Single-Cell and Spatial Transcriptomic Dissection of Macrophage-Associated Inflammatory Signalling in Endometriosis-Associated Pain: A Hypothesis-Generating Study of Candidate Tissue-Level Genes

Background: Endometriosis is an oestrogen-dependent chronic inflammatory disease with chronic pelvic pain as a predominant and disabling symptom that severely compromises patient quality of life. The molecular mechanisms governing endometriosis-associated pain remain poorly elucidated. Given the lack of individual clinical pain scores in public datasets, this hypothesis-generating study aimed to characterise pain-correlated cell subsets and signalling programmes at single-cell resolution using a literature-based inflammatory gene module, and to identify replicable tissue-level candidate genes across independent cohorts. Methods: A refined single-cell atlas containing 103,434 cells was reconstructed from 31 valid biopsies of 14 patients (GSE179640), with irrelevant organoid samples excluded. Comprehensive analyses were performed, including standardised single-cell preprocessing, Harmony batch integration, Louvain clustering, pseudobulk-based differential analysis, transcription factor activity evaluation, CellChat-based intercellular communication prediction, and nested cross-validated Lasso regression. A 60-gene computational module score was applied to proxy pain phenotypes, with biopsies and patients defined as statistical units. Results: Macrophages dominated the high pain-score cell population (68.8%), while stromal fibroblasts also contributed considerably. High-score macrophages exhibited typical M1-like pro-inflammatory features, activated innate immune and NF-κB pathways, and upregulated the activities of NFKB2, CEBPB and STAT4. Macrophage-derived galectin, MIF, SPP1 and chemokine signalling were prominently enriched. The Lasso model showed limited discriminative power. Only TYROBP and CXCL12 maintained statistical significance after multiple-test correction across two bulk cohorts, whereas spatial co-expression patterns were unstable and section-dependent. Conclusions: Macrophages are the primary cellular population enriched with pain-related transcriptional signatures, though such enrichment is largely driven by the inflammatory components of the gene set. This study provides hypothesis-generating findings. The evaluated pain phenotype is a computational surrogate, and all candidate genes await further experimental and clinical validation.

Authors

Institutions

Publication Details

Journal
Biomedicines
Published
2026-11-10
DOI
https://doi.org/10.3390/biomedicines14112583
Primary Topic
Endometriosis Research and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Single-Cell and Spatial Transcriptomic Dissection of Macrophage-Associated Inflammatory Signalling in Endometriosis-Associated Pain: A Hypothesis-Generating Study of Candidate Tissue-Level Genes

胡明, Suting LI, Jianming Tang, Jie Min et al.
Biomedicines
Endometriosis Research and Treatment
article

Single-Cell and Spatial Transcriptomic Dissection of Macrophage-Associated Inflammatory Signalling in Endometriosis-Associated Pain: A Hypothesis-Generating Study of Candidate Tissue-Level Genes

胡明, Suting LI, Jianming Tang, Jie Min, Xiaoli Tian
article en

Abstract

Background: Endometriosis is an oestrogen-dependent chronic inflammatory disease with chronic pelvic pain as a predominant and disabling symptom that severely compromises patient quality of life. The molecular mechanisms governing endometriosis-associated pain remain poorly elucidated. Given the lack of individual clinical pain scores in public datasets, this hypothesis-generating study aimed to characterise pain-correlated cell subsets and signalling programmes at single-cell resolution using a literature-based inflammatory gene module, and to identify replicable tissue-level candidate genes across independent cohorts. Methods: A refined single-cell atlas containing 103,434 cells was reconstructed from 31 valid biopsies of 14 patients (GSE179640), with irrelevant organoid samples excluded. Comprehensive analyses were performed, including standardised single-cell preprocessing, Harmony batch integration, Louvain clustering, pseudobulk-based differential analysis, transcription factor activity evaluation, CellChat-based intercellular communication prediction, and nested cross-validated Lasso regression. A 60-gene computational module score was applied to proxy pain phenotypes, with biopsies and patients defined as statistical units. Results: Macrophages dominated the high pain-score cell population (68.8%), while stromal fibroblasts also contributed considerably. High-score macrophages exhibited typical M1-like pro-inflammatory features, activated innate immune and NF-κB pathways, and upregulated the activities of NFKB2, CEBPB and STAT4. Macrophage-derived galectin, MIF, SPP1 and chemokine signalling were prominently enriched. The Lasso model showed limited discriminative power. Only TYROBP and CXCL12 maintained statistical significance after multiple-test correction across two bulk cohorts, whereas spatial co-expression patterns were unstable and section-dependent. Conclusions: Macrophages are the primary cellular population enriched with pain-related transcriptional signatures, though such enrichment is largely driven by the inflammatory components of the gene set. This study provides hypothesis-generating findings. The evaluated pain phenotype is a computational surrogate, and all candidate genes await further experimental and clinical validation.

BiomedicinesVol. 14(11)
Renmin Hospital of Wuhan University (CN)
Endometriosis Research and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon