Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks

Abstract Cells interpret cues from and interact with fibrous microenvironments through the body based on the mechanics and organization of these environments and the phenotypic state of the cell. This in turn regulates mechanoactive pathways, such as the localization of mechanosensitive factors. Her...

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Autores principales:	Edward D. Bonnevie, Beth G. Ashinsky, Bassil Dekky, Susan W. Volk, Harvey E. Smith, Robert L. Mauck
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d831d02e33214c92a636a23dfbdcf246
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spelling	oai:doaj.org-article:d831d02e33214c92a636a23dfbdcf2462021-12-02T13:18:08ZCell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks10.1038/s41598-021-85276-52045-2322https://doaj.org/article/d831d02e33214c92a636a23dfbdcf2462021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85276-5https://doaj.org/toc/2045-2322Abstract Cells interpret cues from and interact with fibrous microenvironments through the body based on the mechanics and organization of these environments and the phenotypic state of the cell. This in turn regulates mechanoactive pathways, such as the localization of mechanosensitive factors. Here, we leverage the microscale heterogeneity inherent to engineered fiber microenvironments to produce a large morphologic data set, across multiple cells types, while simultaneously measuring mechanobiological response (YAP/TAZ nuclear localization) at the single cell level. This dataset describing a large dynamic range of cell morphologies and responses was coupled with a machine learning approach to predict the mechanobiological state of individual cells from multiple lineages. We also noted that certain cells (e.g., invasive cancer cells) or biochemical perturbations (e.g., modulating contractility) can limit the predictability of cells in a universal context. Leveraging this finding, we developed further models that incorporate biochemical cues for single cell prediction or identify individual cells that do not follow the established rules. The models developed here provide a tool for connecting cell morphology and signaling, incorporating biochemical cues in predictive models, and identifying aberrant cell behavior at the single cell level.Edward D. BonnevieBeth G. AshinskyBassil DekkySusan W. VolkHarvey E. SmithRobert L. MauckNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Edward D. Bonnevie Beth G. Ashinsky Bassil Dekky Susan W. Volk Harvey E. Smith Robert L. Mauck Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
description	Abstract Cells interpret cues from and interact with fibrous microenvironments through the body based on the mechanics and organization of these environments and the phenotypic state of the cell. This in turn regulates mechanoactive pathways, such as the localization of mechanosensitive factors. Here, we leverage the microscale heterogeneity inherent to engineered fiber microenvironments to produce a large morphologic data set, across multiple cells types, while simultaneously measuring mechanobiological response (YAP/TAZ nuclear localization) at the single cell level. This dataset describing a large dynamic range of cell morphologies and responses was coupled with a machine learning approach to predict the mechanobiological state of individual cells from multiple lineages. We also noted that certain cells (e.g., invasive cancer cells) or biochemical perturbations (e.g., modulating contractility) can limit the predictability of cells in a universal context. Leveraging this finding, we developed further models that incorporate biochemical cues for single cell prediction or identify individual cells that do not follow the established rules. The models developed here provide a tool for connecting cell morphology and signaling, incorporating biochemical cues in predictive models, and identifying aberrant cell behavior at the single cell level.
format	article
author	Edward D. Bonnevie Beth G. Ashinsky Bassil Dekky Susan W. Volk Harvey E. Smith Robert L. Mauck
author_facet	Edward D. Bonnevie Beth G. Ashinsky Bassil Dekky Susan W. Volk Harvey E. Smith Robert L. Mauck
author_sort	Edward D. Bonnevie
title	Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
title_short	Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
title_full	Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
title_fullStr	Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
title_full_unstemmed	Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
title_sort	cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d831d02e33214c92a636a23dfbdcf246
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Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks

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