The paper describes the development and application of a computational pipeline for spatial mechano-transcriptomics, which is a new framework for jointly analyzing transcriptional and mechanical signals at single-cell resolution within tissues. This framework integrates spatial transcriptomics data with an image-based mechanical force inference approach, such as the Variational Method of Stress Inference (VMSI), to quantify cellular properties like pressure and tension. Applied to the developing mouse embryo, the analysis demonstrates that tissue compartment boundaries are characterized by elevated cell–cell junctional tension, a finding robustly supported by biophysical simulations. Furthermore, the study utilizes ligand-receptor analysis and structural equation modeling (gSEM) to identify gene modules and signaling pathways, including ephrin signaling, that are associated with and potentially regulate these mechanical properties, often exhibiting nonlinear expression patterns in response to mechanical changes.

References:

• Hallou A, He R, Simons B D, et al. A computational pipeline for spatial mechano-transcriptomics[J]. Nature methods, 2025: 1-14.