Background Differentiation of lymphocytes is frequently accompanied by cell cycle changes interplay that is of central importance for immunity but is still incompletely understood. discrete cell states which we verify by single-cell quantitative HsRad51 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 VX-770 (Ivacaftor) 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. Conclusion The 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 material The online version of this article (doi:10.1186/s13059-016-0957-5) contains supplementary material which is available to authorized users. for Th2 for Th1 for Th17 and for pTregs) [4] and there is considerable insight into their regulatory networks [5]. While much is known in CD8+ (killer) T cells [6] the expansion of CD4+ (helper) T cells during an infection is less well understood at the cellular and molecular levels. How does the coupling between differentiation and the cell routine occur in Compact disc4+ T cells? Will be the two procedures 3rd party and orthogonal as recommended by Duffy and Hodgkin [7] or connected through molecules and therefore VX-770 (Ivacaftor) intertwined [8]? Will differentiation occur inside a progressive way as recommended by many reports including a recently available single-cell evaluation of lung epithelial advancement [9] or inside a cooperative switch-like way? Here we make use of a new method of tackle these queries which can be to draw out biologically intermediate areas of differentiation from an individual chronological period stage. By sorting out distinct cell populations from an individual cell tradition of asynchronized dividing cells we targeted to lessen the natural variability in cytokine publicity confluence etc. With this process we reduce the biological sound inside our data and concentrate entirely for the processes of cell division and differentiation. We used in-depth transcriptome profiling coupled with bioinformatics data analysis to identify three major cell states during Th2 differentiation. By counting cells in each cell generation using flow cytometry we modelled the rates of death division and differentiation using a discrete time Markov branching process. This revealed a higher cell division rate for differentiated cells compared with proliferating activated cells. We validate those finding by DNA staining and by single-cell live imaging of Th2 cells. These in vitro data supported the idea of a fine-tuned relationship between cell cycle speed and differentiation status in CD4+ T cells. Finally we related our findings from an ex vivo cell culture model of Th2 differentiation to single-cell transcriptomes of Th1 cells from a mouse model of malaria infection. The in vivo cytokine secreting Th1 cells also cycle more quickly than in vivo turned on cells VX-770 (Ivacaftor) displaying the general relevance of our leads to major activation of T cells. Therefore an acceleration of effector Compact disc4+ T cell enlargement upon differentiation is certainly area of the immune system system’s system of pathogen clearance during major activation. Outcomes Cell division-linked differentiation of Th2 cells in vivo and in vitro After antigen excitement from the T-cell receptor [10] na?ve Compact disc4+ T cells start dividing quickly plus some cells start expression of particular cytokines which may be the hallmark of differentiated effector cells. To probe this technique in vivo we isolated and sequenced Compact disc3+/Compact disc4+/Compact disc62L- one cells from spleen and both mediastinal and mesenteric lymph nodes of (Nb)-contaminated mice 5 times post-infection (Fig.?1a). We performed quality control evaluation to be able to remove cells with an unhealthy quality collection (start to see the “Methods” section for details and Additional file 1: Physique S1a) and we retained data from 78 cells. All read statistics are reported in Additional file 2: Table S1. In order to individual the fast cycling cells from VX-770 (Ivacaftor) the slow cycling ones we clustered them according to the expression of cell cycle genes VX-770 (Ivacaftor) (Fig.?1b). We ranked the cells according to the expression of aggregated G2/M genes as a measure of “cell cycle score” thus reflecting the velocity of the.