Transcriptome Dynamics Reveals Progressive Transition from Effector to Memory in CD4⁺T cells

Abstract

CD4+ T cells are repositories of immune memory, conferring enhanced immunity to many infectious agents. Studies of acute viral and bacterial infection suggest that memory CD4+ T cells develop directly from effectors. However, delineating these dynamic developmental pathways has been challenging. Here, we used high-resolution single-cell RNA-seq and temporal mixture modelling to examine the fate of Th1 and Tfh effector cells during non-lethal Plasmodium infection in mice. We observed linear Th1 and Tfh pathways towards memory, characterized by progressive halving in the numbers of genes expressed, and partial transcriptomic coalescence. Low-level persisting infection diverted but did not block these pathways. We observed in the Th1-pathway a linear transition from Th1 through a Tr1 state to TEM cells, which were then poised for Th1 re-call. The Tfh-pathway exhibited a modest Th1-signature throughout, with little evidence of Tr1 development, and co-expression of TCM and memory Tfh markers. Thus, we present a high-resolution atlas of transcriptome dynamics for naïve to memory transitions in CD4+ T cells. We also defined a subset of memory-associated genes, including transcription factors Id2 and Maf, whose expression increased progressively against the background of transcriptomic quiescence. Single-cell ATAC-seq revealed substantial heterogeneity in chromatin accessibility in single effectors, which was extensively, though incompletely reset and homogenized in memory. Our data reveal that linear transitions from effector to memory occur in a progressive manner over several weeks, suggesting opportunities for manipulating CD4+ T cell memory after primary infection. Highlights scRNA-seq reveals progressive transition from effector to memory in CD4+ T cells. Transcriptome dynamics suggest linear not branching models for memory development. A subset of genes associates with gradual onset of CD4+ T cell memory. Th1/Tfh predisposition varies among clonotypes with identical antigen-specificity. scATAC-seq uncovers non-coding “memory” elements in the genome.

Using two phases of the CD4 T cell response to blood‐stage murine malaria to understand regulation of systemic immunity and placental pathology in Plasmodium falciparum infection

“…Indeed, these are evident in one of the antibody combinations we have used to evaluate immune phenotype by flow cytometry (CXCR5, T-bet) 36 and using CXCR6, which is not expressed on CXCR5 + cells. 150 . We were able to explore the functional importance of shifts along this spectrum.…”

“…While they consider a third phenotype during peak parasitemia unlikely, they allow for the possibility that the Tfh phenotype defined within the effector phase overlaps significantly with a mitochondrially fueled memory precursor that is protected from death in the contraction phase, which is challenging to detect with this method. 150 In culture, IFN-γ + IL-21 + Th1 cells can be driven towards a Tfh-like phenotype by varying levels of IL-2 in the culture 78,140 . This is interesting, as IL-21 seems to counter-regulate responsiveness to IL-2 135,155 .…”

Immune gene expression changes more during a malaria transmission season than between consecutive seasons.

“…Prior studies have demonstrated development of both a cellular 5 and humoral 6 response to malaria upon repeated exposures. Memory CD4+ T cells specific for Plasmodium blood-stage antigens and skewed towards several T cell phenotypes (e.g., Th1, Tfh, Treg) have been observed 7,8 , but their role in protective antimalarial immunity remains controversial. In mouse models, Th1 cytokine-biased memory cells appear to protect against malaria 9 , but further work is needed to characterize human T cell memory-mediated protection.…”

Dissecting the dynamic transcriptional landscape of early T helper cell differentiation into Th1, Th2, and Th1/2 hybrid cells

“…Individual Th-cell lineages such as Th1 and Th2 cells can be discriminated by expression of the master-regulator transcription factors T-bet and GATA-3 and by production of signature cytokines such as IFN-g and IL-4, respectively (1,2). The differentiation process from naïve Th cells into the various effector cell lineages spans multiple days, and the underlying transcriptional network governing the decision processes changes dynamically throughout differentiation (3,4). The gene-regulatory networks for Th-cell subset-specific differentiation are quite complex and can be modulated by cell-cell interactions (5).…”

“…Nevertheless, assessing the complex interplay of different regulatory elements shaping the phenotypic Th-cell landscape has been exacerbated by the limited availability of kinetic data, which are difficult to obtain experimentally because of small cell numbers occurring in vivo especially at early time points. Indeed, experimental and theoretical studies have underlined the value of time-course information for the quantitative understanding of dynamic processes such as T-cell differentiation (4,(18)(19)(20)(21)(22)(23)(24)(25).…”