Urbanska, MartaGe, YanWinzi, MariaAbuhattum, ShadaAli, Syed ShafatHerbig, MaikKräter, MartinToepfner, NicoleDurgan, JoanneFlorey, OliverDori, MartinaCalegari, FedericoLolo, Fidel-Nicolásdel Pozo, Miguel ÁngelTaubenberger, AnnaCannistraci, Carlo VittorioGuck, Jochen2025-07-092025-07-092025-02-17Elife. 2025 Feb 17:12:RP87930.https://hdl.handle.net/20.500.12105/26812Cell 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.We thank Isabel Richter and Christine Schweitzer for technical assistance, Miguel Sanchez (CNIC, Spain) and Konstantinos Anastasiadis (TU Dresden, Germany) for helpful discussions, Len R Stephens (Babraham Institute, UK) for provision of MCF10A PIK3CA cells, and Kevin Struhl (Harvard Medical School, MA, USA) for provision of MCF10A-ER-Src cells. We further thank the Microstructure Facility at the Center for Molecular and Cellular Bioengineering (CMCB) at the Technische Universität Dresden (in part funded by the State of Saxony and the European Regional Development Fund) for hosting the chip fabrication. The authors acknowledge the following funding: Alexander von Humboldt-Stiftung, Alexander von Humboldt Professorship (JG), European Commission, ERC Starting Grant 'LightTouch' #282060 (JG), Marie Sklodowska-Curie Actions under the European Union’s Horizon 2020 research and innovation programme, BIOPOL ITN, #641639 (MADP, JG), Deutsche Forschungsgemeinschaft, #GU 612/5-1 and #399422891 (JG), Zhou Yahui Chair Professorship of Tsinghua University (CVC), The starting funding of the Tsinghua Laboratory of Brain and Intelligence (THBI) (CVC), The National High-Level Talent Program of the Ministry of Science and Technology of China #20241710001 (CVC), The independent research group leader running funding of the Technische Universität Dresden (CVC), Wellcome Trust, Sir Henry Wellcome Postdoctoral Fellowship, #224074/Z/21/Z (MU), Comunidad Autónoma de Madrid, Tec4Bio-CM, #S2018/NMT-4443 (MADP), Fundació La Marató de TV3, #201936-30-31 (MADP), Mildred Scheel Early Career Center Dresden (MSNZ) funded by the German Cancer Aid (Deutsche Krebshilfe) (AT).engVoRhttp://creativecommons.org/licenses/by/4.0/cell biologydeformability cytometrydiscriminative network analysishumanmechanical phenotypemechanomicsmousephysics of living systemstranscriptomicsunsupervised machine learningAttribution 4.0 International39960760eLifeopen access