Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria

By Stuti Mehta, D. Cronkite, M. Basavappa, Tahnee L. Saunders, Fatemeh Adiliaghdam, Hajera Amatullah, Sara A. Morrison, J. Pagan, R. Anthony, P. Tonnerre and 15 more. Science immunology, 2017

357 citations recorded by the source corpus.

Computational modeling defines T helper cell differentiation toward multiple fates during experimental malaria. Fork in the road for immune cells Immune cell differentiation along T helper pathways can profoundly influence the nature of the immune response—from promoting allergy to enhancing inflammation. Lönnberg et al. use single-cell transcriptomics and computational modeling to delineate the molecular cues that guide this decision. During blood-stage Plasmodium infection in mice, the authors track TH1/TFH bifurcation at both the population and single-clone levels and identify genes associated with each path. They demonstrate roles of particular cell types in shaping this decision. This approach provides a broad framework for modeling immune cell differentiation in vivo. Differentiation of naïve CD4+ T cells into functionally distinct T helper (TH) subsets is crucial for the orchestration of immune responses. Because of extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis with a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of TH1 and TFH (T follicular helper) cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous T cell receptor sequences, we first demonstrated that TH1/TFH bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with TH1 or TFH fates and demonstrated a T cell–intrinsic role for Galectin-1 in supporting TH1 differentiation. We also revealed the close molecular relationship between TH1 and interleukin-10–producing Tr1 cells in this infection. TH1 and TFH fates emerged from a highly proliferative precursor that up-regulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell interaction in driving TH1/TFH fates. In particular, we found that precursor TH cells were coached toward a TH1 but not a TFH fate by inflammatory monocytes. Thus, by integrating genomic and computational approaches, our study has provided two unique resources: a database, www.PlasmoTH.org, which facilitates discovery of novel factors controlling TH1/TFH fate commitment, and, more generally, GPfates, a modeling framework for characterizing cell differentiation toward multiple fates.

Subfields: Genetics, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/76fc180948382684b864f0bc90a406557046f23b

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Citations in the corpus

The Human Cell Atlas (2017, background): “…be inferred from single-cell RNA-seq data (Figure 3) (Angerer et al., 2016; Bendall et al., 2014; Chen et al., 2016b; Haghverdi et al., 2015; Haghverdi et al., 2016; Lönnberg et al., 2017; Marco et al., 2014; Moignard et al., 2015; Setty et al., 2016; Trapnell et al., 2014; Treutlein et al., 2016).”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, background): “For example, in recent studies of murine T-cell responses in vivo, this issue was circumvented by employing transgenic T-cell receptor cells that expressed the same antigen receptor [24, 61].”
Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming (2019, unknown): “: This method (Lönnberg et al., 2017) identifies trajectories by fitting a mixture of Gaussian processes to model a set of branching trajectories over time.”