Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
Nat Methods. 2022 May;19(5):534-546. doi: 10.1038/s41592-022-01409-2. Epub 2022 Mar 10.
The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .
许多生物系统的功能，如胚胎、肝小叶、肠绒毛和肿瘤，都依赖于其细胞的空间组织。在过去的十年中，已经开发出了高通量技术来定量空间中的基因表达，并且已经开发出了计算方法，利用空间基因表达数据来识别具有空间模式的基因，并描绘组织内的邻域。为了全面记录空间基因表达技术和数据分析方法，我们对回溯到 1987 年的空间转录组学文献进行了精心审查，并对该领域的趋势进行了深入分析，例如实验技术的使用、研究的物种、组织以及使用的计算方法。我们的综述将当前的方法置于历史背景下，并从中得出了一些可以指导当前研究策略的见解。一个补充材料提供了对所分析的技术和方法的更详细的介绍：https://pachterlab.github.io/LP_2021/ 。
