Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming c...

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Autores principales:	Vlada S Rozova, Ayad G Anwer, Anna E Guller, Hamidreza Aboulkheyr Es, Zahra Khabir, Anastasiya I Sokolova, Maxim U Gavrilov, Ewa M Goldys, Majid Ebrahimi Warkiani, Jean Paul Thiery, Andrei V Zvyagin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b7bdaabf3ea64e01a3109b0a60295ea9
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spelling	oai:doaj.org-article:b7bdaabf3ea64e01a3109b0a60295ea92021-12-02T19:57:23ZMachine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.1553-734X1553-735810.1371/journal.pcbi.1009193https://doaj.org/article/b7bdaabf3ea64e01a3109b0a60295ea92021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009193https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.Vlada S RozovaAyad G AnwerAnna E GullerHamidreza Aboulkheyr EsZahra KhabirAnastasiya I SokolovaMaxim U GavrilovEwa M GoldysMajid Ebrahimi WarkianiJean Paul ThieryAndrei V ZvyaginPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009193 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Vlada S Rozova Ayad G Anwer Anna E Guller Hamidreza Aboulkheyr Es Zahra Khabir Anastasiya I Sokolova Maxim U Gavrilov Ewa M Goldys Majid Ebrahimi Warkiani Jean Paul Thiery Andrei V Zvyagin Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
description	Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.
format	article
author	Vlada S Rozova Ayad G Anwer Anna E Guller Hamidreza Aboulkheyr Es Zahra Khabir Anastasiya I Sokolova Maxim U Gavrilov Ewa M Goldys Majid Ebrahimi Warkiani Jean Paul Thiery Andrei V Zvyagin
author_facet	Vlada S Rozova Ayad G Anwer Anna E Guller Hamidreza Aboulkheyr Es Zahra Khabir Anastasiya I Sokolova Maxim U Gavrilov Ewa M Goldys Majid Ebrahimi Warkiani Jean Paul Thiery Andrei V Zvyagin
author_sort	Vlada S Rozova
title	Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
title_short	Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
title_full	Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
title_fullStr	Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
title_full_unstemmed	Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
title_sort	machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b7bdaabf3ea64e01a3109b0a60295ea9
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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

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