Key Points
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Single-cell RNA sequencing (scRNA-seq) can be used to identify and characterize distinct immune cell subsets in health and disease. The transcriptional signatures of these immune cells enable the identification of novel pathogenic drivers and biomarkers.
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scRNA-seq can be used to identify stochastic variations in gene expression within a single population, which might drive complex immunological responses.
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scRNA-seq can be used for the reconstruction of developmental 'trajectories' to reveal cell fate decisions of distinct cell subpopulations. Branching points at these trajectories bridge transitional cellular states to distinct fate-specific progenitor populations.
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Combining single-cell technologies will allow for more complete profiling of a cell. With emerging technologies, it will become possible to identify the transcriptional state of a cell together with its chromatin accessibility, epigenetic modifications and cellular ancestry.
Abstract
Advances in single-cell RNA sequencing (scRNA-seq) have allowed for comprehensive analysis of the immune system. In this Review, we briefly describe the available scRNA-seq technologies together with their corresponding strengths and weaknesses. We discuss in depth how scRNA-seq can be used to deconvolve immune system heterogeneity by identifying novel distinct immune cell subsets in health and disease, characterizing stochastic heterogeneity within a cell population and building developmental 'trajectories' for immune cells. Finally, we discuss future directions of the field and present integrated approaches to complement molecular information from a single cell with studies of the environment, epigenetic state and cell lineage.
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Acknowledgements
The authors thank members of the Satija and Littman laboratories for helpful discussions and the anonymous referees for insightful critiques. R.S. is supported by a National Institutes of Health Director's New Innovator Award Program (DP2-HG-009623).
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E.P. and R.S. wrote the article and reviewed and edited the manuscript before submission.
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Glossary
- Flow cytometry
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Laser-based technology that allows for simultaneous quantification of the abundance of up to 17 cell surface proteins using fluorescently labelled antibodies.
- Mass cytometry
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(commercial name CyTOF). Mass spectrometry technique used as an alternative to flow cytometry that allows for the quantification of cellular protein levels by using isotopes that overcome problems associated with the spectral overlap of fluorophores.
- Quantitative PCR
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(qPCR). Polymerase chain reaction used to quantify gene expression levels using fluorescently labelled nucleotides and by tracking fluorescence levels during amplification cycles.
- Microfluidic approaches
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Single-cell RNA-sequencing techniques that use microfluidic devices for single-cell isolation.
- Microarrays
-
Technique used to detect gene expression levels of many genes simultaneously. Microarrays use gene-specific probes that can be hybridized to complementary fluorescently labelled cDNA molecules. The fluorescence intensity is used to quantify gene expression.
- Reverse transcription
-
Conversion of a mRNA molecule to complementary DNA (cDNA) using reverse transcriptase enzymes isolated from RNA viruses.
- Barcode
-
A 12–20 nucleotide sequence that is uniquely assigned to a cell during reverse transcription and is used to trace mRNA transcripts back to their cellular origins.
- Reverse emulsions devices
-
Devices that create oil-in-water emulsions, producing droplets that can encapsulate single cells.
- Chromatin immunoprecipitation-sequencing
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(CHIP-seq). A technique that uses crosslinking of protein–DNA interactions and sequencing to identify protein-binding patterns and motifs on DNA.
- cDC1 or cDC2 lineage
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Functionally distinct conventional dendritic cell subgroups characterized by high levels of expression of the surface markers CD8α and CD103 (cDC1) or CD4 and CD11b (cDC2).
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Papalexi, E., Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 18, 35–45 (2018). https://doi.org/10.1038/nri.2017.76
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DOI: https://doi.org/10.1038/nri.2017.76
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