ROICellTrack：一种用于在肿瘤组织亚细胞空间转录组分析中整合细胞成像模态的深度学习框架。

ROICellTrack: a deep learning framework for integrating cellular imaging modalities in subcellular spatial transcriptomic profiling of tumor tissues.

作者信息

Song Xiaofei, Yu Xiaoqing, Moran-Segura Carlos M, Xu Hongzhi, Li Tingyi, Davis Joshua T, Vosoughi Aram, Grass G Daniel, Li Roger, Wang Xuefeng

机构信息

Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, United States.

Department of Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, United States.

出版信息

Bioinformatics. 2025 May 6;41(5). doi: 10.1093/bioinformatics/btaf152.

DOI:10.1093/bioinformatics/btaf152

PMID:40199763

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12085996/

Abstract

MOTIVATION

Spatial transcriptomic (ST) technologies, such as GeoMx Digital Spatial Profiler, are increasingly utilized to investigate the role of diverse tumor microenvironment components, particularly in relation to cancer progression, treatment response, and therapeutic resistance. However, in many ST studies, the spatial information obtained from immunofluorescence imaging is primarily used for identifying regions of interest (ROIs) rather than as an integral part of downstream transcriptomic data analysis and interpretation.

RESULTS

We developed ROICellTrack, a deep learning-based framework that better integrates cellular imaging with spatial transcriptomic profiling. By analyzing 56 ROIs from urothelial carcinoma of the bladder and upper tract urothelial carcinoma, ROICellTrack identified distinct cancer-immune cell mixtures, characterized by specific transcriptomic and morphological signatures and receptor-ligand interactions linked to tumor content and immune infiltrations. Our findings demonstrate the value of integrating imaging with transcriptomics to analyze spatial omics data, improving our understanding of tumor heterogeneity and its relevance to personalized and targeted therapies.

AVAILABILITY AND IMPLEMENTATION

ROICellTrack is publicly available at https://github.com/wanglab1/ROICellTrack.

摘要

动机

空间转录组学（ST）技术，如GeoMx数字空间剖析仪，越来越多地用于研究多种肿瘤微环境成分的作用，特别是在癌症进展、治疗反应和治疗抗性方面。然而，在许多ST研究中，从免疫荧光成像获得的空间信息主要用于识别感兴趣区域（ROI），而不是作为下游转录组数据分析和解释的一个组成部分。

结果

我们开发了ROICellTrack，这是一个基于深度学习的框架，能更好地将细胞成像与空间转录组分析相结合。通过分析来自膀胱尿路上皮癌和上尿路尿路上皮癌的56个ROI，ROICellTrack识别出了不同的癌症-免疫细胞混合物，其特征是具有特定的转录组和形态学特征以及与肿瘤含量和免疫浸润相关的受体-配体相互作用。我们的研究结果证明了将成像与转录组学相结合以分析空间组学数据的价值，有助于我们更好地理解肿瘤异质性及其与个性化和靶向治疗的相关性。