Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Combining single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics can localize transcriptionally characterized single cells within their native tissue context. This Review discusses methodologies and tools to integrate scRNA-seq with spatial transcriptomics approaches, and illustrates the types of insights that can be gained. Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

Automated summary

Integrating single-cell RNA sequencing with spatial transcriptomics allows for the localization of transcriptionally characterized single cells within their native tissue context. While scRNA-seq identifies cell subpopulations within tissue, it does not capture their spatial distribution or reveal local networks of intercellular communication. Recent techniques like multiplexed in situ hybridization and in situ sequencing, defined as high-plex RNA imaging, can help address these limitations. However, the need for approaches to integrate single-cell and spatial data remains, given that no current method provides as complete a scope of the transcriptome as scRNA-seq.