2026-08-29T09:25:17Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/268122025-12-18T13:01:54Zcom_20.500.12105_19586com_20.500.12105_2202col_20.500.12105_19587

00925njm 22002777a 4500 dc Urbanska, Marta author Ge, Yan author Winzi, Maria author Abuhattum, Shada author Ali, Syed Shafat author Herbig, Maik author Kräter, Martin author Toepfner, Nicole author Durgan, Joanne author Florey, Oliver author Dori, Martina author Calegari, Federico author Lolo, Fidel-Nicolás author del Pozo, Miguel Ángel author Taubenberger, Anna author Cannistraci, Carlo Vittorio author Guck, Jochen author 2025-02-17 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, , changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions. Elife. 2025 Feb 17:12:RP87930. eLife 39960760 https://hdl.handle.net/20.500.12105/26812 cell biology deformability cytometry discriminative network analysis human mechanical phenotype mechanomics mouse physics of living systems transcriptomics unsupervised machine learning De novo identification of universal cell mechanics gene signatures.