spot-level pcw6.5 fh6_1000l2_cn74_d1 spatial transcriptomics slide  (Spatial Transcriptomics Inc)

Journal: bioRxiv

Article Title: STARComm Scalably Detects Emergent Modules of Spatial Cell-Cell Communication in Inflammation and Cancer

doi: 10.1101/2025.08.07.669133

Figure Lengend Snippet: | a , Conceptual framework: cell–cell communication is inferred based on spatial proximity, where ligand-expressing (sender) cells are more likely to interact with nearby receptor-expressing (receiver) cells. b , Tissue samples are collected and processed using spatial transcriptomics (e.g., Xenium). c , Spatial gene expression images are generated, with cell type annotations (top) and receptor–ligand pairs curated from public databases (e.g., CellChat, CellPhoneDB, SpaTalk) or custom lists (bottom). d , Communication is modeled across various scenarios: non-interacting dispersed states, gain of receptor expression, gain of ligand expression, or recurring local interactions. A multicellular communication interaction module (MCIM) is defined as a set of co-localized receptor–ligand interactions. e , Spatial transcriptomics data with annotated cell types visualized by color. f , Communication edges are inferred by identifying ligand-expressing and receptor-expressing cells using TACIT and then connecting cells within a 50µm spatial radius. g , The tissue is partitioned into spatial bins; receptor–ligand edges are counted per bin. h , Kernel density estimation generates a spatial map of communication density for each receptor–ligand pair. i , Communication densities are summarized in a matrix (bins × receptor–ligand pairs), where values represent interaction densities. j , This matrix is clustered to identify MCIMs. k , Heatmap of communication densities for each MCIM. l , MCIMs are grouped by signaling pathway and mapped onto spatial tissue architecture. m–n , MCIMs can be associated with clinical outcomes, such as survival, and may serve as biomarkers or therapeutic targets (e.g., in GVHD).

Article Snippet: However, a fundamental challenge arises because most of these tools were designed for two-dimensional and spot-level spatial transcriptomics .

Techniques: Expressing, Gene Expression, Generated, Biomarker Discovery

Journal: bioRxiv

Article Title: STARComm Scalably Detects Emergent Modules of Spatial Cell-Cell Communication in Inflammation and Cancer

doi: 10.1101/2025.08.07.669133

Figure Lengend Snippet: a , Overview of chronic GVHD manifestations across organ systems in the patient cohort. Percentages indicate the frequency of organ involvement across individuals. b , Cohort composition showing 43 minor salivary gland samples collected from 23 patients, including both healthy controls and GVHD cases. c , Spatial transcriptomics was performed using the Xenium platform on minor salivary gland tissue microarrays (TMAs), enabling single-cell resolution mapping of gene expression. d , Integration of receptor–ligand spatial networks across tissues identified MCIMs based on recurrent cellular interaction patterns (top). These MCIMs were annotated using KEGG and GO enrichment and evaluated for therapeutic relevance using the newly developed spatial Drug2Cell pipeline (bottom). e–f , UMAP visualization of bin-level communication densities across all tissues, colored by GVHD status and MCIM membership. g , Heatmap showing receptor–ligand density within each MCIM, highlighting signaling pathways associated with disease. h , Proportion plot illustrating the distribution of specific MCIMs across 36 tissue samples. i , Heatmap depicting enrichment scores of the top interacting cell types within each MCIM.

Article Snippet: However, a fundamental challenge arises because most of these tools were designed for two-dimensional and spot-level spatial transcriptomics .

Techniques: Gene Expression, Protein-Protein interactions