A computational pipeline for spatial mechano-transcriptomics

• Published in: Nature methods Vol. 22; no. 4; pp. 737 - 750
• Main Authors: Hallou, Adrien, He, Ruiyang, Simons, Benjamin D., Dumitrascu, Bianca
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2025; Nature Publishing Group
• Subjects: 631/114/2415; 631/136/1660; 631/1647/2204; 631/1647/514/1949; 631/208/199; Animals; Bioinformatics; Biological Microscopy; Biological Techniques; Biomechanical Phenomena; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Cell fate; Cells; Computational Biology - methods; Computer applications; Embryo, Mammalian - metabolism; Embryos; Gene expression; Gene Expression Profiling - methods; Life Sciences; Mechanical properties; Mechanical stimuli; Methods; Mice; Morphology; Pattern formation; Proteomics; Spatial analysis; Statistical analysis; Transcriptome; Transcriptomics
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-025-02618-1

Advances in spatial profiling technologies are providing insights into how molecular programs are influenced by local signaling and environmental cues. However, cell fate specification and tissue patterning involve the interplay of biochemical and mechanical feedback. Here we develop a computational framework that enables the joint statistical analysis of transcriptional and mechanical signals in the context of spatial transcriptomics. To illustrate the application and utility of the approach, we use spatial transcriptomics data from the developing mouse embryo to infer the forces acting on individual cells, and use these results to identify mechanical, morphometric and gene expression signatures that are predictive of tissue compartment boundaries. In addition, we use geoadditive structural equation modeling to identify gene modules that predict the mechanical behavior of cells in an unbiased manner. This computational framework is easily generalized to other spatial profiling contexts, providing a generic scheme for exploring the interplay of biomolecular and mechanical cues in tissues. The authors present a computational framework that leverages mechanical force inference and spatial transcriptomics to enable analyses of the interplay between the transcriptomic and mechanical state.