Advances in spatial transcriptomic data analysis

Abstract

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.

Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue

“…Spatial transcriptomics technologies are evolving at a rapid pace and extending beyond transcriptomics into metabolomics and proteomics ( Lundberg and Borner, 2019 ; Ganesh et al, 2021 ; Yuan et al, 2021 ). The integration of unbiased spatial omics technologies will provide a powerful set of tools to characterize disease processes in intact tissue ( Dries et al, 2021a ). We hope that these technologies will not only develop data rich atlases of healthy and diseased tissues, but also provide a platform for advances in the fundamental understanding of disease mechanisms and highlight new therapeutic targets.…”

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

“…The recent development of computational approaches has created new effective paradigms for analyzing high-dimensional data, e.g., in single-cell RNA-seq (scRNA-seq) research [ 18 ]. Likewise, there has been much progress in the field of method development for spatial transcriptomics data analysis [ 19 , 20 ]. Theoretically, many of the computational approaches developed for scRNA-seq data analysis could be adapted to study spatial transcriptomics data.…”

Recent advances in spatially resolved transcriptomics: challenges and opportunities

“…For example, the tumor microenvironment has remained elusive ( 3 , 4 ). In order to overcome this limitation, transcriptomic techniques that capture the spatial information for tissues of interest have been actively developed ( 5 , 6 ).…”