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Mass cytometric and transcriptomic profiling of epithelial-mesenchymal transitions in human mammary cell lines

Wagner, Johanna; Masek, Markus; Jacobs, Andrea; Soneson, Charlotte; Sivapatham, Sujana; Damond, Nicolas; de Souza, Natalie; Robinson, Mark D; Bodenmiller, Bernd (2022). Mass cytometric and transcriptomic profiling of epithelial-mesenchymal transitions in human mammary cell lines. Scientific Data, 9:44-60.

Abstract

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. However, detection, inhibition, and elimination of EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. We comprehensively profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a flow cytometry surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by prolonged exposure to TGFβ1 or 4-hydroxytamoxifen, respectively. Each cell line exhibited a spectrum of EMT transition phenotypes, which we compared to the steady-state phenotypes of fifteen luminal, HER2-positive, and basal breast cancer cell lines. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used here, and for the design of EMT time course experiments.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Physical Sciences > Information Systems
Social Sciences & Humanities > Education
Physical Sciences > Computer Science Applications
Social Sciences & Humanities > Statistics, Probability and Uncertainty
Social Sciences & Humanities > Library and Information Sciences
Language:English
Date:9 February 2022
Deposited On:07 Jul 2022 12:53
Last Modified:19 Mar 2025 04:30
Publisher:Nature Publishing Group
ISSN:2052-4463
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41597-022-01137-4
PubMed ID:35140234
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