A computational pipeline for spatial mechano-transcriptomics

Abstract

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, m providing a generic scheme for exploring the interplay of biomolecular and mechanical cues in tissues.

Morphogen gradients are regulated by porous media characteristics of the developing tissue

“…These could be identified from or validated against gene-expression [43] or spatial transcriptomics data [71, 72], e.g., from RNAscopes [116], single-molecule fluorescence in-situ hybridization (smFISH) [60], sequential fluorescence in-situ hybridization (seqFISH) [63], or single-cell RNA sequencing (scRNAseq) [41, 55] with tissue reference maps [1, 95]. Complementary to recent work that studied the relationship between spatial gene expression and tissue mechanics [39], our model could be extended to enable quantitative analyses of the relationship between spatial gene expression and morphogen gradients.…”