Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories

2026-07-24

AI Summary

This study addresses the challenge of integrating local microenvironments with global pseudotemporal trajectories in spatial transcriptomics, which involves multimodal registration across samples and regions as well as deciphering complex spatiotemporal expression patterns. To this end, the authors propose a multi-region analytical paradigm that jointly models local neighborhoods and global developmental trajectories. They develop an integrated visual analytics system featuring novel glyphs and a computational framework to enable efficient, interactive exploration of spatial transcriptomic data alongside reference cell atlases and simulated temporal dynamics. In case studies involving pathologists and oncologists, as well as external evaluations, the system effectively facilitated the identification of cellular state transitions and the discovery of spatiotemporal gene expression dynamics.

Abstract

We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments. ST is a molecular profiling technology that measures gene expression directly within a thin tissue section while preserving its spatial organization. For practical application-driven analyses, the ST local microenvironment data needs to be integrated with cell reference datasets and temporal simulations of cell behavior. This integration is challenging due to multi-modal registration issues and the complexity of the pseudo-temporal patterns, spatial enrichment data, and gene expression dynamics. Loom leverages a novel glyph coupled with a computational backbone to facilitate the detailed pseudo-temporal exploration of local microenvironments, cross-sample comparisons, and investigation of spatiotemporal biological mechanisms. We evaluate Loom through two case studies developed with experts in tissue pathology and oncologists and through an external usability study. The results demonstrate that Loom supports effectively the discovery of cellular transitions and spatiotemporal expression dynamics.

Problem

• spatial transcriptomics
• pseudo-temporal trajectories
• local microenvironments
• multi-modal integration
• spatiotemporal dynamics

Innovation

• spatial transcriptomics
• pseudo-temporal trajectory
• visual computing
• local microenvironment
• multi-region comparison