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STRAINS: A big data method for classifying cellular response to stimuli at the tissue scale.

Authors :
Zheng J
Wyse Jackson T
Fortier LA
Bonassar LJ
Delco ML
Cohen I
Source :
PloS one [PLoS One] 2022 Dec 08; Vol. 17 (12), pp. e0278626. Date of Electronic Publication: 2022 Dec 08 (Print Publication: 2022).
Publication Year :
2022

Abstract

Cellular response to stimulation governs tissue scale processes ranging from growth and development to maintaining tissue health and initiating disease. To determine how cells coordinate their response to such stimuli, it is necessary to simultaneously track and measure the spatiotemporal distribution of their behaviors throughout the tissue. Here, we report on a novel SpatioTemporal Response Analysis IN Situ (STRAINS) tool that uses fluorescent micrographs, cell tracking, and machine learning to measure such behavioral distributions. STRAINS is broadly applicable to any tissue where fluorescence can be used to indicate changes in cell behavior. For illustration, we use STRAINS to simultaneously analyze the mechanotransduction response of 5000 chondrocytes-over 20 million data points-in cartilage during the 50 ms to 4 hours after the tissue was subjected to local mechanical injury, known to initiate osteoarthritis. We find that chondrocytes exhibit a range of mechanobiological responses indicating activation of distinct biochemical pathways with clear spatial patterns related to the induced local strains during impact. These results illustrate the power of this approach.<br />Competing Interests: The authors have declared that no competing interests exist.<br /> (Copyright: © 2022 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Details

Language :
English
ISSN :
1932-6203
Volume :
17
Issue :
12
Database :
MEDLINE
Journal :
PloS one
Publication Type :
Academic Journal
Accession number :
36480531
Full Text :
https://doi.org/10.1371/journal.pone.0278626