Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA.
Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA.
Genome Res. 2021 Oct;31(10):1706-1718. doi: 10.1101/gr.275224.121.
Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
空间转录组学是一个快速发展的领域，有望全面描述单细胞或亚细胞分辨率下的组织组织和结构。这些信息为深入了解健康和疾病状态下的许多生物学过程提供了坚实的基础，而这些过程是传统技术无法获得的。计算方法的发展在从原始数据中提取生物信号方面发挥了重要作用。已经开发了各种方法来克服特定技术的限制，如空间分辨率、基因覆盖率、灵敏度和技术偏差。下游分析工具将空间组织和细胞间通讯表述为可量化的特性，并提供用于推导这些特性的算法。整合管道进一步将多个工具集成到一个包中，使生物学家能够方便地从头到尾分析数据。在这篇综述中，我们总结了空间转录组数据分析方法和管道的最新进展，并讨论了它们在不同技术平台上的工作原理。
