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Updated: Apr 28, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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Published on: October 31, 2025

Single-Cell Transcriptomic Profiling and Machine Learning Integration Unveil Stromal Cell Heterogeneity in

Huipeng Zhang¹, Yuli Luo²

¹Department of Gynecology, Beijing Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China, bjcy2y.com.

|April 27, 2026

Summary

This study reveals distinct molecular subtypes of endometriosis (EMs) by analyzing single-cell RNA sequencing data. Key genes like HOXA10, ESR1, MMP9, and SPP1 show differential expression, offering potential biomarkers and therapeutic targets for EMs.

Keywords:

biomarkers cell–cell communication ectopic endometrial cell differentiation endometriosis machine learning single-cell RNA sequencing somatic mutations

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Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

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Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Area of Science:

Genomics and Molecular Biology
Computational Biology and Bioinformatics
Reproductive Medicine

Background:

Endometriosis (EMs) affects 10% of reproductive-age women globally, with unclear pathogenesis.
Abnormal cell differentiation and somatic mutations in ectopic endometrium are key to EMs progression and treatment variability.
Understanding molecular mechanisms driving ectopic endometrial cell differentiation is crucial for EMs management.

Purpose of the Study:

To elucidate molecular mechanisms of ectopic endometrial cell differentiation using machine learning (ML) and single-cell RNA sequencing (scRNA-seq).
To identify novel prognostic biomarkers and therapeutic targets for EMs, focusing on mutation-driven transcriptional alterations.
To stratify EMs patients and characterize the ectopic microenvironment.

Main Methods:

Analysis of comprehensive transcriptomic data from GEO and HED, including scRNA-seq data from 162,485 cells across 46 EMs patients.
Application of 10 ML algorithms and 101 hybrid combinations for predictive modeling and patient stratification.
Unsupervised clustering, functional enrichment, pathway analysis, and cell-cell communication network construction.

Main Results:

Identification of 298 genes associated with ectopic endometrial cell differentiation, including mutation-harboring genes.
Discovery of two distinct patient subgroups (high- and low-invasive phenotypes) with different disease trajectories.
Validation of differential expression for HOXA10 (downregulated), ESR1, MMP9, and SPP1 (upregulated) in ectopic vs. normal endometrial stromal cells.

Conclusions:

Comprehensive molecular characterization of EMs cell differentiation via ML and scRNA-seq.
Identification of distinct patient phenotypes, key regulatory genes (HOXA10, ESR1, MMP9, SPP1), and macrophage-centric communication networks.
HOXA10, ESR1, MMP9, and SPP1 as potential diagnostic biomarkers and therapeutic targets for personalized EMs treatment.