Spatial Modeling of Tissues for Morphogenic Field Analysis

Abstract

Abstract Tissues are shaped by extracellular signaling fields which convey information between cells. The cellular composition of tissues, and the extracellular signaling within the tissue, are innately spatially structured. Modern spatialomics data provide unprecedented measurement of ligand and receptor expressivity in situ from tissue sections. Here, we show that by adapting generalizable geospatial statistical models to spatialomics data, we are able to reveal statistically-detailed portraits of morphogenic field interactions within tissues and thereby approach a richer set of biologic questions than is typically pursued. The general methods piloted here can readily be applied to spatialomics data from diverse platforms with no need to alter data collection techniques. Our results demonstrate that the application of spatial statistical modeling to spatialomics data opens many avenues for future experimentation that will be valuable to fundamental biology and to regenerative medicine. Graphical Abstract Highlights Tissue biology & regenerative medicine requires analysis of tissue morphogen fields and morphogenic interactions Spatial statistics can be used to model continuous morphogenic interaction fields in tissues from discrete spatialomics data

Affiliated Institutions

• Bucknell University US
• Yale University US

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