Department of Population and Data Sciences at University of Texas Southwestern Medical Center.
Department of Mathematics Sciences at University of Texas at Dallas.
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa145.
Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.
分子剖析技术，如基因组测序和蛋白质组学，已经改变了生物医学研究，但大多数此类技术需要组织解离，这会导致组织形态和空间信息的丢失。近年来，空间分子剖析技术的发展使得在保持细胞空间和形态学背景完整的情况下，对细胞进行全面的分子特征描述成为可能。分子剖析数据对细胞的遗传、转录和蛋白质组学事件进行了深入的描述，而组织图像则捕获了细胞的空间位置、组织和相互作用，以及它们的形态特征。这些数据，连同细胞和组织成像数据，为研究组织异质性和细胞空间组织提供了前所未有的机会。本综述旨在概述这些空间分子剖析技术的最新进展，以及为分析这些数据而开发的相应计算方法。
