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

Author

J. Zheng
T.W. Jackson
L.A. Fortier
L.J. Bonassar
M.L. Delco
Itai Cohen

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. 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.

Date Published

Journal

PLoS ONE

Volume

17

Issue

12 December

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143563474&doi=10.1371%2fjournal.pone.0278626&partnerID=40&md5=b2bfe04f160cdd37c967154b174e6d5c

DOI

10.1371/journal.pone.0278626

Group (Lab)

Itai Cohen Group

Funding Source

BMMB-1536463 IC
CMMI-1927197
DMR-1807602
K08AR068470
R01AR071394
R03AR075929
DMR-1719875

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