Cheng Xiaojie, Tang Chen, Dong Kejing, You Yuzhou, Zhao Xueying, Duan Bin, Chen Shaoqi, Chuai Guohui, Zhang Zhenbo, Liu Qi
Department of Hematology, Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People's Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Reproductive Medicine Center, Department of Obstetrics and Gynecology, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200065, China.
Department of Hematology, Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-like Intelligence, Shanghai Fourth People's Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
Cell Genom. 2025 Jul 9;5(7):100893. doi: 10.1016/j.xgen.2025.100893. Epub 2025 Jun 5.
The emergence of spatial transcriptomics (ST) technology offers unprecedented opportunities to elucidate the complexity and heterogeneity of the tumor microenvironment (TME). However, quantitatively linking spatially resolved features with clinical phenotypes remains challenging due to the scarcity of clinical annotations of spatial sequencing samples. Herein, we introduce SpaLinker, an innovative integrated framework that utilizes ST data to decipher spatially resolved TMEs at molecular, cellular, and tissue structure levels. Specifically, it assesses the prognostic significance of spatially defined features by integrating well-accumulated bulk RNA sequencing (RNA-seq) data, using a phenotype-driven computational framework. Applying SpaLinker to diverse tumor ST datasets demonstrated its utility and effectiveness in recognizing spatial architectures, including tertiary lymphoid structures and tumor-normal interfaces, and in establishing links to distinct clinical outcomes. Overall, this study presents a valuable and comprehensive pan-cancer analytical platform to de novo identify phenotype-associated spatial TME features, significantly enhancing the clinical utility of spatial sequencing technology.
空间转录组学（ST）技术的出现为阐明肿瘤微环境（TME）的复杂性和异质性提供了前所未有的机会。然而，由于空间测序样本的临床注释稀缺，将空间分辨特征与临床表型进行定量关联仍然具有挑战性。在此，我们介绍了SpaLinker，这是一个创新的综合框架，利用ST数据在分子、细胞和组织结构水平上解析空间分辨的TME。具体而言，它通过使用表型驱动的计算框架，整合积累丰富的批量RNA测序（RNA-seq）数据，评估空间定义特征的预后意义。将SpaLinker应用于不同的肿瘤ST数据集，证明了其在识别空间结构（包括三级淋巴结构和肿瘤-正常界面）以及建立与不同临床结果的联系方面的实用性和有效性。总体而言，本研究提出了一个有价值且全面的泛癌分析平台，用于从头识别与表型相关的空间TME特征，显著提高了空间测序技术的临床实用性。
