Computational prediction of cellular elastic modulus from mechanosensitive gene expression at multiple biological levels.

BACKGROUND: Understanding cellular mechanical properties is crucial for investigating cell fate determination, embryonic development, and disease progression. Traditional methods of measuring cellular mechanical properties, such as atomic force microscopy, are time-consuming, labor-intensive, and low-throughput. Computational models which can capture the relationship between mechanosensitive gene expression, a readily accessible and cost-effective data source, and cellular mechanical properties offer promising alternatives. RESULTS: In this study, we identified mechanosensitive genes from 104 cell lines, using RNA-seq data and corresponding elastic modulus from the MechanoBase database. Several statistical learning models were tested and gradient boosting regression emerged as the most effective, outperforming other models in accuracy. We termed this model MechanoGEPred. The model demonstrated its ability to predict elastic modulus variations across tissue samples, single cells, and tissue spatial domains, capturing complex relationships between gene expression and mechanical properties. CONCLUSIONS: By enabling predictions at multiple biological levels, MechanoGEPred offers a useful framework for inferring cellular elastic modulus directly from gene expression data. The model reveals biologically meaningful patterns and context-dependent differences, suggesting potential applications in biomechanics and cancer research, and providing a proof of concept for studying mechanical heterogeneity and its role in health and disease.