Spatial Neighborhood Construction and NICHES Connectomics for Differential Ligand-Receptor Analysis in Fibrotic Human...

Spatial transcriptomics enables measurement of gene expression within intact tissue architecture, yet most analytical workflows emphasize cell-type classification over cellular interaction. Workflows that do infer intercellular signaling typically aggregate inferred interactions across clusters, regions, or spots, and fix neighborhood definitions, ligand-receptor (LR) databases, and scoring functions that users cannot easily inspect or change. The unmet need is therefore an analysis workflow that preserves signaling inference at the resolution of individual cell-cell pairs, constrains candidate interactions using measured physical distance, and exposes every modeling choice as a user-set parameter. Here, we describe a computational protocol that meets these requirements by inferring LR signaling at cell-cell pair resolution, grounded in physical tissue space, from imaging-based spatial transcriptomics data. The workflow defines spatial neighborhoods and constructs a directed connectomics graph in which edges represent the set of inferred LR interactions between cells. Using the NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling) package, three complementary representations are derived: a Cell-to-Cell (C2C) edge-level connectome, a Cell-to-Neighborhood (C2N) outgoing signaling summary, and a Neighborhood-to-Cell (N2C) incoming signaling summary. A parallel Neighborhood Composition assay characterizes the local tissue architecture of the spatial neighborhoods. The framework is modular and tunable: users may adjust neighborhood definitions, select alternative LR databases, and modify scoring and aggregation methods. These node- and edge-aligned data layers support visualization, clustering, and differential analysis of spatially organized signaling patterns. Applied to fibrotic lung tissue, the workflow segregates architecturally defined neighborhoods and identifies differential LR mechanisms that distinguish fibrotic regions from surrounding tissue. This protocol provides a reproducible framework for studying spatially organized intercellular communication across tissues and disease contexts.