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

Nature Reviews Genetics volume 22, pages 627–644 (2021)Cite this article

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Abstract

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.

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Fig. 1: Adding spatial information to transcriptomes: integration of single-cell and spatial transcriptomics data.
Fig. 2: Common spatial transcriptomics techniques.
Fig. 3: Model workflow integrating scRNA-seq and spatial transcriptomics: the four As.
Fig. 4: Deconvolution and mapping methods.
Fig. 5: Principles used to decode mechanisms of intercellular communication via expression of ligands and receptors in physically proximal cell subpopulations.

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Acknowledgements

The authors thank members of the Khavari, Nolan, Altman, Zou and Lundeberg laboratories for helpful discussions. This work was supported by the US Veterans Affairs Office of Research and Development I01BX00140908, National Institutes of Health, National Cancer Institute (NIH/NCI) CA142635, NIH, National Institute for Arthritis and Musculoskeletal and Skin Diseases (NIH/NIAMS) AR43799 and AR49737 (P.A.K.), and a Physician-Scientist Training Award from the Damon Runyon Cancer Research Foundation (A.L.J.).

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Authors and Affiliations

  1. Program in Epithelial Biology, Stanford University, Stanford, CA, USA

    Sophia K. Longo, Margaret G. Guo, Andrew L. Ji & Paul A. Khavari

  2. Stanford Cancer Institute, Stanford University, Stanford, CA, USA

    Sophia K. Longo, Margaret G. Guo, Andrew L. Ji & Paul A. Khavari

  3. Program in Biomedical Informatics, Stanford University, Stanford, CA, USA

    Margaret G. Guo

  4. Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA

    Paul A. Khavari

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  1. Sophia K. Longo
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Contributions

S.K.L. researched the literature and wrote the article. M.G.G., A.L.J. and P.A.K. contributed substantially to discussions of the content. All authors reviewed and/or edited the manuscript.

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Correspondence to Paul A. Khavari.

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Glossary

Single-cell RNA sequencing

(scRNA-seq). A method that sequences RNA transcripts (primarily mRNA) isolated from each cell of a tissue, thereby characterizing individual cells’ transcriptomes; aggregated data characterize the gene expression distribution of tissue cell subpopulations.

Intercellular communication

Communication between cells, typically through ligand–receptor interaction. Juxtracrine denotes direct cellular contact for signalling molecules to be passed between cells, whereas paracrine refers to diffusion of signalling molecules from a sender to a receiver cell.

Spatial transcriptomics

A method that localizes mRNA transcripts to precise spatial locations (single cells or capture spots, formerly regions) in a tissue. In this Review, the term refers broadly to all spatial transcriptomics methods, not exclusively spatial barcoding.

Bulk RNA-seq

A method that sequences a mixture of RNA transcripts (primarily mRNA) from the whole tissue to generate an average expression level for each gene across all cells sequenced.

High-plex RNA imaging

(HPRI). A targeted spatial transcriptomics method that localizes and quantifies RNA transcripts through multiplexed fluorescent microscopy imaging. Can typically target up to ~100–200 genes simultaneously in intact tissue sections.

Spatial barcoding

Spatial transcriptomic methods that use a microarray of poly-T oligonucleotides to ‘capture’ mRNA transcripts of tissue cross-sections, typically followed by next-generation sequencing.

Coverage

In the context of single-cell or spatial transcrtipomics assays, the number of distinct genes that are represented from captured RNA molecules.

Capture spots

Individual coordinates or capture locations on the microarray used to ‘capture’ mRNA transcripts for spatial barcoding and identified by a DNA barcode; each capture spot generally captures mRNA from multiple cells.

Resolution

In spatial data, the distance between spatial coordinates that identify the source of molecules. For spatial barcoding, higher resolution refers to a smaller distance between capture spot coordinates with smaller capture spot diameter.

Depth

In the context of single-cell or spatial transcriptomics assays, the number of unique RNA molecules captured for a particular gene.

Fluorescent-activated cell sorting

(FACS). A cell sorting technique based on flow cytometry for isolating and identifying different cell types using fluorescent antibodies targeting known cell type-specific cell surface proteins.

Microdissection

A method, such as laser-capture microdissection (LCM), that finely isolates different tissue parts from cryo-sections. LCM-seq is a spatial transcriptomics method that couples LCM with RNA sequencing.

Deconvolution

The process of predicting cell-type proportions at a given spatial barcoding capture spot based on its mRNA mixture. Cell types are typically derived from single-cell RNA sequencing profiles of the tissue.

Pseudo-time analysis

Computational ordering of single cells, namely of single-cell RNA sequencing, based on a gradual evolution of their transcriptomes to measure progress through a given biological process (for example, differentiation or proliferation)

Mapping

The process of assigning a single-cell RNA sequencing (scRNA-seq)-based cell type to each cell spatially resolved by high-plex RNA imaging assays; secondarily, predicting the spatial location of each scRNA-seq cell based on its transcriptome

Statistical regression

In the context of deconvolution, linear regression models are fit to determine to what extent each cell type explains the gene expression values for each capture spot.

Bayesian statistical framework

In the deconvolution context, statistical models that rely on inferences about the distribution of transcripts for each cell type to yield a probability that a mixture of transcripts can be explained by a specific single-cell RNA sequencing cell type.

Maximum a posteriori

A Bayesian estimate of an unknown conditional. In the context of spatial transcriptomics integration methods, the maximum a posteriori estimate most often refers to the cell-type distribution given the gene distribution at a capture spot.

Enrichment

Refers to a particular class of genes that is over-represented in a large set of genes. In the deconvolution and mapping contexts, this class is often the cell type.

Mismatch

Incongruity between cell types detected as present in single-cell RNA sequencing and spatial transcriptomics data that can complicate spatial barcoding deconvolution, but less so high-plex RNA imaging mapping.

Mutual nearest neighbour

A robust algorithm for clustering single cells between two different single-cell transcriptomic data sets (that is, single-cell RNA sequencing with high-plex RNA imaging) based on the cell subtype.

Dimensionality reduction

A mathematical technique to represent high-dimensional data in lower dimensions. Mostly used in transcriptomic analysis for visualization of cell subtype clustering in 2D, sometimes 3D, space to establish subtypes.

Non-negative matrix factorization

A method commonly used in bioinformatics for dimensionality reduction of gene expression data as the non-negativity constraint reflects that genes are either expressed or not and cannot be negatively expressed.

Factor loading

In the context of transcriptomics, correlation coefficients between known gene transcript levels and latent cell subtype information. Can be plotted in low-dimensional space prior to joint clustering of single-cell RNA sequencing and high-plex RNA imaging data.

Impute

In the context of spatial transcriptomics, the computational process of determining unknown gene expression values of spatially resolved single cells (high-plex RNA imaging) with corresponding values from mapped single-cell RNA sequencing data.

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Longo, S.K., Guo, M.G., Ji, A.L. et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 22, 627–644 (2021). https://doi.org/10.1038/s41576-021-00370-8

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