Dataset Information

Divide-and-conquer matrisome protein (DC-MaP) strategy: an optimal approach to proteomic matrisome characterization

ABSTRACT: Currently, the extracellular matrix (ECM) is considered a pivotal complex meshwork of macromolecules playing a plethora of biomolecular functions in health and disease beyond its commonly known mechanical role. Only by unravelling its composition, we can leverage related tissue engineering and pharmacological efforts. Nevertheless, its unbiased proteomic identification still encounters major limitations mainly due to partial ECM enrichment by precipitation, sequential fractionation using unfriendly mass spectrometry (MS) detergents and resuspension with harsh reagents that need to be entirely removed prior to analysis. These methods can be technically challenging and labor-intensive, which affects the reproducibility of ECM identification and induces protein loss. Here, we present a simple new method applicable to tissue fragments of 10 mg and more. The technique involves a standardized procedure for sample processing with an MS-compatible detergent and combined centrifugation. This two-step protocol eliminates the need for laborious sample clarification and divides our samples into 2 fractions, soluble and insoluble, successively enriched with matrisome-associated (ECM-interacting) and core matrisome (structural ECM) proteins.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Ovarian Follicle Layer, Extracellular Matrix Constituent Secretion

SUBMITTER: Didier Vertommen

LAB HEAD: Amorim Christiani

PROVIDER: PXD020823 | Pride | 2020-12-03

REPOSITORIES: Pride

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Divide-and-Conquer Matrisome Protein (DC-MaP) Strategy: An MS-Friendly Approach to Proteomic Matrisome Characterization.

Ouni Emna E Ruys Sebastien Pyr Dit SPD Dolmans Marie-Madeleine MM Herinckx Gaëtan G Vertommen Didier D Amorim Christiani A CA

International journal of molecular sciences 20201130 23

Currently, the extracellular matrix (ECM) is considered a pivotal complex meshwork of macromolecules playing a plethora of biomolecular functions in health and disease beyond its commonly known mechanical role. Only by unraveling its composition can we leverage related tissue engineering and pharmacological efforts. Nevertheless, its unbiased proteomic identification still encounters some limitations mainly due to partial ECM enrichment by precipitation, sequential fractionation using unfriendly ...[more]

PMID: 33266304

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Project description:Our modern era is witnessing an increasing infertility rate worldwide. Although some of the causes can be attributed to our modern lifestyle (eg. persistent organic polluants, late pregnancy), our knowledge of the human ovarian tissue has remained limited and insufficient to reverse the infertility statistics. Indeed, all efforts have been focused on the endocrine and cellular function in support of the cell theory that dates back to the 18th century, while the human ovarian matrisome is still under-described. Therefore, hereby we provide the first systematic study focusing on the human ovarian matrisome and its remodeling during a woman lifetime, from prepuberty til menopause. We apply our matrisome tailored fractionation method, the DC-MaP strategy and mass spectrometry-based quantitative proteomics using TMT pro 16-plex isobaric labeling to delineate proteome-wide and ECM-specific shifts between prepubertal, reproductive-age and menopausal stages. Post-translational modifications, namely proline hydroxylation and advanced glycation ends products have also been detected at different intensities according to age. Matrisome age-biomarkers have been validated with lightsheet microscopy-based-3D imaging, following immunofluorescence and tissue clearing with an ovary-tailored protocol. We used machine learning and statistics to build a neural network that can predict predict the probability of a specific ovarian tissue matrisome to resemble to prepubertal, reproductive-age or menopausal tissue and potentially conclude its fertility potency or evaluate in the future the efficiency of ovarian rejuvenation treatments. We also used NLP medscan technology for advanced literature text-mining to predict all possible-connections between the extra- and intra-cellular compartments and how chemo-radiotherapy, known for its gonadotoxicity can also affect key matrisome proteins.This comprehensive proteomics analysis represents the ovarian proteomic codex and contributes to an improved understanding of the critical roles that ECM plays throughout ovarian life span. By publishing our proteomic data, we hope to open new avenues in fertility restoration, ovarian rejuvenation and reproductive tissue engineering

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