Single-cell RNA sequencing in immunology


Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of the immune system and now has a wide range of applications in immunology. This technology spans the whole genome and provides an unbiased gene expression profile of individual cells.

Bulk vs single-cell RNA-seq

Traditional bulk RNA-seq is often performed on well-identified groups of cells thought to be homogeneous. However, quantification of molecular changes is made by estimating the mean value from millions of cells and averaging the signal of individual cells, thus ignoring cell-to-cell heterogeneity, which is a hallmark of adaptive immune cell subsets such as B and T lymphocytes.

The need to identify new and discrete immune cell populations and to understand molecular changes that occur at the single cell level has favored the development of low‐input RNA‐seq protocols, that now have a multitude of different applications and come with a bunch of new analysis tools.

Single-Cell RNA-Sequencing Analysis Outline. From Neu et al.

Main applications of scRNA-seq in immunology

Identification of new cell types and functions

By spanning the whole genome in search of unknown molecular markers, scRNA-seq can be used to identify new cell types and functions. While traditional qPCR approaches are sensitive and easy to perform, they require prior knowledge and are based on the measurement of a preselected pool of genes, which introduces bias. Using scRNA-seq technology in the context of immune response to a stimulus (infection, vaccination, autoimmunity) can lead to the identification of new activities and functions. Gene expression and quantification tools originally designed for bulk RNA-seq have now been successfully adopted for scRNA-seq data, including STAR, RSEM and Kallisto.

Characterization of heterogeneous populations

Adaptive immune cells such as B and T lymphocytes use V(D)J recombination to generate a highly diverse repertoire of receptors to recognize antigens. By combining single-cell identification of clonotypes with cell phenotype (eg responsive/autoreactive/anergic), researchers can find strategies to augment or lower specific immune responses. Several tools have now been developed to help you reconstruct full-length T and B cell receptors from scRNA-seq data, such as TraCeR, BASIC, and ImReP.

TCR sequences assembled from scRNA-seq reads during Salmonella infection in mice. From Stubbington et al.

Mapping transition states and cell fate decisions

Immune cell populations arise from precursor cells and go through a succession of checkpoints and states before becoming fully mature and functional. Mapping transition states and cell lineages with scRNA-seq can provide insights into developmental aspects of the immune system in health and disease. Specific tools let you organize individual cells in pseudotime and bifurcating developmental trajectories, such as Monocle and TSCAN.

Bifurcating pseudotime trajectory. From Stubbington et al.

Personalized medicine

In the near future, scRNA-seq could revolutionize the field of personalized medicine in cancer by enabling researchers to identify individual clones and biomarkers in a tumor, and select precision drugs for each of them. Because one particular tumor cell can drive drug resistance or metastasis, scRNA-seq can provide critical information for rapid and personalized treatment. Of particular interest, the ESTIMATE algorithm can be applied to scRNA-seq data to identify the tumor phenotype and the proportion of tumor, immune, or stromal cells.

scRNA-seq applications in cancer medicine. From Shalek and Benson.

Future directions

From flow cytometry to microscopy, the study of the immune system has often relied on technologies that operate at a single-cell resolution. With next-generation sequencing (NGS) technologies becoming cheaper, scRNA-seq will probably be routinely used by researchers in the near future.

Upcoming challenges will include data management and development of integrated multiplex tools to combine transcriptomics with other genomic data.

Based on recent papers:

(Neu et al., 2016) Single-Cell Genomics: Approaches and Utility in Immunology. Trends in Immunology

(Papalexi and Satija, 2017) Single-cell RNA sequencing to explore immune cell heterogeneity. Nature Reviews Immunology

(Shalek and Benson, 2017) Single-cell analyses to tailor treatments. Science Translational Medecine.

(Stubbington et al., 2017) Single-cell transcriptomics
 to explore the immune system in health and disease. Science.