Machine learning interpretable models of cell mechanics from protein images.

Autor:	Schmitt MS; James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA; Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA., Colen J; James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA; Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA., Sala S; Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA., Devany J; James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA., Seetharaman S; James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA., Caillier A; Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA., Gardel ML; James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA. Electronic address: gardel@uchicago.edu., Oakes PW; Department of Cell & Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA. Electronic address: poakes@luc.edu., Vitelli V; James Franck Institute, University of Chicago, Chicago, IL 60637, USA; Department of Physics, University of Chicago, Chicago, IL 60637, USA; Kadanoff Center for Theoretical Physics, University of Chicago, Chicago, IL 60637, USA. Electronic address: vitelli@uchicago.edu.
Jazyk:	angličtina
Zdroj:	Cell [Cell] 2024 Jan 18; Vol. 187 (2), pp. 481-494.e24. Date of Electronic Publication: 2024 Jan 08.
DOI:	10.1016/j.cell.2023.11.041
Abstrakt:	Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understanding processes such as cell adhesion and migration. Here, we develop a data-driven modeling pipeline to learn the mechanical behavior of adherent cells. We first train neural networks to predict cellular forces from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion (FA) protein, such as zyxin, are sufficient to predict forces and can generalize to unseen biological regimes. Using this observation, we develop two approaches-one constrained by physics and the other agnostic-to construct data-driven continuum models of cellular forces. Both reveal how cellular forces are encoded by two distinct length scales. Beyond adherent cell mechanics, our work serves as a case study for integrating neural networks into predictive models for cell biology. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2023 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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