Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data driven approach paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.Competing Interest StatementS.A., M.K., and J.G. are co-founders and shareholders of the company Rivercyte GmbH that is commercializing deformability cytometry technology. The remaining authors declare no competing interests.Footnotes* (1) Added the phrase: across the studied mouse and human systems, to the statement: the identified gene markers are universal, trustworthy and specific to the mechanical phenotype - in abstract, introduction, and discussion. (2) In the captions of Figures: 2, 3, 5, 6, S2, S9, and S11, clarified the meaning of symbol shapes. (3) Added a clarifying sentence explaining how PC-corr is derived at the first time of appearance in the results section. - Introduced a new paragraph in the results section discussing the absolute values of measured Youngs moduli in relation to probing frequencies, accompanied by two new display items in the supplementary materials: Fig. S10 and Table S9. (4) Added three new supplementary figures (Fig. S4-S6) to display the expression matrices for the genes from the identified modules in carcinoma datasets used for validation. (5) At the end of the first paragraph of the discussion, we have added a statement indicating open ends of the current study. (6) Introduced a new paragraph in the discussion section to indicate the known intracellular origins of resistance to deformation and the dominance of the actin cortex at low deformations. (7) Cited Swift et al. 2013, which identifies PTRF, another caveolar component, as being associated with tissue stiffness. (8) Updated Figure S7 to include additional regression lines in each panel, representing origin-grouped sub-selections of data. (9) Made further minor edits to the text and figure captions to correct typos and improve readability.* https://doi.org/10.6084/m9.figshare.c.5399826* https://doi.org/10.6084/m9.figshare.20123159* https://github.com/biomedical-cybernetics/Joint-View-trustworthiness-JVT...

De novo identification of universal cell mechanics gene signatures

10.1101/2021.04.26.441418