Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics.
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.
1. Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets.
2. Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data.
3. Giotto: a toolbox for integrative analysis and visualization of spatial expression data.
4. Inferring spatial and signaling relationships between cells from single cell transcriptomic data.
5. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas.
1. ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes
2. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas
3. High-density spatial transcriptomics arrays for in situ tissue profiling
4. Spatial reconstruction of single-cell gene expression
5. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations