Comprehensive Spatial Methods (CSM): a Toolbox for Spatially Analyzing Tissues in Histopathology

The image-based determination of proteins with spatial context has revolutionized our understanding of biology in different fields, including developmental biology, immunology, and oncology. The popularization of multiplex and high-plex tissue imaging methods has allowed researchers to simultaneously interrogate multiple tissue proteins with high spatial resolution in a single tissue section. Although these technologies offer many opportunities, analytical challenges have also emerged. Currently, no single analytical pipeline covers the entire spectrum of analyses required to harness the potential of these spatial platforms. Here, we present Comprehensive Spatial Methods (CSM), an R-based analysis toolbox designed to analyze multiplex and high-plex omics with high spatial resolution. CSM covers all the analytical steps, from cell and tissue segmentation and protein expression normalization to cell phenotyping, spatial heterogeneity analysis, cell-to-cell spatial interaction determination, and cellular neighborhood analysis. CSM relies on top-performing R libraries to deliver a user-friendly experience. We test the performance of CSM on a set of multiplex and high-plex images of endometrial, breast, and colorectal carcinomas and nontumoral lymph node, skin, and lung tissue. We demonstrate that the ability of CSM to phenotype and quantify cells is better than that of other state-of-the-art resources. In addition, we show that the different approaches implemented in CSM for assessing cell phenotypes, spatial heterogeneity, cell-to-cell interactions, and tissue neighborhoods cover a broad range of analytical scenarios. The freely available CSM toolbox covers many of the analytical needs of researchers working with spatially resolved histopathology data.