npj Biological Physics and Mechanics.BiophysicsPeer-reviewed journal article
Tissue-dependent mechanosensing by cells derived from human tumors
Kshitiz ParihariD, Jonathan Nukpezah, Daniel V. IwamotoiD, Katrina Cruz, Fitzroy J. Byfield, LiKang ChiniD, Maria E. Murray, Melissa G. MendeziD, Anne van Oosten, Anne Herrmann, Elisabeth E. CharrieriD, Peter A. GalieiD, Megan Donlick, Tongkeun LeeiD, Paul A. JanmeyiD, Ravi RadhakrishnaniD
Abstract
Alterations of the extracellular matrix (ECM), including both mechanical (such as stiffening of the ECM) and chemical (such as variation of adhesion proteins and deposition of hyaluronic acid (HA)) changes, in malignant tissues have been shown to mediate tumor progression. To survey how cells from different tissue types respond to various changes in ECM mechanics and composition, we measured physical characteristics (adherent area, shape, cell stiffness, and cell speed) of 25 cancer and 5 non-tumorigenic cell lines on 7 different substrate conditions. Our results indicate substantial heterogeneity in how cell mechanics changes within and across tissue types in response to mechanosensitive and chemosensitive changes in ECM. The analysis also underscores the role of HA in ECM with some cell lines showing changes in cell mechanics in response to presence of HA in soft substrate that are similar to those observed on stiff substrate. This pan-cancer investigation also highlights the importance of tissue-type and cell line specificity for inferences made based on comparison between physical properties of cancer and normal cells. Lastly, using unsupervised machine learning, we identify phenotypic classes that characterize the physical plasticity, i.e. the distribution of physical feature values attainable, of a particular cell type in response to different ECM-based conditions.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- approach: machine learning (classical)
- Citations
- 5
- FWCI
- 2.50
- Field percentile
- top 11%
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