Dataset Information

Proteomics of endometriosis

ABSTRACT: This project aims at comparing endometrium from women with and without endometriosis during the secretory phase of menstrual cycle. The present results constitute a first step towards identifying potential diagnosis biomarkers and may provide a better understanding of endometriosis especially the etiology of the disease.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Uterine Endometrium

DISEASE(S): Endometriosis

SUBMITTER: E Com

LAB HEAD: Charles Pineau

PROVIDER: PXD012981 | Pride | 2022-04-01

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	1827001-10_E5_Slot1-12_1_270.d.mzid.gz	Mzid
	1827001-10_E5_Slot1-12_1_270.d.pride.mztab.gz	Mztab
	1827001-10_E5_Slot1-12_1_270.d.zip	Other
	1827001-10_E5_Slot1-12_1_270_DA5.1.201_1.mgf	Mgf
	1827001-10_E5_Slot1-12_1_270_DA5.1.201_1.pride.mgf.gz	Mgf

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Publications

The Eutopic Endometrium Proteome in Endometriosis Reveals Candidate Markers and Molecular Mechanisms of Physiopathology.

Méar Loren L Com Emmanuelle E Fathallah Khadija K Guillot Laetitia L Lavigne Régis R Guével Blandine B Fauconnier Arnaud A Vialard François F Pineau Charles C

Diagnostics (Basel, Switzerland) 20220206 2

Endometriosis is a common chronic gynaecological disease causing various symptoms, such as infertility and chronic pain. The gold standard for its diagnosis is still laparoscopy and the biopsy of endometriotic lesions. Here, we aimed to compare the eutopic endometrium from women with or without endometriosis to identify proteins that may be considered as potential biomarker candidates. Eutopic endometrium was collected from patients with endometriosis (n = 4) and women without endometriosis (n = ...[more]

PMID: 35204508

Similar Datasets

Project description:Endometriosis is a debilitating gynecological disorder affecting approximately 10% of the female population. Despite its prevalence, robust methods to classify and treat endometriosis remain elusive. Changes throughout the menstrual cycle in tissue size, architecture, cellular composition, and individual cell phenotypes make it extraordinarily challenging to identify markers or cell types associated with uterine pathologies since disease-state alterations in gene and protein expression are convoluted with cycle phase variations. Here, we developed an integrated workflow to generate both proteomic and single-cell RNA-sequencing (scRNA-seq) data sets using tissues and cells isolated from the uteri of control and endometriotic donors. Using a linear mixed effect model (LMM), we identified proteins associated with cycle stage and disease, revealing a set of genes that drive separation across these two biological variables. Further, we analyzed our scRNA-seq data to identify cell types expressing cycle and disease- associated genes identified in our proteomic data. A module scoring approach was used to identify cell types driving the enrichment of certain biological pathways, revealing several pathways of interest across different cell subpopulations. Finally, we identified ligand-receptor pairs including Axl/Tyro3 – Gas6, that may modulate interactions between endometrial macrophages and/or endometrial stromal/epithelial cells. Analysis of these signaling pathways in an independent cohort of endometrial biopsies revealed a significant decrease in Tyro3 expression in patients with endometriosis compared to controls, both transcriptionally and through histological staining. This measured decrease in Tryo3 in patients with disease could serve as a novel diagnostic biomarker or treatment avenue for patients with endometriosis. Taken together, this integrated approach provides a framework for integrating LMMs, proteomic and RNA-seq data to deconvolve the complexities of complex uterine diseases and identify novel genes and pathways underlying endometriosis.