StereoSiTE: A framework to spatially and quantitatively profile the cellular neighborhood organized iTME

Spatial Transcriptomics (ST) technology enables systematic depiction of regional milieu of a tissue, like tumor immuno-microenvironment (iTME). However, a powerful algorithmic framework to dissect spatially resolved niches, and to quantitatively evaluate spatial cell interaction intensity will pave the ways to understand the spatial signature associated mechanism. In this study, we provide a promising framework (StereoSiTE), which is based on space nearest neighbor graph and gene expression profile to spatially resolve iTME and to quantitatively define cell-cell communication intensity. We applied StereoSiTE to dissect the iTME of xenograft model receiving immunoagonist treatment, 7 distinct cellular neighborhoods (CN) were identified, and each CN was considered as the functional unit with exclusive cell type (CT) composition. Further deconvolving the joint matrix covering CNs and CTs indicated the importance of neutrophils in CN6, which was confirmed by pathway enrichment analysis. What’s more, analysis of interaction intensity indicated that the recruited neutrophils preserved tumor protection activity through paired IL-1β/IL-1R after immunoagonist treatment exclusively in CN6. This evidence provided a new possible vision of tumor immune evasion orchestrated by neutrophils. StereoSiTE is believed to be a promising framework of mapping iTME niches using spatial transcriptomics, which could be utilized to spatially reveal tumoribiology mechanisms. Highlight A framework based on space nearest neighbor graph and gene expression profile to spatially resolve iTME and to quantitatively define cell-cell communication intensity (StereoSiTE) ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes