SpatialKNifeY (SKNY): Extending from spatial domain to surrounding area to identify microenvironment features with single-cell spatial omics data

Single-cell spatial omics analysis requires consideration of biological functions and mechanisms in a microenvironment. However, microenvironment analysis using bioinformatic methods is limited by the need to detect histological morphology and extend it to the surrounding area. In this study, we developed SpatialKNifeY (SKNY), an image-processing-based toolkit that detects spatial domains that potentially reflect histology and extends these domains to the microenvironment. Using spatial transcriptomic data from breast cancer, we applied the SKNY algorithm to identify tumor spatial domains, followed by clustering of the domains, trajectory estimation, and spatial extension to the tumor microenvironment (TME). The results of the trajectory estimation were consistent with the known mechanisms of cancer progression. We observed tumor vascularization and immunodeficiency at mid- and late-stage progression in TME. Furthermore, we applied the SKNY to integrate and cluster the spatial domains of 14 patients with metastatic colorectal cancer, and the clusters were divided based on the TME characteristics. In conclusion, the SKNY facilitates the determination of the functions and mechanisms in the microenvironment and cataloguing of the features.Author summary: The advent of high-resolution and high-density spatial omics platforms has created a growing need for practical analytical tools in cancer research. While significant efforts have been made to develop unsupervised clustering methods, advancements in downstream analyses have been relatively slower. To address this issue, we developed SpatialKNifeY (SKNY), a versatile toolkit designed to analyze spatial omics data by defining the spatial domains of cancer cells and their microenvironment. SKNY offers a suite of analyses, including clustering and trajectory analysis, with a unique capability to extract spatial domains and their surrounding regions. The tool enables integrated studies of cancer cells alongside their stroma, immune cells, and vascular environment. Using SKNY, we quantified the vascular and immune environments surrounding cancer cells during progression, revealing insights consistent with established cancer pathology and progression models. These results highlight the toolkit’s utility and the biological interpretability of its analyses, providing a valuable resource for spatial omics research.