An introduction to spatial transcriptomics for biomedical research

Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.

Automated summary

Single-cell transcriptomics (scRNA-seq) has become essential in biomedical research, especially in the fields of developmental biology, cancer, immunology, and neuroscience. However, most available protocols require intact and viable cells, limiting the scope of study and destroying spatial context. This review introduces different methods of spatial transcriptomics and discusses their features, such as tissue area, spatial resolution, and gene profiling capabilities. It also provides guidance on platform selection, data analysis, and integration with existing scRNA-seq data, aiming to help researchers utilize spatial transcriptomics in their own biomedical research.