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BP: Fachverband Biologische Physik
BP 13: Cell Mechanics I
BP 13.10: Vortrag
Dienstag, 17. März 2020, 12:30–12:45, SCH A251
Elucidating cell mechanics regulators from mechano-transcriptomics data using unsupervised machine learning — •Marta Urbanska1,2, Yan Ge1, Maria Winzi1, Konstantinos Anastasiadis1, Jochen Guck1,2, and Carlo V. Cannistraci1 — 1Biotechnology Center, CMCB, TU Dresden, Dresden, Germany — 2Max Planck Institute for the Science of Light, Erlangen, Germany
Mechanical properties of cells determine their capability to perform many physiological functions, such as migration, cell-fate specification or circulation through vasculature. Identifying the molecular factors that govern the mechanical phenotype is therefore a subject of great interest. Here we present an approach that enables establishing links between mechanophenotype changes and the genes responsible for driving them. In particular, we employ an unbiased machine learning method termed PC-corr to correlate cell mechanical states, measured by real-time deformability cytometry (RT-DC), with large-scale transcriptome datasets across different biological systems. We validate the obtained functional gene module in silico on four further datasets and show that the five identified genes have the capacity to discriminate between stiffer and softer cell states of 70 to 93%. Finally, we validate experimentally the influence of the top scoring gene on cell mechanics by its down- and up-regulation. The data-driven approach presented here has the power of de novo identification of genes involved in the regulation of cell mechanics and will extend the toolbox for tuning the mechanical properties of cells on demand to enable biological function or prevent pathologies.