Single-cell transcriptomics to explore the immune system in health and disease

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Science  06 Oct 2017:
Vol. 358, Issue 6359, pp. 58-63
DOI: 10.1126/science.aan6828


  • Fig. 1 Single-cell genomics in immunology.

    The immune system is well suited to studies at single-cell resolution. Immune responses involve a wide variety of cell types that can be further subdivided into more fine-grained subtypes and distinct cell states that can be followed throughout the duration of a response. Furthermore, single-cell analyses provide insights into intercellular networks and allow us to compare immune responses across individuals and species. HLA, human leukocyte antigen.

  • Fig. 2 Inferring cellular trajectories from single-cell data.

    (A) During a differentiation process, individual cells can be aligned along “pseudotime,” which represents their progression within the differentiation pathway. The processes described in this way can be linear or can involve branches to multiple eventual fates. (B) Examples of biological processes analyzed in terms of cellular trajectories include the progression of stem cells to terminally differentiated fates, the response of naïve immune cells to infection, and the adaptation of circulating immune cells to the tissues where they ultimately reside. (C) A bifurcating pseudotime trajectory inferred from scRNA-seq data generated from a mouse malaria infection model [adapted from (53), reprinted with permission from AAAS, and modified with permission from the authors]. Each point represents an individual cell following dimensionality reduction using a Bayesian Gaussian process latent variable model. These cells are then ordered in pseudotime, and two simultaneous and bifurcating developmental trajectories (red and blue lines) are inferred using overlapping mixtures of Gaussian processes. The color of each point indicates the probability that a cell belongs to either the red or the blue trend.

  • Fig. 3 Single-cell analysis of antigen receptor sequences reveals clone distributions between transcriptional states.

    (A) Independent component analysis of scRNA-seq data from mouse splenic CD4+ T cells during Salmonella infection. Each point represents an individual cell. Shaded areas indicate likely functional identities associated with each region of reduced dimensionality space. Starred points indicate cells that are clonally related and share one particular set of TCR sequences. Clonally related cells are distributed throughout the gene expression space [adapted from (56)]. (B) Cell type assignment of cells with scRNA-seq data from mouse splenic CD4+ T cells at varying time points during malaria infection. Each point represents an individual cell, with y-axis position indicating the likelihood that it is a TH1 cell rather than a TFH cell (high values imply a TH1 identity, low values a TFH identity). Colored points indicate pairs of cells inferred to be clonally related due to shared TCR sequences. Sibling cells can be found such that one is a TH1 cell, whereas the other is a TFH cell [adapted from (53), reprinted with permission from AAAS, and modified with permission from the authors]. (C) Data sets (59, 60) of linked α and β TCR chains provide enough power to allow machine learning inference of common sequence motifs from diverse T cells recognizing the same antigen. This opens the door for the future possibility to more systematically associate TCR sequences with their cognate peptide–MHC sequences. [Figure adapted by the authors from (66)]

  • Fig. 4 Network interactions within the immune system.

    Immune responses involve networks at multiple scales, ranging from intracellular gene regulatory networks to long-distance intercellular communication mediated by cytokines or chemokines. A systems approach to understanding these networks will be crucial if we are to fully understand immune biology and will be accelerated by the application of multiple, different single-cell analysis methods.


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