Journal article
Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
Scientific reports, v 11(Mar (E-published)), pp 5950-5950
15 Mar 2021
PMID: 33723274
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Cell morphology and mechanosensing can be decoupled in fibrous microenvironments and identified using artificial neural networks
- Creators
- Edward D. Bonnevie - University of PennsylvaniaBeth G. Ashinsky - Philadelphia VA Medical CenterBassil Dekky - University of PennsylvaniaSusan W. Volk - University of PennsylvaniaHarvey E. Smith - Philadelphia VA Medical CenterRobert L. Mauck - University of PennsylvaniaAlan T Murray
- Publication Details
- Scientific reports, v 11(Mar (E-published)), pp 5950-5950
- Publisher
- Nature Publishing Group UK
- Grant note
- IK2 RX001476; I01 RX002274 / ; F32 AR072478; F30 AG060670; R01 GM124091; R01 AR056624 / ;
- Resource Type
- Journal article
- Language
- English
- Web of Science ID
- WOS:000630516300026
- Scopus ID
- 2-s2.0-85102524498
- Other Identifier
- 991019357635904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Cell Biology