Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
Rozova, VS
Anwer, AG
Guller, AE
Es, HA
Khabir, Z
Sokolova, AI
Gavrilov, MU
Goldys, EM
Warkiani, ME
Thiery, JP
Zvyagin, AV
- Publisher:
- Public Library of Science (PLoS)
- Publication Type:
- Journal Article
- Citation:
- PLoS Comput Biol, 2021, 17, (7), pp. e1009193
- Issue Date:
- 2021-07
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Rozova, VS | |
dc.contributor.author | Anwer, AG | |
dc.contributor.author | Guller, AE | |
dc.contributor.author | Es, HA | |
dc.contributor.author | Khabir, Z | |
dc.contributor.author | Sokolova, AI | |
dc.contributor.author | Gavrilov, MU | |
dc.contributor.author | Goldys, EM | |
dc.contributor.author | Warkiani, ME | |
dc.contributor.author | Thiery, JP | |
dc.contributor.author | Zvyagin, AV | |
dc.date.accessioned | 2022-05-09T05:13:17Z | |
dc.date.available | 2021-06-17 | |
dc.date.available | 2022-05-09T05:13:17Z | |
dc.date.issued | 2021-07 | |
dc.identifier.citation | PLoS Comput Biol, 2021, 17, (7), pp. e1009193 | |
dc.identifier.issn | 1553-734X | |
dc.identifier.issn | 1553-7358 | |
dc.identifier.uri | http://hdl.handle.net/10453/157152 | |
dc.description.abstract | 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. | |
dc.format | Electronic-eCollection | |
dc.language | eng | |
dc.publisher | Public Library of Science (PLoS) | |
dc.relation.ispartof | PLoS Comput Biol | |
dc.relation.isbasedon | 10.1371/journal.pcbi.1009193 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject | 01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences | |
dc.subject.classification | Bioinformatics | |
dc.subject.mesh | Adaptation, Physiological | |
dc.subject.mesh | Antigens, CD | |
dc.subject.mesh | Biomarkers, Tumor | |
dc.subject.mesh | Biophysical Phenomena | |
dc.subject.mesh | Cadherins | |
dc.subject.mesh | Cell Adhesion | |
dc.subject.mesh | Cell Count | |
dc.subject.mesh | Cell Line, Tumor | |
dc.subject.mesh | Cell Proliferation | |
dc.subject.mesh | Cell Shape | |
dc.subject.mesh | Computational Biology | |
dc.subject.mesh | Epithelial-Mesenchymal Transition | |
dc.subject.mesh | Extracellular Matrix | |
dc.subject.mesh | Female | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Models, Biological | |
dc.subject.mesh | Neoplasm Metastasis | |
dc.subject.mesh | Triple Negative Breast Neoplasms | |
dc.subject.mesh | Tumor Microenvironment | |
dc.subject.mesh | Vimentin | |
dc.subject.mesh | Cell Line, Tumor | |
dc.subject.mesh | Extracellular Matrix | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Neoplasm Metastasis | |
dc.subject.mesh | Vimentin | |
dc.subject.mesh | Cadherins | |
dc.subject.mesh | Antigens, CD | |
dc.subject.mesh | Cell Count | |
dc.subject.mesh | Computational Biology | |
dc.subject.mesh | Adaptation, Physiological | |
dc.subject.mesh | Cell Adhesion | |
dc.subject.mesh | Cell Proliferation | |
dc.subject.mesh | Cell Shape | |
dc.subject.mesh | Models, Biological | |
dc.subject.mesh | Female | |
dc.subject.mesh | Biophysical Phenomena | |
dc.subject.mesh | Epithelial-Mesenchymal Transition | |
dc.subject.mesh | Tumor Microenvironment | |
dc.subject.mesh | Triple Negative Breast Neoplasms | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Biomarkers, Tumor | |
dc.title | Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness. | |
dc.type | Journal Article | |
utslib.citation.volume | 17 | |
utslib.location.activity | United States | |
utslib.for | 01 Mathematical Sciences | |
utslib.for | 06 Biological Sciences | |
utslib.for | 08 Information and Computing Sciences | |
utslib.copyright.status | open_access | * |
dc.date.updated | 2022-05-09T05:13:11Z | |
pubs.issue | 7 | |
pubs.publication-status | Published online | |
pubs.volume | 17 | |
utslib.citation.issue | 7 |
Abstract:
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.
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