{"paper_id":"4bc7f736-fc75-48ef-a802-f419cee2760d","body_text":"Abstract\nBackground\nEndometriosis encompasses heterogeneous lesions, primarily peritoneal and ovarian subtypes, with distinct immunological and fibrotic features. The subtype-specific roles of the immune microenvironment in pathogenesis remain unclear. Integrated analysis combining high-resolution cellular atlases with spatial context is needed to decipher these mechanisms.\nMethods\nWe performed an integrative analysis of published single-cell RNA sequencing (n = 81,676 cells) from control endometrium (Ctrl), eutopic endometrium (EuE), peritoneal endometriosis (EcP), and ovarian endometriosis (EcO), and spatial transcriptomic (n = 60 segments) data from Ecp and match EuE. Analytical pipelines include cellular atlas construction, differential expression, spatial co-expression, and cell-cell communication inference.\nResults\nOur single-cell atlas revealed fundamentally distinct cellular ecosystems. EcP was enriched for CCL19 + perivascular cells (6.4-fold vs. control) and immune-active niches, whereas EcO exhibited pronounced smooth muscle differentiation and NNMT upregulation. Spatial analysis compartmentalized these programs, with the CCL19-CCR7 chemokine axis localized to stromal niches in EcP strongly correlated (ρ = 0.872). In contrast, EcO showed enhanced smooth muscle gene expression (MYH11, log₂FC = 3.87). Cell-cell communication networks diverged, with EcP dominated by chemokine and TGF-β signaling, and EcO dominated by smooth muscle and PDGF pathways. Therapeutic target analysis revealed subtype-specific patterns: immune checkpoints (CTLA4 and PDCD1) were upregulated in EcP, whereas MMP9 was dramatically downregulated in EcO.\nConclusion\nEcP and EcO exhibit fundamentally divergent programs. We propose to conceptualize these as an ‘immune-hot’, chemokine-driven inflammatory niche (EcP) versus an ‘immune-cold’, fibromuscular survival niche (EcO). These findings underscore that lesion-specific immune-stromal crosstalk may dictates pathogenesis and suggest a potential paradigm shift toward immunologically-informed, subtype-specific precision therapeutics, which warrant further experimental validation.\nSimilar content being viewed by others\nData availability\nAll the data analyzed in this study were available from public repositories. ScRNA-seq data are available under GEO accession number GSE179640. Spatial transcriptomic data are available under GEO accession GSE263897. Processed data and analysis codes will be made available in the GitHub repository upon publication.\nAbbreviations\n- EcP:\n-\nPeritoneal endometriosis\n- EcO:\n-\nOvarian endometriosis\n- EuE:\n-\nEutopic endometrium\n- Ctrl:\n-\nControl endometrium\n- scRNA-seq:\n-\nSingle-cell RNA sequencing\n- UMAP:\n-\nUniform Manifold Approximation and Projection\n- PCA:\n-\nPrincipal Component Analysis\n- GO:\n-\nGene Ontology\n- CAF:\n-\nCancer-associated fibroblast\n- MDSC:\n-\nMyeloid-derived suppressor cell\nReferences\nZondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN. Viganò P. Endometriosis. Nat Rev Dis Primers. 2018;4(1):9.\nSaunders PTK, Horne AW. Endometriosis: Etiology, pathobiology, and therapeutic prospects. Cell. 2021;184(11):2807–24.\nHudelist G, Darici Kurt E, Jurkovic D, Tellum T. Endometriosis-The scapegoat for pelvic pain? Acta Obstet Gynecol Scand. 2025;104(9):1599–602.\nCrispim PCA, Jammal MP, Murta EFC, Nomelini RS. Endometriosis: What is the Influence of Immune Cells? Immunol Invest. 2020;50(4):372–88.\nSingh SS, Allaire C, Al-Nourhji O, Bougie O, Bridge-Cook P, Duigenan S, et al. Guideline 449: Diagnosis and Impact of Endometriosis - A Canadian Guideline. J Obstet Gynaecol Can. 2024;46(5):102450.\nMikhaleva LM, Radzinsky VE, Orazov MR, Khovanskaya TN, Sorokina AV, Mikhalev SA, et al. Current Knowledge on Endometriosis Etiology: A Systematic Review of Literature. Int J Womens Health. 2021;13:525–37.\nImperiale L, Nisolle M, Noël JC, Fastrez M. Three Types of Endometriosis: Pathogenesis, Diagnosis and Treatment. State of the Art. J Clin Med. 2023;12(3):994.\nMijatovic V. Towards a more biologically informative system of endometriosis classification. Hum Reprod. 2020;35(12):2658–9.\nPapalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18(1):35–45.\nAldridge S, Teichmann SA. Single cell transcriptomics comes of age. Nat Commun. 2020;11(1):4307.\nTan Y, Flynn WF, Sivajothi S, Luo D, Bozal SB, Davé M, et al. Single-cell analysis of endometriosis reveals a coordinated transcriptional programme driving immunotolerance and angiogenesis across eutopic and ectopic tissues. Nat Cell Biol. 2022;24(8):1306–18.\nLiu Y, Dai Y, Wang L. Spatial omics at the forefront: emerging technologies, analytical innovations, and clinical applications. Cancer Cell. 2026;44(1):24–49.\nHui T, Zhou J, Yao M, Xie Y, Zeng H. Advances in Spatial Omics Technologies. Small Methods. 2025;9(5):e2401171.\nBurns GW, Fu Z, Vegter EL, Madaj ZB, Greaves E, Flores I, et al. Spatial transcriptomic analysis identifies epithelium-macrophage crosstalk in endometriotic lesions. iScience. 2025;28(2):111790.\nZhang M, Xu T, Tong D, Li S, Yu X, Liu B, et al. Research advances in endometriosis-related signaling pathways: A review. Biomed Pharmacother. 2023;164:114909.\nDiao R, Wei W, Zhao J, Tian F, Cai X, Duan YG. CCL19/CCR7 contributes to the pathogenesis of endometriosis via PI3K/Akt pathway by regulating the proliferation and invasion of ESCs. Am J Reprod Immunol. 2017;78:e12744.\nYan J, Zhou L, Liu M, Zhu H, Zhang X, Cai E, et al. Single-cell analysis reveals insights into epithelial abnormalities in ovarian endometriosis. Cell Rep. 2024;43(3):113716.\nShin S, Chung YJ, Moon SW, Choi EJ, Kim MR, Chung YJ, et al. Single-cell profiling identifies distinct hormonal, immunologic, and inflammatory signatures of endometriosis-constituting cells. J Pathol. 2023;261(3):323–34.\nLi Y, Zhou W, Ding J, Song D, Cheng W, Yu J, et al. Integrative Single-Cell Analysis Reveals Iron Overload-Induced Senescence and Metabolic Reprogramming in Ovarian Endometriosis-Associated Infertility. Adv Sci (Weinh). 2025;12(29):e17528.\nShih AJ, Adelson RP, Vashistha H, Khalili H, Nayyar A, Puran R, et al. Single-cell analysis of menstrual endometrial tissues defines phenotypes associated with endometriosis. BMC Med. 2022;20(1):315.\nXia W, Liu J, Chen R, Feng J, Wu L, Wang Y, et al. Molecular subtypes and prognostic signature rooted in disulfidptosis highlight tumor microenvironment in lung adenocarcinoma. Chin J Cancer Res. 2025;37(5):796–820.\nGong Z, Du M, Li Y, Ye B, Huang Y, Gong H, et al. Machine learning identifies TIME subtypes linking EGFR mutations and immune states in lung adenocarcinoma. NPJ Digit Med. 2025;8(1):796.\nZhang P, Liang X, Ye B, Wang X, Wang Y, Gong Z, et al. Metabolic reprogramming signature predicts immunotherapy efficacy in lung adenocarcinoma: Targeting SLC25A1 to overcome immune resistance. Chin J Cancer Res. 2025;37(6):1000–19.\nShao W, Ju H, Xiahou Z, Fang S, Yan R, Li C, et al. Fibroblast heterogeneity and FN1-mediated signaling in endometriosis revealed by single-cell and spatial transcriptomics. Front Immunol. 2025;16:1680849.\nLiu S, Li X, Gu Z, Wu J, Jia S, Shi J, et al. Single-cell and spatial transcriptomic profiling revealed niche interactions sustaining growth of endometriotic lesions. Cell Genom. 2025;5(1):100737.\nHao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573–e8729.\nWolock SL, Lopez R, Klein AM, Scrublet. Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Syst. 2019;8(4):281–e919.\nHafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20(1):296.\nKorsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–96.\nBecht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37:38–44.\nTraag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9(1):5233.\nMatsuzaki S, Canis M, Darcha C, Dechelotte P, Pouly JL, Bruhat MA. Fibrogenesis in peritoneal endometriosis. A semi-quantitative analysis of type-I collagen. Gynecol Obstet Invest. 1999;47(3):197–9.\nSymons LK, Miller JE, Kay VR, Marks RM, Liblik K, Koti M, et al. The Immunopathophysiology of Endometriosis. Trends Mol Med. 2018;24(9):748–62.\nVigano P, Candiani M, Monno A, Giacomini E, Vercellini P, Somigliana E. Time to redefine endometriosis including its pro-fibrotic nature. Hum Reprod. 2018;33(3):347–52.\nvan Kaam KJ, Schouten JP, Nap AW, Dunselman GA, Groothuis PG. Fibromuscular differentiation in deeply infiltrating endometriosis is a reaction of resident fibroblasts to the presence of ectopic endometrium. Hum Reprod. 2008;23(12):2692–700.\nMishra A, Galvankar M, Singh N, Modi D. Spatial and temporal changes in the expression of steroid hormone receptors in mouse model of endometriosis. J Assist Reprod Genet. 2020;37(5):1069–81.\nZhang T, De Carolis C, Man GCW, Wang CC. The link between immunity, autoimmunity and endometriosis: a literature update. Autoimmun Rev. 2018;17(10):945–55.\nLei L, Xu X, Gong C, Lin B, Li F. Integrated analysis of genome-wide gene expression and DNA methylation profiles reveals candidate genes in ovary endometriosis. Front Endocrinol (Lausanne). 2023;14:1093683.\nRai G, Ashish A, Rai S, Mishra S, Singh S, Singh R. Differential gene expression profiling of ectopic endometrium reveals key molecular pathways in endometriosis pathogenesis. Mol Biol Rep. 2026;53(1):351.\nZhu S, Wang A, Xu W, Hu L, Sun J, Wang X. The heterogeneity of fibrosis and angiogenesis in endometriosis revealed by single-cell RNA-sequencing. Biochim Biophys Acta Mol Basis Dis. 2023;1869(2):166602.\nRiccio LDGC, Santulli P, Marcellin L, Abrão MS, Batteux F, Chapron C. Immunology of endometriosis. Best Pract Res Clin Obstet Gynaecol. 2018;50:39–49.\nLei Z, Tang R, Wu Y, Mao C, Xue W, Shen J, et al. TGF-β1 induces PD-1 expression in macrophages through SMAD3/STAT3 cooperative signaling in chronic inflammation. JCI Insight. 2024;9(7):e165544.\nHeide J, Bilecz AJ, Patnaik S, Allega MF, Donle L, Yang K, et al. NNMT inhibition in cancer-associated fibroblasts restores antitumour immunity. Nature. 2025;645(8082):1051–9.\nAcknowledgements\nWe would like to thank Editage (www.edita ge.cn) for the English language editing.\nFunding\nThis study was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (2023Y9359 and 2024Y9538), Qihang Fund of Fujian Medical University (2023QH1190 and 2023QH1193), and Fujian Provincial Health Technology Project (2024GGA057). The sponsors had no involvement in the study design, collection, analysis, and interpretation of data, writing of the report, or decision to submit the article for publication.\nAuthor information\nAuthors and Affiliations\nContributions\nFangjie He : Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing. Shuiling Zu : Data curation, Formal analysis, Funding acquisition, Project administration, Visualization, Writing – original draft. Yan Lin : Data curation, Formal analysis, Visualization, Writing – original draft. Jun Shi : Data curation, Writing – review & editing. Shunhe Lin : Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing.\nCorresponding authors\nEthics declarations\nEthics approval and consent to participate\nThis study only utilized publicly available anonymized datasets. Original studies involving human participants were reviewed and approved by their respective Institutional Review Boards. As this work involved the re-analysis of existing de-identified data, it did not constitute human subject research requiring additional ethical approval.\nCompeting interests\nThe authors declare no competing interests.\nAdditional information\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nSupplementary Information\nRights and permissions\nOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\nAbout this article\nCite this article\nHe, F., Zu, S., Lin, Y. et al. Integrated single-cell and spatial transcriptomics reveal divergent immunological and stromal programs in peritoneal versus ovarian endometriosis. BMC Women's Health (2026). https://doi.org/10.1186/s12905-026-04456-5\nReceived:\nAccepted:\nPublished:\nDOI: https://doi.org/10.1186/s12905-026-04456-5","source_license":"CC-BY-4.0","license_restricted":false}