Spatial Modeling of Tissues for Morphogenic Field Analysis

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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/692715v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@467a2eorg.highwire.dtl.DTLVardef@ff7082org.highwire.dtl.DTLVardef@33936dorg.highwire.dtl.DTLVardef@13e791_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITissue biology & regenerative medicine requires analysis of tissue morphogen fields and morphogenic interactions C_LIO_LISpatial statistics can be used to model continuous morphogenic interaction fields in tissues from discrete spatialomics data C_LI