Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation

Abstract

BackgroundDifferentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells.ResultsWe perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing.ConclusionThe link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0957-5) contains supplementary material, which is available to authorized users.

Diverse continuum of CD4+T-cell states is determined by hierarchical additive integration of cytokine signals

“…We found that cell populations respond to cytokine combinations in a way that is additive in nature, such that the mean response to a combination of cytokines equals the sum of the responses to the individual cytokines. Additive signal integration was observed in other studies of cellular responses (45,(54)(55)(56)(57)(58). We show that, in the case of CD4 + T-cell differentiation, however, there is a hierarchy that modulates the summation of signals: some inputs are more dominant, and if present, they modify the coefficients of the additive model.…”

“…Previous models described differentiation of Th cells toward a specific fate (55)(56)(57) or focused on key molecular components (38,(58)(59)(60). Other models describe Th differentiation at a higher level of detail by modeling regulatory networks of increasing complexity (61)(62)(63)(64).…”

Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis

“…The functional diversity of natural killer T (NKT) cells is difficult to characterize using cytometry alone; however, single cell RNA-seq analysis revealed differential patterns of gene expression that resolve NKT subsets and indicate potential functions [66]. Single cell transcriptomic profiling is also particularly useful for understanding T cell differentiation and proliferation, as the expression of key transcription factors and other regulatory genes can be easily ascertained and used to assign cells to differentiation trajectories [67,68]. …”

Writ large: Genomic dissection of the effect of cellular environment on immune response

“…While these studies profiled cell populations defined by cell surface, cytokine or TF expression, recent single cell genomic studies (6, 43) have increased the resolution at which we characterize cellular populations and their fluidity. For example, Th17 cells were shown to span a continuum of states, from higher expression of a program associated with pathogenic effect to one characteristic of regulatory cells, with distinct regulators for each program (6).…”