# Leveraging Single-Cell Technologies to Elucidate Niche Environments and Immune Mechanisms Involved in Endometriosis Pathogenesis, Pathophysiology, and Disease Stratification

> **NIH NIH P01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $417,284

## Abstract

ABSTRACT – PROJECT 1
Endometriosis is a chronic, estrogen-dependent, inflammatory disease that affects ~10% of reproductive age
women, resulting in debilitating pelvic pain, infertility, and compromised quality of life. It is characterized by
anatomically and phenotypically diverse lesions of endometrial-like tissue superficially on the pelvic peritoneum,
the ovaries and deeply infiltrating into pelvic organs, with resulting neuroangiogenesis, fibrosis, adhesions, pelvic
pain and infertility. The pathogenesis of endometriosis relies on complex interactions between endometrial,
peritoneal mesothelial and connective tissue cells and activation of local immune cell responses. There is
profound dysfunction of the innate and adaptive immune systems, associated with inefficient lesion clearance
and pelvic and systemic inflammation. As clinical classifications of endometriosis are maladapted to the
heterogeneity of disease expression, diagnostics as well as effective treatments are lacking. Thus, precise
understanding of the cellular and molecular pathobiology of endometriosis is a critical prerequisite to improve
disease classification and inform diagnostic and therapeutic interventions. The goal of Project 1 is to determine
the contribution of the immune system to the pathobiology of endometriosis on a single cell level, and using a
data-driven strategy to derive and molecularly characterize objective disease classification. In Aim 1, we will
determine the cellular composition and functional attributes of endometriosis lesions, their surrounding
peritoneal/serosal niches, and eutopic endometrium through the lens of transcriptomic signatures at single cell
resolution. Our hypothesis is that lesions and their niche environments have unique and functionally relevant
transcriptomic signatures. In Aim 2, we will determine the contribution of the local and peripheral immune system
to the pathobiology of endometriosis leveraging CYTOF technology. We will test the hypothesis that the local
and peripheral myeloid phagocyte systems are dysfunctional in women with endometriosis. Local and systemic
immunological data will be integrated to identify immunological signatures of dysfunctionality and to differentiate
endometriosis disease types, along with functional studies. Finally, in Aim 3, we will leverage unsupervised
machine learning techniques to integrate single-cell assessment of endometriosis lesions, surrounding tissue,
endometrium, the local and peripheral immune systems and clinical data into a cross-tissue predictive model of
disease classification. Our integrated approach will leverage hundreds of existing, clinically well-annotated
biospecimens in our well established Human Endometrial Tissue & DNA Bank and ongoing accrual through our
extensive network of physician and surgeon collaborators. The impact of this study will be to derive a replete
transcriptomic and proteomic taxonomy of endometriosis lesions, their niche environments, eutopic endometrium...

## Key facts

- **NIH application ID:** 10458758
- **Project number:** 5P01HD106414-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** LINDA C GIUDICE
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $417,284
- **Award type:** 5
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458758

## Citation

> US National Institutes of Health, RePORTER application 10458758, Leveraging Single-Cell Technologies to Elucidate Niche Environments and Immune Mechanisms Involved in Endometriosis Pathogenesis, Pathophysiology, and Disease Stratification (5P01HD106414-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10458758. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*