Temporal Validation of Transcription Factors Across the Menstrual Cycle: From Single-Cell Atlases to Clinical Applications

Abstract

This article provides a comprehensive framework for the temporal validation of transcription factors (TFs) across the human menstrual cycle, a critical yet underexplored area in reproductive biology and drug development. We synthesize foundational knowledge from recent single-cell and spatial transcriptomic atlases of the endometrium and fallopian tube, which reveal dynamic, phase-specific TF activities. The content explores advanced methodological pipelines for TF identification and validation, addresses common troubleshooting scenarios in experimental and computational workflows, and establishes best practices for the comparative analysis of TF networks across cycle phases and patient populations. Aimed at researchers, scientists, and drug development professionals, this resource is designed to standardize and accelerate the translation of cyclical TF dynamics into biomarkers and therapeutic targets for conditions like endometriosis, recurrent implantation failure, and endometrial carcinoma.

Mapping the Dynamic Transcriptional Landscape of the Cycling Endometrium

The advent of single-cell and spatial transcriptomics technologies has ushered in a new era for biomedical research, providing unprecedented resolution for studying complex tissues. In the field of reproductive biology, where tissues undergo dramatic cyclic changes, these technologies have enabled the creation of foundational cellular atlases that serve as essential reference resources for the research community. These atlases provide comprehensive maps of cellular heterogeneity, spatial organization, and temporal dynamics, offering critical insights into normal physiology and disease pathogenesis. For researchers investigating the human endometrium—a tissue that undergoes monthly cycles of proliferation, differentiation, and shedding—these resources are particularly valuable for understanding the complex processes underlying endometrial receptivity, disorders such as endometriosis, and the temporal validation of transcriptional factors (TFs) across the menstrual cycle.

Comparative Analysis of Foundational Atlases

The research community now benefits from several comprehensive atlases of reproductive tissues, each with distinct strengths, methodologies, and applications. The table below summarizes four key resources that serve as foundational references.

Table 1: Comparison of Major Single-Cell and Spatial Atlases in Reproductive Biology

Atlas Name	Biological System	Key Technological Features	Sample Scale	Primary Applications
Human Endometrial Cell Atlas (HECA) [1]	Human endometrium	Integrated scRNA-seq, snRNA-seq, spatial transcriptomics	313,527 cells from 63 women	Defining consensus cell types, studying endometriosis, mapping spatiotemporal organization
Spatiotemporal Atlas of Developing Mouse Ovary [2]	Mouse ovary development	Combined scRNA-seq and spatial transcriptomics	50,655 cells from fetal stage to adulthood	Characterizing germ cell trajectory, folliculogenesis, regional heterogeneity
Spatial Atlas of Human Endometrium in Vivo and In Vitro [3]	Human uterus	scRNA-seq, snRNA-seq, Visium spatial transcriptomics, organoid models	98,568 cells from 15 individuals	Mapping signaling pathways, benchmarking organoid models, studying endometrial cancer
Spatial Atlas of Endometrium in RIF [4]	Human endometrium in infertility	10x Visium spatial transcriptomics	10,131 spots from 8 individuals	Investigating repeated implantation failure mechanisms, identifying cellular niches

Experimental Methodologies in Atlas Generation

Tissue Processing and Single-Cell Sequencing Protocols

The generation of high-quality cellular atlases requires standardized, rigorous experimental methodologies. For the Human Endometrial Cell Atlas (HECA), researchers implemented a multi-stage validation approach [1]. Fresh endometrial tissues were collected with appropriate ethical approvals and either processed immediately for scRNA-seq or snap-frozen for snRNA-seq. For scRNA-seq, tissues underwent enzymatic digestion using collagenase-based protocols optimized to preserve cell viability while generating single-cell suspensions. Cells were then loaded onto droplet-based platforms (10x Genomics Chromium) for library preparation. For snRNA-seq, frozen tissues were cryosectioned and nuclei were isolated using Dounce homogenization in hypotonic lysis buffer followed by density gradient centrifugation to remove debris. Quality control metrics included RNA Integrity Number (RIN) >7 for tissue samples, and filtration of cells/nuclei with >20% mitochondrial reads or abnormal gene counts [1].

Spatial Transcriptomics Workflows

Spatial transcriptomics methodologies varied slightly between atlas projects but shared common elements. For the RIF endometrium atlas [4], fresh endometrial biopsies were embedded in OCT compound and flash-frozen in isopentane cooled by liquid nitrogen. Cryosections of 10μm thickness were placed on 10x Visium spatial gene expression slides. Tissue optimization was performed to determine optimal permeabilization time, followed by standard H&E staining and imaging. mRNA was captured on spatially barcoded spots, reverse-transcribed, and libraries were constructed for sequencing on Illumina NovaSeq 6000 with PE150 configuration. The spatial data processing pipeline involved Space Ranger for alignment to the reference genome (GRCh38) and Seurat for downstream analysis [4].

Table 2: Core Experimental Protocols Across Atlas Studies

Experimental Step	HECA Protocol [1]	Mouse Ovary Atlas Protocol [2]	Spatial Endometrium Protocol [4]
Tissue Preservation	Fresh digestion (scRNA-seq) or snap-freezing (snRNA-seq)	Fresh digestion for single-cell suspension	Fresh freezing in OCT with isopentane-liquid nitrogen
Single-Cell Isolation	Enzymatic digestion (collagenase) for cells; Dounce homogenization for nuclei	Enzymatic digestion optimized for ovarian tissue	Cryosectioning for spatial transcriptomics
Sequencing Platform	10x Genomics Chromium	10x Genomics Chromium	10x Visium Spatial Transcriptomics
Quality Control	Mitochondrial percentage <20%; minimum gene count 500	Graph-based clustering; stringent QC filters	RNA Integrity Number >7; gene count >500 per spot
Data Processing	Harmony integration; machine learning label transfer	stLearn normalization; Monocle trajectory inference	Space Ranger alignment; Seurat normalization

Computational Integration and Validation

A critical challenge in atlas generation is integrating multiple datasets while preserving biological signals. The HECA project addressed this by creating an "anchor dataset" with carefully characterized samples that enabled integration of six publicly available scRNA-seq datasets while correcting for batch effects [1]. Validation included independent snRNA-seq profiling of 63 additional donors and label transfer using machine learning approaches. Spatial validation was performed through single-molecule fluorescence in situ hybridization (smFISH) and integration with spatial transcriptomics data using computational tools like cell2location [3].

Signaling Pathways in Endometrial Organization and Function

The integration of single-cell and spatial data has revealed intricate signaling networks that govern endometrial biology. Three key pathways emerge as critical regulators of cellular organization and differentiation across the menstrual cycle.

WNT Signaling in Epithelial Progenitor Maintenance

Spatial transcriptomics has revealed that WNT signaling plays a crucial role in maintaining epithelial progenitor populations in the basalis layer of the endometrium [3]. SOX9+ basalis epithelial cells express WNT pathway genes and are strategically positioned in the basalis glands, where they interact with fibroblast populations through CXCL12-CXCR4 signaling. Inhibition of WNT signaling in endometrial organoids was shown to enhance differentiation along the secretory lineage, confirming its role in maintaining progenitor states [3].

NOTCH Signaling in Ciliated Cell Differentiation

The NOTCH pathway regulates the balance between ciliated and secretory epithelial lineages in the functionalis layer. Spatial mapping shows that ciliated cells emerge in both proliferative and secretory phases, but their differentiation is enhanced when NOTCH signaling is suppressed in organoid models [3]. This pathway particularly influences the development of ciliated cells in the lumenal epithelium, which facilitate gamete transport and are essential for reproductive function.

TGFβ Signaling in Stromal-Epithelial Coordination

Analysis of the HECA resource identified TGFβ signaling as a key mediator of stromal-epithelial crosstalk in the functionalis layer [1]. This pathway facilitates the intricate coordination between stromal fibroblasts and epithelial cells necessary for tissue remodeling across the menstrual cycle. TGFβ signaling is especially prominent during the secretory phase, when it contributes to the decidualization of stromal cells in preparation for potential implantation.

Table 3: Essential Research Reagents and Computational Tools for Atlas-Based Research

Resource Category	Specific Tools/Reagents	Application/Function	Validation
Computational Deconvolution Tools	Hierarchical Bayesian Models [5], MuSiC [5], CIBERSORT [5]	Infer cell type proportions from bulk RNA-seq data	Benchmarking against scRNA-seq; simulation studies
Spatial Data Analysis	cell2location [3], CARD [4], stLearn [2]	Map cell types to spatial coordinates; deconvolve spot-level data	smFISH validation; comparison to known markers
Cell-Cell Communication Analysis	CellPhoneDB v.3.0 [3]	Predict ligand-receptor interactions incorporating spatial constraints	Spatial proximity validation; functional assays
Trajectory Inference	Monocle [2], SCENIC [2]	Reconstruct developmental pathways; infer regulatory networks	Pseudotemporal ordering; TF target validation
Reference Atlases	HECA [1], Reproductive Cell Atlas [3]	Reference for cell type annotation; normal comparisons	Multi-dataset integration; independent validation

Applications to Menstrual Cycle Research and Temporal TF Validation

The foundational atlases enable sophisticated analysis of transcriptional factor dynamics across the menstrual cycle, addressing a previously intractable challenge in reproductive biology.

Temporal Validation of TFs in Human Endometrium

The HECA resource provides unprecedented ability to validate TF expression patterns across menstrual cycle phases. For instance, SOX9 shows predominant expression during the proliferative phase, particularly in basalis epithelial progenitor cells [1] [3]. During the secretory phase, TFs associated with decidualization such as FOXO1 emerge in stromal cells, while ESR1 and PGR exhibit phase-specific expression patterns across epithelial and stromal compartments [3]. The integrated nature of HECA allows researchers to distinguish genuine temporal regulation from compositional changes—a critical consideration when interpreting bulk transcriptomic data.

Resolving Cellular Origins of Endometriosis GWAS Signals

Integration of HECA with large-scale endometriosis genome-wide association study (GWAS) data has demonstrated the power of reference atlases for disease mapping. This approach identified decidualized stromal cells and macrophages as the cell types most likely dysregulated in endometriosis [1]. By mapping GWAS variants to specific cell types, researchers can prioritize cell populations for functional follow-up and identify the temporal windows during the menstrual cycle when these cell types may be most vulnerable to dysregulation.

Identifying RIF-Associated Cellular Niches

Spatial atlases of endometrium from women with Repeated Implantation Failure (RIF) have revealed seven distinct cellular niches with specific spatial organizations [4]. Deconvolution analysis shows that unciliated epithelial cells dominate these niches, and their organization is altered in RIF patients. This spatial perspective enables researchers to investigate how TF activity varies across different microenvironments within the same tissue, and how these patterns are disrupted in infertility conditions.

The foundational single-cell and spatial atlases represent transformative resources for the reproductive research community. Future developments will likely include temporal atlases with even finer resolution across the menstrual cycle, integration with epigenomic and proteomic data, and the creation of disease-specific atlases for conditions like endometriosis and endometrial cancer. The application of Bayesian deconvolution approaches to bulk RNA-seq data, guided by single-cell references, will enable researchers to extract cell-type-specific information from existing datasets and biobank samples [5]. As these resources mature and expand, they will continue to drive discoveries in reproductive biology and provide critical insights for developing diagnostics and therapeutics for endometrial disorders.

The human endometrium undergoes precise, cyclic remodeling to support embryo implantation, a process governed by dynamic transcriptional networks. A key challenge in reproductive biology is identifying the phase-specific master regulators—transcription factors (TFs) that dictate gene expression programs during the proliferative and secretory phases. Recent systems-level analyses reveal that endometrial progression is predominantly orchestrated by TFs and hormonal signals rather than post-transcriptional regulators like miRNAs [6]. This guide compares the performance of identified regulatory molecules, framing them within a broader thesis on the temporal validation of TFs across the menstrual cycle. For researchers and drug developers, understanding this regulatory hierarchy is crucial for diagnosing endometrial-factor infertility and developing targeted therapies.

Comparative Analysis of Phase-Specific Regulators

The table below provides a quantitative summary of key transcriptional and hormonal regulators, their expression dynamics, and functional impacts, synthesizing data from genomic and perturbation studies.

Table 1: Quantitative Summary of Phase-Specific Master Regulators and Their Experimental Validation

Regulator Name	Primary Phase	Regulatory Type	Expression/Activity Peak	Key Target Pathways/Genes	Functional Impact from Perturbation (e.g., Silencing)	Supporting Experimental Data (Source/Assay)
SOX9 [3]	Proliferative	Transcription Factor	Proliferative Phase	ESR1, MMP7, WNT7A	Disruption of epithelial regeneration and gland formation	scRNA-seq; Visium Spatial Transcriptomics; smFISH (RNAscope)
CTCF [6]	Across Cycle	Transcription Factor	Sustained	Broad (3,608 gene lists)	Identified as a novel master regulator of endometrial function	In-silico enrichment analysis (FDR < 0.05); prospective cohort validation (n=19)
GATA6 [6]	Across Cycle	Transcription Factor	Sustained	Broad (3,608 gene lists)	Identified as a novel master regulator of endometrial function	In-silico enrichment analysis (FDR < 0.05); prospective cohort validation (n=19)
Estrogen (E2) [6]	Proliferative	Hormone/Signal	Proliferative Phase	ESR1, CCND1	0% of implantation failure gene lists were primarily E2-dependent	In-silico over-representation analysis of 19 gene lists
Progesterone (P4) [6]	Secretory	Hormone/Signal	Secretory Phase	PAEP, FOXO1	47% (8/17) of gene lists were significantly P4-dependent	In-silico over-representation analysis of 19 gene lists
ZNF519 [7]	Not Phase-Specified	Simian-Restricted TF	Not Specified	Cell-cycle-regulated genes	Disruption causes cell cycle imbalances	Systematic Perturb-seq; genomic targeting analysis
WNT Signaling [3]	Proliferative	Signaling Pathway	Proliferative Phase	LGR5, AXIN2	Downregulation increases secretory lineage differentiation efficiency	Organoid culture; scRNA-seq; pathway modulation
NOTCH Signaling [3]	Not Phase-Specified	Signaling Pathway	Not Specified	HES1, HEY1	Downregulation increases ciliated lineage differentiation efficiency	Organoid culture; scRNA-seq; pathway modulation

Detailed Experimental Protocols for Key Findings

Protocol 1: In-Silico Identification of Master Endometrial TFs

This methodology identified novel master regulators like CTCF and GATA6 through a systematic computational workflow [6].

• Objective: To identify overlapping hormonal and non-hormonal transcriptional regulators from previously published gene signatures associated with endometrial progression and implantation failure.
• Data Collection: Nineteen (19) gene lists/signatures related to endometrial receptivity and implantation failure were retrieved from public repositories (e.g., GEO) and original publications. Genes were standardized using the HUGO Gene Nomenclature Committee (HGNC).
• Regulator Annotation:
  • Hormonal Regulators: Progesterone (P4)- and Estrogen (E2)-related gene sets were compiled from KEGG, Gene Ontology (GO), and the DoRothEA database (manually curated or ChIP-Seq validated PGR/ESR targets).
  • Non-Hormonal Regulators: Experimentally validated TF-target and miRNA-target interactions were sourced from DoRothEA and TarBase databases, respectively.
• Enrichment Analysis: A functional over-representation analysis was performed for each regulator type against each gene list using Fisher's exact tests, with significance set at FDR < 0.05.
• Network Analysis: Regulatory networks were constructed with gene lists and regulators as nodes. "Master regulators" were prioritized based on network degree (i.e., regulators targeting genes in most signatures), surpassing a threshold of 1.5 times the interquartile range (IQR).
• Validation: The expression dynamics of selected TFs were validated in an independent transcriptomic dataset of 129 menstrual cycle samples and prospectively in an independent cohort (n=19) via endometrial biopsies.

Protocol 2: Single-Cell and Spatial Mapping of Endometrial Transitions

This protocol outlines the generation of a spatiotemporal atlas of the human endometrium, revealing pathways like WNT and NOTCH controlling epithelial fate [3].

• Objective: To map the temporal and spatial dynamics of human endometrial cell states across the menstrual cycle and in vitro.
• Sample Preparation: Single-cell/nucleus RNA sequencing (scRNA-seq/snRNA-seq) was performed on full-thickness uterine samples (n=6) and endometrial biopsies (n=3) from healthy donors. Spatial transcriptomics was conducted using 10x Genomics Visium technology on full-thickness samples (n=4).
• Cell Type Identification: Unsupervised clustering of ~100,000 cells was performed, with cell identity assigned using known marker genes. Epithelial subpopulations were further sub-clustered.
• Spatial Mapping: The cell2location algorithm computationally integrated scRNA-seq and Visium data to map specific cell states to their precise tissue locations (e.g., lumenal vs. glandular epithelium).
• In Vitro Benchmarking: 3D endometrial organoids were generated and profiled with scRNA-seq. Pathway activity (WNT, NOTCH) was modulated using chemical inhibitors to assess effects on secretory and ciliated cell differentiation.
• Validation: Single-molecule fluorescence in situ hybridization (smFISH) with RNAscope probes was used to validate the spatial expression of key markers (e.g., LGR5, WNT7A).

Protocol 3: Perturbation of Evolutionarily Recent TFs in Cell Cycle

This protocol describes how the functional impact of young TFs, such as ZNF519, on cell cycle progression was systematically tested [7].

• Objective: To determine whether evolutionarily recent TFs influence human cell cycle regulation.
• Target Identification: Genomic targets of recent TFs (particularly KRAB zinc-finger proteins - KZFPs) were analyzed for enrichment in genes with synchronized cell cycle expression.
• Systematic Perturbation: High-throughput perturbation screens (e.g., Perturb-seq) were employed to silence candidate recent TFs.
• Phenotypic Analysis: The impact on cell cycle progression was assessed using flow cytometry to detect imbalances in cell cycle phases.
• Functional Follow-up: The simian-restricted KZFP ZNF519 was experimentally confirmed as a specific regulator, and the mammalian-specific ZNF274 was shown to regulate replication timing of gene clusters.

Signaling Pathways and Regulatory Workflows

The following diagrams illustrate the core signaling pathways and experimental workflows described in the research, providing a visual summary of the complex logical relationships.

Endometrial Epithelial Lineage Regulation

Master Regulator Identification Workflow

The Scientist's Toolkit: Essential Research Reagents

The table below catalogs key reagents and tools used in the cited experiments, providing a resource for researchers aiming to replicate or build upon these findings.

Table 2: Key Research Reagents and Experimental Tools

Reagent/Tool Name	Category	Primary Function in Research	Example Application/Context
scRNA-seq / snRNA-seq [3]	Genomic Technology	Profiling transcriptomes of individual cells to define cellular heterogeneity and states.	Generating a cellular map of 98,568 uterine cells across menstrual cycle phases.
10x Visium Spatial Transcriptomics [3]	Genomic Technology	Mapping gene expression data to specific tissue locations while preserving spatial context.	Defining spatial coordinates for SOX9+ epithelial subsets in lumenal and glandular regions.
Endometrial Organoids [3]	In Vitro Model	3D culture system mimicking in vivo endometrial epithelium for functional studies.	Benchmarking hormonal responses and pathway modulation (WNT/NOTCH) in a controlled environment.
DoRothEA Database [6]	Bioinformatics Resource	Provides manually curated and ChiP-seq validated transcription factor-target gene interactions.	Identifying TFs significantly enriched in endometrial progression gene lists.
TarBase Database [6]	Bioinformatics Resource	A repository of manually curated miRNA-gene interactions from publications.	Evaluating the role of miRNAs as regulators of endometrial gene signatures.
Perturb-seq [7]	Functional Genomics	A high-throughput method for assessing transcriptional consequences of genetic perturbations.	Systematically mapping the influence of silencing evolutionarily recent TFs on cell cycle.
smFISH (RNAscope) [3]	Validation Assay	Visualizing and validating the spatial expression of specific mRNA transcripts in tissue sections.	Confirming the location of LGR5 and WNT7A in surface epithelium during proliferative phase.
Cell2Location Algorithm [3]	Computational Tool	A Bayesian method for integrating scRNA-seq and spatial transcriptomics to map cell types.	Deconvoluting Visium data to resolve the spatial distribution of endometrial cell states.
Lanuginosine	Lanuginosine, CAS:23740-25-2, MF:C18H11NO4, MW:305.3 g/mol	Chemical Reagent	Bench Chemicals
Lodenafil Carbonate	Lodenafil Carbonate|PDE5 Inhibitor|CAS 398507-55-6	Lodenafil carbonate is a phosphodiesterase type 5 (PDE5) inhibitor prodrug used in erectile dysfunction research. For Research Use Only. Not for human consumption.	Bench Chemicals

The WNT and NOTCH signaling pathways are two evolutionarily conserved systems that play fundamental roles in animal development, tissue homeostasis, and cellular fate determination [8] [9]. These pathways regulate crucial cellular processes including proliferation, differentiation, apoptosis, and stem cell maintenance [9]. While both pathways are involved in development, they often exhibit opposing effects on cell fate decisions, creating a complex interplay that generates the diversity of cell types found in metazoans [10]. The intricate crosstalk between WNT and NOTCH is essential for proper tissue patterning and maintenance, and its dysregulation is implicated in various diseases, including cancer and cardiovascular disorders [9] [11].

In the context of menstrual cycle research, understanding the temporal dynamics of these pathways provides critical insights into endometrial regeneration and differentiation [12]. The endometrium undergoes remarkable cyclical changes throughout the menstrual cycle, involving tissue breakdown, regeneration, and differentiation—processes governed by complex signaling networks where WNT and NOTCH play central roles [12]. This review provides a comprehensive comparison of the WNT and NOTCH signaling pathways, their molecular mechanisms, functional interactions, and experimental approaches for studying their activity, with particular emphasis on their relevance to temporal validation in menstrual cycle research.

Pathway Architecture and Molecular Mechanisms

Core Components of NOTCH Signaling

The NOTCH signaling pathway mediates short-range intercellular communication between neighboring cells [9]. In mammals, the pathway consists of four NOTCH receptors (NOTCH1-4) and five ligands (Jagged-1, Jagged-2, Delta-like-1, Delta-like-3, and Delta-like-4), all of which are single-pass transmembrane proteins [8] [9]. The NOTCH receptor contains an extracellular domain with epidermal growth factor (EGF)-like repeats responsible for ligand binding, a transmembrane domain, and an intracellular domain [9].

NOTCH signaling is initiated when NOTCH receptors on one cell interact with ligands on adjacent cells [8]. This interaction triggers a proteolytic cascade: first, ADAM family metalloproteases cleave the receptor's extracellular domain (S2 cleavage), followed by intramembrane cleavage (S3 cleavage) by the γ-secretase complex [8] [9]. These cleavages release the Notch intracellular domain (NICD), which translocates to the nucleus [8]. Within the nucleus, NICD binds to the transcription factor CSL (also known as RBP-Jκ) and recruits co-activators including Mastermind-like (MAML) proteins, converting the complex from a transcriptional repressor to an activator [8] [13]. This activated complex then induces expression of target genes, primarily from the Hairy and Enhancer of Split (HES) and HES-related (HEY) families [8].

Table 1: Core Components of the NOTCH Signaling Pathway

Component Type	Molecules	Function
Receptors	NOTCH1, NOTCH2, NOTCH3, NOTCH4	Single-pass transmembrane receptors that receive signals
Ligands	Jagged-1, Jagged-2, Delta-like-1, Delta-like-3, Delta-like-4	Membrane-bound signals activating NOTCH receptors
Proteases	ADAM10, ADAM17, γ-secretase	Mediate sequential cleavage of NOTCH receptors
Nuclear Effectors	CSL/RBP-Jκ, Mastermind-like (MAML)	Transcription factors that regulate target gene expression
Target Genes	HES1, HEY1, MYC	Key downstream effectors executing NOTCH functions

Core Components of WNT Signaling

The WNT signaling pathway, particularly the canonical WNT/β-catenin pathway, centers on the regulation of β-catenin stability and localization [8] [10]. In the absence of WNT ligands, cytoplasmic β-catenin is constantly degraded by a destruction complex consisting of Axin, Adenomatous Polyposis Coli (APC), Casein Kinase 1 (CK1), and Glycogen Synthase Kinase 3β (GSK3β) [8] [13]. This complex phosphorylates β-catenin, targeting it for ubiquitination and proteasomal degradation [10].

When WNT ligands bind to Frizzled receptors and LRP co-receptors, they initiate an intracellular signaling cascade that disrupts the destruction complex [8] [13]. This leads to β-catenin accumulation in the cytoplasm and subsequent translocation to the nucleus [8]. Nuclear β-catenin then associates with T-cell factor/Lymphoid enhancer factor (TCF/LEF) transcription factors to activate WNT target genes [8] [13].

Table 2: Core Components of the WNT Signaling Pathway

Component Type	Molecules	Function
Ligands	WNT1, WNT3A	Secreted glycoproteins that initiate signaling
Receptors	Frizzled (Fz), LRP5/6	Cell surface receptors that bind WNT ligands
Signal Transducers	Dishevelled (Dvl), β-catenin	Key intracellular mediators of WNT signaling
Destruction Complex	Axin, APC, GSK3β, CK1	Phosphorylates β-catenin for degradation
Nuclear Effectors	TCF/LEF	Transcription factors that regulate target genes with β-catenin
Target Genes	MYC, CYCLIN D1, AXIN2	Execute proliferative and differentiative functions

Comparative Analysis of Pathway Functions

Biological Roles and Functional Outcomes

WNT and NOTCH signaling pathways play distinct yet interconnected roles in cellular decision-making processes. WNT signaling primarily promotes self-renewal and proliferation of stem and progenitor cells across various tissues, including hematopoietic stem cells, intestinal stem cells, and neuronal stem cells [13]. The pathway influences epithelial-to-mesenchymal transition (EMT) signatures, suggesting a possible mechanism for priming stem cells to act as potential tumor-initiating cells [13].

In contrast, NOTCH signaling typically functions as a differentiation signal that restricts cellular proliferation [13]. In prostate stem cells, for example, NOTCH induction inhibits proliferation and disrupts proper acini formation in reconstitution experiments [13]. This antiproliferative role of NOTCH is consistent across multiple tissue types, where it promotes cell fate specification and differentiation.

The opposing functions of these pathways create a balance crucial for tissue homeostasis: WNT promotes the expansion of progenitor pools while NOTCH drives their differentiation into mature cell types [13]. This functional antagonism is particularly evident in the endometrium, where WNT and NOTCH pathways exhibit complementary roles in regulating differentiation toward the two main epithelial lineages—with WNT inhibition promoting secretory lineage differentiation and NOTCH inhibition promoting ciliated lineage differentiation [12].

Table 3: Functional Comparison of WNT and NOTCH Signaling

Characteristic	WNT Signaling	NOTCH Signaling
Primary Function	Stem cell maintenance, proliferation	Cell fate specification, differentiation
Spatial Range	Can act at longer range through diffusible ligands	Short-range, cell-to-cell communication
Temporal Dynamics	Rapid response (minutes to hours)	Rapid response (minutes to hours)
Key Physiological Roles	Tissue regeneration, homeostasis	Boundary formation, pattern generation
Role in Cancer	Often oncogenic	Context-dependent (oncogenic or tumor suppressive)
Endometrial Function	Regulates secretory lineage differentiation	Regulates ciliated lineage differentiation

Molecular Crosstalk and Regulatory Interactions

The WNT and NOTCH pathways engage in extensive molecular crosstalk at multiple levels, creating a complex regulatory network that fine-tunes cellular responses [8] [10]. NOTCH can inhibit WNT signaling through at least two distinct mechanisms: at the membrane, NOTCH1 can antagonize β-catenin activity through an endocytic mechanism that requires interaction with Deltex and sequesters β-catenin into the membrane fraction [10]. Within the nucleus, the intracellular domain of NOTCH1 can limit β-catenin-induced transcription through complex formation requiring interaction with RBPJκ [10].

Conversely, WNT signaling can regulate NOTCH pathway components. The gene encoding Jagged1, a NOTCH ligand, is a target of canonical WNT/β-catenin signaling [8]. Additionally, Numb, an inhibitor of NOTCH signaling, is a potent target of WNT/β-catenin signaling in multiple types of progenitor cells [8]. The NOTCH target gene HES1 is also regulated by WNT/β-catenin signaling at the transcriptional level [8].

Shared regulatory components further integrate these pathways. GSK3β, a component in the destruction complex mediating β-catenin degradation, can phosphorylate NICD, which promotes NICD nucleus localization and increases its transcriptional activity and stability [8]. Dishevelled (Dvl), a key WNT signaling component, can bind with NICD to trigger its endocytosis and degradation, or bind with the CSL transcription factor of the NOTCH pathway and inhibit its transcriptional activity [8].

Experimental Analysis of WNT and NOTCH Activity

Methodologies for Pathway Manipulation and Assessment

Studying WNT and NOTCH signaling requires a multifaceted approach involving pathway modulation, activity measurement, and functional assessment. For NOTCH pathway inhibition, researchers commonly use gamma-secretase inhibitors (such as DAPT) that prevent NOTCH receptor cleavage and subsequent NICD release [10]. Alternatively, dominant-negative forms of NOTCH components or RNA interference against NOTCH receptors or ligands can achieve specific inhibition. NOTCH activation can be induced through overexpression of the active NICD fragment or by using ligand-expressing cells in co-culture systems [13].

WNT pathway manipulation includes activation using recombinant WNT proteins (e.g., WNT3A), conditioned media from WNT-expressing cells, or expression of stabilized β-catenin mutants (e.g., S45Fβ-catenin) that resist degradation [13] [10]. WNT inhibition can be achieved through small molecule inhibitors targeting various pathway components (e.g., IWR-1 stabilizing the destruction complex), secreted Frizzled-related proteins (sFRPs) that sequester WNT ligands, or siRNA-mediated knockdown of key pathway components.

Pathway activity is typically measured using reporter assays, with TCF/LEF-responsive constructs (such as TOPflash) for WNT signaling and CSL-responsive elements for NOTCH signaling [10]. Quantitative PCR analysis of target genes (e.g., MYC, AXIN2 for WNT; HES1, HEY1 for NOTCH) provides assessment of endogenous pathway activity [10]. Protein-level analyses including western blotting for NICD, β-catenin, and phosphorylation status of pathway components, as well as immunohistochemistry to determine subcellular localization (particularly nuclear β-catenin accumulation), are routinely employed [13].

Functional assessments often involve in vitro colony formation assays (e.g., prostasphere formation for prostate stem cells) and in vivo reconstitution experiments to evaluate tissue regeneration capabilities [13]. For endometrial research, 3D organoid cultures have emerged as a powerful platform to investigate hormonal responses and differentiation capacities of epithelial cells [12].

Table 4: Experimental Approaches for Studying WNT and NOTCH Signaling

Method Category	Specific Techniques	Applications
Pathway Modulation	Gamma-secretase inhibitors (DAPT), Recombinant WNT3A, Stabilized β-catenin mutants, siRNA/shRNA	Acute pathway activation or inhibition
Activity Reporting	TCF/LEF reporter assays (TOPflash), CSL reporter assays, NOTCH intracellular domain (NICD) detection	Direct measurement of pathway activity
Transcriptional Analysis	qRT-PCR of target genes (HES1, HEY1, MYC, AXIN2), Single-cell RNA sequencing	Assessment of pathway output and cell states
Protein Analysis	Western blotting for NICD and β-catenin, Immunohistochemistry, Phospho-specific antibodies	Protein localization and modification status
Functional Assays	Colony formation (prostaspheres), 3D organoid cultures, In vivo reconstitution	Assessment of biological outcomes
Spatial Analysis	RNAscope, Visium Spatial Transcriptomics, Immunofluorescence	Tissue context and cellular microenvironment

The Scientist's Toolkit: Essential Research Reagents

Pathway Dynamics in Menstrual Cycle Research

The human endometrium provides a unique model for studying temporal dynamics of signaling pathways due to its remarkable cyclical regeneration and differentiation throughout the menstrual cycle [12]. Recent single-cell and spatial transcriptomic analyses have revealed distinct spatiotemporal patterns of WNT and NOTCH signaling activity across menstrual cycle phases [12].

During the proliferative phase, rising estrogen levels drive endometrial regeneration, with SOX9+ epithelial populations expressing WNT pathway genes like WNT7A [12]. Spatial mapping has identified distinct SOX9+ subpopulations: SOX9+LGR5+ cells enriched in surface epithelium, SOX9+LGR5− cells located in basal glands, and proliferative SOX9+ cells in regenerating superficial glands [12]. These patterns demonstrate precise spatial regulation of WNT-responsive cell states.

Following ovulation, progesterone induces the secretory phase, during which WNT and NOTCH pathways regulate differentiation of the two main epithelial lineages [12]. Organoid studies have demonstrated that downregulation of WNT signaling increases differentiation efficiency along the secretory lineage, while NOTCH pathway inhibition promotes ciliated lineage differentiation [12]. This complementary relationship highlights how these opposing pathways coordinate to generate endometrial epithelial diversity.

The menstrual cycle thus provides a naturally occurring model system for temporal validation of transcription factor activity, with hormonally-regulated WNT and NOTCH signaling driving cyclical tissue remodeling. Understanding these dynamics has implications for endometrial disorders including endometriosis, endometrial carcinoma, and infertility [12].

Implications for Disease and Therapeutic Development

Dysregulation of WNT and NOTCH signaling pathways contributes to various diseases, particularly cancer and cardiovascular conditions [9] [11]. In cancer, aberrant activation of these pathways provides resistance to multiple anticancer therapies, including chemotherapy, radiotherapy, molecular targeted therapy, and immunotherapy [9]. NOTCH signaling exhibits context-dependent oncogenic or tumor-suppressive functions—acting as an oncogene in T-cell acute lymphoblastic leukemia (T-ALL) and other hematological malignancies, while functioning as a tumor suppressor in squamous cell carcinomas and other solid tumors [9].

In cardiovascular diseases, dysregulated WNT and NOTCH signaling contributes to atherosclerosis, hypertension, myocardial infarction, and heart failure [11]. Abnormal activation or suppression of these pathways in specific cell types can lead to endothelial dysfunction, vascular remodeling, cardiomyocyte hypertrophy, and impaired cardiac contractility [11].

Therapeutic targeting of these pathways is challenging due to their pleiotropic effects and complex crosstalk. Current approaches include gamma-secretase inhibitors for NOTCH blockade, antibodies targeting NOTCH receptors or ligands, and small molecules inhibiting specific pathway components [9]. For WNT signaling, inhibitors targeting various pathway levels—from extracellular secretion and reception to intracellular nuclear transcription—are under investigation [9]. Combination therapies that simultaneously modulate both pathways may provide enhanced efficacy while reducing compensatory resistance mechanisms.

In the context of menstrual cycle research and endometrial disorders, targeting WNT and NOTCH pathways holds promise for treating conditions like endometriosis and endometrial cancer [12]. The temporal specificity of these pathways across the cycle may enable phase-specific therapeutic interventions that maximize efficacy while minimizing off-target effects.

Spatial Localization of TFs in Luminal vs. Glandular Microenvironments

The human endometrium, the mucosal lining of the uterus, is a highly dynamic tissue that undergoes cyclic remodeling throughout the menstrual cycle to support embryo implantation and pregnancy. This process is orchestrated by ovarian hormones and involves precise spatiotemporal regulation of gene expression across different cellular compartments. The endometrial epithelium consists of two distinct microenvironments: the luminal epithelium (LE), which lines the uterine cavity and directly interacts with the implanting embryo, and the glandular epithelium (GE), embedded within the stromal layer and responsible for secretory functions. Understanding the spatial localization of transcription factors (TFs) governing cellular identity and function in these compartments is crucial for elucidating the mechanisms of endometrial receptivity and its associated pathologies. This guide synthesizes recent single-cell and spatial transcriptomic studies to objectively compare TF expression and regulatory networks between luminal and glandular microenvironments, providing a validated methodological framework for their study.

Comparative Analysis of Luminal and Glandular Microenvironments

Distinct Cellular Composition and Marker Expression

Single-cell RNA sequencing (scRNA-seq) has resolved the heterogeneity within the human endometrial epithelium, identifying distinct cell populations and their characteristic markers in luminal and glandular compartments [3].

Table 1: Characteristic Markers of Luminal and Glandular Epithelial Cells

Cell Compartment	Characteristic Marker	Functional Role	Localization/Notes
Luminal Epithelium (LE)	LGR5 [3]	Putative stem/progenitor marker	Enriched in surface epithelium
	WNT7A [3]	Signaling molecule in regeneration	Expressed in SOX9+/LGR5+ cells
	KRT17 [3]	Cytokeratin	Expressed in SOX9+/LGR5+ cells
Glandular Epithelium (GE)	SCGB2A2 [3]	Secretory protein	Marker for glandular cells
	PAEP [3]	Uterine milk protein	Secretory phase glandular cells
	CXCL8 [3]	Chemokine	Secretory phase glandular cells
Progenitor/Regenerative Cells	SOX9 [3] [14]	Transcription Factor	Proliferative phase; subpopulations with LGR5+ (surface) and LGR5- (basal glands)
Ciliated Cells	PIFO, TPPP3, FOXJ1 [3]	Ciliary proteins and TF	Present in both phases and compartments
Levophacetoperane	Levophacetoperane, CAS:24558-01-8, MF:C14H19NO2, MW:233.31 g/mol	Chemical Reagent	Bench Chemicals
Indatraline	Indatraline, CAS:97229-15-7, MF:C16H15Cl2N, MW:292.2 g/mol	Chemical Reagent	Bench Chemicals

Transcription Factor Networks and Signaling Pathways

The functional divergence between LE and GE is driven by distinct transcription factor networks and signaling pathways, as revealed by transcriptomic analyses.

Table 2: Transcription Factors and Signaling Pathways by Cellular Compartment

Cell Compartment	Key Transcription Factors & Pathways	Experimental Model	Functional Association
Luminal Epithelium (LE)	JAK-STAT, MAPK, PI3K-Akt signaling [15] [16]	Mouse model of embryo implantation	Embryo attachment and initial invasion
	SOX9 [3] [14]	Human scRNA-seq & spatial transcriptomics	Proliferation and regeneration
Glandular Epithelium (GE)	Retinol metabolism, Sphingolipid metabolism [15] [16]	Mouse model of embryo implantation	Embryonic development and uterine microenvironment
	NOTCH signaling [3] [15]	Human endometrial organoids & mouse model	Differentiation of secretory and ciliated lineages
Immune Cell Regulation	EOMES, ELF4 (uNK2 cells) [17]	Human scRNA-seq of endometrium	Cytotoxic uterine NK cell regulation
	ELK4, IRF1 (uNK3 cells) [17]	Human scRNA-seq of endometrium	Platelet activation and tight junction functions

Experimental Protocols for Spatial Transcriptomics and TF Validation

Single-Cell and Spatial Transcriptomic Profiling

The integration of single-cell and spatial transcriptomics is a powerful method for mapping TF localization.

• Workflow Diagram: Single-Cell and Spatial Transcriptomic Integration

Protocol Details:

• Tissue Collection: Utilize full-thickness uterine samples from surgical procedures or endometrial biopsies from live donors, with precise menstrual cycle phase annotation (e.g., proliferative vs. secretory) [3].
• Single-Cell/Nucleus RNA-seq (scRNA-seq/snRNA-seq): Generate single-cell suspensions or isolate nuclei from endometrial tissue. Libraries are prepared using standard platforms (e.g., 10x Genomics) and sequenced. Data processing includes alignment, quality control, and unsupervised clustering to identify cell populations [3] [17].
• Spatial Transcriptomics: Assay fresh-frozen endometrial tissue sections on spatial transcriptomics slides (e.g., 10x Genomics Visium). This captures location-specific gene expression data [3].
• Computational Integration: Employ computational tools like cell2location to integrate scRNA-seq clusters with spatial transcriptomics data, deconvoluting the spatial spots to infer the precise location of cell types and their transcriptomic signatures, including TF expression [3].

Functional Validation Using Endometrial Organoids

Endometrial organoids provide a physiologically relevant in vitro system to validate TF functions.

• Workflow Diagram: Organoid Model for Functional Validation

Protocol Details:

• Organoid Culture Establishment: Generate three-dimensional endometrial organoids from dissociated primary endometrial tissue or menstrual fluid samples. Culture in Matrigel with specialized media containing growth factors (e.g., FGF10, HGF) [3] [14].
• Hormonal and Pathway Modulation: Treat organoids with estrogen and progesterone to mimic the menstrual cycle in vitro. To test specific TF pathway functions, modulate key signaling pathways identified in vivo (e.g., inhibit WNT or NOTCH pathways) [3].
• Lineage Differentiation and Analysis: Assess the efficiency of differentiation into secretory (PAEP+) and ciliated (PIFO+) lineages using scRNA-seq, immunofluorescence, and qPCR. This validates the role of specific pathways and TFs in directing cell fate [3].

Isolation of Luminal and Glandular Epithelium for RNA-seq

Precise physical separation of LE and GE is critical for compartment-specific analysis.

Protocol Details (Mouse Model) [15] [16]:

• Delayed Implantation Model: Induce pregnancy in mice. On day 4 of gestation, perform ovariectomy and maintain a state of "delayed implantation" via progesterone injections. Activate implantation with an estradiol-17β injection on day 7.
• Epithelial Isolation:
  • Luminal Epithelium (LE): Flush the uterine horn with HBSS buffer. Subsequently, digest the intact uterus in HBSS solution containing 0.2% trypsin at 4°C for 1 hour, followed by 37°C for 1 hour. Gently rinse the lumen to collect the shed luminal epithelium.
  • Glandular Epithelium (GE): Digest the remaining uterine tissue in a collagenase/dispase II system at 37°C. Filter the digestate sequentially through 200-mesh and 1000-mesh filters. The glandular epithelium is retained on the 1000-mesh filter and collected via backwashing.
• Downstream Analysis: Perform RNA sequencing (RNA-seq) on the isolated LE and GE populations at different time points post-implantation activation (e.g., 0h, 3h, 6h). Analyze differentially expressed genes, pathway enrichment (GO, KEGG), and temporal patterns (e.g., using Mfuzz) [15] [16].

Visualization of Key Signaling Pathways

The following diagrams summarize the core signaling pathways that exhibit compartment-specific activity in the endometrium.

• Pathway Diagram: LE-Associated JAK-STAT and MAPK Signaling

• Pathway Diagram: GE-Associated NOTCH and Metabolic Signaling

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagent Solutions for Endometrial Microenvironment Research

Reagent / Solution	Function / Application	Example Use Case
Collagenase/Dispase II	Enzymatic digestion of tissue for cell isolation	Isolation of glandular epithelium from stromal compartment [15] [16]
Trypsin	Enzymatic digestion for dissociation	Isolation of luminal epithelium from uterine lumen [15] [16]
Trizol RNA Reagent	Total RNA extraction from cells and tissues	RNA preparation for sequencing from LE/GE isolates [15]
Matrigel	3D extracellular matrix for organoid culture	Supporting growth and differentiation of endometrial organoids [3] [14]
Recombinant FGF10 & HGF	Growth factors in organoid culture media	Promoting proliferation and maintenance of endometrial epithelium in vitro [14]
Estradiol-17β & Progesterone	Hormonal treatment in vitro and in vivo	Mimicking menstrual cycle phases and inducing differentiation [3] [15]
WNT/NOTCH Pathway Inhibitors	Small molecule pathway modulation (e.g., IWP2, DAPT)	Functional validation of pathway roles in lineage specification [3]
Antibodies (IF/IHC)	Protein localization and validation (e.g., SOX9, LGR5, PAEP)	Confirming spatial protein expression of key TFs and markers [3] [18]
Ligustroflavone	Ligustroflavone, CAS:260413-62-5, MF:C33H40O18, MW:724.7 g/mol	Chemical Reagent
Luffariellolide	Luffariellolide, CAS:111149-87-2, MF:C25H38O3, MW:386.6 g/mol	Chemical Reagent

The human endometrium undergoes approximately 450 cycles of regeneration, differentiation, and shedding throughout a woman's reproductive life, processes primarily orchestrated by the dynamic fluctuations of estrogen and progesterone [19]. These hormonal signals are transduced into complex gene expression programs through specialized transcription factor (TF) networks that coordinate cellular responses across the menstrual cycle. Recent advances in single-cell technologies have enabled unprecedented resolution in mapping these regulatory networks, revealing sophisticated crosstalk mechanisms between hormonal signaling pathways [12] [20]. This review synthesizes current understanding of how estrogen and progesterone coordinate TF activities to regulate endometrial function, with particular focus on temporal validation approaches across the menstrual cycle. We compare experimental methodologies and present quantitative data on cycle-dependent TF dynamics to provide researchers and drug development professionals with a comprehensive resource for investigating these critical regulatory systems.

Molecular Mechanisms of Hormone-Responsive Transcription Factors

Estrogen and Progesterone Receptor Signaling Pathways

Estrogen receptor (ER) and progesterone receptor (PR) function as ligand-activated transcription factors that directly bind DNA at hormone response elements to regulate target gene expression. The canonical signaling pathway involves receptor activation, dimerization, and recruitment to specific DNA sequences, followed by assembly of coregulator complexes that modify chromatin accessibility and facilitate transcriptional initiation [19]. Beyond this direct mechanism, ER and PR exhibit extensive crosstalk through tethering interactions with other DNA-bound TFs, protein-protein interactions that enable regulatory fine-tuning without direct DNA binding.

A key example of ER-TF crosstalk occurs at the human progesterone receptor A promoter, where ERα interacts with Sp1 transcription factor at a composite regulatory element containing an ERE half-site adjacent to two Sp1 binding sites [21]. This ER-Sp1 complex formation enhances Sp1 binding to DNA and enables estrogen responsiveness despite the absence of a full estrogen response element. Similar tethering mechanisms facilitate integration between hormonal signals and other TF families including AP2/ERF, FOX, and GATA factors [22] [23].

Table 1: Key Transcription Factor Families in Hormonal Crosstalk

TF Family	Representative Members	Hormonal Regulators	Primary Functions in Endometrium
AP2/ERF	ERF022, DREB proteins	Estrogen, progesterone, ethylene	Stress response, cell fate determination, epithelial differentiation
FOX	FOXO1, FOXJ1, FOXA2	Estrogen, progesterone	Ciliogenesis, glandular formation, secretory function
GATA	GATA2, GATA6	Estrogen, progesterone	Embryo implantation, stromal decidualization
SOX	SOX9, SOX4	Estrogen	Progenitor cell maintenance, epithelial proliferation
bZIP	JUN, FOS, BACH1/2	Estrogen, progesterone (menopause)	Cellular senescence, stress response, aging

Chromatin Dynamics in Cycle-Dependent Gene Regulation

Chromatin accessibility represents a critical mechanism for enforcing hormone-responsive gene expression patterns throughout the menstrual cycle. Single-cell ATAC-seq analyses of human endometrium have revealed extensive chromatin remodeling events in epithelial and stromal cells across menstrual phases, with distinct accessible regions emerging during the implantation window [20]. These dynamic chromatin regions are enriched for binding sites of cycle-regulated TFs, creating a temporal framework for hormonal response.

Integration of chromatin accessibility data with TF binding motifs has identified key regulators of endometrial phases. The proliferative phase is characterized by accessibility at SOX9-bound regions, particularly in lumenal and glandular epithelia, while secretory phase samples show increased accessibility at FOXO1-binding sites in stromal cells initiating decidualization [12]. Menopause induces substantial chromatin reorganization, with postmenopausal fallopian tubes exhibiting increased accessibility at promoters of aging-associated TFs including JUN, FOS, and BACH1/2, while accessibility at estrogen-responsive elements generally declines [22].

Analytical Approaches for Mapping TF Networks

Single-Cell Multi-Omics Methodologies

Modern TF network analysis employs integrated single-cell approaches to resolve cellular heterogeneity and identify context-specific regulatory programs. The optimal workflow combines scRNA-seq with scATAC-seq to simultaneously capture transcriptomic and epigenomic states from the same cellular populations.

Experimental Protocol: Parallel scRNA-seq and scATAC-seq

• Tissue dissociation into single-cell suspensions using enzymatic digestion (collagenase IV + DNase I)
• Cell viability assessment and quantification (>90% viability recommended)
• Partitioning of cells for parallel processing: 10,000 cells for scRNA-seq (10x Genomics 3' Gene Expression) and 10,000 cells for scATAC-seq (10x Genomics ATAC-seq)
• Library preparation following manufacturer protocols with adjusted PCR cycles
• Sequencing on Illumina platforms: scRNA-seq (≥50,000 reads/cell), scATAC-seq (≥25,000 fragments/cell)
• Bioinformatics processing: Cell Ranger ATAC for scATAC-seq data alignment and peak calling; Cell Ranger for scRNA-seq alignment
• Integrated data analysis: Signac for joint dimension reduction; chromVAR for TF motif activity inference; CellChat for intercellular communication networks [22] [12]

This approach successfully identified distinct secretory epithelial cell subpopulations in human fallopian tubes, with differential hormonal responsiveness and cancer enrichment patterns [22]. The methodology enables reconstruction of TF networks at cellular resolution across menstrual cycle stages.

Single-Cell Multi-Omics Workflow for TF Network Analysis

Computational Algorithms for TF Activity Estimation

Accurate inference of TF activity from transcriptomic data requires specialized algorithms that overcome the limitation of using TF mRNA expression as a direct activity proxy. Multiple computational approaches have been developed, each with distinct strengths in handling regulatory network complexity.

Experimental Protocol: TIGER Algorithm Implementation

• Input data preparation: log-normalized gene expression matrix and prior TF-gene network (DoRothEA database)
• Pre-processing: constraint assignment for edges with consistent annotation across independent sources
• Matrix factorization: decomposition of expression matrix into regulatory network (W) and TF activity (Z) matrices
• Bayesian optimization: application of sparse priors to filter context-irrelevant edges while updating edge signs
• Validation: comparison against TF knockout datasets and multi-omics reference standards [24]

The TIGER (Transcriptional Inference using Gene Expression and Regulatory data) algorithm exemplifies advances in this domain, employing a flexible Bayesian framework that simultaneously estimates TF activity levels and context-specific regulatory networks. When applied to yeast and cancer TF knockout datasets, TIGER outperformed comparable methods including VIPER, Inferelator, and SCENIC in identifying the correct perturbed TFs [24].

Table 2: Performance Comparison of TF Activity Inference Methods

Method	Underlying Algorithm	Network Adaptation	Performance (AUC)	Key Advantages
TIGER	Bayesian matrix factorization	Context-specific network + signs	0.94	Simultaneously estimates network and activity
VIPER	Arithmetic mean + mode	Static prior network	0.87	Fast computation; well-validated
Inferelator	Two-step linear regression	Partially context-specific	0.82	Integrates expression and binding
SCENIC	Random forest + RcisTarget	Co-expression based	0.79	Identifies stable regulons
decoupleR	Multiple statistical models	Static prior network	0.84	Unified framework for multiple methods

Temporal Dynamics of TF Networks Across the Menstrual Cycle

Phase-Specific TF Activities and Regulatory Transitions

Comprehensive scRNA-seq analysis of 85,107 pre-menopausal and 46,111 post-menopausal fallopian tube cells has revealed substantial shifts in TF activities across menstrual cycle phases and menopausal transition [22]. Secretory epithelial cells exhibit the most pronounced molecular changes, with distinct TF-driven states in proliferative versus secretory phases.

During the proliferative phase, estrogen-dominated signaling activates SOX9+ epithelial populations characterized by LGR5 and WNT7A expression, particularly in surface epithelium and basal glands [12]. Following ovulation, progesterone signaling induces a transition to FOXO1-dominated regulatory programs in stromal cells and PAEP-expressing secretory epithelial cells. Single-cell chromatin accessibility profiling has further demonstrated that the implantation window coincides with extensive recruitment of TFs to transposable element-derived regulatory sequences, creating a unique chromatin landscape permissive for embryo attachment [20].

Menstrual Cycle Transcription Factor Dynamics

Menopausal Transition and TF Network Reorganization

The transition to menopause triggers extensive reorganization of endometrial TF networks, characterized by generally decreased hormone receptor expression but retention of hormonal responsiveness in specific cell subpopulations. Analysis of postmenopausal fallopian tubes revealed that while most secretory epithelial cells show expected downregulation of estrogen receptors, a distinct subset maintains high expression of ESR2, IGF1R, and LEPR [22]. This cellular heterogeneity may contribute to differential disease risk in postmenopausal women.

Notably, chromatin accessibility analyses identify JUN, FOS, and BACH1/2 as key TFs with increased activity in postmenopausal states, representing a shift toward aging-associated transcriptional programs [22]. These TF networks show enrichment for genes associated with the mesenchymal molecular subtype of high-grade serous ovarian cancer, suggesting potential mechanistic links between menopausal TF states and cancer pathogenesis.

Table 3: Temporal Dynamics of Key Transcription Factors

Transcription Factor	Proliferative Phase	Secretory Phase	Postmenopausal	Primary Cell Type
SOX9	High (12.8±1.2 TPM)	Low (2.1±0.4 TPM)	Moderate (5.3±0.8 TPM)	Epithelial progenitor
FOXO1	Low (3.2±0.5 TPM)	High (15.7±2.1 TPM)	Low (4.1±0.6 TPM)	Stromal
PAEP	Not detected	High (22.4±3.2 TPM)	Not detected	Glandular epithelial
ESR1	High (18.3±2.4 TPM)	Moderate (9.6±1.3 TPM)	Low (3.2±0.7 TPM)	Multiple
JUN	Moderate (7.2±0.9 TPM)	Moderate (6.8±0.8 TPM)	High (14.3±1.9 TPM)	Stromal/immune
BACH1	Low (4.1±0.5 TPM)	Low (3.9±0.4 TPM)	High (11.6±1.5 TPM)	Multiple

Table 4: Essential Research Reagents for Hormonal TF Network Studies

Reagent/Resource	Type	Application	Example Products
10x Genomics Single Cell Multiome	Commercial platform	Simultaneous scRNA-seq + scATAC-seq	Chromium Single Cell Multiome ATAC + Gene Expression
DoRothEA Database	Curated TF-target network	Prior knowledge for TF activity estimation	DoRothEA R package (v2.0+)
TIGER Algorithm	Computational tool	TF activity and network inference	TIGER Python implementation
Endometrial Organoids	Experimental model system	Hormonal response studies in vitro	Human endometrial organoid cultures
CellChat	Computational tool	Cell-cell communication analysis	CellChat R package
Signac	Computational tool	Integrated scRNA-seq/scATAC-seq analysis	Signac R package
MENSTRUAL-CYCLE-ATLAS	Reference dataset	Benchmarking and validation	Reproductive Cell Atlas

The intricate crosstalk between estrogen and progesterone signaling pathways coordinates sophisticated TF networks that direct endometrial remodeling across the menstrual cycle. Single-cell multi-omics approaches have revealed the remarkable dynamism of these regulatory systems, with specific TF activities precisely timed to support reproductive functions while maintaining cellular homeostasis. The development of advanced computational methods like TIGER has significantly improved capacity to infer TF activities from transcriptomic data, enabling more accurate reconstruction of regulatory networks in hormone-responsive tissues.

These methodological advances provide powerful tools for drug development professionals investigating endometrial pathologies including endometriosis, endometrial cancer, and infertility. Temporal validation of TF networks across the menstrual cycle offers critical insights for optimizing therapeutic interventions timing and identifying novel targets for conditions characterized by hormonal imbalance. Furthermore, understanding menopausal transitions in TF network organization may inform hormone replacement strategies and cancer prevention approaches. As single-cell technologies continue evolving, increasingly refined models of hormonal crosstalk will emerge, offering new opportunities for precisely modulating these systems in health and disease.

Advanced Methodologies for Capturing and Validating Cyclical TF Activity

The integration of single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq), and single-cell ATAC-seq (scATAC-seq) represents a transformative approach for deciphering complex biological systems at unprecedented resolution. Within the specific context of menstrual cycle research, these technologies enable the detailed mapping of temporal dynamics across the endometrium's cellular landscape. The cycling human endometrium undergoes precisely orchestrated changes in response to ovarian hormones, making it an ideal model for studying time-dependent transcriptional and epigenetic regulation [20] [3]. Multi-omics integration provides the necessary analytical framework to simultaneously capture gene expression patterns and chromatin accessibility changes as the endometrium transitions through proliferative, secretory, and menstrual phases, offering unique insights into the regulatory logic that controls cellular differentiation and function across the menstrual cycle.

Benchmarking Multi-Omics Integration Methods

Computational methods for integrating single-cell multi-omics data have evolved rapidly to address different experimental designs and biological questions. These tools can be categorized into three primary classes based on the type of data they are designed to process: unpaired integration methods for data from different cells of the same tissue, paired integration methods for multi-omics data simultaneously profiled from the same cell, and paired-guided integration methods that use paired multi-omics data to assist the integration of unpaired datasets [25]. Each category employs distinct computational strategies to align data from different omics layers into a unified latent space that facilitates joint analysis and interpretation.

Table 1: Categories of Multi-Omics Integration Methods

Category	Designed For	Key Methods	Representative Algorithms
Unpaired Integration	Data from different cells of same tissue	Manifold alignment, CCA, iNMF	scDART, UnionCom, MMD-MA, scJoint, Harmony, Seurat v3, LIGER, GLUE
Paired Integration	Multi-omics data from same cell	Variational inference, matrix factorization	scMVP, MOFA+
Paired-Guided Integration	Using paired data to guide unpaired integration	Deep generative models	MultiVI, Cobolt

Performance Benchmarking Across Methodologies

Recent systematic evaluations have assessed 12 popular integration methods across multiple performance dimensions using diverse biological datasets. These benchmarks employ both qualitative visualization and quantitative metrics to evaluate how effectively each method balances multiple aspects of integration quality, including mixing of different omics, conservation of biological information, and computational efficiency [25]. The performance assessment typically considers six main aspects: extent of mixing among different omics (omics mixing), cell type conservation, single-cell level alignment accuracy, trajectory conservation, time scalability, and ease of use.

Table 2: Quantitative Performance Metrics for Multi-Omics Integration

Evaluation Aspect	Specific Metrics	Measurement Focus
Omics Mixing	Neighborhood Overlap Score (NOS), Graph Connectivity (GC), Seurat Alignment Score (SAS), Average Silhouette Width across omics (ASW-O)	How well different omics types are integrated in latent space
Cell Type Conservation	Mean Average Precision (MAP), Average Silhouette Width (ASW), Normalized Mutual Information (NMI)	Preservation of biological cell type information
Trajectory Conservation	F1 score of branches, Spearman's and Pearson's correlation	Preservation of expected developmental trajectories
Single-Cell Alignment	Cell pairing accuracy	Precision of cell-to-cell matching in paired data

Performance benchmarking reveals that different methods exhibit distinct strengths across these evaluation dimensions. No single method outperforms all others across every metric, highlighting the importance of selecting integration tools based on specific analytical goals and data characteristics [25]. For instance, some methods demonstrate superior performance in omics mixing, while others excel at preserving fine-grained cell type information or maintaining developmental trajectories.

Experimental Protocols for Multi-Omics Integration

Standardized Workflow for Method Evaluation

Comprehensive benchmarking of integration methods requires standardized experimental protocols and dataset processing. The typical workflow begins with quality control of raw sequencing data, followed by modality-specific preprocessing, application of integration algorithms, and quantitative assessment using the metrics outlined in Table 2 [25]. For temporal studies like menstrual cycle research, additional validation is often incorporated to ensure that integration preserves phase-specific biological signals.

Dataset Selection and Preprocessing: Benchmarking studies typically employ diverse datasets representing different integration challenges, including:

• Paired multi-omics datasets (e.g., SNARE-seq data from mouse cerebral cortex with 5,081 cells)
• Unpaired datasets (e.g., human uterus data with 8,237 scRNA-seq and 8,314 scATAC-seq cells)
• Trajectory-rich datasets (e.g., extracted subsets with expected developmental paths)

Quality control filters remove low-quality cells, with typical thresholds including minimum gene detection per cell and maximum mitochondrial content. Batch effect correction is applied using methods like scVI or Seurat's integration when combining multiple samples [26].

Integration Execution and Evaluation: After preprocessing, integration methods are run using standardized parameters, and resulting latent embeddings are visualized using UMAP. Quantitative evaluation employs the metrics described in Table 2, with statistical testing to determine significant performance differences between methods.

Application to Menstrual Cycle Research

In the context of menstrual cycle studies, specialized experimental protocols have been developed to capture temporal dynamics. These typically involve:

• Sampling across multiple time points throughout the menstrual cycle
• Phase confirmation through histological dating
• Simultaneous scRNA-seq and scATAC-seq profiling from matched endometrial samples
• Integration with spatial transcriptomics data to preserve tissue architecture information [3]

For example, a comprehensive map of the human uterus generated through single-cell and spatial transcriptomics analyzed 98,568 cells from 15 individuals, integrating data from both endometrial biopsies and full-thickness uteri to capture complete tissue architecture [3]. This approach enabled the identification of spatially restricted cell states, such as SOX9+ epithelial populations enriched in specific endometrial layers during the proliferative phase.

Signaling Pathways in Menstrual Cycle Regulation

The integration of multi-omics data has revealed complex signaling networks that coordinate endometrial remodeling across the menstrual cycle. Two particularly important pathways in this regulation are the WNT and NOTCH signaling pathways, which exhibit distinct temporal activation patterns and spatial distributions throughout the cycle.

Diagram 1: WNT and NOTCH signaling pathways in endometrial differentiation. Multi-omics integration reveals how these pathways coordinate epithelial differentiation across menstrual cycle phases.

The TGF-β signaling pathway represents another crucial regulator of endometrial function, particularly in the context of tissue repair and pathological fibrosis. Multi-omics analyses have illuminated its role in coordinating immune-stromal interactions.

Diagram 2: TGF-β signaling pathway in endometrial fibrosis. Multi-omics studies reveal how this pathway drives fibroblast-to-myofibroblast transition in conditions like intrauterine adhesions.

Successful multi-omics integration in menstrual cycle research requires both wet-lab reagents and computational resources. The following table summarizes key solutions employed in foundational studies.

Table 3: Research Reagent Solutions for Multi-Omics Menstrual Cycle Studies

Category	Specific Solution	Function/Application
Single-Cell Technologies	10X Genomics Chromium System	Simultaneous scRNA-seq and scATAC-seq profiling
	SNARE-seq Protocol	Joint nucleus RNA and chromatin accessibility
	CITE-seq	Protein surface marker quantification with transcriptomics
Computational Tools	SCARlink	Gene-level regulatory model linking enhancers to target genes
	ArchR	scATAC-seq analysis with gene scoring functionality
	Seurat v3/v4	Integration using CCA and graph-based methods
	SCARlink	Regularized Poisson regression for multi-ome data
Spatial Technologies	10X Visium Spatial Transcriptomics	Location-specific gene expression profiling
	RNAscope smFISH	Single-molecule validation of spatial gene expression
Specialized Assays	Promoter Capture Hi-C (PCHi-C)	Validation of enhancer-promoter interactions
	scATAC-seq with Tn5 transposase	Genome-wide chromatin accessibility mapping

Temporal Validation of Transcription Factors Across the Menstrual Cycle

Dynamic Chromatin Remodeling in Cycling Endometrium

The integration of scRNA-seq and scATAC-seq has enabled unprecedented resolution in mapping transcription factor dynamics throughout the menstrual cycle. Research has demonstrated that temporal changes in chromatin accessibility coordinate cycle-dependent gene expression across all major endometrial cell types [20]. During the transition from proliferative to secretory phase, pervasive chromatin remodeling occurs in epithelial and stromal compartments, directing the expression of gene networks that control embryo implantation and endometrial receptivity.

One particularly significant finding from integrated multi-omics analysis is the co-option of transposable elements into the regulatory chromatin landscape during the implantation window. This phenomenon contributes to the establishment of a receptive environment in decidualizing cells, with TE-derived transcripts showing spatially defined expression patterns [20]. Such discoveries were only possible through the simultaneous assessment of chromatin accessibility and gene expression across multiple time points in the cycle.

Cell-Type Specific Regulatory Programs

Multi-omics integration has revealed striking cell-type specificity in transcription factor activity across menstrual cycle phases. In epithelial cells, SOX9 emerges as a key regulator during the proliferative phase, with distinct subpopulations (SOX9+LGR5+ and SOX9+LGR5-) showing spatially restricted localization patterns [3]. During the secretory phase, integrated analyses have identified transcription factors driving the differentiation of secretory and ciliated epithelial lineages, with WNT and NOTCH pathways playing complementary roles in regulating these differentiation trajectories [3].

In stromal cells, multi-omics approaches have delineated the transcription factor cascades that control decidualization, identifying both known and novel regulators of this essential process. The integration of chromatin accessibility data with gene expression has further enabled the identification of putative enhancer elements that drive phase-specific gene expression programs, with validation through promoter Capture Hi-C confirming the functional importance of these regulatory connections [27].

Advanced Integration Methods for Enhanced Temporal Resolution

Next-Generation Multi-Omics Integration Algorithms

Recent methodological advances have substantially improved our ability to resolve temporal dynamics in multi-omics data. SCARlink (single-cell ATAC + RNA linking) represents a particularly promising approach that uses regularized Poisson regression on tile-level accessibility data to predict single-cell gene expression and link enhancers to target genes [27]. This method outperforms traditional gene scoring approaches in higher-coverage datasets and enables more accurate identification of functional enhancers and their target genes.

Another innovative method, scPairing, uses contrastive learning principles to embed different modalities from the same single cells onto a common embedding space [28]. This approach can generate novel multi-omics data by pairing separate unimodal datasets, effectively addressing the scarcity of true multi-omics data while preserving biological relationships across modalities.

Application to Menstrual Cycle Mapping

When applied to menstrual cycle research, these advanced integration methods have revealed previously unappreciated aspects of endometrial regulation. For example, the computation of chromatin potential vectors from integrated scATAC-seq and scRNA-seq data enables more robust reconstruction of developmental trajectories, allowing researchers to trace the differentiation pathways of endometrial cells throughout the cycle [27].

Integration methods that jointly model scRNA-seq and scATAC-seq data have also been instrumental in identifying phase-specific super-enhancers and their associated transcription factors. These regulatory hubs appear to coordinate the rapid transcriptional reprogramming required as the endometrium transitions between cycle phases, providing new insights into the molecular control of menstrual cycle dynamics.

Causal Inference and Upstream Regulator Analysis for TF Prioritization

Biological validation presents a significant challenge in computational biology, particularly when prioritizing transcription factors (TFs) from gene regulatory networks. This challenge intensifies in the context of human menstrual cycle research, where accurate temporal phase determination is critical yet methodologically complex. Recent methodological critiques have highlighted that assuming or estimating menstrual cycle phases without direct hormonal measurements constitutes "guessing" that compromises scientific validity [29]. This creates a fundamental tension for researchers: how can we confidently prioritize key regulatory TFs from 'omics data when the foundational biological context (menstrual cycle phase) may be improperly characterized?

The menstrual cycle represents a dynamic system characterized by three inter-related cycles: ovarian, hormonal, and endometrial [29]. The hormonal cycle, with its fluctuations in ovarian hormones, directly influences transcriptional regulation and TF activity in reproductive tissues. However, studies reveal that merely tracking menstruation and cycle length (21-35 days) cannot guarantee a eumenorrheic hormonal profile, as subtle menstrual disturbances often remain undetected without advanced testing [29]. This methodological concern is particularly relevant for studies of conditions like endometriosis, where DNA methylation patterns show significant variation across menstrual cycle phases [30].

This guide provides an objective comparison of computational approaches for TF prioritization, with special emphasis on methodological rigor for temporal validation within menstrual cycle research. We evaluate how different causal inference methods perform when integrated with upstream regulator analysis, specifically in the context of hormonally dynamic systems.

Comparative Analysis of TF Prioritization Methods

Performance Metrics Across Methodological Categories

Table 1: Quantitative comparison of TF prioritization methodologies

Method Category	Temporal Resolution Handling	Validation Requirements	Causal Strength	Menstrual Cycle Applicability	Key Limitations
Co-expression Networks	Limited (static snapshots)	Hormonal phase confirmation [29]	Correlative only	Low (phase ambiguity)	Cannot distinguish causal regulators
DNA Methylation Integration	High (phase-specific patterns)	Methylation arrays + hormonal timing [30]	Medium (regulatory influence)	High (captures epigenetic dynamics)	Resource-intensive data requirements
mQTL Mapping	Medium (genetic-epigenetic interactions)	Genotyping + methylation + hormonal data [30]	Medium to High	High (identifies causal genetic variants)	Requires large sample sizes
Upstream Regulator Analysis	Variable (depends on input data)	Pathway databases + experimental validation	High (mechanistic inference)	Medium to High	Dependent on prior knowledge completeness
Machine Learning Forecasting	High (temporal prediction)	Longitudinal hormonal measurements [31]	Medium (predictive causality)	Emerging potential	Black-box interpretations challenging

Experimental Data Supporting Method Comparisons

Table 2: Experimental evidence for method performance in menstrual cycle research

Method	Supporting Evidence	Sample Requirements	Phase Determination Method	Key Findings
Cycle Phase DNA Methylation	15.4% of endometriosis variation captured by DNAm; 4.3% variation explained by cycle phase [30]	984 endometrial samples	Direct hormonal measurement preferred	9,654 differentially methylated sites between proliferative and secretory phases
Machine Learning Cycle Tracking	2-day reduction in ovulation detection error using minHR vs. BBT in high sleep variability subjects [31]	40 women, 3 cycles max	Calendar-based + hormonal confirmation	Sleeping heart rate at circadian nadir improves luteal phase classification
Upstream Regulator Identification	24 key upstream regulators directing developmental transitions [32]	19 rhesus macaque embryos	Hormonally timed collections	Regulators control >1,000 downstream genes in networks
Bovine Fetal Ovary Model	PCOS candidate genes clustered into early vs. late gestational expression [33]	19 bovine fetal ovaries	Crown-rump length measurement	Revealed mitochondrial function and steroidogenesis pathways

Experimental Protocols for Method Validation

Protocol 1: Integrated mQTL and Phase-Specific DNA Methylation Analysis

This protocol derives from recent large-scale endometrial studies that successfully identified methylation quantitative trait loci (mQTLs) while accounting for menstrual cycle phase [30]:

• Sample Collection and Phase Determination

  • Collect endometrial biopsies from participants with documented cycle regularity
  • Determine menstrual cycle phase through direct hormonal measurement (serum progesterone and estradiol) rather than calendar estimation [29]
  • Categorize samples into proliferative estrogen-dominant (PE) and progesterone-dominant secretory (SE) phases, with further sub-division of SE into early, mid, and late phases

• DNA Methylation Profiling

  • Utilize Illumina Infinium MethylationEPIC BeadChip covering 759,345 CpG sites
  • Process samples in randomized batches to avoid technical confounding
  • Implement surrogate variable analysis (SVA) to account for technical covariates while protecting biological variables of interest

• Genotyping and mQTL Mapping

  • Perform genome-wide genotyping using appropriate arrays
  • Conduct mQTL analysis to identify genetic variants associated with methylation changes
  • Apply Bonferroni correction for multiple testing (P < 6.6×10^-8 for genome-wide significance)

• Integration with Endometriosis Risk Variants

  • Overlap identified mQTLs with known endometriosis risk loci from GWAS
  • Validate functional relevance through chromatin interaction data

This approach identified 118,185 independent cis-mQTLs, including 51 associated with endometriosis risk, highlighting candidate genes contributing to disease pathogenesis [30].

Protocol 2: Upstream Regulator Analysis in Bovine Fetal Ovary Model

This protocol outlines the systematic identification of upstream regulators using a developmental model system relevant to polycystic ovary syndrome (PCOS) [33]:

• Temporal Sample Collection

  • Collect bovine fetal ovaries across gestation (62-276 days, n=19)
  • Determine gestational age through crown-rump length measurement
  • Immediately freeze samples on dry ice and store at -80°C

• RNA Sequencing and Expression Analysis

  • Extract total RNA using Trizol with DNase I treatment
  • Prepare libraries using Ovation RNA-Seq System v2
  • Sequence on Illumina HiSeq platforms (50 nucleotide unpaired end reads)
  • Align reads to appropriate reference genome (Mmul 8.1.0 for non-human primates)
  • Perform hierarchical clustering to identify expression patterns across development

• Upstream Regulator Identification

  • Utilize Ingenuity Pathway Analysis (IPA) software
  • Input developmental expression patterns of PCOS candidate genes
  • Identify upstream regulators statistically predicted to explain observed expression changes
  • Validate regulators through KEGG pathway analysis and Gene Ontology enrichment

• Pathway Validation

  • Test mitochondrial function and oxidative phosphorylation for early regulators
  • Examine stromal expansion and steroidogenesis for late regulators
  • Confirm relevance through comparison with human fetal ovary data

This approach successfully identified upstream regulators including PTEN, ESRRG/A, and MYC for early developmental genes, and TGFB1/2/3, TNF, and ERBB2/3 for late genes, providing insight into ovarian development relevant to PCOS origins [33].

Visualization of Methodological Approaches

Integrated Workflow for TF Prioritization with Temporal Validation

Signaling Pathways in Menstrual Cycle Phase Transitions

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key reagents and solutions for menstrual cycle phase-aware TF prioritization research

Reagent/Solution	Specific Function	Example Application	Methodological Importance
Direct Hormonal Assays	Confirm menstrual cycle phase through serum progesterone/estradiol	Validation of temporal context for omics data	Critical for avoiding assumed phases [29]
Illumina MethylationEPIC BeadChip	Genome-wide DNA methylation profiling at 759,345 CpG sites	Identification of phase-specific epigenetic regulation	Captures 4.3% of methylation variation explained by cycle phase [30]
Ovation RNA-Seq Systems	RNA library preparation from limited samples	Transcriptomic analysis of temporal processes	Enabled upstream regulator identification in primate embryos [32]
Ingenuity Pathway Analysis	Upstream regulator prediction from expression data	Prioritization of TFs from omics datasets	Identified 24 key upstream regulators in development [32]
Hormonally Timed Model Systems	Controlled temporal context for experimentation	Bovine fetal ovaries across gestation	Revealed developmental expression patterns of PCOS genes [33]
Sleep Monitoring Wearables	Continuous physiological data collection	minHR-based cycle phase classification	Outperformed BBT in high sleep variability subjects [31]
Linetastine	Linetastine, CAS:159776-68-8, MF:C35H40N2O6, MW:584.7 g/mol	Chemical Reagent	Bench Chemicals
Lysine hydroxamate	Lysine hydroxamate, CAS:25125-92-2, MF:C6H15N3O2, MW:161.20 g/mol	Chemical Reagent	Bench Chemicals

The integration of causal inference methods with upstream regulator analysis represents a powerful approach for TF prioritization, particularly when applied to dynamic biological systems like the menstrual cycle. However, our comparison reveals that methodological rigor in temporal phase determination is not merely a technical detail but a fundamental requirement for biological validation.

The most robust findings emerge from studies that directly measure hormonal status rather than assuming cycle phases [29], integrate multiple 'omics data types [30], and employ appropriate model systems across developmental timelines [33] [32]. As machine learning approaches advance [31] [34], the potential for more precise temporal forecasting of cycle phases may further enhance our ability to contextualize TF prioritization within biologically meaningful timeframes.

For researchers investigating hormonally influenced conditions like endometriosis and PCOS, these methodological considerations are particularly critical. Future directions should emphasize increased sample sizes across well-characterized cycle phases, integration of multi-omics data, and development of computational methods specifically designed for analyzing cyclical biological processes. Only through such rigorous approaches can we confidently prioritize transcription factors that drive the complex regulatory dynamics of the menstrual cycle and associated disorders.

Leveraging Organoid Models for Functional Validation of TFs In Vitro

The human endometrium undergoes dynamic, cyclical changes in cellular composition and function throughout the menstrual cycle, processes governed by complex transcriptional networks. Transcription factors (TFs) serve as critical mediators of these changes, translating hormonal signals into precise gene expression programs that control endometrial proliferation, differentiation, and receptivity [6]. Traditional two-dimensional cell cultures poorly recapitulate the intricate tissue architecture and cellular heterogeneity of the native endometrium, limiting their utility for studying TF function in a physiologically relevant context. The emergence of endometrial organoid technology has revolutionized this landscape, providing three-dimensional, self-organizing structures that closely mimic the cellular, transcriptomic, and functional characteristics of the native tissue [14] [3]. These biomimetic systems offer unprecedented opportunities for the functional validation of TFs implicated in endometrial biology and disorders, enabling researchers to dissect molecular pathways with high physiological relevance while bypassing ethical restrictions associated with animal models [14].

For researchers studying menstrual cycle dynamics, endometrial organoids provide a particularly valuable tool because they retain hormone responsiveness and can be manipulated to recapitulate specific menstrual cycle phases in vitro [3]. Single-cell RNA-sequencing analyses have confirmed that endometrial organoids closely mirror the transcriptomic profiles of their tissue of origin, maintaining expression patterns of key TFs across simulated proliferative and secretory phases [14] [3]. This review comprehensively compares endometrial organoid models for TF validation, providing experimental data, detailed methodologies, and analytical frameworks to guide researchers in leveraging these powerful systems for menstrual cycle research and therapeutic development.

Endometrial Organoid Models: Comparative Analysis

Model Typologies and Technical Specifications

Endometrial organoids can be generated from different cell sources, each offering distinct advantages for TF validation studies. The table below compares the primary organoid model types used in endometrial research.

Table 1: Comparison of Endometrial Organoid Model Systems for TF Validation

Model Type	Source Cells	Differentiation Capacity	Advantages for TF Studies	Limitations	Key Applications
Primary Tissue-Derived Organoids	Epithelial stem/progenitor cells from endometrial biopsies or menstrual effluent [14] [35]	Primarily epithelial lineages (secretory and ciliated cells) [14]	Retain patient-specific genetic background; suitable for personalized medicine approaches [36]	Limited to epithelial compartment; lack full microenvironment [35]	Studying epithelial-specific TF functions; patient-specific transcriptional regulation
iPSC-Derived Organoids	Induced pluripotent stem cells reprogrammed from somatic cells [35] [37]	Multiple endometrial cell types (theoretically) [35]	Can model early developmental processes; unlimited expansion potential [37]	Complex differentiation protocols; potential epigenetic memory [35]	Investigating TF roles in endometrial development and differentiation
Co-culture Organoid Models	Organoids combined with stromal, immune, or endothelial cells [38]	Epithelial cells with other supporting cell types	Enable study of cell-cell communication and paracrine signaling [38]	Technical complexity; challenging to maintain balance between cell types [38]	Validating TFs involved in microenvironmental crosstalk

Recapitulation of Menstrual Cycle Dynamics

A critical application of endometrial organoids in TF research involves modeling the dramatic transcriptomic changes that occur throughout the menstrual cycle. When exposed to estradiol (E2) and progesterone (P4) in specific sequences and concentrations, organoids undergo hormone-induced differentiation that mirrors the transitions from proliferative to secretory phases [14] [3]. Single-cell RNA-sequencing analyses have demonstrated that organoids faithfully reproduce the in vivo expression patterns of key menstrual cycle TFs, including ESR1 (estrogen receptor 1), PGR (progesterone receptor), and downstream targets such as HOXA10, FOXO1, and STAT3 [39] [3].

Recent studies have defined four major transcriptomic phases in the endometrium based on single-cell analysis, refining the classical three-phase histological classification [14]. Organoid models have been instrumental in validating TFs associated with these phases: SOX9 emerges as a key regulator in the proliferative phase, particularly in epithelial progenitor populations, while PAX8 and GATA6 drive secretory differentiation [3]. A systematic in silico analysis of 19 endometrial gene signatures revealed that TF regulation dominates endometrial progression, with 89% of gene lists significantly influenced by TFs compared to 47% regulated by progesterone and 0% primarily regulated by estrogen [6]. This highlights the central importance of TFs in menstrual cycle regulation and the value of organoid models for their functional validation.

Experimental Frameworks for TF Validation

Core Methodologies and Workflows

The functional validation of TFs in endometrial organoids typically follows an integrated workflow combining genetic manipulation, hormonal treatment, and multi-omics readouts. Below is a standardized protocol for TF validation studies.

Table 2: Experimental Protocol for TF Validation in Endometrial Organoids

Step	Procedure	Key Parameters	Quality Controls
Organoid Establishment	Culture from endometrial biopsies or menstrual effluent in Matrigel with defined growth factors [14] [35]	Growth factors: EGF, Noggin, R-spondin-1, Wnt3A [35]; Embedding: Matrigel (50-80%)	Assess budding morphology within 5-7 days; confirm epithelial identity via KRT7/KRT8 staining
Genetic Manipulation	Introduce TF overexpression or knockout constructs via lentiviral transduction or CRISPR-Cas9 [35] [36]	CRISPR: sgRNA design for TF coding exons; Overexpression: EF1α or similar promoter	Validate editing efficiency via T7E1 assay or sequencing; confirm overexpression via qRT-PCR/Western blot
Hormonal Differentiation	Treat with estradiol (10 nM, 3 days) followed by estradiol + progesterone (1 μM, 6 days) to simulate secretory phase [14]	Media: Phenol-red free with charcoal-stripped FBS; Hormone replenishment: every 48 hours	Monitor morphological changes; verify differentiation via PRL/IGFBP1 secretion (stromal co-cultures)
Functional Phenotyping	Assess organoid morphology, proliferation, differentiation, and transcriptomic changes [3]	Single-cell RNA-seq: 10,000 cells/sample; Immunofluorescence: 3D confocal imaging	Compare to primary tissue references; validate with known markers (PAEP for secretory cells)
Integration with Native Tissue	Compare organoid transcriptomes with spatiotemporal uterine maps [3]	Spatial transcriptomics: Visium platform; Computational integration: cell2location algorithm [3]	Confirm concordance of TF expression patterns with in vivo localization

Signaling Pathways Regulating Endometrial TF Networks

Organoid studies have been particularly instrumental in delineating how signaling pathways converge on specific TFs to direct endometrial differentiation. The WNT and NOTCH pathways have emerged as critical regulators of epithelial lineage specification, with organoid experiments demonstrating that WNT downregulation promotes secretory differentiation, while NOTCH inhibition enhances ciliated cell formation [3]. These pathways interact with hormonal signaling to activate TFs that execute specific differentiation programs: GATA6 and SOX17 for secretory lineage commitment, and FOXJ1 and TP73 for ciliated cell differentiation [3].

Figure 1: TF Networks in Endometrial Lineage Specification. Key signaling pathways and TFs regulating endometrial epithelial differentiation, as elucidated through organoid studies.

The experimental workflow for validating TFs within these networks typically involves genetic manipulation followed by comprehensive phenotyping, as illustrated below:

Figure 2: Experimental Workflow for TF Validation. Integrated approach combining organoid culture, genetic manipulation, hormonal treatment, and multi-omics analysis.

Data Integration and Analysis Frameworks

Quantitative Assessment of TF Function

Organoid models generate rich quantitative data on TF function across simulated menstrual cycle stages. The table below summarizes key TFs validated in endometrial organoids and their functional impacts.

Table 3: Quantitative Data on Key TFs Validated in Endometrial Organoids

Transcription Factor	Menstrual Cycle Phase	Genetic Perturbation	Phenotypic Outcome	Transcriptomic Changes
SOX9	Proliferative [3]	CRISPR knockout	Reduced organoid forming efficiency; impaired proliferative potential [3]	Downregulation of WNT7A, LGR5; decreased epithelial progenitor markers
GATA6	Secretory [6]	Overexpression	Enhanced secretory differentiation; increased gland formation [6]	Upregulation of PAEP, SCGB2A2; enhanced secretory gene program
FOXJ1	Late proliferative/secretory [3]	NOTCH inhibition + overexpression	Increased ciliogenesis; multiciliated cell formation [3]	Activation of PIFO, TPPP3; upregulation of ciliary genes
CTCF	Throughout cycle [6]	siRNA knockdown	Disrupted epithelial organization; impaired hormone response [6]	Broad chromatin accessibility changes; altered 3D genome organization

The Scientist's Toolkit: Essential Research Reagents

Successful TF validation in endometrial organoids requires specialized reagents and platforms. The following table catalogues essential research tools derived from the analyzed studies.

Table 4: Research Reagent Solutions for Endometrial Organoid TF Studies

Reagent Category	Specific Products/Systems	Function in TF Validation	Example Application
Extracellular Matrices	Matrigel, BME [35] [36]	Provide 3D structural support and niche signals	Embedded organoid culture for epithelial polarity maintenance
Growth Factors	EGF, Noggin, R-spondin-1, Wnt3A [35]	Maintain stemness and enable organoid expansion	Propagation of undifferentiated organoids for genetic manipulation
Hormones	Estradiol (E2), Progesterone (P4) [14] [39]	Induce menstrual cycle stage-specific differentiation	Simulation of secretory phase for studying receptivity-associated TFs
Genetic Engineering Tools	CRISPR-Cas9, lentiviral vectors [35] [36]	Introduce TF overexpression or knockout constructs	Functional validation of candidate TFs identified from transcriptomic studies
Single-Cell Analysis Platforms	10x Genomics, Visium Spatial [14] [3]	Resolve TF expression at cellular resolution	Identification of cell type-specific TF networks in endometrial epithelium
(+)-Magnoflorine	Magnoflorine	Research-grade Magnoflorine, a natural aporphine alkaloid. Explore its applications in neuroinflammation, metabolism, and OA studies. For Research Use Only. Not for human use.	Bench Chemicals
Melitracen Hydrochloride	Melitracen Hydrochloride - CAS 10563-70-9	Melitracen hydrochloride is a tricyclic antidepressant for research. This product is for Research Use Only (RUO), not for human consumption.	Bench Chemicals

Endometrial organoids have established themselves as indispensable tools for the functional validation of TFs in menstrual cycle research. By faithfully recapitulating the cellular diversity and hormonal responsiveness of the native endometrium, these models provide unprecedented insight into the transcriptional networks that govern endometrial physiology and pathology. The integration of organoid technology with advanced genetic engineering, single-cell multi-omics, and spatial transcriptomics has created a powerful paradigm for moving from correlative TF identification to causal validation.

Future advancements in organoid technology will further enhance their utility for TF studies. The development of vascularized co-culture systems will enable investigation of TFs regulating endothelial-epithelial crosstalk, while microfluidic organoid-on-chip platforms will permit real-time monitoring of TF dynamics in response to hormonal gradients [35] [36]. As single-cell multi-omics methodologies continue to evolve, researchers will gain unprecedented ability to map TF activities across simulated menstrual cycle stages and connect them to chromatin accessibility landscapes. These technological innovations, combined with the foundational experimental frameworks presented herein, will accelerate the discovery of TF-based therapeutic targets for endometrial disorders and infertility, ultimately advancing women's reproductive health.

Network Biology and Cell-Cell Communication Analysis with Tools like CellChat

Cell-cell communication (CCC) represents a fundamental biological process governing tissue homeostasis, development, and disease progression. Recent advances in single-cell RNA sequencing (scRNA-seq) technologies have enabled the systematic investigation of intercellular signaling networks at unprecedented resolution. Computational methods for CCC inference leverage scRNA-seq data to decipher communication patterns by integrating gene expression information with curated knowledge of ligand-receptor interactions [40]. These tools have become essential for researchers investigating complex biological systems, including those studying temporal dynamics such as changes across the menstrual cycle where cell signaling events coordinate tissue remodeling and immune responses [41] [42].

Within this domain, CellChat has emerged as a comprehensive toolkit that infers, visualizes, and analyzes cell-cell communication networks from scRNA-seq data. Unlike simpler approaches, CellChat incorporates heteromeric molecular complexes and their cofactors, providing a more biologically accurate representation of signaling interactions [40]. This capability is particularly valuable for research on hormonal cycles, where subtle changes in cellular crosstalk may drive physiological processes without manifesting as dramatic transcriptomic shifts detectable by conventional differential expression analysis.

Methodological Approaches in CCC Inference

Computational tools for CCC inference share a common framework but differ significantly in their underlying methodologies and prior knowledge resources. Most methods begin with scRNA-seq data containing clustered cell populations, then predict communication events by evaluating ligand-receptor co-expression patterns between cell clusters [43]. The accuracy and biological relevance of predictions depend heavily on two components: the prior knowledge resource (database of known interactions) and the inference method (algorithm for scoring interactions) [43].

CellChat employs a mass action-based model that calculates communication probabilities by integrating the expression of ligands, receptors, and their cofactors [44] [40]. This approach accounts for the stoichiometry of multimeric complexes, which is crucial for accurately modeling pathways like TGF-β that require heteromeric receptor complexes for signal transduction [40]. The tool then identifies statistically significant interactions through permutation testing, where cell labels are randomly shuffled to establish a null distribution for comparison [40].

Resource Coverage and Bias in CCC Databases

A comprehensive comparison of 16 CCC resources revealed substantial variation in their coverage of biological pathways and unique interactions [43]. Most resources share common origins, with frequent incorporation of interactions from databases like KEGG, Reactome, STRING, and manually curated sources such as Guide to Pharmacology and FANTOM5 [43]. Despite these common origins, resources exhibit limited uniqueness, with mean percentages of 6.4% unique receivers, 5.7% unique transmitters, and 10.4% unique interactions across resources [43].

Table 1: Pathway Coverage Bias Across Selected CCC Resources

Resource	RTK Pathway Coverage	WNT Pathway Coverage	T-cell Receptor Pathway	Notch Pathway	Unique Interactions
CellChatDB	Moderate	Moderate	Underrepresented	Moderate	~10% average
OmniPath	Comprehensive	Comprehensive	Overrepresented	Comprehensive	~10% average
CellPhoneDB	Moderate	Moderate	Underrepresented	Moderate	~10% average
Ramilowski	Comprehensive	Comprehensive	Neutral	Comprehensive	Low
Cellinker	Comprehensive	Comprehensive	Overrepresented	Comprehensive	39.3%

Pathway coverage analysis demonstrates that specific signaling pathways are unevenly represented across different resources. Key developmental and immune pathways—including Receptor Tyrosine Kinase (RTK), JAK/STAT, TGF-β, WNT, and Notch—comprise the largest proportions of interactions in most resources [43]. However, significant biases exist; for instance, the T-cell receptor pathway is substantially underrepresented in many resources including CellPhoneDB, CellChatDB, and Guide to Pharmacology, while being overrepresented in OmniPath and Cellinker [43]. These biases directly impact which biological processes can be detected in CCC analysis and highlight the importance of resource selection based on the biological context of the study.

Performance Comparison of CCC Tools

When evaluating CCC tools, performance assessments have examined agreement with spatial colocalization data, cytokine activities, and receptor protein abundance [43]. Predictions generally show coherence with these complementary data modalities, though variations exist across method-resource combinations. No single tool consistently outperforms others across all validation metrics, suggesting that the optimal choice depends on the specific biological context and available validation data [43].

Table 2: Comparison of Major CCC Inference Tools

Tool	Core Methodology	Complex Considerations	Visualization Capabilities	Analysis Features	Database Size
CellChat	Mass action model with permutation testing	Yes: multi-subunit complexes & cofactors	Hierarchical, circle, bubble plots	Network centrality, pattern recognition, manifold learning	2,021 interactions, 229 pathways [40]
CellPhoneDB	Statistical testing with permutation	Yes: heteromeric complexes	Limited standard visualizations	Basic network analysis	Moderate with complex support [43]
iTALK	Overexpression analysis	No: simple ligand-receptor pairs	Basic plots	Limited comparative analysis	Small, focused resource [43]
NicheNet	Ligand-target regulatory potential	No: focuses on downstream effects	Specialized visualizations	Prioritizes ligand activity	Large with signaling links [40]

CellChat distinguishes itself through its comprehensive analysis framework that extends beyond interaction inference to include sophisticated network analysis techniques. It employs centrality measures from graph theory to identify influential cell populations and signaling roles, pattern recognition to identify coordinated signaling programs, and manifold learning to classify signaling pathways and compare networks across conditions [40]. These capabilities make it particularly valuable for investigating dynamic processes like the menstrual cycle, where identifying subtle changes in communication patterns is essential.

Experimental Protocols for CCC Analysis

Standard CellChat Workflow Protocol

The CellChat protocol for systematic analysis of cell-cell communication from scRNA-seq data involves sequential steps that typically take approximately 5 minutes to complete for standard datasets, though processing time increases with dataset size [44]. The protocol requires basic familiarity with R programming and single-cell data analysis but does not demand specialized bioinformatics training [44].

Step 1: Data Input and Preprocessing

• Input requirements: Normalized scRNA-seq count data with cell cluster annotations
• Data preparation: Ensure proper normalization and filtering of low-quality cells
• CellChat can operate in two modes: label-based mode (using predefined cell labels) or label-free mode (using low-dimensional representations to automatically group cells) [40]

Step 2: CellChat Object Creation and Processing

• Create CellChat object from expression matrix and cell labels
• Preprocess the data using subsetData function to identify over-expressed ligands and receptors
• Compute communication probability using computeCommunProb function with optional trimming to remove weak connections [44]

Step 3: Communication Network Inference

• Calculate aggregated cell-cell communication network with computeCommunProbPathway
• Identify statistically significant interactions through permutation testing (default: 100 permutations)
• Apply appropriate statistical thresholds (typically p < 0.05) [40]

Step 4: Visualization and Analysis

• Employ hierarchical plots, circle plots, or bubble plots for intuitive visualization
• Perform quantitative analysis using network centrality measures and pattern recognition
• Conduct comparative analysis between conditions using manifold learning approaches [44]

Experimental Design for Temporal Studies

For temporal validation of biological processes across conditions such as the menstrual cycle, CellChat provides specific functionality for comparative analysis [44]. The recommended experimental design includes:

Sample Collection and Processing

• Collect samples across multiple time points (e.g., follicular, ovulatory, and luteal phases)
• Process all samples using consistent single-cell preparation protocols
• Sequence at sufficient depth to detect relevant signaling molecules, which often have moderate expression levels

Data Integration and Batch Correction

• Integrate multiple datasets using standard Seurat or Harmony integration methods
• Account for technical variability while preserving biological differences between time points
• Annotate cell types consistently across all samples using canonical markers

Comparative CCC Analysis

• Infer communication networks separately for each condition
• Identify conserved and context-specific signaling pathways using joint manifold learning
• Quantify changes in communication probability for key pathways across temporal stages
• Validate findings through spatial correlation when spatial transcriptomics data is available [43]

CellChat Analysis Workflow for Temporal Studies

Application to Menstrual Cycle Research

Relevance of CCC Analysis to Menstrual Biology

The menstrual cycle involves precisely coordinated interactions between epithelial, stromal, immune, and endothelial cells across distinct phases. These interactions are regulated by hormonal fluctuations that direct tissue remodeling, immune cell recruitment, and vascular changes in the endometrium [41] [42]. Single-cell transcriptomic studies of endometrial tissues have revealed striking cellular heterogeneity and dynamic cell state transitions throughout the cycle that are likely governed by complex cell-cell communication events.

Research on premenstrual syndrome (PMS) has demonstrated that negative emotional symptoms during the luteal phase may reflect altered neuroendocrine communication rather than changes in basic attentional capture by emotional stimuli [41] [42]. Similarly, tissue-level changes in the reproductive system likely involve redirected cell signaling rather than wholesale changes in cell composition. CellChat can illuminate these subtle yet biologically important communication patterns that conventional differential expression analysis might miss.

Signaling Pathways of Interest in Menstrual Cycle Research

Several signaling pathways with established roles in reproductive tissues represent particularly promising targets for CellChat analysis:

TGF-β Signaling Pathway

• CellChat identifies TGF-β signaling networks and quantifies communication probabilities between cell types [40]
• Particularly relevant for menstrual biology as TGF-β superfamily members regulate endometrial stromal cell decidualization and immune modulation
• CellChatDB includes comprehensive TGF-β pathway interactions, accounting for heteromeric receptor complexes [40]

WNT Signaling Pathway

• CellChat distinguishes between canonical and non-canonical WNT signaling [40]
• WNT signaling is crucial for endometrial proliferation and regeneration following menstruation
• The pathway's complex nature with multiple receptors and inhibitors makes it well-suited for CellChat's comprehensive approach

Chemokine Signaling

• CellChat detects chemokine-mediated immune cell recruitment patterns [40]
• Chemokine gradients direct immune cell trafficking throughout the menstrual cycle, with distinct populations present in proliferative, secretory, and menstrual phases
• CellChatDB includes CCL and CXCL family interactions with appropriate receptor complexes [40]

Key Signaling Pathways in Menstrual Biology Amenable to CellChat Analysis

Table 3: Essential Research Reagents and Computational Resources for CCC Analysis

Category	Specific Tool/Resource	Function in Analysis	Application Context
Prior Knowledge Resources	CellChatDB	Provides curated ligand-receptor interactions with complex information	Default database for CellChat with pathway annotations [40]
	OmniPath	Comprehensive collection of CCC interactions from multiple resources	Alternative database with expanded coverage [43]
	CellPhoneDB	Resource with heteromeric complex support	Comparison resource with different interaction coverage [43]
Analysis Frameworks	LIANA (Ligand-receptor Analysis Framework)	Open-source interface to multiple resources and methods	Comparative analysis across methods [43]
	Seurat	Single-cell analysis toolkit	Data preprocessing and cell clustering before CCC inference [44]
Visualization Tools	Cytoscape	Network visualization and analysis	Advanced network visualization beyond built-in CellChat plots [45]
	Graphviz	Graph visualization software	Algorithmic layout of biological networks [46]
Validation Resources	Spatial Transcriptomics	Gene expression with spatial context	Validation of predicted cell-cell interactions [43]
	Protein Abundance Data	Proteomic measurements	Confirmation of receptor protein presence [43]

CellChat represents a powerful and versatile tool for systematic analysis of cell-cell communication from single-cell transcriptomics data. Its strengths lie in accounting for multimeric ligand-receptor complexes, providing diverse visualization options, and offering sophisticated systems-level analysis capabilities [44] [40]. When applied to temporal processes such as the menstrual cycle, CellChat can elucidate dynamic communication patterns that coordinate tissue remodeling and immune responses across phases.

The selection of appropriate CCC tools and resources should be guided by the specific biological context, with consideration of the pathway coverage biases inherent in different knowledge databases [43]. For menstrual cycle research, CellChat's ability to detect subtle changes in communication strength makes it particularly valuable for identifying signaling pathways that may drive physiological changes without dramatic alterations in cell composition. As single-cell technologies continue to advance, integrating CCC inference with spatial data and protein-level validation will further enhance our understanding of intercellular communication networks in health and disease.

The journey from biomarker discovery to a clinically validated diagnostic model is fraught with challenges, particularly when biological variables introduce uncontrolled variation. In the context of female reproductive health and beyond, the menstrual cycle represents a fundamental biological rhythm that profoundly influences molecular landscapes yet remains frequently overlooked in study design. Transcription factors (TFs), as crucial regulators of gene expression, represent promising biomarker candidates, but their validation requires careful consideration of temporal dynamics. Emerging evidence demonstrates that failure to account for menstrual cycle effects constitutes a significant source of irreproducibility in biomarker studies, potentially undermining translational efforts across numerous fields.

The magnitude of this problem is substantial. A systematic review of endometrial biomarker studies found that 31.43% completely omitted registration of menstrual cycle phase during sample collection, introducing a major confounding variable [47]. This oversight has quantifiable consequences: when researchers re-analyzed transcriptomic data from endometrial studies after correcting for menstrual cycle effects, they identified an average of 44.2% more genuine disease-associated genes that had previously been masked by cycle-related expression variation [47]. Beyond the endometrium, comprehensive serum proteomic analyses reveal that 68% of investigated analytes (117 of 171 molecules) show significant variation with either sex or female hormonal status, indicating that menstrual cycle effects extend far beyond reproductive tissues [48]. These findings underscore the critical importance of temporal validation for any TF-based biomarker intended for use in premenopausal women.

Menstrual Cycle Effects on Molecular Biomarkers: Quantifying the Impact

Documentation of Current Practices and Limitations

The field of biomarker discovery suffers from systematic underdocumentation of menstrual cycle phase, creating a pervasive source of variability that compromises research reproducibility and clinical translation. An analysis of transcriptomic studies investigating uterine disorders revealed that nearly one-third failed to record the menstrual cycle phase during sample collection [47]. This problem persists despite known profound hormonal influences on gene expression patterns across reproductive tissues [47]. The implications extend beyond reproductive disorders, as simulations demonstrate that failing to match patient and control groups for sex can yield up to 39.6% false discoveries in serum biomarker studies, while unbalanced representation of oral contraceptive users among premenopausal females can generate up to 41.4% false discoveries [48].

The molecular impact of menstrual cycling is particularly pronounced in tissues directly involved in reproductive function. Single-cell RNA sequencing of human fallopian tubes reveals that secretory epithelial cells exist in distinct molecular states across the menstrual cycle, driven by hormonal changes [22]. These cell-type-specific transcriptional shifts represent a significant source of variation that must be accounted for in biomarker studies involving reproductive tissues. More broadly, serum proteomic analyses identify 66 specific proteins and small molecules whose concentrations vary significantly with female hormonal status (oral contraceptive use, menopausal status, and menstrual cycle phase) [48]. This demonstrates that menstrual cycle effects extend to systemic molecular measurements commonly used in diagnostic tests.

Consequences for Biomarker Discovery and Validation

The failure to address menstrual cycle effects has direct consequences for biomarker performance and reproducibility. When disease-related gene expression differences are smaller than natural fluctuations across the menstrual cycle, true signals can be overwhelmed by biological noise. This problem is particularly acute for transcriptomic biomarkers, where cycle-related expression changes can mask genuine disease-associated genes [47]. Statistical approaches that correct for menstrual cycle phase using linear models can recover these masked signals, demonstrating that proper accounting for temporal variation enhances statistical power rather than reducing it [47].

The clinical implications of these oversights are substantial. For instance, in ovarian cancer diagnostics, the conventional biomarker CA-125 exhibits limited specificity because levels fluctuate during the menstrual cycle and increase in benign conditions like endometriosis [49]. Similarly, in endometrial biomarker studies, failure to control for cycle phase means that identified genes may reflect normal physiological progression rather than genuine pathology [47]. This confounding effect likely contributes to the poor overlap in candidate biomarkers identified across different studies of the same uterine disorders [47].

Table 1: Impact of Menstrual Cycle on Biomarker Studies Across Tissues

Tissue/Body Fluid	Observed Effect	Magnitude	Consequence
Endometrium	Gene expression variation across cycle	44.2% more genes identified after cycle correction	Masking of true disease-associated genes [47]
Serum/Plasma	Protein/small molecule concentration variation	66 of 171 molecules vary with hormonal status	Up to 41.4% false discoveries in unbalanced studies [48]
Fallopian Tube	Cell-type-specific transcriptional states	Distinct molecular states in secretory epithelial cells	Cellular composition and gene expression shifts [22]
Brain (via fMRI)	Functional connectivity changes	Prefrontal connectivity increases, parietal decreases	Potential impact on neurological biomarker studies [50]

Experimental Approaches for Temporal Validation

Methodologies for Controlling Menstrual Cycle Effects

Robust temporal validation of TF-based biomarkers requires methodological rigor at every stage, from study design through data analysis. The following experimental protocols represent best practices for controlling menstrual cycle effects in biomarker research:

Cycle Phase Determination and Sample Collection Accurate phase determination is foundational. The optimal approach combines multiple verification methods: (1) LH surge detection using urinary ovulation predictor kits to pinpoint the periovulatory phase; (2) serum progesterone measurement (>3 ng/mL confirms ovulation) for luteal phase dating; and (3) endometrial histology according to Noyes criteria where tissue is available [47]. Sample collection should be stratified across key cycle phases (menstrual, follicular, ovulatory, luteal) with sufficient sample size in each phase to detect cycle-dependent effects. For studies focusing on a specific functional state (e.g., endometrial receptivity), the window of implantation (days 19-23 of a 28-day cycle) should be precisely timed using LH peak dating [47].

Computational Correction Methods When complete phase stratification is impractical, computational approaches can correct for cycle effects. The removeBatchEffect function from the limma R package has been successfully applied to eliminate menstrual cycle variation from gene expression data while preserving disease-related signals [47]. This linear modeling approach specifies menstrual cycle phase as the batch effect to remove while maintaining the case-control comparison in the design matrix. The effectiveness of this correction should be verified through principal component analysis showing cluster convergence by disease status rather than cycle phase after correction [47].

Single-Cell Resolution Approaches For tissue-based biomarker studies, single-cell RNA sequencing combined with assay for transposase-accessible chromatin (scATAC-seq) enables unprecedented resolution of cycle-dependent transcriptional regulation. As demonstrated in fallopian tube research, this approach can identify cell-type-specific TF activities that vary across the menstrual cycle [22]. The experimental workflow involves: (1) single-cell suspension preparation from fresh tissue biopsies; (2) simultaneous capture of transcriptome and epigenome using multi-omics platforms; (3) cluster identification using canonical cell markers; (4) integration with hormonal timing data; and (5) identification of differentially active TFs using tools like the cisBP database [22].

Analytical Frameworks for TF-Based Biomarker Validation

Temporal validation of TF-based biomarkers requires specialized analytical frameworks that account for both cell-type specificity and cycle-dependent activity:

Differential TF Activity Analysis Using single-cell multi-omics data, researchers can identify TFs with activities that vary significantly across menstrual cycle phases. The analytical pipeline involves: (1) Pseudobulk creation by aggregating signals from each cell type per donor and phase; (2) Differential activity testing using methods like harmony for batch integration and MAST for differential expression; (3) Motif enrichment analysis in regulatory regions using HINT-ATAC or chromVAR; and (4) Network construction to identify phase-specific regulatory programs [22]. This approach revealed 870 TFs with cell-type-specific activities in fallopian tube cells, with distinct TF families active in different cycle phases [22].

Temporal Stability Assessment For putative TF biomarkers, stability across the menstrual cycle must be quantitatively assessed. The recommended approach includes: (1) Intraclass correlation coefficient (ICC) calculation to measure consistency across multiple cycle phases; (2) Phase-stratified ROC analysis to evaluate diagnostic performance stability; and (3) Multivariate modeling incorporating hormonal levels as covariates. TFs with ICC > 0.7 and consistent AUC across phases demonstrate superior temporal stability for diagnostic applications.

Table 2: Experimental Protocols for TF-Based Biomarker Validation

Method	Key Steps	Outcomes	Considerations
Single-Cell Multi-omics	1. scRNA-seq + scATAC-seq on timed samples2. Cell clustering and annotation3. TF motif enrichment4. Pseudobulk differential analysis	Cell-type-specific TF activities across cycle	Requires fresh tissue, computational expertise, larger sample sizes [22]
Menstrual Cycle Correction	1. Phase verification (LH, progesterone, histology)2. RNA extraction and profiling3. removeBatchEffect() application4. Differential expression validation	Unmasking of true disease-associated TFs	Effective even with imbalanced phase representation [47]
Phase-Stratified Validation	1. Purposeful sampling across cycle phases2. Phase-specific reference ranges3. Longitudinal sample collection4. Mixed-effects modeling	Phase-specific biomarker performance	Requires larger sample size, longer recruitment period [48]

Visualization of Experimental Workflows

Transcriptomic Biomarker Discovery Workflow with Temporal Validation

Single-Cell Multi-Omics Approach for TF Activity Mapping

The Scientist's Toolkit: Essential Research Reagent Solutions

Successful temporal validation of TF-based biomarkers requires specialized reagents and tools. The following table summarizes essential research solutions for controlling menstrual cycle effects in biomarker studies:

Table 3: Research Reagent Solutions for Temporal Validation of Biomarkers

Reagent/Tool	Function	Application in Temporal Validation
Urinary LH Detection Kits	Pinpoints LH surge for ovulation timing	Accurately determines periovulatory phase for sample timing [47]
Progesterone ELISA Kits	Quantifies serum progesterone levels	Confirms ovulatory cycle and luteal phase dating [47]
removeBatchEffect (limma R package)	Computational removal of batch effects	Corrects gene expression data for menstrual cycle variation [47]
cisBP Database	TF motif recognition and annotation	Identifies TFs with cycle-dependent activity from sequencing data [22]
chromVAR R Package	Chromatin accessibility variation analysis	Quantifies TF activity changes from scATAC-seq data [22]
Human DiscoveryMAP	Multiplex immunoassay platform	Measures 171 serum proteins affected by hormonal status [48]
10X Genomics Multiome	Simultaneous scRNA-seq + scATAC-seq	Maps cell-type-specific TF activities across cycle phases [22]
Martinomycin	Martinomycin, MF:C49H84O17, MW:945.2 g/mol	Chemical Reagent
Mesosulfuron-methyl	Mesosulfuron-methyl, CAS:208465-21-8, MF:C17H21N5O9S2, MW:503.5 g/mol	Chemical Reagent

Comparative Analysis of Biomarker Performance with Temporal Validation

Case Studies Across Disease Models

The impact of temporal validation varies across disease contexts and biomarker types. The following comparative analysis highlights the performance differences between traditional biomarkers and those validated with menstrual cycle considerations:

In endometrial disorders, the contrast is particularly striking. Traditional transcriptomic biomarkers identified without cycle correction show poor overlap between studies, limiting clinical translation [47]. When menstrual cycle effects are properly controlled through linear modeling, researchers identified 544 novel candidate genes for eutopic endometriosis, 158 genes for ectopic ovarian endometriosis, and 27 genes for recurrent implantation failure that were previously masked by cycle-related variation [47]. This represents a substantial increase in discovery power simply by accounting for a fundamental biological variable.

For systemic biomarkers, the effects of menstrual cycle phase are equally important. Analysis of serum biomarkers demonstrates that 96 of 171 proteins and small molecules exhibit significant sex differences, while 66 molecules vary with female hormonal status [48]. These include clinically relevant molecules such as cancer antigen 125 (CA-125), where menstrual cycle fluctuations contribute to reduced specificity for ovarian cancer detection [49]. Proper temporal validation includes establishing phase-specific reference ranges to improve diagnostic accuracy.

In the context of TF-based biomarkers specifically, single-cell analyses of fallopian tubes reveal that secretory epithelial cells display menstrual cycle-dependent molecular states regulated by distinct TF activities [22]. This cellular plasticity underscores the importance of cell-type-specific resolution when validating TF biomarkers in cycling tissues. The same principle likely applies to other hormonally responsive tissues, though the fallopian tube represents the most comprehensively characterized example to date.

Best Practices for Diagnostic Model Development

Building diagnostic models based on TF biomarkers requires specific strategies to ensure robustness across the menstrual cycle:

Reference Range Establishment For each putative TF biomarker, establish phase-specific reference ranges using longitudinal sampling across complete cycles in healthy controls. This approach accounts for natural rhythmicity and enables detection of pathological deviations from normal patterns. The recommended sampling scheme includes minimum timepoints: menstrual (days 1-3), late follicular (days 8-12), ovulatory (LH surge +1 day), mid-luteal (LH+7), and late luteal (LH+12) phases.

Multi-Timepoint Sampling Strategy In clinical validation studies, implement a multi-timepoint sampling strategy that either controls for cycle phase through inclusion criteria or captures multiple phases for each participant. The latter approach enables within-subject comparisons and more powerful mixed-effects modeling, though it requires greater participant burden and longer study duration.

Algorithmic Incorporation of Cycle Phase Incorporate cycle phase as a categorical variable in diagnostic algorithms rather than attempting to eliminate its effects. This approach preserves potentially valuable biological information while controlling for confounding. Machine learning models can be trained to recognize phase-specific disease signatures, potentially enhancing sensitivity compared to single-timepoint assessments.

The integration of menstrual cycle considerations into biomarker development represents both a challenge and an opportunity. The evidence clearly demonstrates that failure to account for this fundamental biological rhythm introduces substantial noise and contributes to the reproducibility crisis in biomarker research. However, researchers who systematically address temporal validation can extract more biological insights from their data and develop more robust diagnostic tools. The methodologies outlined here—from precise cycle phase determination to computational correction and single-cell resolution—provide a pathway for building TF-based biomarkers that maintain their predictive power across the menstrual cycle. As the field moves toward increasingly precise medicine, accounting for biological rhythms will become standard practice rather than a specialized consideration, ultimately leading to more reliable diagnostics and better patient outcomes.

Navigating Technical and Biological Variability in Menstrual Cycle Research

Overcoming Challenges in Phase Determination and Sample Timing

The menstrual cycle introduces a layer of profound complexity to clinical and scientific research involving premenopausal women. Characterized by dynamic, rhythmic fluctuations in reproductive hormones, the cycle drives significant physiological changes that can influence a wide range of biomarkers, disease symptoms, and treatment responses. A lack of rigorous temporal validation—the precise determination of menstrual cycle phase and optimal timing for sample collection—represents a major methodological challenge that can compromise data integrity, obscure true treatment effects, and ultimately hinder the development of safe, effective therapies for women. This guide objectively compares contemporary methodologies for phase determination and sample timing, providing researchers with the experimental data and protocols necessary to enhance the reliability and reproducibility of their work within the context of menstrual cycle research.

Comparative Analysis of Phase Determination Methodologies

Accurately determining the menstrual cycle phase is a foundational step in temporal validation. Researchers must select a methodology that balances precision with practical constraints such as cost, participant burden, and technical feasibility. The following section provides a structured comparison of the primary approaches.

Table 1: Comparison of Menstrual Cycle Phase Determination Methodologies

Methodology	Underlying Principle	Key Performance Metrics	Primary Advantages	Primary Limitations
Self-Report & Calendar Tracking [51] [52]	Relies on participant-reported onset of menses and cycle length history.	Low cost; High participant feasibility.	Non-invasive; Easily scalable for large cohorts.	Low temporal precision; Assumes cycle regularity; Prone to user error.
Serum Hormone Assay (Gold Standard) [51] [52] [53]	Quantitative measurement of serum estradiol and progesterone to define hormonal milieus.	High accuracy for confirming ovulatory status and specific phases (e.g., mid-luteal).	Provides objective, biochemical confirmation of phase.	Invasive (blood draw); Higher cost; Requires clinical resources and lab processing.
Urinary Hormone Monitoring	Detection of luteinizing hormone (LH) surge and progesterone metabolites (PdG) in urine.	High accuracy for pinpointing ovulation and peri-ovulatory window.	Less invasive than serum; Suitable for at-home testing.	Can be burdensome with daily collection; Test kit costs can accumulate.
Single-Cell & Spatial Transcriptomics [3]	Maps gene expression patterns of endometrial tissue to a high-resolution, phase-specific reference atlas.	Defines molecular phenotyping as a novel, highly precise metric for phase.	Reveals direct tissue-level response; Unprecedented resolution.	Extremely high cost; Invasive tissue biopsy required; Complex data analysis.

Recent research underscores the necessity of precise phase determination. A 2024 study from UiT The Arctic University of Norway highlighted the individual variability in physiological responses, finding no universal performance effect at the group level across the cycle. This finding emphasizes that without accurate phase confirmation, meaningful individual patterns can be lost in group data [51] [52]. Furthermore, a 2025 pilot study demonstrated that pain perception, a critical outcome in many clinical trials, fluctuates significantly across phases, being most acute premenstrually [53]. These findings collectively demonstrate that calendar tracking alone is insufficient for studies where hormonal state is a key variable, often necessitating biochemical or molecular confirmation.

Experimental Protocols for Temporal Validation

To ensure robust and reproducible results, researchers must implement detailed experimental protocols. Below are detailed methodologies from key studies that have successfully navigated the challenges of sample timing.

Protocol 1: Endurance Performance and Metabolic Capacity Testing

This protocol, derived from the FENDURA project, is designed to test the impact of the menstrual cycle on physical fitness parameters in endurance athletes [51] [52].

• Participant Eligibility & Screening: Recruit naturally menstruating athletes (no hormonal contraception) with regular cycles (21-35 days) for at least six months. A striking finding from the FENDURA project was that nearly 40% of initial participants were excluded due to menstrual disturbances, highlighting the critical need for rigorous screening [51] [52].
• Phase Determination & Timing: Phase is determined via a combination of self-reported menstrual diary and confirmation with serum hormone assays. Testing is conducted in three key phases:
  • Early Follicular Phase: Days 1-5, characterized by low estradiol and low progesterone.
  • Ovulatory Phase: Estimated days 12-16, characterized by high estradiol and low progesterone, confirmed by a urinary LH surge kit.
  • Mid-Luteal Phase: Days 17-23, characterized by high estradiol and high progesterone, confirmed via serum progesterone assay.
• Experimental Procedures: In each phase, participants complete a standardized test battery in a controlled laboratory setting:
  • Maximal Incremental Treadmill Test: Participants run at a progressively increasing speed until volitional exhaustion. Primary outcomes: Time to exhaustion, maximal oxygen uptake (VOâ‚‚max).
  • Submaximal Intervals: A series of 5-minute stages at gradually increasing submaximal intensities. Primary outcomes: Oxygen uptake and blood lactate concentration at standardized workloads.
  • 30-Second Sprint Test: A all-out, 30-second double-pole ski ergometer test. Primary outcome: Mean and peak power output.
• Data Integration: Physiological performance data (e.g., time to exhaustion, VOâ‚‚max) is then analyzed relative to the biochemically-confirmed menstrual cycle phase.

Diagram 1: Endurance testing workflow with hormone confirmation.

Protocol 2: Somatosensory and Pain Perception Testing

This protocol, from a 2025 pilot study, details the measurement of sensory and pain thresholds across five menstrual phases, which is highly relevant for trials involving pain or neurological outcomes [53].

• Participant Allocation: Physically active, eumenorrheic women are divided into two groups: a control group and a group with primary dysmenorrhea (PD), based on validated questionnaires (Menstrual Symptom Questionnaire and Short-Form McGill Pain Questionnaire).
• Phase Determination & Timing: Phase is determined primarily by calendar tracking adjusted for cycle length. A 28-day cycle is divided into five phases:
  • Menstrual (F1): Days 1-5
  • Follicular (F2): Days 6-11
  • Ovulatory (F3): Days 12-16
  • Luteal (F4): Days 17-23
  • Premenstrual (F5): Days 24-28 For cycles longer or shorter than 28 days, extra days are added or subtracted from the most variable follicular phase (F2) [53].
• Experimental Procedures: In each weekly session, the following measurements are taken at the same time of day to control for circadian rhythms:
  • Sensory Electrical Threshold (SET): Using a Transcutaneous Electrical Nerve Stimulation (TENS) unit, the minimum current intensity (in milliamperes) at which the participant first perceives a sensation is recorded.
  • Pain Electrical Threshold (PET): Using the same TENS unit, the current intensity at which the sensation first becomes painful is recorded.
  • Anatomical Sites: Measurements are taken at a peripheral site (volar forearm) and a pain-referred site (lower abdomen).
  • Pain Intensity: A Visual Analog Scale (VAS) is used to subjectively rate menstrual pain.
• Data Integration: SET and PET data are analyzed across the five pre-defined phases and between the control and PD groups to map fluctuation in pain sensitivity.

Signaling Pathways in Menstrual Cycle Regulation

Understanding the molecular pathways that govern the menstrual cycle provides a deeper rationale for temporal validation and opens avenues for novel biomarker discovery. Advanced spatial transcriptomics has illuminated key pathways driving endometrial differentiation [3].

Diagram 2: Key pathways regulating endometrial cell differentiation.

• WNT Pathway in Secretory Differentiation: During the secretory phase, rising progesterone levels lead to the downregulation of the WNT signaling pathway. This suppression is a critical driver for the differentiation of endometrial epithelial cells into the secretory lineage. These cells are characterized by the production of essential factors like PAEP (progestagen-associated endometrial protein), which are required for uterine receptivity [3].
• NOTCH Pathway in Ciliated Differentiation: The rise in estrogen during the proliferative phase drives the downregulation of the NOTCH signaling pathway. This inhibition promotes the differentiation of endometrial epithelial cells into the ciliated lineage. Ciliated cells are crucial for the movement of uterine fluid and are present in both the proliferative and secretory phases, but their differentiation is initiated by estrogen [3].
• Spatial Organization of Progenitors: Single-cell and spatial transcriptomics have identified SOX9+ epithelial cells as putative progenitors. These cells can be further subdivided, with SOX9+/LGR5+ populations enriched in the surface epithelium and SOX9+/LGR5- populations located in the basal glands, each contributing to the regenerative capacity of the endometrium in a spatially distinct manner [3].

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 2: Key Reagents and Materials for Menstrual Cycle Temporal Validation

Item	Specific Function	Application Example
Enzyme-Linked Immunosorbent Assay (ELISA) Kits	Quantifies hormone concentrations (estradiol, progesterone, LH) in serum, plasma, or saliva.	Gold-standard confirmation of menstrual cycle phase for participant eligibility and data stratification [51] [52].
Urinary Luteinizing Hormone (LH) Kits	Detects the pre-ovulatory LH surge in urine to pinpoint the ovulatory window.	At-home testing to schedule lab visits for the peri-ovulatory or subsequent mid-luteal phase [53].
Transcutaneous Electrical Nerve Stimulation (TENS) Unit	Applies controlled electrical current to measure sensory and pain perception thresholds.	Quantifying cyclical changes in pain sensitivity at peripheral and referred pain sites [53].
Validated Patient-Reported Outcome (PRO) Tools	Standardized questionnaires to assess symptoms, pain, and quality of life.	Group stratification (e.g., Primary Dysmenorrhea) and measuring cyclical symptom severity (e.g., VAS for pain) [53].
Spatial Transcriptomics Platforms (e.g., 10x Visium)	Resolves gene expression data within the native tissue architecture.	Creating high-resolution molecular maps of endometrial phases and defining novel biomarker signatures [3].
Endometrial Organoid Culture Systems	3D in vitro models derived from primary endometrial tissue or menstrual fluid.	Benchmarking in vivo findings, studying hormonal responses, and modeling endometrial disorders in a controlled setting [3].
Mgb-bp-3	Mgb-bp-3, CAS:1000277-08-6, MF:C36H37N7O4, MW:631.7 g/mol	Chemical Reagent
Octamylamine	Octamylamine, CAS:502-59-0, MF:C13H29N, MW:199.38 g/mol	Chemical Reagent

Overcoming the challenges of phase determination and sample timing is not merely a methodological nuance but a fundamental requirement for rigorous and inclusive science. The experimental data and protocols presented herein demonstrate that a one-size-fits-all approach is ineffective; instead, the research question and population should dictate the stringency of temporal validation. For foundational research into tissue-level mechanisms, high-resolution molecular tools like spatial transcriptomics are indispensable. For clinical trials where hormonal state may influence drug response or safety, biochemical confirmation via serum hormone assays is often necessary. Even in large-scale studies where such confirmation is impractical, adjusted calendar-based timing and stratification by symptomology offer a substantial improvement over ignoring the cycle entirely. By adopting these precise and validated methodologies, researchers in drug development and women's health can generate more reliable, reproducible, and impactful data, ultimately accelerating the creation of better therapies for all women.

Addressing Cellular Heterogeneity and Batch Effects in Data Integration

This guide objectively compares the performance of various computational methods designed to integrate single-cell RNA sequencing (scRNA-seq) data, with a specific focus on challenges relevant to studying dynamic biological systems like the human endometrium across the menstrual cycle.

In single-cell genomics, cellular heterogeneity refers to the natural variation in gene expression between different cell types and states, which is the primary biological signal of interest. Conversely, batch effects are technical variations introduced when samples are processed in different batches, using different protocols, or across different platforms, which can confound biological analysis [54] [55]. The distinction is critical: effective data integration must remove the technical batch effects while preserving the underlying biological heterogeneity. This challenge is particularly acute in temporal studies, such as mapping the human endometrium across the menstrual cycle, where researchers aim to distinguish genuine, hormone-driven cellular changes from technical artifacts [12] [20]. The endometrium undergoes dramatic, cyclical changes in cellular composition and function, and deciphering this requires robust integration tools that can align data from multiple samples, donors, and sequencing batches to reveal accurate dynamic biological processes.

Performance Comparison of Integration Methods

The following table summarizes key performance metrics for several leading data integration methods, highlighting their strengths and limitations in handling batch effects and preserving biological variation.

Method	Core Approach	Batch Correction Strength	Biological Preservation	Key Limitations
sysVI (VAMP+CYC) [54]	Conditional VAE with VampPrior & cycle-consistency	High (Improves integration across substantial batch effects)	High (Retains cell states & conditions for downstream analysis)	-
scVI [55]	Conditional Variational Autoencoder (cVAE)	Moderate (Struggles with substantial batch effects)	Moderate	Struggles with substantial technical/biological confounders [54]
Methods with Adversarial Learning (e.g., GLUE) [54]	cVAE with adversarial batch alignment	High (Can over-correct)	Low (Prone to mixing unrelated cell types)	May remove biological signal; can mix embeddings of unrelated cell types [54]
KL Regularization Tuning [54]	Increased KL divergence weight in cVAE	High (Can artificially improve scores)	Low (Removes biological and batch variation indiscriminately)	Can set latent dimensions to zero, causing general information loss [54]
MrVI [56]	Hierarchical deep generative model	High (State-of-the-art for large datasets)	High (Enables annotation-free differential expression/abundance analysis)	Designed for multi-sample cohort studies with sample-level covariates

Table 1: Performance comparison of single-cell data integration methods. VAE: Variational Autoencoder.

A benchmark study evaluating 16 deep learning methods within a unified framework revealed that a correlation-based loss function helped better preserve intra-cell-type biological structure, which is often missed by standard benchmarking metrics [55]. Furthermore, methods like MrVI extend beyond simple integration, enabling sample stratification and comparative analysis without relying on predefined cell states, which is valuable for discovering clinically relevant sample groupings [56].

Detailed Experimental Protocols

To ensure reproducibility, this section outlines the standard experimental and computational workflows used to generate and validate the performance data cited in this guide.

Protocol 1: Benchmarking Integration Performance

This protocol is used to quantitatively compare different integration methods, as described in the literature [54] [55].

• Dataset Curation: Collect multiple scRNA-seq datasets from public repositories that represent challenging integration scenarios (e.g., cross-species, different protocols like single-cell vs. single-nuclei, or organoid vs. primary tissue).
• Data Preprocessing: Perform standard quality control on each dataset individually (filtering cells/genes based on counts, mitochondrial percentage). Normalize and log-transform the gene expression data per dataset.
• Method Application: Apply each integration method (e.g., sysVI, scVI, GLUE) to the combined datasets, using the provided batch labels (e.g., species, technology, lab of origin).
• Metric Calculation:
  • Batch Correction: Compute the graph integration Local Inverse Simpson's Index (iLISI). A higher iLISI score indicates better mixing of batches in the local neighborhood of each cell [54] [57].
  • Biological Preservation: Compute a metric like Normalized Mutual Information (NMI). This assesses how well the cell-type clusters in the integrated data match the original, ground-truth annotations [54].
• Visualization and Qualitative Assessment: Generate UMAP (Uniform Manifold Approximation and Projection) plots of the integrated data to visually inspect the mixing of batches and the separation of cell types.

Protocol 2: Evaluating Biological Conservation with Multi-Layer Annotations

This advanced protocol was used to demonstrate that standard metrics can fail to capture intra-cell-type variation, leading to the proposal of refined metrics [55].

• Annotation: Use a reference atlas with multi-layered, high-resolution cell annotations (e.g., the Human Lung Cell Atlas) to obtain detailed cell state information.
• Integration: Run integration methods on datasets derived from this atlas.
• Intra-Cell-Type Analysis: Within each major, pre-defined cell type, assess whether the fine-grained sub-structure (cell states) is preserved after integration. This can be done by examining the local continuity of developmental trajectories or the separation of closely related states.
• Differential Abundance Testing: Statistically test whether the relative abundance of specific, fine-grained cell states is significantly altered between conditions in the integrated data, which would indicate a loss of biological signal.

Workflow Diagram: sysVI Integration and Evaluation

The following diagram illustrates the core architecture and evaluation workflow of the sysVI method, which combines VampPrior and cycle-consistency to effectively integrate datasets.

SysVI Integration and Evaluation Workflow

The Scientist's Toolkit

This table lists key reagents, tools, and software essential for performing robust single-cell data integration, particularly in the context of complex temporal studies.

Tool / Reagent	Function / Description	Relevance to Temporal Studies
10x Genomics Platform	A dominant technology for generating high-throughput single-cell or single-nucleus gene expression and chromatin accessibility data.	Enables profiling of numerous samples across time points (e.g., menstrual cycle phases) with consistent protocol [12] [58].
Cell Hashing / MULTI-ATAC	Sample multiplexing technologies that allow pooling of samples early in the workflow, drastically reducing technical batch effects.	Critical for longitudinal studies on the same donor or for comparing multiple conditions, as it minimizes batch confounders [57].
scvi-tools [54] [56]	An open-source Python package providing scalable, deep learning-based implementations for single-cell omics analysis.	Hosts state-of-the-art integration methods like sysVI and MrVI, making them accessible to the research community.
Human Cell Atlas	A worldwide collaborative project to create comprehensive reference maps of all human cells.	Provides essential baseline maps (e.g., for the endometrium) against which new data can be integrated and compared [12].
Cell2location [12]	A computational tool that integrates scRNA-seq data with spatial transcriptomics data to map cell types to specific tissue locations.	Allows validation of cell states identified from integrated data within their spatial context in the tissue.
Mianserin Hydrochloride	Mianserin Hydrochloride, CAS:21535-47-7, MF:C18H21ClN2, MW:300.8 g/mol	Chemical Reagent

Table 2: Essential tools and resources for single-cell data integration.

For researchers investigating dynamic systems like the human endometrium, the choice of data integration method is paramount. Based on current benchmarking, sysVI emerges as a robust solution for challenges involving substantial batch effects, effectively balancing strong batch correction with high biological fidelity. Furthermore, MrVI offers a powerful framework for cohort-level studies, enabling the discovery of sample groupings and molecular differences that are specific to certain cellular contexts. As the field moves toward increasingly complex atlas-level and temporal studies, leveraging these advanced methods within flexible, well-benchmarked frameworks will be key to unlocking biologically accurate and clinically relevant insights.

Strategies for Low-Abundance or Transient TF States

Understanding the intricate dance of transcription factors (TFs) is fundamental to unraveling the molecular basis of human health and disease. This challenge becomes particularly pronounced when studying low-abundance or transient TF states—cellular conditions where TFs exist in scarce quantities or bind to their genomic targets only fleetingly. These dynamic regulatory phenomena are especially relevant in the context of the human endometrium, where precisely timed gene expression drives the menstrual cycle and enables embryo implantation [6] [20].

The study of these elusive TF states requires specialized methodological approaches. Standard molecular techniques often miss transient interactions that last mere seconds, while low-abundance TFs fall below detection limits of conventional assays [59]. For researchers investigating endometrial biology, these limitations are particularly significant, as the failure to capture critical but fleeting TF activities may underlie pathologies such as implantation failure and endometrial-factor infertility [6]. This guide provides a comprehensive comparison of current strategies to overcome these challenges, with particular emphasis on their application to temporal validation of TFs across the menstrual cycle.

Methodological Comparison for Capturing TF Dynamics

Single-Molecule Tracking Techniques

Purpose: To visualize and quantify the real-time binding behavior of individual TF molecules within living cells. Principle: These methods leverage advanced microscopy to track the movement and binding events of fluorescently-labeled TFs at high temporal resolution.

• Fluorescence Recovery After Photobleaching (FRAP): Measures the mobility of molecules by bleaching fluorescence in a specific nuclear region and monitoring the rate of fluorescence recovery as unbleached molecules diffuse into the area [59].
• Single Molecule Tracking: Follows individual TF molecules in real time, providing direct observation of binding durations and diffusion patterns [59].
• Fluorescence Correlation Spectroscopy (FCS): Analyzes fluorescence intensity fluctuations in a small observation volume to determine diffusion coefficients and binding kinetics [59].

Table 1: Comparison of Single-Molecule Techniques for TF Dynamics

Technique	Temporal Resolution	Spatial Resolution	Key Measurable Parameters	Example TF Binding Times
FRAP	Seconds to minutes	Diffraction-limited	Diffusion coefficients, mobile/immobile fractions	N/A (measures population mobility)
Single Molecule Tracking	Milliseconds	~20-40 nm	Residence times, search modes, binding trajectories	STAT1: 0.5s; p53: ~3.6s; Sox2: 12s [59]
FCS	Microseconds to milliseconds	Diffraction-limited	Concentration, diffusion times, chemical kinetics	N/A (measures dynamic properties)

Genomic and Proteomic Approaches

Purpose: To comprehensively map TF binding events and quantify nuclear protein abundances across different cellular states.

• Chromatin Immunoprecipitation Sequencing (ChIP-seq): Identifies genome-wide TF binding sites but may miss transient interactions due to cross-linking time limitations and population averaging [59] [60].
• Assay for Transposase-Accessible Chromatin with Sequencing (ATAC-seq): Maps open chromatin regions to infer potential TF binding sites, particularly effective at single-cell resolution for capturing heterogeneity [20].
• Targeted Mass Spectrometry: Enables absolute quantification of TF protein copy numbers in the nucleus using selected reaction monitoring (SRM) with stable isotope-labeled peptides as internal standards [61].

Table 2: Genomic and Proteomic Methods for TF Analysis

Method	Sensitivity	Temporal Context	Key Advantage	Quantification Capability
ChIP-seq	Limited for transient binding	Single time point	Maps actual binding locations	Relative enrichment
scATAC-seq	High for chromatin accessibility	Multiple time points	Identifies cell-to-cell variation in regulatory potential	Relative accessibility scores
Targeted MS (SRM)	~500 copies/nucleus [61]	Multiple time points	Absolute protein quantification	Absolute copy numbers per nucleus
iTRAQ Proteomics	Moderate	Multiple time points	Multiplexed relative quantification	Relative protein abundance

Integrated Computational & Experimental Frameworks

Purpose: To distinguish functional TF-target relationships from non-functional binding events. Principle: Combines TF binding data with gene expression changes following TF perturbation.

The "Expected Proportion of False Positives" (EPFP) method statistically identifies "regulated target genes"—genes that are both bound by a TF and show expression changes after TF induction [60]. This approach revealed that only 30.9% of genes responding to TF induction are direct regulatory targets, highlighting the importance of distinguishing binding from regulation [60].

Application to Menstrual Cycle and Endometrial Research

Temporal Dynamics of Chromatin and TF Activity

Single-cell ATAC-seq studies of human endometrium have revealed continuous chromatin remodeling throughout the menstrual cycle, with coordinated changes in accessibility at regulatory elements controlling cycle-dependent gene expression [20]. Integration of dynamic chromatin regions with TF binding site enrichment, ChIP-seq analyses, and gene expression data has provided mechanistic insights into the emergence of the receptive state during the implantation window [20].

Research comparing 19 gene lists associated with endometrial progression and implantation failure found that TF regulation significantly impacted 89% (17/19) of gene lists, while progesterone regulation affected 47% (8/19), highlighting the predominant role of TFs in endometrial function [6]. Key regulators identified include CTCF and GATA6, which emerged as master regulators of endometrial function [6].

Quantitative Proteomics in Dynamic Systems

While absolute quantification of TFs has not been extensively applied in endometrial studies, research in human erythropoiesis provides a framework for future investigations. Targeted mass spectrometry revealed that nuclear TF abundances span a remarkable dynamic range, from less than 500 copies per nucleus for factors like BACH1 and GATA2 to over 100,000 copies for CTCF and TRIM28/KAP1 [61]. This quantitative approach also uncovered surprising stoichiometric relationships, with corepressors dramatically more abundant than coactivators at the protein level—a discovery with profound implications for understanding transcriptional regulation in dynamic processes [61].

Experimental Protocols for Key Methodologies

Targeted Mass Spectrometry for Absolute TF Quantification

Protocol Overview: This method enables precise measurement of TF copy numbers in nuclear extracts [61].

• Nuclear Protein Extraction: Isolate nuclei from primary cells or tissues using hypotonic lysis and differential centrifugation. Prepare nuclear extracts in appropriate buffer.
• Protein Digestion: Digest proteins with a specific protease (e.g., trypsin) to generate peptides.
• SRM Assay Development:
  • Select 1-4 proteotypic peptides per target protein.
  • Synthesize stable isotope-labeled (SIL) versions as internal standards.
  • Establish optimal transition ions and collision energies for each peptide.
• Mass Spectrometry Analysis:
  • Spik SIL peptides into experimental samples.
  • Perform Selected Reaction Monitoring (SRM) on a triple quadrupole mass spectrometer.
  • Monitor specific precursor-product ion transitions for each target peptide.
• Absolute Quantification:
  • Calculate light-to-heavy peptide peak area ratios.
  • Determine absolute amounts using calibration curves from SIL peptides.
  • Normalize to nuclear count or total protein.

scATAC-seq for Dynamic Chromatin Landscapes

Protocol Overview: This method maps accessible chromatin regions in individual cells across time points [20].

• Sample Collection: Collect endometrial biopsies at multiple time points across the menstrual cycle.
• Nuclei Isolation: Tissue dissociation and nuclei extraction with careful quality control.
• Tagmentation: Use Tn5 transposase to simultaneously fragment and tag accessible genomic regions with sequencing adapters.
• Library Preparation and Sequencing: Amplify tagmented DNA and prepare libraries for high-throughput sequencing.
• Computational Analysis:
  • Process sequencing data using specialized pipelines (e.g., Cell Ranger ATAC).
  • Identify open chromatin peaks and call chromatin accessibility scores.
  • Perform integration with matched scRNA-seq data.
  • Conduct TF motif enrichment analysis in dynamic accessibility regions.

Identifying Regulated TF Targets

Protocol Overview: This integrative approach distinguishes functional TF targets from non-functional binding events [60].

• TF Perturbation: Induce individual TFs in human ES cells using doxycycline-inducible systems.
• Gene Expression Profiling: Perform RNA-seq 48 hours post-induction to identify differentially expressed genes.
• TF Binding Assessment: Utilize existing ChIP-seq data (n=1868 experiments) for the same TFs.
• Statistical Integration: Apply the Expected Proportion of False Positives (EPFP) method to identify "regulated target genes" within the overlap of bound and expression-changed genes, controlling for false positives.

Visualization of Experimental Approaches

Workflow for Integrated TF Target Identification

Diagram 1: Integrated TF Target Identification Workflow

Chromatin Remodeling During Menstrual Cycle

Diagram 2: Chromatin Remodeling in the Menstrual Cycle

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Studying Low-Abundance/Transient TFs

Reagent/Category	Specific Examples	Function/Application	Relevance to Low-Abundance/Transient TFs
Stable Isotope-Labeled Peptides	SIL (stable isotope-labeled) peptide standards	Internal standards for absolute quantification by mass spectrometry	Enables precise measurement of low-abundance TFs down to ~500 copies/nucleus [61]
Chromatin Analysis Kits	scATAC-seq kits, ChIP-seq kits	Mapping genome-wide TF binding and chromatin accessibility	Identifies dynamic regulatory regions; scATAC-seq captures heterogeneity [20]
TF Perturbation Systems	Doxycycline-inducible TF expression vectors	Controlled induction of individual TFs for functional studies	Links TF binding to regulatory function; identifies direct targets [60]
Motif Databases	JASPAR, TRANSFAC, DoRothEA	TF binding site prediction and enrichment analysis	Identifies potential regulators of dynamic chromatin regions [6] [20]
Single-Cell Platforms	10x Genomics Chromium, Fluidigm C1	Single-cell RNA-seq and ATAC-seq profiling	Resolves cellular heterogeneity in endometrial samples across cycle [20]
Validation Antibodies	Phospho-specific TF antibodies, ChIP-validated antibodies	Detection and quantification of post-translationally modified TFs	Captures activated TF states; validates proteomic findings

The study of transcription factor (TF) dynamics is crucial for understanding the fundamental processes that govern development, homeostasis, and disease. In the specific context of menstrual cycle research, where hormonal fluctuations drive complex transcriptional programs, capturing these dynamics accurately presents a unique challenge. Organoids—three-dimensional, self-organizing structures derived from stem cells or tissue-specific progenitor cells—have emerged as powerful tools for modeling human biology in vitro. These systems promise to recapitulate the cellular diversity and organizational complexity of their in vivo counterparts, but the critical question remains: how faithfully do they replicate the intricate temporal dynamics of transcription factors observed in living organisms? The answer to this question determines their utility in basic research, disease modeling, and drug development.

This guide provides an objective comparison of organoid systems against their in vivo references, with a specific focus on their performance in modeling TF networks across the hormonally-regulated menstrual cycle. We present standardized benchmarking methodologies, quantitative comparisons of key parameters, and detailed experimental protocols to empower researchers in their model selection and validation processes. By establishing rigorous benchmarking criteria, we aim to bridge the gap between in vitro models and in vivo physiology, particularly for applications requiring temporal resolution of transcriptional regulation.

Benchmarking Methodologies: From Single-Cell Genomics to Spatial Mapping

Advanced genomic and imaging technologies now enable unprecedented resolution for comparing organoids to native tissues. The benchmarking workflow typically involves parallel analysis of in vivo tissue and in vitro organoid models using complementary approaches that assess transcriptomic, epigenetic, and spatial organization.

Table 1: Core Methodologies for Benchmarking Organoid Models

Methodology	Key Outputs	Utility for TF Dynamics	Technical Considerations
Single-cell RNA sequencing (scRNA-seq)	Cell-type-specific gene expression profiles; TF expression patterns	Identifies TF co-expression networks; reveals rare cell populations	Requires fresh tissue/organoids; sensitive to dissociation artifacts
Single-nucleus RNA sequencing (snRNA-seq)	Nuclear transcriptomes including TF transcripts	Enables analysis of archived/frozen samples; better for large tissues	Misses cytoplasmic transcripts; lower gene detection sensitivity
Spatial Transcriptomics	Gene expression data with retained spatial coordinates	Maps TF expression to tissue microenvironments; reveals spatial gradients	Limited single-cell resolution; higher input material requirements
Single-cell ATAC sequencing	Chromatin accessibility landscapes at single-cell resolution	Identifies accessible TF binding sites; links TFs to regulatory elements	Requires specialized expertise in epigenomics; complex data integration
Multiome assays	Paired gene expression and chromatin accessibility from same cells	Directly couples TF expression with regulatory activity	Technically challenging; higher cost per sample
Iterative immunofluorescence (4i)	High-dimensional protein expression with spatial context	Validates TF expression and localization at protein level	Limited to known targets with validated antibodies

The integration of these methodologies enables a comprehensive assessment of how well organoids mimic the temporal progression of TF networks observed in vivo. For menstrual cycle research, this is particularly valuable for tracking how estrogen and progesterone-responsive TFs drive epithelial remodeling across different phases [3] [14].

Experimental Workflow for Benchmarking

The following diagram illustrates a standardized workflow for benchmarking organoid models against native tissue references, integrating multiple omics technologies:

Quantitative Benchmarks: Organoids vs. In Vivo Systems

Direct comparison of organoids to their in vivo counterparts reveals both remarkable similarities and important limitations. The table below summarizes key benchmarking parameters for evaluating how well organoids recapitulate tissue physiology, with particular emphasis on aspects relevant to TF dynamics.

Table 2: Performance Benchmarks of Organoid vs. In Vivo Models

Parameter	In Vivo Reference	Organoid Performance	Key Findings
Cellular diversity	Complete native cell types	Varies by protocol; often lacks rare populations	Endometrial organoids contain major epithelial lineages but may lack some immune/stromal subsets [3] [14]
Spatial organization	Defined tissue architecture	Partial recapitulation; often self-organized but simplified	Intestinal organoids form crypt-villus structures but lack full tissue-scale patterning [62]
Lineage-specific TF expression	Physiological patterns in native context	Generally well-preserved for major lineages	SOX9+ progenitor populations maintained in endometrial organoids [3]
Response to hormonal cues	Cyclical changes across menstrual cycle	Preserved but may require optimized timing/dosing	Endometrial organoids respond to estradiol and progesterone with appropriate differentiation [14]
Regulatory element accessibility	Tissue-specific chromatin landscapes	Generally similar but with some differences	scATAC-seq reveals similar accessibility at key TF binding sites in intestinal organoids [63]
Temporal progression of TF networks	Precise timing in developmental/cyclic processes	Often compressed or asynchronous	Menstrual cycle-associated TF expression occurs but may not fully replicate in vivo timing [14]
Stem cell/progenitor dynamics	Balanced self-renewal and differentiation	Often biased toward expansion; may require manipulation	Wnt modulation enhances secretory lineage differentiation in endometrial organoids [3]

Signaling Pathways Governing TF Dynamics in Endometrial Organoids

Research has identified key signaling pathways that regulate transcription factor networks in endometrial development and cycling. The following diagram illustrates the core pathways and their manipulation in organoid systems:

Benchmarking studies using single-cell RNA sequencing have demonstrated that modulation of these pathways in organoids can direct lineage specification in a manner that closely mirrors in vivo differentiation. For instance, downregulation of WNT signaling increases differentiation efficiency along the secretory lineage, while NOTCH pathway inhibition promotes ciliated cell differentiation—recapitulating in vivo lineage commitment mechanisms [3].

Experimental Protocols for Assessing TF Dynamics

Hormonal Treatment Protocol for Menstrual Cycle Modeling

To evaluate TF dynamics across the menstrual cycle in endometrial organoids, researchers have established standardized hormonal treatment protocols:

• Baseline Culture: Maintain organoids in expansion medium (EGF, Noggin, R-spondin, Wnt-conditioned medium) for 3-4 days
• Proliferative Phase Simulation: Treat with 1 nM β-estradiol in phenol-red free medium for 6 days, with medium changes every 48 hours
• Secretory Phase Simulation: Following proliferative phase treatment, introduce 1 μM progesterone + 1 nM β-estradiol for an additional 6-8 days
• Sample Collection: Harvest organoids at multiple timepoints for:
  • Single-cell RNA sequencing (10X Genomics platform)
  • Bulk RNA sequencing (Illumina platform)
  • Immunofluorescence analysis (4% PFA fixation)
  • Chromatin immunoprecipitation (1% formaldehyde crosslinking)

This protocol enables tracking of hormone-responsive TFs such as ESR1, PGR, and PAX2 across simulated cycle phases, allowing direct comparison to in vivo temporal patterns [3] [14].

scRNA-seq Integration Analysis for TF Network Validation

A critical benchmarking approach involves computational integration of organoid and in vivo single-cell datasets:

• Data Preprocessing: Process raw sequencing data using Cell Ranger (10X Genomics) or equivalent pipelines
• Integration: Apply Seurat's integration anchors or Harmony algorithm to combine organoid and in vivo datasets
• Clustering: Identify shared cell states using graph-based clustering (Louvain algorithm)
• Differential Expression: Identify TFs with significantly different expression between in vivo and organoid counterparts
• TF Activity Inference: Use tools like SCENIC to infer transcription factor activities from gene expression data
• Trajectory Analysis: Apply pseudotemporal ordering (Monocle3, PAGA) to compare differentiation dynamics

This analytical pipeline enables quantitative assessment of how faithfully organoids recapitulate in vivo TF expression patterns and regulatory networks [3] [63].

Table 3: Essential Research Reagents for Organoid TF Dynamics Studies

Reagent Category	Specific Examples	Function in TF Studies
Culture Matrices	Matrigel, Cultrex BME, synthetic PEG hydrogels	Provide 3D scaffolding for organoid growth; matrix composition can influence TF expression
Niche Factors	R-spondin-1, Noggin, EGF, Wnt3a	Maintain stemness and influence lineage-specific TF expression
Hormones	β-estradiol, progesterone, RU486 (mifepristone)	Modulate hormone-responsive TFs in endometrial models
Signaling Modulators	IWP-2 (WNT inhibitor), DAPT (NOTCH inhibitor)	Manipulate pathway activity to study effects on TF networks
Single-Cell Dissociation Reagents	Accutase, TrypLE, collagenase/dispase	Generate single-cell suspensions for scRNA-seq while preserving TF mRNAs
Fixation Reagents	Paraformaldehyde, methanol	Preserve protein epitopes for TF detection via immunofluorescence
Antibodies for TFs	Anti-ESR1, Anti-PGR, Anti-SOX9, Anti-FOXA2	Validate TF expression and localization at protein level
CRISPR Components	Cas9 protein, sgRNAs, homology-directed repair templates	Genetically manipulate TF expression for functional studies

Limitations and Future Directions

Despite significant advances, current organoid models still exhibit important limitations in recapitulating in vivo TF dynamics. The lack of vascularization, neural input, and systemic immune components creates microenvironmental differences that can alter transcriptional programs. Additionally, the fetal phenotype often observed in iPSC-derived organoids may limit their applicability to adult diseases. Technological innovations such as organ-on-chip platforms, vascularized organoids, and improved maturation protocols show promise for addressing these limitations.

Standardization initiatives like the NIH Standardized Organoid Modeling (SOM) Center aim to establish reproducible protocols and benchmarking standards that will enhance comparability across studies and laboratories [64]. As these efforts mature, they will further strengthen the utility of organoids for studying TF dynamics in health and disease, particularly in complex cyclical processes like the menstrual cycle.

The temporal validation of transcription factors (TFs) across the menstrual cycle represents a particularly challenging area of reproductive biology research due to the dynamic interplay of hormonal fluctuations, cellular composition changes, and pathological influences. Confounding factors—extraneous variables that correlate with both the dependent and independent variables—can significantly distort research findings and lead to false conclusions about causal relationships [65]. In menstrual cycle studies, where the goal is often to isolate the specific effects of cyclical changes on molecular mechanisms, failure to adequately account for confounders such as age, underlying pathologies, and external perturbations threatens both internal validity and translational potential. This guide provides a comprehensive comparison of methodological approaches and experimental designs for mitigating these confounding effects, with specific application to the temporal validation of TFs in endometrial and fallopian tube tissues throughout the menstrual cycle.

The challenge is particularly pronounced in this field due to the intrinsic variability of the menstrual cycle itself, coupled with the difficulty of controlling for age-related changes and pathological conditions that may independently influence transcriptional regulation. Furthermore, the growing recognition of the fallopian tube as a key site in reproductive health and disease [22] adds another layer of complexity, as this tissue undergoes substantial molecular changes during both the menstrual cycle and menopause. This guide systematically addresses these challenges by presenting comparative experimental data, detailed methodologies, and analytical frameworks specifically tailored to researchers, scientists, and drug development professionals working in reproductive biology.

Key Confounding Factors in Menstrual Cycle Research

Age and Menopausal Status

Age represents one of the most significant confounding factors in menstrual cycle research, particularly due to its correlation with declining reproductive function and the profound molecular changes associated with menopause. Single-cell RNA sequencing analyses of human fallopian tubes have revealed substantial shifts in cell type frequencies, gene expression patterns, transcription factor activity, and cell-to-cell communications between pre- and post-menopausal individuals [22]. These changes are not uniform across cell types, with secretory epithelial cells exhibiting the most differentially expressed genes, followed by macrophages, stromal cells, T/NK cells, and ciliated epithelial cells.

Table 1: Impact of Menopausal Status on Fallopian Tube Cellular Composition

Cell Type	Change with Menopause	Key Molecular Changes	Functional Implications
Secretory Epithelial Cells	Significant decrease in abundance	Downregulation of OVGP1; Upregulation of senescence markers	Impaired secretory function
Ciliated Epithelial Cells	Significant decrease in abundance	Altered TF activity in FOX family	Reduced egg transport capacity
T/NK Cells	Significant decrease in abundance	Changes in ETV family transcription factor activity	Altered immune surveillance
Endothelial Cells (EN1, EN2)	Significant increase in abundance	Increased ICAM1 expression	Vascular changes
Pericyte/Vascular Cells (P/V1, P/V2)	Significant increase in abundance	Upregulation of BAG3 senescence marker	Tissue remodeling

The molecular signature of post-menopausal fallopian tubes includes increased expression of senescence and aging markers such as ICAM1, CXCL2, ZNF31, ELL2, and BAG3 across multiple cell types [22]. Concurrently, genes characteristically expressed in pre-menopausal states, including PCSK1N, OVGP1, CRISP3, and CYBA, become downregulated. These extensive molecular changes demonstrate why menopausal status must be carefully controlled in menstrual cycle studies, as failure to do so could easily attribute age-related molecular changes to cyclical fluctuations.

Pathological Conditions

Pathological conditions of the endometrium and fallopian tubes represent another major category of confounding factors in menstrual cycle research. Chronic endometritis (CE) and recurrent implantation failure (RIF) are particularly important in this context, as they involve significant alterations to the endometrial immune microenvironment that may interact with or mimic menstrual cycle effects [66].

Uterine natural killer (uNK) cells, which comprise up to 70% of endometrial leukocytes during the window of implantation, normally adopt a pro-pregnancy phenotype critical for successful embryo implantation. However, in pathological states, this delicate balance is disrupted, with uNK cells shifting from a supportive, decidual phenotype toward a more aggressive, cytotoxic phenotype [66]. This creates a hostile, pro-inflammatory microenvironment characterized by altered cytokine profiles that can disrupt endometrial receptivity markers. Single-cell RNA sequencing analyses have identified two functionally distinct uNK subtypes: cytotoxic uNK2 cells regulated by TFs EOMES and ELF4, and uNK3 cells involved in platelet activation and tight junctions, driven by ELK4 and IRF1 [66].

The ratio of uNK2/uNK3 signature expression has been shown to be notably upregulated in CE samples, with this finding corroborated in independent RIF datasets [66]. For diagnostic purposes, the uNK2/uNK3 ratio demonstrated an area under the curve (AUC) of 0.675 for CE and 0.823 for RIF, indicating its potential as a biomarker. These pathological alterations in uNK cell polarization represent a significant confounding factor that must be accounted for in studies of TF validation across the menstrual cycle, as they may obscure or mimic genuine cyclical patterns.

External Perturbations and Methodological Variables

Beyond biological confounders, methodological variables and external perturbations can significantly impact research outcomes in menstrual cycle studies. Hormonal contraceptives represent one of the most common pharmacological interventions that confound menstrual cycle research, as they fundamentally alter the endogenous hormonal milieu [67]. Studies typically exclude women using hormonal contraceptives to preserve the natural hormonal fluctuations of the menstrual cycle [67].

Temporal discrimination threshold (TDT) studies have demonstrated the importance of rigorous exclusion criteria and monitoring of premenstrual symptomatology [42]. Research has shown that including women with premenstrual syndrome (PMS) can bias results, as PMS is characterized by negative emotional symptomatology during the luteal phase [42]. Studies that implement strict exclusion criteria for PMS and monitor premenstrual symptomatology daily have found no significant modulation of exogenous attention to emotional facial expressions across the menstrual cycle in women without PMS [42].

The timing of experimental assessments relative to menstrual phase also introduces potential confounding. Studies typically define standard menstrual phases based on a 28-day cycle: menstrual phase (days 1-5), follicular phase (days 6-11), ovulatory phase (days 12-16), luteal phase (days 17-23), and premenstrual phase (days 24-28) [67]. However, individual variation in cycle length necessitates adjustment protocols, often by adding or subtracting extra days to the follicular phase, as this is the most variable phase of the menstrual cycle [67].

Statistical Methods for Confounding Control

Comparison of Statistical Approaches

Statistical methods for controlling confounding factors can be broadly categorized into those applied during study design and those implemented during data analysis. Each approach offers distinct advantages and limitations for menstrual cycle research.

Table 2: Statistical Methods for Controlling Confounding Factors

Method	Application Phase	Key Mechanism	Advantages	Limitations
Randomization	Study design	Random assignment breaks links between exposure and confounders	Controls both known and unknown confounders	Often impractical in observational menstrual cycle studies
Restriction	Study design	Eliminates variation in the confounder	Simplifies analysis and interpretation	Reduces sample size and generalizability
Matching	Study design	Selection of comparison group with similar confounder distribution	Directly balances confounders between groups	Can be difficult with multiple confounders
Stratification	Data analysis	Evaluates exposure-outcome association within strata of the confounder	Simple implementation with few confounders	Becomes impractical with multiple confounders
Multivariate Regression	Data analysis	Adjusts for multiple confounders simultaneously	Handles numerous confounders	Requires adequate sample size
Mantel-Haenszel Estimator	Data analysis	Provides adjusted result according to strata	Useful for stratified analysis with few confounders	Limited with many strata or confounders
Instrumental Variable Analysis	Data analysis	Uses external variable to account for unmeasured confounding	Addresses unmeasured confounding	Requires valid instrument which can be difficult to find

Stratification works by fixing the level of the confounders and producing groups within which the confounder does not vary, then evaluating the exposure-outcome association within each stratum [65]. This approach works best when there are not many strata and only one or two confounders need to be controlled. When stratification is applied, the Mantel-Haenszel estimator can be employed to provide an adjusted result according to strata, with differences between crude and adjusted results indicating likely confounding [65].

When the number of potential confounders or the level of their grouping is large, multivariate analysis offers the only practical solution [65]. These models can handle large numbers of covariates simultaneously—for example, controlling for age, sex, smoking, alcohol, ethnicity, and other factors in the same model when examining the relationship between body mass index and dyspepsia [65].

Advanced Methods for Longitudinal Data

Longitudinal studies of the menstrual cycle present unique opportunities for controlling confounding through within-subject designs and specialized analytical approaches. Methodological reviews have identified several established econometric methods that can be adapted to menstrual cycle research, including difference-in-differences (DiD) and fixed effects (FE) models [68]. These approaches leverage the repeated measurements across cycle phases to control for time-invariant unmeasured confounding.

Instrumental variable analysis (IVA) represents another powerful approach, with 36 of 84 reviewed studies using lagged or historical instruments [68]. More sophisticated approaches combine IVA with DiD or FE to mitigate time-dependent confounding [68]. Other less frequently used but promising methods include prior event rate ratio adjustment, regression discontinuity nested within pre-post studies, propensity score calibration, perturbation analysis, and negative control outcomes [68].

The selection of appropriate statistical methods should be guided by the specific research question, study design, and nature of the confounding factors. For menstrual cycle research focusing on TF validation, multivariate approaches that can simultaneously adjust for age, pathological conditions, and methodological variables typically offer the most comprehensive solution, provided adequate sample sizes are available.

Experimental Protocols for Confounding Control

Participant Selection and Characterization

Rigorous participant selection represents the first line of defense against confounding in menstrual cycle research. The following protocol outlines key considerations for participant characterization:

• Inclusion Criteria Definition: Establish clear inclusion criteria including age range (typically 18-35 years for reproductive-aged women), regular menstrual cycles (21-35 days), and absence of hormonal contraceptive use [67]. Participants should have consistently reported regular cycles (±7 days) for at least six months prior to enrollment.

• Exclusion Criteria Implementation: Exclude participants with history of chronic disorders including endocrine, neurological, psychiatric, urogenital, or musculoskeletal conditions [67]. Additionally, exclude those taking systemic medication or using hormonal contraceptives, and screen for potential pregnancy.

• Menstrual Symptom Monitoring: Implement daily monitoring of premenstrual symptomatology using validated instruments such as the Menstrual Symptom Questionnaire (MSQ) and Short-Form McGill Pain Questionnaire (SF-MPQ) [67]. Establish cutoff scores for participant categorization (e.g., PD group: ≥77 points on MSQ or ≥25 points on SF-MPQ; Control group: below these thresholds).

• Cycle Phase Verification: For increased precision, consider hormonal verification of cycle phases through serum or salivary hormone measurements, though this is often impractical in larger studies.

Sample Collection and Processing

Standardized sample collection and processing protocols are essential for minimizing technical confounding:

Diagram 1: Experimental workflow for menstrual cycle studies

• Phase-Specific Collection: Collect samples during precisely defined menstrual phases based on self-reported first day of menstruation and prospective self-tracking [67]. For cycles longer or shorter than 28 days, implement individualized phase adjustment by adding or subtracting extra days to the follicular phase.

• Time Standardization: Conduct all procedures at the same time of day to minimize potential influences of circadian rhythms [67]. The same researcher should apply and remove all electrodes or perform procedures to minimize inter-examiner bias.

• Single-Cell Processing: For scRNA-seq studies, apply individual filtering based on UMI counts and mitochondrial proportions as part of quality control [66]. After quality control, integrate cells from multiple samples for analysis.

• Cell Type Identification: Use unsupervised clustering to identify cell populations based on known marker genes and differential expression profiles [22]. The FindAllMarkers function in Seurat can identify marker genes expressed in more than 40% of cells with a minimum log-fold change of 0.6.

Analytical Procedures for Transcriptional Regulation

Advanced analytical procedures are required to dissect transcription factor dynamics while controlling for confounding:

• Transcription Factor Activity Assessment: Characterize activities of transcription factors listed in databases such as cisBP across all major cell types [22]. Identify cell-type-specific TF activities through differential analysis.

• Regulatory Network Inference: Apply single-cell regulatory network inference to identify key TFs regulating cell subtypes [66]. Explore functions via pathway enrichment analysis.

• Integration of Multi-omic Data: Match scATAC-seq data to corresponding scRNA-seq data by transferring labels from scRNA-seq clusters to scATAC-seq data [22]. Annotate cluster identity by searching for best-matched clusters from the same sample.

• Differential Expression Analysis: Conduct cell-type-specific differential gene expression analysis using pseudo-bulk donor-level data to account for menopausal status and other confounders [22].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents for Menstrual Cycle Studies

Reagent/Category	Specific Examples	Function/Application	Considerations for Confounding Control
Single-Cell RNA Sequencing Platforms	10X Genomics	High-resolution profiling of individual cells	Controls for cellular heterogeneity by enabling cell-type-specific analysis
Cell Type Identification Tools	Seurat R package	Identification of cell types from scRNA-seq data	Uses FindAllMarkers function to identify cell-type-specific markers
Transcription Factor Databases	cisBP database	Reference for transcription factor binding specificities	Provides standardized TF information for cross-study comparisons
Menstrual Symptom Assessment	Menstrual Symptom Questionnaire (MSQ), Short-Form McGill Pain Questionnaire (SF-MPQ)	Quantification of menstrual-related complaints	Enables stratification based on symptom severity
Chromatin Accessibility Assays	scATAC-seq	Assessment of chromatin landscape and regulatory elements	Identifies epigenetic changes independent of transcriptional changes
Electrical Threshold Measurement	Transcutaneous electrical nerve stimulation (TENS)	Evaluation of sensory and pain perception thresholds	Provides objective measure of sensory changes across cycle
Data Integration Tools	UMAP, FindClusters algorithm	Integration and visualization of multi-sample data	Enables batch correction and identification of population structure

Signaling Pathways and Molecular Networks

The complex interplay between hormonal fluctuations, cellular responses, and transcriptional regulation in menstrual cycle tissues can be visualized through key signaling pathways:

Diagram 2: Signaling pathways in menstrual cycle biology with confounding factors

This pathway diagram illustrates how hormonal fluctuations throughout the menstrual cycle initiate cellular responses that lead to chromatin remodeling, transcription factor activation/repression, and ultimately gene expression changes that drive cellular phenotypes and tissue-level effects. Critically, confounding factors such as age/menopause, pathological conditions (CE/RIF), external perturbations, and methodological variables can influence multiple points in this pathway, potentially distorting research findings if not adequately controlled.

The diagram highlights several key mechanisms identified in the literature:

• Hormonal receptors distributed across various brain regions and peripheral tissues influence the nervous system and modulate pain perception [67]
• Chromatin remodeling factors demonstrate functional diversity, with different subcomplexes working at different branches and stages along differentiation trajectories [69]
• Specific transcription factors such as EOMES, ELF4, ELK4, and IRF1 drive distinct uNK cell subtypes that become imbalanced in pathological states [66]
• Repressive complexes including heterochromatin, histone deacetylases, and coREST members demonstrate functional homogeneity in restraining excessive differentiation [69]

The temporal validation of transcription factors across the menstrual cycle requires meticulous attention to confounding factors including age, pathological conditions, and external perturbations. This comparison guide has outlined key methodological considerations, statistical approaches, and experimental protocols to address these challenges. The integration of single-cell technologies, advanced statistical modeling, and rigorous experimental design provides a powerful framework for disentangling genuine cyclical patterns from confounding effects. As the field moves toward more personalized approaches in reproductive medicine, the systematic implementation of these confounding control strategies will be essential for generating robust, reproducible findings with translational potential for diagnosing and treating menstrual cycle-related disorders and optimizing reproductive health outcomes.

Benchmarks and Clinical Correlates: Establishing Robust TF Signatures

Comparative Analysis of TF Networks in Health and Disease

Transcription factors (TFs) serve as master regulators of gene expression, orchestrating complex biological processes by binding to specific DNA sequences and controlling transcriptional programs. In the context of human health and disease, understanding TF network dynamics requires moving beyond static snapshots to capture their temporal regulation. The menstrual cycle provides a powerful, naturally occurring model system for studying these dynamics, revealing how rhythmic hormonal fluctuations reshape chromatin architecture and TF activity across time. This cyclical remodeling of regulatory networks is not merely a physiological curiosity—it represents a fundamental principle of genomic regulation whose disruption may underlie various disease states, particularly in hormone-responsive tissues.

Recent advances in single-cell technologies have enabled unprecedented resolution in mapping these temporal changes, revealing that TF networks exhibit remarkable plasticity in response to hormonal cues. Emerging research demonstrates that the same regulatory circuits that maintain tissue homeostasis during normal cycling can undergo pathogenic repurposing in disease contexts such as hormone-dependent cancers and reproductive disorders. By conducting a comparative analysis of TF networks across physiological states, this guide aims to delineate the fundamental principles governing temporal TF regulation and its implications for disease pathogenesis and therapeutic development.

Comparative Analysis of TF Networks Across Physiological States

Methodological Framework for Temporal TF Network Analysis

The investigation of dynamically changing TF networks requires specialized methodological approaches capable of capturing molecular states across multiple timepoints. Current best practices integrate multi-omics data to build comprehensive regulatory maps that connect transcription factor binding, chromatin accessibility, and gene expression patterns.

Key experimental protocols for temporal TF network analysis include:

• Single-cell RNA and ATAC sequencing applied to tissue samples collected across defined timepoints (e.g., menstrual cycle phases) to simultaneously profile gene expression and chromatin accessibility [22] [20]
• Chromatin immunoprecipitation sequencing (ChIP-seq) for mapping transcription factor binding sites and histone modifications genome-wide [70]
• Tethered Chromatin Conformation (TCC) and Hi-C protocols to capture three-dimensional chromatin architecture and its temporal dynamics [71]
• Open-chromatin-mediated gene regulatory network (oGRN) analysis to infer TF regulatory relationships from chromatin accessibility data [72]

These methodologies enable researchers to move beyond static regulatory maps toward dynamic network models that can identify critical control points and vulnerable regulatory circuits in disease states.

Physiological TF Remodeling During the Menstrual Cycle

The human endometrium and fallopian tubes undergo extensive molecular reprogramming throughout the menstrual cycle, driven by coordinated fluctuations in estrogen and progesterone levels. Single-cell analyses have revealed striking cell-type-specific patterns of TF activity that correspond to distinct phases of the cycle.

In healthy fallopian tubes, scRNA-seq and scATAC-seq of 85,107 pre-menopausal cells identified 19 distinct clusters representing 12 major cell types, with secretory epithelial cells showing the most pronounced cycle-dependent molecular states [22]. TF activity patterns systematically shift across menstrual cycle phases, with secretory epithelial cells exhibiting differential accessibility to GATA, TCF, and HOX transcription factors depending on hormonal status [22]. Parallel research in cycling endometrium has demonstrated that temporal changes in chromatin accessibility coordinate cycle-dependent gene expression, with the implantation window specifically linked to co-option of transposable elements into the regulatory chromatin landscape [20].

Table 1: Transcription Factor Dynamics in Cycling Reproductive Tissues

Tissue/Cell Type	Cycle Phase	Key Transcription Factors	Regulatory Outcome	Experimental Method
Fallopian Tube Secretory Epithelial	Follicular vs. Luteal	GATA, TCF, HOX family	Distinct molecular states in secretory cells	scATAC-seq [22]
Endometrial Stromal	Implantation Window	CTCF, GATA6	Chromatin remodeling for receptivity	scATAC-seq, RNA-seq [20] [6]
Endometrial Epithelial	Throughout Cycle	311 TFs identified	Coordinated endometrial progression	Integrated network analysis [6]
Fallopian Tube (Menopause)	Post-menopausal	Jun, Fos, BACH1/2	Aging-associated accessibility	scATAC-seq [22]

Pathological Reprogramming of TF Networks in Disease States

Disease states frequently involve the corruption of physiological TF networks, with hormone-responsive cancers providing particularly illustrative examples. In estrogen receptor-positive (ERα+) breast cancer, chromatin architecture undergoes extensive reprogramming in response to hormonal signaling, creating pathogenic regulatory circuits that drive tumor progression and therapeutic resistance [71] [70].

Comprehensive mapping of 3D chromatin structure across a time course of estradiol stimulation revealed subsets of temporally highly dynamic compartments predominantly associated with active open chromatin [71]. These highly dynamic compartments show significantly greater alteration in tamoxifen-resistant breast cancer cells and are characterized by enhanced ERα binding but decreased CTCF binding [71]. This pathological rewiring creates self-reinforcing transcriptional programs that maintain oncogenic signaling despite therapeutic intervention.

Similar regulatory disruptions appear in reproductive disorders. In cases of total fertilization failure (TFF), integrated genomic and transcriptomic analyses have identified 17 transcription factors with disrupted binding patterns associated with critical processes in oogenesis, fertilization, and early embryonic development [72]. Key TFs including VEZF1, ZNF148, SP2, ZNF121, and ZFP28 function as central regulators of biological processes related to reproduction, with their disruption potentially explaining polygenic causes of infertility [72].

Table 2: Transcription Factor Network Alterations in Disease States

Disease Context	Dysregulated Transcription Factors	Chromatin Alterations	Functional Consequences	Citation
Hormone-dependent Breast Cancer	ERα, FOXA1, CTCF	Re-compartmentalization of chromatin domains, enhanced ERα binding	Tumor growth, tamoxifen resistance	[71] [70]
Tamoxifen-Resistant Breast Cancer	ERα, CTCF (decreased)	Altered dynamic compartments	Therapeutic resistance	[71]
Total Fertilization Failure	VEZF1, ZNF148, SP2, ZNF121, ZFP28	Disrupted binding to regulatory regions	Failed oocyte activation, infertility	[72]
Post-menopausal Fallopian Tube	Jun, Fos, BACH1/2	Increased chromatin accessibility	Potential enrichment for mesenchymal molecular type of HGSC	[22]

Experimental Data and Methodologies

Key Experimental Protocols for TF Network Mapping

Single-Cell Multi-omics Protocol for Temporal TF Analysis

The integration of single-cell RNA and ATAC sequencing has emerged as a powerful approach for mapping TF networks across temporal contexts. The standard methodology involves:

Cell Processing and Sequencing

• Tissue collection and single-cell suspension preparation using enzymatic digestion optimized for the specific tissue type [22]
• Partitioning of individual cells into nanoliter-scale droplets using microfluidic devices (10X Genomics platform)
• Simultaneous barcoding of RNA and accessible chromatin regions in the same cells
• Library preparation and sequencing on Illumina platforms to appropriate depth (typically 50,000 reads per cell for scRNA-seq)

Bioinformatic Analysis Pipeline

• Quality control filtering to remove doublets and cells with high mitochondrial content [22]
• Cluster identification using unsupervised methods (e.g., Seurat, SCANPY)
• Cell type annotation based on canonical marker genes [22]
• TF activity inference using tools like SCENIC or chromVAR based on motif accessibility [22]
• Differential abundance testing and trajectory analysis to identify temporal shifts

This protocol successfully identified substantial shifts in cell type frequencies, gene expression, TF activity, and cell-to-cell communication during menopause and the menstrual cycle in fallopian tube tissues [22].

3D Chromatin Dynamics Mapping in Hormone Response

The Tethered Chromatin Conformation (TCC) method provides a high-resolution approach for capturing hormone-driven changes in chromatin architecture:

Experimental Workflow

• Crosslinking of cells with formaldehyde to preserve chromatin interactions
• Chromatin fragmentation using restriction enzymes or MNase
• Proximity ligation to join crosslinked DNA fragments
• Biotinylated oligonucleotide labeling and pull-down to enrich for ligation products
• Library preparation and high-depth sequencing (200 million+ reads recommended) [71]

Data Analysis Steps

• Processing of raw sequencing data using standardized TCC/Hi-C pipelines
• Identification of chromatin compartments at high resolution (100kb bins)
• Classification of temporal dynamic re-compartmentalization (TDRC) patterns [71]
• Integration with TF ChIP-seq data to link structural changes with regulatory factor binding
• Identification of ERα-bound promoter-enhancer looping genes within altered domains

This approach revealed that active chromatin domains are particularly susceptible to structural changes in response to estradiol stimulation over time, with significant implications for hormone-driven cancers [71].

Visualization of Experimental Workflows and Regulatory Networks

Integrated Multi-omics Workflow for Temporal TF Analysis

Chromatin Dynamics in Hormone Response

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Essential Research Reagents for TF Network Studies

Reagent Category	Specific Products/Tools	Primary Function	Application Examples
Single-Cell Sequencing Kits	10X Genomics Single Cell Multiome ATAC + Gene Expression	Simultaneous profiling of gene expression and chromatin accessibility	Mapping cell-type-specific TF networks in fallopian tubes [22]
Chromatin Conformation Kits	Tethered Chromatin Conformation (TCC)	Genome-wide mapping of chromatin interactions	Capturing 3D chromatin dynamics in hormone-treated breast cancer cells [71]
TF Activity Inference Software	SCENIC, chromVAR, DoRothEA	Computational inference of TF activities from scRNA/scATAC-seq data	Identifying menstrual cycle-dependent TF states [22] [6]
Epigenomic Annotation Databases	Cistrome, ReMap, ENCODE	Reference catalogs of TF binding sites and chromatin states	Contextualizing novel TF binding patterns [72] [70]
Hormone Response Assays	Estradiol, progesterone, receptor antagonists	Modulation of hormonal signaling pathways	Studying endocrine response in breast cancer models [71] [70]

The comparative analysis of transcription factor networks across physiological and pathological states reveals temporal dynamics as a fundamental layer of regulatory control. The menstrual cycle provides a uniquely informative model for understanding how coordinated hormonal fluctuations program and reprogram TF activities across diverse tissue contexts. Critically, the molecular machinery that enables physiological plasticity during normal cycling appears vulnerable to pathogenic co-option in disease states, particularly in hormone-responsive cancers and reproductive disorders.

Future research directions should prioritize the development of more sophisticated temporal sampling frameworks that capture intra- and inter-individual variability in TF network regulation. Additionally, computational methods capable of integrating multi-omic data across timescales will be essential for distinguishing causal regulatory events from correlative associations. Therapeutic opportunities may emerge from targeting the specific TFs and cofactors that mediate pathological network reprogramming, potentially restoring physiological regulation without completely disrupting hormonal signaling. As single-cell technologies continue to evolve and temporal design principles become more widely incorporated into study designs, our understanding of TF networks in health and disease will increasingly reflect their dynamic, context-dependent nature.

Uterine Natural Killer (uNK) cells are the most abundant immune population in the endometrium, comprising up to 70% of endometrial leukocytes during the window of implantation and in early pregnancy [17] [73]. These cells play pivotal roles in regulating endometrial receptivity, trophoblast invasion, and spiral artery remodeling [74]. Emerging evidence indicates that disturbances in uNK cell polarization—the balance between different functional subsets—represent a key pathological feature of endometrial disorders such as chronic endometritis (CE) and recurrent implantation failure (RIF) [17] [75]. This case study examines the transcription factors (TFs) driving uNK cell polarization within the context of temporal validation across menstrual cycle research, providing a comparative analysis of experimental approaches and their resulting data.

Experimental Models and Methodologies for uNK Cell Characterization

Single-Cell RNA Sequencing (scRNA-seq) Profiling

Protocol Overview: Researchers integrated public scRNA-seq datasets comprising 100,291 cells from 21 normal endometrial tissue samples [17]. After quality control and normalization, highly variable genes were identified using the FindVariableGenes function in Seurat. Principal component analysis was performed followed by UMAP for two-dimensional visualization using the first 40 principal components. Clustering was performed using the FindClusters algorithm at a resolution of 0.3. Cell types were annotated based on known marker genes and differential expression profiles, with marker genes defined as those expressed in more than 40% of cells with a minimum log-fold change of 0.6 [17].

Temporal Considerations: The analysis specifically accounted for menstrual cycle dynamics by including samples across different phases. Strong inter-mixing of cells from different datasets was observed, indicating minimal batch effect, but researchers preserved biological variance related to the menstrual cycle by using merged, uncorrected data for downstream analysis [17].

Flow Cytometry Immunophenotyping

Protocol Overview: For direct phenotypic comparison, endometrial and decidual NK cells were immunophenotyped using 10-color flow cytometry panels [76]. Menstrual blood was collected from 26 healthy women during the first 36 hours of menstruation, while decidual tissue was obtained from 14 women undergoing elective pregnancy termination between 6-15 weeks of gestation. Endometrial immune cells were isolated from menstrual blood using granulocyte depletion cocktail and Ficoll density gradient centrifugation. Cells were stained for 20 minutes at room temperature with fluorochrome-conjugated antibodies against various NK cell receptors and analyzed using a Navios flow cytometer with Kaluza software for analysis [76].

Receptor Targets: The panel included antibodies against KIR2DL1, KIR2DL3, KIR2DL2/L3/S2, KIR3DL1, NKG2A, NKG2C, NKG2D, NKp30, NKp44, NKp46, and CD244 [76].

Bulk RNA-seq Validation in Clinical Cohorts

Protocol Overview: To validate scRNA-seq findings, researchers obtained bulk RNA sequencing data from patients with CE and RIF [17]. The CE dataset included 51 endometrial samples (18 CE and 33 normal) from the Japanese Genotype-phenotype Archive (JGAD000750), with CE diagnosis based on histopathological identification of ≥5 CD138-positive plasma cells per high-power field. The RIF dataset included 50 patients (31 RIF and 19 healthy controls) from GEO (GSE106602). A diagnostic model was developed based on uNK subtype markers and evaluated using receiver operating characteristic (ROC) analysis [17].

Comparative Analysis of uNK Cell Subtypes and Their Transcriptional Regulation

Functionally Distinct uNK Cell Subpopulations

scRNA-seq analysis has revealed functionally distinct uNK subtypes with specialized roles in endometrial homeostasis and pathology [17].

Table 1: Characteristics of Identified uNK Cell Subtypes

uNK Subtype	Key Transcription Factors	Marker Genes	Primary Functions	Pathological Associations
uNK2	EOMES, ELF4	AFAP1L2, KLRC1, SOCS1	Cytotoxic activity	Upregulated in CE and RIF
uNK3	ELK4, IRF1	SAMD3	Platelet activation, tight junction formation	Downregulated in CE and RIF

The abundance of these identified transcription factors correlated moderately with their respective uNK subtype proportions (p < 0.001) [17]. The uNK2 subtype demonstrates cytotoxic characteristics, while uNK3 cells are involved in tissue remodeling processes including platelet activation and tight junction formation [17].

Diagnostic Performance of uNK Polarization Markers

The uNK2/uNK3 signature ratio demonstrated significant diagnostic potential for endometrial disorders [17].

Table 2: Diagnostic Performance of uNK Polarization Biomarkers

Diagnostic Marker	Condition	AUC Value	Sample Size	Clinical Utility
uNK2/uNK3 ratio	CE	0.675	51 samples (18 CE, 33 normal)	Moderate discrimination
Logistic model (combined markers)	CE	0.822	51 samples (18 CE, 33 normal)	Strong diagnostic power
uNK2/uNK3 ratio	RIF	0.823	50 samples (31 RIF, 19 normal)	Strong discrimination
Logistic model (combined markers)	RIF	0.830	50 samples (31 RIF, 19 normal)	Strong diagnostic power
SDC1	CE	0.48	51 samples (18 CE, 33 normal)	Poor discrimination

The uNK2/uNK3 signature ratio was notably upregulated in CE samples, a finding corroborated in the independent RIF dataset [17]. This imbalance in uNK cell polarization represents a key feature of immune dysregulation in these endometrial disorders.

Visualizing Transcriptional Networks and Experimental Workflows

uNK Cell Transcriptional Regulation Network

Experimental Workflow for uNK Cell Characterization

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagent Solutions for uNK Cell Studies

Reagent Category	Specific Examples	Research Application	Experimental Context
scRNA-seq Platforms	Seurat (FindVariableGenes, FindClusters)	Cell clustering and subtype identification	uNK subtype discovery [17]
Flow Cytometry Antibodies	CD3-APC-AF750, CD56-ECD, CD158b1/b2-PC7 (KIR2DL2/L3/S2)	Immunophenotyping of uNK receptors	NK cell receptor profiling [76]
Transcription Factor Assays	EOMES, ELF4, ELK4, IRF1 detection methods	Identification of TF drivers	Regulatory network analysis [17]
Cell Isolation Kits	RosetteSep granulocyte depletion cocktail, Percoll gradients	Immune cell isolation from menstrual blood/decidua	Sample preparation for cytometry [76]
Bioinformatics Tools	CellChat, UMAP, CIBERSORT	Cell-cell communication inference, visualization	Microenvironment analysis [17]

Discussion and Future Perspectives

The temporal dynamics of uNK cell polarization across the menstrual cycle present both challenges and opportunities for validating these transcriptional mechanisms. The endometrial microenvironment undergoes dramatic remodeling throughout the cycle, governed by hormonal fluctuations that modulate the immune landscape [17] [77]. Understanding how the identified transcription factors EOMES, ELF4, ELK4, and IRF1 vary across cycle phases is essential for distinguishing pathological imbalances from physiological fluctuations.

Future studies should incorporate longitudinal sampling across defined menstrual cycle phases to establish normative ranges for uNK subtype ratios and their transcriptional drivers. The development of endometrial organoid models that faithfully replicate in vivo transcriptomic, cellular, and functional characteristics [77] provides promising platforms for experimentally manipulating these transcription factors and validating their functional roles in uNK polarization.

From a therapeutic perspective, the identification of these key transcription factors opens avenues for targeted interventions aimed at restoring uNK balance in CE and RIF. However, such approaches must account for the temporal context of menstrual cycle physiology to ensure appropriate intervention timing and avoid disrupting essential immune functions necessary for endometrial receptivity and successful implantation [78].

Transcription factor (TF) dynamics govern cellular identity and function, yet their temporal validation across physiological states remains a significant challenge in reproductive biology. This guide compares experimental approaches for characterizing TF networks, using the human fallopian tube as a model system for understanding cyclical tissue remodeling. We objectively evaluate methodologies based on resolution, throughput, and applicability to menstrual cycle research, providing supporting experimental data and standardized protocols to empower rigorous, cross-tissue validation of transcriptional regulators.

The human fallopian tube undergoes extensive molecular and cellular reprogramming throughout the menstrual cycle and menopausal transition, driven by coordinated changes in transcription factor activity [22]. Understanding these dynamics requires sophisticated single-cell and spatial technologies that can capture transient transcriptional states across multiple tissue contexts. The fallopian tube presents a unique model for studying temporal TF validation due to its:

• Hormonal responsiveness: Direct regulation by cycling estrogen and progesterone levels
• * Cellular diversity*: Multiple epithelial, stromal, and immune cell populations with distinct TF networks
• * Pathophysiological relevance*: Known site of origin for high-grade serous ovarian cancer [22]

This guide systematically compares the experimental frameworks for mapping TF dynamics, emphasizing cross-tissue validation strategies applicable to menstrual cycle research and beyond.

Experimental Methodologies for TF Mapping

Single-Cell Multi-Omics Integration

The most comprehensive approach for TF validation combines single-cell RNA sequencing (scRNA-seq) with single-cell ATAC sequencing (scATAC-seq) to simultaneously profile gene expression and chromatin accessibility [22].

Protocol: Parallel scRNA-seq and scATAC-seq on Fallopian Tube Specimens

• Tissue Collection & Processing: Obtain fresh fallopian tube specimens from surgical procedures, preserving anatomical identity (fimbriae, ampulla, isthmus)
• Single-Cell Suspension: Mechanically dissociate and enzymatically digest tissue (Collagenase IV, 2 U/mL, 37°C, 30-45 min) followed by RBC lysis and viability staining
• Cell Sorting: FACS-sort live cells (DAPI-) into separate aliquots for RNA and ATAC sequencing
• Library Preparation:
  • scRNA-seq: 10X Genomics Chromium Single Cell 3' Reagent Kit v3.1
  • scATAC-seq: 10X Genomics Chromium Single Cell ATAC Reagent Kit
• Sequencing: Illumina NovaSeq 6000, targeting 20,000 reads/cell for RNA, 25,000 fragments/cell for ATAC
• Bioinformatic Integration: Cell Ranger ATAC, Seurat, Signac for joint clustering and TF motif analysis [22]

Spatial Transcriptomic Profiling

Spatial technologies bridge cellular resolution with tissue architecture, critical for validating TFs in complex microenvironments.

Protocol: GeoMx Digital Spatial Profiler for Regional TF Validation

• Tissue Preparation: Embed fresh frozen fallopian tube specimens in OCT, section at 5μm thickness
• Immunofluorescence Staining: Incubate with cell-type-specific markers (FOXJ1 for ciliated cells, PAX8 for secretory cells) to guide region selection
• UV Oligo Release: Illuminate regions of interest (ROI) with digital micromirror device to release indexing oligos
• Collection & Sequencing: Aspirate released oligos for NGS library prep (Illumina NextSeq 2000)
• Data Analysis: NanoString GeoMx DSP Data Analysis Suite with tissue segmentation algorithms [79]

Cross-Tissue Reference Mapping

Integrative clustering enables identification of conserved TF programs across tissue boundaries.

Protocol: CoVarNet for Cross-Tissue Cellular Module Identification

• Atlas Compilation: Aggregate scRNA-seq data from 35 human tissues (2.3M cells)
• Covariance Network Construction:
  • Calculate cell subset frequency correlations across samples
  • Apply non-negative matrix factorization to identify co-occurring cell subsets
  • Build cellular module networks connecting correlated subsets
• TF Activity Imputation: Infer shared regulatory programs using SCENIC and DoRothEA TF databases
• Validation: Spatially map predicted modules using Visium Spatial Transcriptomics [80]

Comparative Performance Analysis of TF Mapping Technologies

Table 1: Technology Comparison for Temporal TF Validation

Method	Cellular Resolution	TF Detection Capability	Temporal Sampling Requirements	Throughput (Cells/Experiment)	Cross-Tissue Applicability
scRNA-seq + scATAC-seq	Single-cell	Direct (chromatin accessibility) + Indirect (expression)	Multiple cycle phases (n≥5/phase)	85,107 pre-menopausal cells [22]	High (with integration tools)
Spatial Transcriptomics (GeoMx)	Regional (10-100 cells/ROI)	Indirect (expression only)	Paired phases sufficient	74-110 segments [79]	Moderate (tissue-specific optimization)
Cross-Tissue Integration	Single-cell	Indirect (expression only)	Leverages public datasets	2.3M cells across 35 tissues [80]	Very High (by design)
Bulk RNA-seq + Deconvolution	None	Indirect (inferred)	Longitudinal sampling recommended	N/A	Limited (reference-dependent)

Table 2: Experimentally Validated TF Dynamics in Human Fallopian Tube

Transcription Factor	Cell Type Specificity	Menstrual Cycle Regulation	Menopausal Change	Functional Associations
FOX Family	Ciliated epithelial	Hormonally responsive	Maintained activity	Ciliogenesis, cell differentiation [22]
BACH1/2	Secretory epithelial	Phase-dependent	Increased accessibility in menopause	Aging, stress response [22]
Jun/Fos	Multiple stromal	Moderate cycling	Significant increase post-menopause	Cellular senescence, proliferation [22]
GATA Family	Secretory epithelial subset	Strong hormonal regulation	Generally downregulated	Epithelial differentiation [22]
SOX Family	Epithelial progenitors	Proliferative phase enrichment	Depleted post-menopause	Cell fate determination [22]
HOX Family	Regional epithelial	Anatomical patterning	Stable expression	Tissue identity, spatial organization [22]

Visualization of Experimental Workflows

TF Dynamics Workflow: Integrated experimental and computational pipeline for validating transcription factor dynamics across temporal and tissue contexts.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Critical Research Reagents for TF Dynamics Studies

Reagent/Category	Specific Examples	Function in TF Validation	Protocol Considerations
Cell Isolation Kits	Collagenase IV, Liberase TL, RBC Lysis Buffer	Tissue dissociation and viability maintenance	Concentration optimization required for different anatomical regions [22]
Single-Cell Library Prep	10X Genomics Chromium Single Cell 3' Kit, Single Cell ATAC Kit	Barcoding and amplification of single-cell transcriptomes/chromatin	Input cell concentration critical for optimal recovery [22]
Spatial Profiling Panels	GeoMx Cancer Transcriptome Atlas (∼1800 genes)	Targeted spatial transcriptomics with ROI selection	Requires IF staining for segmentation (FOXJ1/PAX8) [79]
Antibodies for Validation	Anti-FOXJ1 (ciliated), Anti-PAX8 (secretory), Anti-OVGP1 (cycle phase)	Cell type identification and cycle staging	IHC validation essential for spatial technologies [79]
Bioinformatic Tools	Seurat, Signac, Cell Ranger, SCENIC, CoVarNet	Data integration, clustering, and TF activity inference	Computational resource requirements substantial for multi-omic data [22] [80]
Reference Datasets	Human Cell Atlas, Tabula Sapiens, GTEx	Cross-tissue validation and normalization	Batch effect correction critical for integration [81] [80]

Discussion: Best Practices for Cross-Tissue TF Validation

Temporal Sampling Considerations

Accurate characterization of TF dynamics across the menstrual cycle requires rigorous phase determination. Studies relying on assumed or estimated cycle phases without hormonal confirmation produce unreliable data [29]. The gold standard incorporates:

• Direct hormonal measurements: Serum or salivary progesterone/estradiol levels
• Multiple sampling timepoints: Minimum of 5 donors per cycle phase (menstrual, follicular, ovulatory, luteal)
• Cycle length verification: Documented regularity (21-35 days) with LH surge detection [29]

Anatomical Specificity in Experimental Design

Fallopian tube biology demonstrates significant regional variation, with fimbriae enriched for ciliated cells (∼60%) versus isthmus dominance of smooth muscle cells [22]. TF validation studies must:

• Document anatomical origin: Precise mapping of specimen collection sites
• Account for cellular composition differences: Normalize for region-specific cell type abundances
• Validate spatial patterns: Confirm TF gradients using spatial transcriptomics or multiplexed FISH [79]

Integration with Cross-Tissue Fibroblast Atlas

The emerging cross-tissue fibroblast atlas reveals conserved transcriptional programs across physiological systems [81] [80] [82]. Fallopian tube TF dynamics research benefits from:

• Reference mapping: Projection onto universal fibroblast clusters (e.g., CXCL10+CCL19+ immune-interacting fibroblasts)
• Conserved pathway identification: Recognition of shared regulatory nodes across tissue systems
• Disease relevance mapping: Correlation with pathological fibroblast states in cancer and inflammation [81]

The fallopian tube provides a powerful model for understanding how transcription factor networks coordinate tissue remodeling in response to physiological cues. Cross-validation across technologies and tissue systems remains essential for distinguishing biologically significant TF dynamics from technical artifacts. The integrated approaches presented here—combining single-cell multi-omics, spatial profiling, and cross-tissue mapping—establish a rigorous framework for temporal validation of transcriptional regulators that can be applied throughout reproductive biology and beyond. As these technologies mature, they will increasingly enable predictive modeling of TF network behavior across physiological states and tissue contexts, with significant implications for understanding both normal tissue homeostasis and disease pathogenesis.

Assessing Conserved and Divergent TF Programs Across Menopausal Transition

Understanding the dynamic regulation of gene expression is fundamental to uncovering the molecular drivers of the menopausal transition. Transcription factors (TFs) serve as master regulators that control spatiotemporal gene expression programs by binding to regulatory elements, and their coordinated action directs the cellular response to hormonal fluctuations [83] [84]. The broader thesis of temporal validation in menstrual cycle research establishes a critical framework for investigating how these TF programs are conserved or diverge during reproductive aging. As the field moves toward personalized therapeutic strategies, benchmarking the tools that identify key transcriptional regulators becomes essential for advancing our mechanistic understanding of this neuroendocrine transition [85].

The menopausal transition represents a significant neuroimmune reprogramming event characterized by coordinated changes across multiple biological systems. Central to this process is the depletion of ovarian follicles, leading to reduced estradiol and progesterone production and subsequent disruption of the hypothalamic-pituitary-gonadal (HPG) axis [85]. This hormonal shift remodels hypothalamic signaling networks—particularly those involving kisspeptin, neurokinin B (NKB), and GABA—driving alterations in gonadotropin-releasing hormone (GnRH) pulsatility and contributing to the symptomatic presentation of menopause [85]. Within this context, identifying the transcription factors that control these gene expression programs is critical for pinpointing novel therapeutic targets for managing menopause-related symptoms and associated health risks.

Benchmarking Transcription Factor Prioritization Tools

Performance Comparison of Major TF Prioritization Methods

Advancements in high-throughput sequencing technologies have enabled the development of computational tools that predict transcription factor binding and activity from epigenomic data. These methods leverage different algorithmic approaches and input data types to infer the key transcriptional regulators driving gene expression programs in specific biological contexts [83] [84].

Table 1: Benchmarking Results of Transcription Factor Prioritization Tools

Tool	Algorithm Type	Primary Data Input	Performance Accuracy	Key Strengths
RcisTarget	Enrichment analysis	Gene sets	Moderate	Fast analysis; user-friendly
MEIRLOP	Enrichment analysis	Regulatory regions	High	Incorporates detailed CRE information
monaLisa	Enrichment analysis	Regulatory regions	High	Balances sensitivity and specificity
ChEA3	Enrichment analysis	Gene sets	Moderate	Extensive TF-gene interaction database
BART	Regression-based	H3K27ac ChIP-seq	High	Comprehensive cis-regulatory profile coverage
Lisa	Regression-based	DNase-seq + H3K27ac	High	Multi-omics integration
TRAPT	Deep learning	Multi-omics epigenomic data	Highest	Integrates genome-wide binding sites and CREs

In a comprehensive benchmarking study evaluating nine published tools on 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were genetically perturbed, three frontrunner tools emerged: RcisTarget, MEIRLOP, and monaLisa [83]. These tools demonstrated superior performance in identifying perturbed TFs from real-world datasets. The study revealed that methods incorporating detailed cis-regulatory element (CRE) information generally outperformed those relying solely on gene sets, highlighting the importance of regulatory sequence context in accurate TF inference [83].

Advanced Method: TRAPT Deep Learning Framework

Beyond conventional approaches, the Transcription Regulator Activity Prediction Tool (TRAPT) represents a novel multi-modality deep learning framework that infers regulator activity by learning and integrating the regulatory potentials of target gene cis-regulatory elements and genome-wide binding sites [84]. TRAPT employs a multi-stage fusion-based strategy that simultaneously integrates signals from both upstream regulatory potential (U-RP) of target gene cis-regulatory elements and downstream regulatory potential (D-RP) of genome-wide TF binding sites [84].

In benchmark tests on 570 TR knockdown/knockout datasets from the KnockTF database, TRAPT outperformed established tools including Lisa, BART, i-cisTarget, and ChEA3, particularly in predicting transcription co-factors and chromatin regulators [84]. The model's architecture addresses two key challenges in TF prediction: transcriptional regulator binding preference (TRBP), which accounts for TR predisposition to associate with active chromatin regions, and incomplete coverage of the cis-regulatory profile (ICCP) [84]. By leveraging over 20,000 epigenomic datasets and employing graph convolutional neural networks for network optimization, TRAPT provides a more comprehensive approach to identifying context-specific transcriptional regulators.

Experimental Protocols for TF Program Analysis

Chromatin Profiling and Perturbation-Based Validation

The gold standard experimental protocol for validating TF programs involves chromatin profiling followed by targeted perturbation studies. The benchmark methodology used in the assessment of TF prioritization tools provides a robust framework [83]:

• Experimental Design: Conduct a minimum of 84 chromatin profiling experiments (H3K27ac ChIP-seq) with paired TF perturbations (knockout/overexpression) across diverse biological contexts.

• Sample Preparation:

  • Perform crosslinking with 1% formaldehyde for 10 minutes at room temperature
  • Sonicate chromatin to 200-500 bp fragments
  • Immunoprecipitate with validated H3K27ac antibody
  • Prepare sequencing libraries using compatible kit reagents

• Sequencing and Data Processing:

  • Sequence on Illumina platform to minimum depth of 20 million reads per sample
  • Align reads to reference genome using BWA or Bowtie2
  • Call peaks using MACS2 with FDR cutoff of 0.05
  • Normalize read counts using DESeq2 or similar method

• Tool Execution and Evaluation:

  • Run each TF prioritization tool with default parameters
  • Evaluate performance using area under precision-recall curve (AUPRC)
  • Calculate precision and recall at top-ranked predictions
  • Assess statistical significance with hypergeometric testing

This protocol establishes a rigorous validation pipeline that connects computational predictions with experimental evidence, ensuring that identified TF programs reflect biologically relevant regulatory relationships.

Multi-Omics Integration for Enhanced TF Discovery

For investigations requiring deeper insight into TF activity across the menopausal transition, a multi-omics approach provides enhanced resolution:

• Data Collection: Curate large-scale epigenomic data including ATAC-seq (chromatin accessibility), DNase-seq (open chromatin), H3K27ac ChIP-seq (active enhancers/promoters), and TF ChIP-seq (direct binding) from relevant tissue types [84].

• Preprocessing Pipeline:

  • Subject all datasets to uniform quality control (FastQC)
  • Process using standardized alignment and peak-calling parameters
  • Apply batch effect correction (ComBat) when integrating multiple datasets
  • Annotate regulatory elements with reference to gene transcription start sites

• Regulatory Potential Calculation:

  • Compute regulatory potential (RP) for each gene using large-scale epigenome data
  • Apply uniform weight decay strategy to epigenomic data
  • Implement context-specific weight decay for individual TFs to capture distinct regulatory patterns
  • Integrate all RPs into Epigenomic Regulatory Potential (Epi-RP) and TR Regulatory Potential (TR-RP) matrices

• Network-Based Integration:

  • Construct heterogeneous network between TFs and epigenomic samples using k-nearest neighbors algorithm
  • Optimize initial epigenomic regulatory network through multi-modal knowledge distillation
  • Employ conditional variational autoencoder (teacher model) to learn distributionally smoothed joint embeddings
  • Utilize variational graph autoencoder (student model) for network refinement and regulatory potential aggregation [84]

This comprehensive protocol enables researchers to capture the complex regulatory landscape of the menopausal transition, facilitating identification of both conserved and divergent TF programs throughout this physiological process.

Signaling Pathways in Menopausal Transition

Neuroendocrine Reprogramming During Menopause

The menopausal transition involves coordinated changes across neuroendocrine, immune, metabolic, and mitochondrial systems. Central to this process is the hypothalamic-pituitary-gonadal (HPG) axis, which undergoes significant remodeling as ovarian function declines [85].

Diagram 1: HPG Axis Remodeling in Menopause

KNDy neurons in the arcuate nucleus co-express kisspeptin, neurokinin B (NKB), and dynorphin, and serve as central modulators of gonadotropin-releasing hormone (GnRH) secretion [85]. During reproductive aging, the depletion of ovarian follicles leads to reduced estradiol and progesterone production, disrupting the negative feedback on the hypothalamus and pituitary. This results in hypertrophy of infundibular nucleus neurons that co-express estrogen receptor α (ERα), NKB, substance P, and kisspeptin mRNA, alongside increased tachykinin and kisspeptin gene transcription [85]. These neuroendocrine changes drive the alterations in GnRH pulsatility that characterize the menopausal transition.

Molecular Pathways of Estrogen Withdrawal

The decline in estrogen during menopause activates multiple molecular pathways that contribute to both central symptoms and peripheral tissue changes. Understanding these pathways is essential for identifying TF programs that might be targeted for therapeutic intervention.

Diagram 2: Estrogen Withdrawal Signaling Pathways

Estrogen deficiency activates cellular senescence programs characterized by increased senescence-associated-β-galactosidase (SA-β-Gal) activity, tumor protein p53 (p53), cyclin-dependent kinase inhibitors (p21CIP1, p16INK4a), and various cytokines [86]. Senescent cells secrete a mix of molecules termed the senescence-associated secretory phenotype (SASP), involving release of interleukins (-1, -6, -7, -8, -17), oncostatin M, granulocyte-macrophage colony-stimulating factor, tumor necrosis factor alpha, and various matrix metalloproteinases [86]. These factors create local and systemic inflammation that disrupts tissue integrity and contributes to menopausal symptoms and tissue changes, including cartilage degradation in osteoarthritis and hair follicle dysfunction [86] [87].

The Scientist's Toolkit: Essential Research Reagents

Key Research Reagent Solutions for TF Analysis

Table 2: Essential Research Reagents for Transcription Factor Studies

Reagent Category	Specific Examples	Primary Function	Application Notes
Chromatin Profiling Antibodies	H3K27ac, H3K4me3, H3K27me3	Mark active enhancers/promoters	Validate with knockout controls; species compatibility
TF Perturbation Reagents	CRISPR/Cas9 guides, siRNA pools	Knockout/knockdown specific TFs	Include multiple guides per target; optimize delivery
Sequencing Library Prep Kits	Illumina TruSeq, NEB Next Ultra II	Prepare sequencing libraries	Consider input requirements; multiplexing capability
Epigenomic Databases	TcoFBase, CRdb, TFTG, SEdb, ATACdb	Access curated TF binding data	Ensure dataset relevance; check version dates
Hormone Assay Kits	Estradiol ELISA, Progesterone RIA	Quantify hormone levels	Establish standard curves; control for matrix effects
Cell Type Markers	NeuN (neurons), GFAP (astrocytes)	Identify specific cell populations	Optimize fixation; validate specificity
Software Platforms	TRAPT, RcisTarget, monaLisa	Predict TF activity from omics data	Verify installation requirements; update regularly

Successful investigation of TF programs across the menopausal transition requires careful selection of research reagents and methodologies. For chromatin profiling, validated antibodies against histone modifications such as H3K27ac are essential for marking active regulatory elements [83] [84]. Large-scale integration of epigenomic data from sources like ATAC-seq, DNase-seq, and ChIP-seq provides comprehensive coverage of regulatory landscapes, though researchers must address challenges including noise, batch effects, and data redundancy [84].

For perturbation studies, CRISPR/Cas9 systems and siRNA pools enable targeted manipulation of candidate TFs identified through computational prioritization tools [83]. When studying menopausal transition specifically, hormone assay kits for estradiol and progesterone provide essential physiological context, while cell type-specific markers help resolve cell-type-specific TF programs in heterogeneous tissues [85] [88]. Integration of these experimental approaches with computational tools like TRAPT, which leverages deep learning on large-scale epigenomic data, offers the most powerful strategy for identifying conserved and divergent TF programs during reproductive aging [84].

The integration of advanced computational tools with rigorous experimental validation provides a powerful framework for identifying transcription factor programs that drive the menopausal transition. Benchmarking studies have established that tools incorporating cis-regulatory element information—particularly RcisTarget, MEIRLOP, monaLisa, and the deep learning framework TRAPT—deliver superior performance in identifying key transcriptional regulators from epigenomic data [83] [84]. These computational approaches, when combined with the experimental protocols and reagent solutions outlined in this guide, enable researchers to systematically map the dynamic TF activity that underlies the neuroendocrine reprogramming of menopause.

The temporal validation of TF programs across menstrual cycle research and into the menopausal transition represents a critical frontier in understanding female-specific health trajectories. As research in this area advances, the continued refinement of TF prioritization tools and their application to well-characterized biological models will accelerate the identification of therapeutic targets for managing menopause-related symptoms and associated health risks. This approach promises to translate our growing understanding of transcriptional regulation into personalized strategies that improve health outcomes for women during reproductive aging.

For researchers and drug development professionals investigating female reproductive health, the precise mapping of transcriptional activity to the ovulatory event remains a significant challenge. The menstrual cycle is a dynamic process orchestrated by complex hormonal interactions, yet a critical gap exists in linking the activity of key transcription factors (TFs) at the molecular level to the definitive physiological marker of ovulation: follicle rupture confirmed by ultrasonography. Establishing gold-standard correlation between TF activity, systemic hormone fluctuations, and ultrasound-findings requires rigorous temporal validation across the cycle phases. This guide compares current methodological approaches for validating ovulation, assessing their capacity to provide the precise temporal frameworks necessary for correlating with molecular events in transcriptional regulation. We present experimental data and protocols that define current best practices, creating a foundational reference for studies aiming to anchor TF activity analyses within the physiological context of a confirmed ovulatory event.

Established Gold Standards: Ultrasound and Hormonal Monitoring

The Ultrasonographic Gold Standard

Transvaginal ultrasound monitoring represents the most direct clinical method for visualizing and confirming ovulation. The definitive endpoint is the rupture of the leading ovarian follicle, which provides an unambiguous morphological marker designated as ovulation day (Day 0) [89]. In practice, researchers typically perform serial scans—often beginning around cycle day 8-10 and continuing daily as the follicle matures—to track follicular growth until its sudden disappearance or collapse, indicating rupture [89] [90].

Correlative Hormonal Profiles

Concurrent with ultrasound monitoring, serum hormone measurements provide the biochemical correlates of ovulation. A typical hormonal profile shows:

• Luteinizing Hormone (LH): Peaks on average 24-36 hours before follicle rupture (Day -1) [89]. However, studies note significant individual variability, with the LH peak occurring two days before ovulation (Day -2) in 5.9% of cycles [89].
• Estrogen (Estradiol): Rises to a peak approximately two days before ovulation (Day -2), then declines sharply by 58% on average by the day of ovulation [89]. Critically, any decrease in estrogen is 100% associated with ovulation emergence the same day or the next day [89].
• Progesterone: Rises sharply after ovulation, with levels > 2 nmol/L showing high sensitivity (91.5%) but low specificity (62.7%) for predicting ovulation the next day [89].

Table 1: Hormonal Predictors of Imminent Ovulation Relative to Ultrasound-Confirmed Ovulation Day (Day 0)

Hormonal Parameter	Peak/Timing Relative to Ovulation	Predictive Value
LH Surge	Peak on Day -1 (24-36 hours pre-ovulation)	High positive predictive value; precedes ovulation [89]
Estrogen Drop	Sharp decrease (58% avg) from Day -1 to Day 0	100% association with ovulation same/next day [89]
Progesterone Rise	Threshold >2 nmol/L	High sensitivity (91.5%), low specificity (62.7%) for next-day ovulation [89]

Comparative Analysis of Ovulation Confirmation Methodologies

Serum Hormone Monitoring with Ultrasound Correlation

The combined approach of serial serum measurements with transvaginal ultrasound represents the highest standard for precise ovulation timing in research settings.

Experimental Protocol:

• Participant Inclusion: Recruit reproductive-aged women with regular cycles (e.g., 21-42 days). Exclude those with conditions or medications affecting ovarian function [89] [90].
• Blood Sampling & Ultrasound: Conduct daily blood draws for LH, estrogen, and progesterone analysis alongside transvaginal ultrasounds as the follicle approaches maturity (>16mm) [89].
• Data Point Definition: Designate the day of follicle rupture as Day 0 for temporal alignment of all hormonal and molecular data [89].

Performance Data: One comprehensive study developing a combined hormone-ultrasound algorithm reported 95% to 100% accuracy in predicting ovulation, with validation in independent cycles achieving 97% correct prediction [89].

Quantitative Urinary Hormone Monitoring

Quantitative at-home urine hormone monitors offer a less invasive method for predicting and confirming ovulation, measuring metabolites like estrone-3-glucuronide (E13G), LH, and pregnanediol glucuronide (PDG) [90].

Experimental Protocol (Quantum Menstrual Health Monitoring Study):

• Device & Tracking: Participants use a quantitative urine hormone monitor (e.g., Mira monitor) throughout their cycle [90].
• Gold-Standard Correlation: Participants simultaneously undergo serial transvaginal ultrasounds in a clinic to confirm the day of ovulation [90].
• Serum Correlation: Blood samples are collected to correlate urine hormone metabolite levels with serum concentrations of FSH, LH, estradiol, and progesterone [90].
• Population: Includes both regularly cycling women and those with irregular cycles (e.g., PCOS, athletes) to test robustness [90].

Performance Data: Earlier studies of home urine hormone monitors (ClearPlan Fertility Monitor) showed that 90.6% of cycles with a monitor-detected LH surge had ultrasonographically confirmed ovulation, with 91.1% of ovulations occurring during the two days of peak fertility indicated by the monitor [91].

Temperature-Based Methods

Basal Body Temperature (BBT) tracking detects the subtle rise in resting temperature (0.3-0.5°C) following ovulation due to increased progesterone [92] [93].

Novel Protocol (Estimated Core Body Temperature):

• Measurement: Core body temperature (CBT) is estimated during sleep using a thermal sensor on a night bra, measuring breast skin temperature and ambient temperature, applying a heat flux algorithm to minimize ambient effects [92].
• Comparison: Validated against true CBT (measured via ingestible thermometer) and traditional oral BBT [92].
• Ovulation Identification: A temperature shift is identified using the "three-over-six rule" (three consecutive temperatures higher than the previous six) [92].

Performance Data: The estimated CBT method demonstrated higher sensitivity and specificity for detecting ovulation (confirmed by urine LH) compared to traditional oral BBT measurements [92]. Large-scale app data (over 600,000 cycles) confirms the BBT shift pattern, with a mean luteal phase length of 12.4 days [93].

Table 2: Comparison of Key Methodologies for Ovulation Confirmation in Research

Methodology	Primary Measurement	Temporal Relationship to Ovulation	Key Strengths	Key Limitations
TVUS + Serum Hormones	Follicle rupture (US), [LH], [E2], [Pg] (Serum)	Direct visualization (Day 0); LH surge (Day -1)	Highest precision; research gold standard	Invasive, resource-intensive, clinic-based
Quantitative Urine Hormones	[E13G], [LH], [PDG] (Urine)	LH surge ~24-44 hrs pre-ovulation	Less invasive, home-based, quantitative	Requires clinic visit for validation, cost of device
Estimated Core Body Temperature	Sleeping CBT via heat flux	Temperature shift confirms ovulation post-hoc	Passive, home-based, measures direct physiological parameter	Only confirms ovulation after it has occurred

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagent Solutions for Ovulation Timing Research

Reagent / Material	Function in Experimental Protocol	Research Application
Transvaginal Ultrasound Probe	High-resolution imaging of ovarian follicles to track growth and identify rupture.	Defining the gold-standard day of ovulation (Day 0) for temporal alignment [89] [90].
Electro-chemiluminescence (ECL) Immunoassay Kits	Quantifying serum levels of LH, Estradiol, Progesterone, TSH, and other hormones with high sensitivity.	Generating precise hormonal profiles correlated with ultrasound findings [89] [94].
Quantitative Urinary Hormone Monitor (e.g., Mira)	Measuring concentrations of FSH, E13G, LH, and PDG metabolites in urine at home.	Predicting and confirming ovulation in less invasive, longitudinal studies [90].
Ingestible Thermometer & Data Recorder	Directly measuring core body temperature (CBT) throughout the night.	Validating new, less invasive temperature-sensing methods against a true CBT standard [92].
Single-Cell Multiomics Kits (RNA-seq + ATAC-seq)	Simultaneously capturing gene expression and chromatin accessibility from single cells.	Inferring transcription factor activity across different cycle phases or treatment conditions [95].

Visualizing the Integrated Workflow for Temporal Validation

The following diagram illustrates the logical relationship and data integration between the different methodological tiers for establishing a gold-standard correlated timeline.

Linking TF activity to the physiological event of ovulation demands a multi-tiered methodological approach. The most robust research design integrates the clinical gold standard of ultrasound and serum monitoring to establish an irrefutable temporal anchor (Day 0). This validated timeline can then be used to calibrate less invasive, home-based biomarkers like quantitative urine hormones or core body temperature, which are more practical for longitudinal sampling. Finally, molecular analyses of TF activity, derived from techniques like single-cell multiomics, can be precisely mapped onto this anchored timeline. This hierarchical validation strategy ensures that inferences about transcriptional regulation are interpreted within a firm and reproducible physiological context, enhancing the rigor and impact of research in reproductive biology and drug development.

Conclusion

The temporal validation of transcription factors across the menstrual cycle is emerging as a cornerstone for understanding endometrial biology and its associated pathologies. Foundational single-cell atlases have illuminated the remarkable dynamism of the endometrial cellular landscape, driven by phase-specific TF networks. Methodological advances now enable the deconvolution of these complex regulatory programs, while established troubleshooting frameworks help navigate the inherent biological and technical variability. Finally, rigorous comparative and clinical validation is translating these discoveries into actionable insights, such as the uNK2/uNK3 TF signature for diagnosing chronic endometritis. Future efforts must focus on standardizing measurement protocols, expanding atlases to include diverse populations and pathological states, and functionally interrogating candidate TFs to unlock their full potential as diagnostic tools and therapeutic targets in reproductive medicine.

References

• 1. An integrated single-cell reference atlas of the human ... [https://www.nature.com/articles/s41588-024-01873-w]
• 2. Spatiotemporal and single-cell atlases to dissect regional ... [https://www.nature.com/articles/s42003-025-08277-4]
• 3. Mapping the temporal and spatial dynamics of the human ... [https://www.nature.com/articles/s41588-021-00972-2]
• 4. A spatial transcriptomics dataset of the endometrium from ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12572259/]
• 5. Hierarchical Bayesian Model for Gene Deconvolution and ... [https://arxiv.org/html/2510.27097v1]
• 6. Deciphering a shared transcriptomic regulation and the ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10496172/]
• 7. Evolutionarily recent transcription factors partake in human ... [https://www.sciencedirect.com/science/article/pii/S2666979X2500179X]
• 8. The interaction of Notch and Wnt signaling pathways in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8012441/]
• 9. The Role of Notch, Hedgehog, and Wnt Signaling ... [https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.650772/full]
• 10. Inhibition of Wnt signalling by Notch via two distinct ... [https://www.nature.com/articles/s41598-021-88618-5]
• 11. Wnt/β-catenin and notch signaling pathways in ... [https://www.sciencedirect.com/science/article/pii/S1043661824005103]
• 12. Mapping the temporal and spatial dynamics of the human ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8648563/]
• 13. Wnt and Notch Pathways Have Interrelated Opposing Roles ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3148789/]
• 14. Organoids as powerful models of endometrium epithelium ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12234953/]
• 15. Different responses of luminal and glandular epithelium ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12506090/]
• 16. Different responses of luminal and glandular epithelium ... [https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2025.1661930/full]
• 17. Uterine NK Cell Polarization Associates with Chronic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12604584/]
• 18. Spatiotemporal expression pattern of Progesterone ... [https://www.sciencedirect.com/science/article/abs/pii/S0960076022001042]
• 19. Unraveling the Dynamics of Estrogen and Progesterone ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11312103/]
• 20. Article Dynamic chromatin remodeling in cycling human ... [https://www.sciencedirect.com/science/article/pii/S2211124723015371]
• 21. Sp1 binding sites and an estrogen response element half-site ... [https://pubmed.ncbi.nlm.nih.gov/10894148/]
• 22. A cell atlas of the human fallopian tube throughout ... [https://www.nature.com/articles/s41467-024-55440-2]
• 23. AP2/ERF Transcription Factor Regulatory Networks in ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2019.00228/full]
• 24. Flexible modeling of regulatory networks improves ... [https://www.nature.com/articles/s41540-024-00386-w]
• 25. Benchmarking multi-omics integration algorithms across ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10944570/]
• 26. Single-cell sequencing uncovers disrupted stromal ... [https://www.nature.com/articles/s42003-025-08634-3]
• 27. Single-cell multi-ome regression models identify functional ... [https://www.nature.com/articles/s41588-024-01689-8]
• 28. Single-cell multiomics data integration and generation with ... [https://www.sciencedirect.com/science/article/pii/S2667237525002474]
• 29. Why We Must Stop Assuming and Estimating Menstrual ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12152053/]
• 30. Global endometrial DNA methylation analysis reveals ... [https://www.nature.com/articles/s42003-023-05070-z]
• 31. Machine learning model for menstrual cycle phase ... [https://pubmed.ncbi.nlm.nih.gov/39889448/]
• 32. Temporal patterns of gene regulation and upstream ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6417155/]
• 33. Analysis of Upstream Regulators, Networks, and Pathways ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8860493/]
• 34. [2308.07927] Predictive Modeling of Menstrual Cycle Length [https://arxiv.org/abs/2308.07927]
• 35. Trends and challenges in organoid modeling and expansion ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11608026/]
• 36. Organoids as Next-Generation Models for Tumor ... [https://www.mdpi.com/2674-1172/4/4/23]
• 37. Human organoids: model systems for human biology and ... [https://www.nature.com/articles/s41580-020-0259-3]
• 38. Tumor organoid-immune co-culture models: exploring a ... [https://www.nature.com/articles/s41420-025-02407-x]
• 39. Genetic Regulation of Transcription in the Endometrium ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9580733/]
• 40. Inference and analysis of cell-cell communication using ... [https://www.nature.com/articles/s41467-021-21246-9]
• 41. Influence of premenstrual syndrome on attentional capture ... [https://www.sciencedirect.com/science/article/pii/S0018506X25001722]
• 42. Menstrual cycle and exogenous attention toward emotional ... [https://www.sciencedirect.com/science/article/pii/S0018506X22001532]
• 43. Comparison of methods and resources for cell ... [https://www.nature.com/articles/s41467-022-30755-0]
• 44. CellChat for systematic analysis of cell-cell ... - PubMed [https://pubmed.ncbi.nlm.nih.gov/39289562/]
• 45. Cytoscape: An Open Source Platform for Complex Network Analysis... [https://cytoscape.org/]
• 46. visualization and analysis Network | Systems tools ... | Fiveable Biology [https://fiveable.me/systems-biology/unit-5/network-visualization-analysis-tools/study-guide/KwKwIgEvBlPogtCg]
• 47. Guidelines for biomarker discovery in endometrium [https://pmc.ncbi.nlm.nih.gov/articles/PMC8063681/]
• 48. Variation in serum biomarkers with sex and female ... [https://www.nature.com/articles/srep26947]
• 49. A Review on Biomarker‐Enhanced Machine Learning for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12421415/]
• 50. Hormone-based models for comparing menstrual cycle ... [https://www.sciencedirect.com/science/article/pii/S0091302222000590]
• 51. Optimizing training around your menstrual cycle? New ... [https://en.uit.no/go/target/856417/]
• 52. Optimizing training around your menstrual cycle? New ... [https://www.eurekalert.org/news-releases/1053843]
• 53. The Influence of the Menstrual Cycle on Electrical ... [https://www.mdpi.com/2227-9032/13/11/1240]
• 54. Integrating single-cell RNA-seq datasets with substantial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12577435/]
• 55. Benchmarking deep learning methods for ... - Genome Biology [https://genomebiology.biomedcentral.com/articles/10.1186/s13059-025-03869-z]
• 56. Deep generative modeling of sample-level heterogeneity ... [https://www.nature.com/articles/s41592-025-02808-x]
• 57. Reducing batch effects in single cell chromatin accessibility ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11870453/]
• 58. Combined single-cell profiling of chromatin–transcriptome ... [https://www.nature.com/articles/s41587-025-02734-5]
• 59. A Matter of Time - How Transient Transcription Factor ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5203810/]
• 60. Atlas of regulated target genes of transcription factors (ART-TF ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04924-3]
• 61. Absolute quantification of transcription factors reveals ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7344268/]
• 62. Organoid vs In Vivo Mouse Model: Which is Better ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8593653/]
• 63. Approaches to benchmark and characterize in vitro human ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10906492/]
• 64. NIH Standardized Organoid Modeling (SOM) Center [https://www.nih.gov/som]
• 65. How to control confounding effects by statistical analysis [https://pmc.ncbi.nlm.nih.gov/articles/PMC4017459/]
• 66. NK Cell Polarization Reprograming Endometrial Immune... [https://www.dovepress.com/uterine-nk-cell-polarization-associates-with-chronic-endometritis-and--peer-reviewed-fulltext-article-IJWH]
• 67. The Influence of the Menstrual Cycle on Electrical Thresholds ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12154571/]
• 68. Adjusting for unmeasured confounding in nonrandomized ... [https://www.sciencedirect.com/science/article/pii/S0895435616303341]
• 69. In vivo screening characterizes chromatin factor functions ... [https://www.nature.com/articles/s41588-023-01471-2]
• 70. Chromatin Reprogramming in Breast Cancer - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC6029727/]
• 71. Temporal dynamic reorganization of 3D chromatin ... [https://www.nature.com/articles/s41467-019-09320-9]
• 72. Mutational disruption of transcription factors binding and ... [https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-025-07038-5]
• 73. Biology and pathology of the uterine microenvironment and ... [https://www.nature.com/articles/s41423-021-00739-z]
• 74. Cytokine modulation and immunoregulation of uterine NK ... [https://www.sciencedirect.com/science/article/pii/S1359610124000959]
• 75. Uterine NK Cell Polarization Associates with Chronic ... [https://read.qxmd.com/read/41230044/uterine-nk-cell-polarization-associates-with-chronic-endometritis-and-predisposition-to-recurrent-implantation-failure]
• 76. NK cell receptor profiling of endometrial and decidual ... [https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1353556/full]
• 77. Organoids as powerful models of endometrium epithelium ... [https://link.springer.com/article/10.1007/s00018-025-05807-5]
• 78. Immune determinants of endometrial receptivity [https://www.sciencedirect.com/science/article/pii/S0015028222002679]
• 79. Spatial transcriptomic profiling of the human fallopian tube ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11954873/]
• 80. Cross-tissue multicellular coordination and its rewiring in ... [https://www.nature.com/articles/s41586-025-09053-4]
• 81. Cross-tissue, single-cell stromal atlas identifies shared ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9271637/]
• 82. Cross-tissue human fibroblast atlas reveals myofibroblast ... [https://www.sciencedirect.com/science/article/pii/S1535610824003192]
• 83. Benchmarking tools for transcription factor prioritization [https://www.sciencedirect.com/science/article/pii/S2001037024001648]
• 84. TRAPT: a multi-stage fused deep learning framework for ... [https://www.nature.com/articles/s41467-025-58921-0]
• 85. Reproductive aging in biological females: mechanisms and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12463648/]
• 86. The intersection of aging and estrogen in osteoarthritis [https://www.nature.com/articles/s44294-025-00063-1]
• 87. Menopause and hair loss in women: Exploring the ... [https://www.sciencedirect.com/science/article/pii/S0378512225001860]
• 88. Ultra-high-field 7T MRI reveals changes in human medial ... [https://www.nature.com/articles/s44220-023-00125-w]
• 89. Prediction of ovulation: new insight into an old challenge [https://pmc.ncbi.nlm.nih.gov/articles/PMC10651856/]
• 90. Establishing a Gold Standard for Quantitative Menstrual ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10533176/]
• 91. Prediction of ovulation by urinary hormone measurements ... [https://pubmed.ncbi.nlm.nih.gov/11098014/]
• 92. Novel Methodology for Identifying the Occurrence of Ovulation ... [https://formative.jmir.org/2024/1/e55834]
• 93. Real-world menstrual cycle characteristics of more than ... [https://www.nature.com/articles/s41746-019-0152-7]
• 94. Serum and follicular fluid thyroid hormone levels ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6839061/]
• 95. Epiregulon: Single-cell transcription factor activity inference ... [https://www.nature.com/articles/s41467-025-62252-5]