Data from: STRAINS: A Big Data Method for Classifying Cellular Response to Stimuli at the Tissue Scale

Please cite as: Zheng, Jingyang, Thomas Wyse Jackson, Lisa Fortier, Lawrence Bonassar, Michelle Delco, and Itai Cohen (2022). Data from: STRAINS: A Big Data Method for Classifying Cellular Response to Stimuli at the Tissue Scale [Dataset] Cornell University Library eCommons Digital Repository. https://doi.org/10.7298/3kwt-pm43These files contain data supporting all results reported in Zheng et. al., STRAINS: A Big Data Method for Classifying Cellular Response to Stimuli at the Tissue Scale. 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.The work was supported by the NIH National Institute of Arthritis and Musculoskeletal and Skin Diseases, Contract: 5R01AR071394-04, K08AR068470, R03AR075929, and The Harry M. Zweig Fund for Equine Research. Additionally, this work was supported by the National Science Foundation grants DMR-1807602, DMR-1808026, CBET-1604712, CMMI 1927197, and BMMB-1536463. Lastly, this work made use of the Cornell Center for Materials Research Shared Facilities which are supported through the NSF MRSEC program (DMR-1719875)