De novo identification of universal cell mechanics gene signatures

• Published in: eLife Vol. 12
• Main Authors: Urbanska, Marta, Ge, Yan, Winzi, Maria, Abuhattum, Shada, Ali, Syed Shafat, Herbig, Maik, Kräter, Martin, Toepfner, Nicole, Durgan, Joanne, Florey, Oliver, Dori, Martina, Calegari, Federico, Lolo, Fidel-Nicolás, del Pozo, Miguel Ángel, Taubenberger, Anna, Cannistraci, Carlo Vittorio, Guck, Jochen
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications, Ltd 17.02.2025; eLife Sciences Publications Ltd
• Subjects: Animals; Biomechanical Phenomena; Cell Biology; deformability cytometry; discriminative network analysis; Gene Expression Profiling; Humans; Machine Learning; mechanical phenotype; mechanomics; Mice; Physics of Living Systems; Single-Cell Analysis; Transcriptome; transcriptomics; unsupervised machine learning; discriminative network analysis; mouse; deformability cytometry; unsupervised machine learning; cell biology; transcriptomics; mechanical phenotype; mechanomics; human; physics of living systems
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.87930

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 toward engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.