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  De novo identification of universal cell mechanics regulators

Urbanska, M., Ge, Y., Winzi, M., Abuhattum Hofemeier, S., Herbig, M., Kräter, M., et al. (2021). De novo identification of universal cell mechanics regulators. bioRxiv:10.1101/2021.04.26.441418.

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Urbanska, Marta1, 2, Author              
Ge, Yan2, Author
Winzi, Maria2, Author
Abuhattum Hofemeier, Shada1, 2, Author              
Herbig, Maik2, 3, Author              
Kräter, Martin1, 2, Author              
Toepfner, Nicole2, Author
Durgan, Joanna4, Author
Florey, Oliver4, Author
Dori, Martina2, Author
Calegari, Frederico2, Author
Lolo, Fidel-Nicolás4, Author
del Pozo, Miguel Ángel4, Author
Taubenberger, Anna V.2, Author
Cannistraci, Carlo Vittorio2, 4, Author
Guck, Jochen1, 2, 5, Author              
Affiliations:
1Guck Division, Max Planck Institute for the Science of Light, Max Planck Society, ou_3164416              
2Technische Universität Dresden, ou_persistent22              
3Guests, Max Planck Institute for the Science of Light, Max Planck Society, ou_2364696              
4external, ou_persistent22              
5Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society, ou_3164414              

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 Abstract: Mechanical proprieties determine many cellular functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors governing cell mechanical phenotype is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between mechanical phenotype changes and the genes involved in driving them. We employ a machine learning-based discriminative network analysis method termed PC-corr to associate cell mechanical states, measured by real-time deformability cytometry (RT-DC), with large-scale transcriptome datasets ranging from stem cell development to cancer progression, and originating from different murine and human tissues. By intersecting the discriminative networks inferred from two selected datasets, we identify a conserved module of five genes with putative roles in the regulation of cell mechanics. We validate the power of the individual genes to discriminate between soft and stiff cell states in silico, and demonstrate experimentally that the top scoring gene, CAV1, changes the mechanical phenotype of cells when silenced or overexpressed. The data-driven approach presented here has the power of de novo identification of genes involved in cell mechanics regulation and paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

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Language(s): eng - English
 Dates: 2021-04-27
 Publication Status: Published online
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 Identifiers: arXiv: 10.1101/2021.04.26.441418
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Title: bioRxiv:10.1101/2021.04.26.441418
Source Genre: Commentary
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