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

IF 5.4 Bioinformatics (Oxford, England) Pub Date : 2025-05-06 DOI:10.1093/bioinformatics/btaf152

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

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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.

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ROICellTrack：一个深度学习框架，用于整合肿瘤组织亚细胞空间转录组分析的细胞成像模式。

动机：空间转录组学（ST）技术，如GeoMx数字空间分析器，越来越多地用于研究不同肿瘤微环境成分的作用，特别是与癌症进展、治疗反应和治疗耐药性有关。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的区域，而不是作为下游转录组数据分析和解释的组成部分。结果：我们开发了ROICellTrack，这是一个基于深度学习的框架，可以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（UCB）和上尿路上皮癌（UTUC）的56个roi， ROICellTrack发现了不同的癌症-免疫细胞混合物，其特征是特异性转录组学和形态学特征以及与肿瘤内容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结合起来分析空间组学数据的价值，提高了我们对肿瘤异质性及其与个性化和靶向治疗的相关性的理解。可用性：ROICellTrack可在https://github.com/wanglab1/ROICellTrack.Supplementary信息网站公开获取；补充数据可在Bioinformatics网站在线获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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