STopover利用空间转录组学数据中的拓扑分析来捕获肿瘤微环境中的空间共定位和相互作用。

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data.

作者信息

Bae Sungwoo, Lee Hyekyoung, Na Kwon Joong, Lee Dong Soo, Choi Hongyoon, Kim Young Tae

机构信息

Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea.

Portrai, Inc., Seoul, Republic of Korea.

出版信息

Genome Med. 2025 Apr 1;17(1):33. doi: 10.1186/s13073-025-01457-1.

DOI:10.1186/s13073-025-01457-1

PMID:40170080

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11963362/

Abstract

Unraveling the spatial configuration of the tumor microenvironment (TME) is crucial for elucidating tumor-immune interactions based on immuno-oncology. We present STopover, a novel approach utilizing spatially resolved transcriptomics (SRT) data and topological analysis to investigate the TME. By gradually lowering the feature threshold, connected components (CCs) are extracted based on spatial distance and persistence, with Jaccard indices quantifying their spatial overlap, and transcriptomic profiles are permutated to assess statistical significance. Applied to lung and breast cancer SRT, STopover revealed immune and stromal cell infiltration patterns, predicted key cell-cell communication, and identified relevant regions, shedding light on cancer pathophysiology (URL: https://github.com/bsungwoo/STopover ).

摘要

解析肿瘤微环境（TME）的空间构型对于基于免疫肿瘤学阐明肿瘤-免疫相互作用至关重要。我们提出了STopover，这是一种利用空间分辨转录组学（SRT）数据和拓扑分析来研究TME的新方法。通过逐步降低特征阈值，基于空间距离和持续性提取连通分量（CCs），用杰卡德指数量化它们的空间重叠，并对转录组图谱进行置换以评估统计显著性。将STopover应用于肺癌和乳腺癌的SRT，揭示了免疫和基质细胞浸润模式，预测了关键的细胞间通讯，并确定了相关区域，为癌症病理生理学提供了新的见解（网址：https://github.com/bsungwoo/STopover ）。