Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis

TLDR

Spatial transcriptomics advances tissue mapping but still struggles to delineate pathology‑relevant cellular localizations and interactions unique to spatial niches such as tumor boundaries. The authors develop Cottrazm, a tool that integrates spatial transcriptomics, histology, and single‑cell data to delineate the tumor boundary linking malignant and non‑malignant cell spots, deconvolute cell‑type composition, and reconstruct sub‑spot gene expression profiles. Cottrazm’s performance is validated along the malignant‑boundary‑non‑malignant spatial axis. Cottrazm reveals macrophage and fibroblast subtypes at the tumor boundary that form a structural barrier limiting T‑cell infiltration and promoting immune exclusion, and it enables the discovery of functional insights and therapeutic targets in oncologic spatial transcriptomics datasets.

Abstract

Abstract Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets.