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• W1817768716 abstract "Recent studies using limiting-dilution strategies or novel cell-tracing techniques have demonstrated that a single naïve CD4+ or CD8+ T lymphocyte can generate a heterogeneous adaptive immune response. Application of emerging single-cell technologies has revealed an early divergence of terminal effector and self-renewing memory lymphocyte fates that can only be observed at the single-cell level. Impaired asymmetric division at the initiation of the adaptive immune response reduces early molecular heterogeneity of CD8+ T lymphocytes, thus altering the balance of effector- and memory-fated precursor cells and reducing differentiation into the memory T lymphocyte fates. Single-cell approaches will be essential to improving our understanding of T lymphocyte fate determination and to advancing vaccination and therapeutic approaches that enhance long-term protective immune responses. Immunological protection against microbial pathogens is dependent on robust generation of functionally diverse T lymphocyte subsets. Upon microbial infection, naïve CD4+ or CD8+ T lymphocytes can give rise to effector- and memory-fated progeny that together mediate a potent immune response. Recent advances in single-cell immunological and genomic profiling technologies have helped elucidate early and late diversification mechanisms that enable the generation of heterogeneity from single T lymphocytes. We discuss these findings here and argue that one such mechanism, asymmetric cell division, creates an early divergence in T lymphocyte fates by giving rise to daughter cells with a propensity towards the terminally differentiated effector or self-renewing memory lineages, with cell-intrinsic and -extrinsic cues from the microenvironment driving the final maturation steps. Immunological protection against microbial pathogens is dependent on robust generation of functionally diverse T lymphocyte subsets. Upon microbial infection, naïve CD4+ or CD8+ T lymphocytes can give rise to effector- and memory-fated progeny that together mediate a potent immune response. Recent advances in single-cell immunological and genomic profiling technologies have helped elucidate early and late diversification mechanisms that enable the generation of heterogeneity from single T lymphocytes. We discuss these findings here and argue that one such mechanism, asymmetric cell division, creates an early divergence in T lymphocyte fates by giving rise to daughter cells with a propensity towards the terminally differentiated effector or self-renewing memory lineages, with cell-intrinsic and -extrinsic cues from the microenvironment driving the final maturation steps. Heterogeneity in T lymphocyte responses to microbial infection is essential for establishing protective immunity. Upon activation, naïve antigen-specific T cells differentiate into two distinct classes of cellular progeny: terminal effector T cells (see Glossary) that mediate acute protection and self-renewing memory cells that provide long-term protective immunity. It is now well appreciated that substantial heterogeneity in effector and memory subsets can arise from a single activated T lymphocyte. However, a fundamental question in the field remains unanswered: how does this cellular diversity arise from a single cell? Various models incorporating the influence of T cell receptor (TCR) signal-strength, environmental cues, and transcriptional changes on T cell differentiation have been proposed and discussed extensively by others (reviewed by Kaech and Cui [1Kaech S.M. Cui W. Transcriptional control of effector and memory CD8+ T cell differentiation.Nat. Rev. Immunol. 2012; 12: 749-761Crossref PubMed Scopus (394) Google Scholar] and discussed by Ahmed et al. [2Ahmed R. et al.The precursors of memory: models and controversies.Nat. Rev. Immunol. 2009; 9: 662-668Crossref PubMed Scopus (92) Google Scholar]). One possibility is that the progeny of an activated T lymphocyte differentiate along a linear pathway, with cells transiting through an equipotent effector phase before a subset of these cells later diverges to form the memory lymphocyte pool [3Ahmed R. Gray D. Immunological memory and protective immunity: understanding their relation.Science. 1996; 272: 54-60Crossref PubMed Google Scholar, 4Buchholz V.R. et al.Disparate individual fates compose robust CD8+ T cell immunity.Science. 2013; 340: 630-635Crossref PubMed Scopus (156) Google Scholar]. This model can be envisaged as one of late divergence, such that diversification into distinct T cell fates does not occur until later stages of the immune response. The signal-strength model proposes that the initial strength of TCR and cytokine signals received during priming dictates whether the progeny of an activated naïve T cell will adopt a terminal effector or memory cell fate. The decreasing-potential model proposes that memory-fated T cells arise as a result of fewer cumulative encounters with antigen and cytokines during the immune response, and are therefore less differentiated than effector-fated T cells. A fourth model is an early divergent model of T lymphocyte diversification whereby a propensity towards terminal effector or self-renewing memory fates can be conferred as early as the first cell division owing to asymmetric division [5Chang J.T. et al.Asymmetric T lymphocyte division in the initiation of adaptive immune responses.Science. 2007; 315: 1687-1691Crossref PubMed Scopus (502) Google Scholar], with continued maturation toward the final cell fates being controlled by a combination of cell-intrinsic and -extrinsic signals. During an asymmetric cell division, important fate determinants and cellular components, including protein, RNA, and organelles, are unequally inherited by the two nascent daughter cells, thus enabling them to adopt distinct fates [6Knoblich J.A. Mechanisms of asymmetric stem cell division.Cell. 2008; 132: 583-597Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar]. Importantly, it should be emphasized that these models are not mutually exclusive, and data supporting one model do not necessarily rule out other models. Differentiation into CD4+ and CD8+ T cell effector and memory fates is driven, in part, by the expression and activities of several transcription factors, as demonstrated by prior investigation of the transcriptional networks and molecular mechanisms that control T lymphocyte fate specification [7Best J.A. et al.Transcriptional insights into the CD8+ T cell response to infection and memory T cell formation.Nat. Immunol. 2013; 14: 404-412Crossref PubMed Scopus (91) Google Scholar, 8Holmes S. et al.Memory T cells have gene expression patterns intermediate between naive and effector.Proc. Natl. Acad. Sci. U.S.A. 2005; 102: 5519-5523Crossref PubMed Scopus (0) Google Scholar, 9Kaech S.M. et al.Molecular and functional profiling of memory CD8 T cell differentiation.Cell. 2002; 111: 837-851Abstract Full Text Full Text PDF PubMed Scopus (652) Google Scholar, 10Liu K. et al.Augmentation in expression of activation-induced genes differentiates memory from naive CD4+ T cells and is a molecular mechanism for enhanced cellular response of memory CD4+ T cells.J. Immunol. 2001; 166: 7335-7344Crossref PubMed Google Scholar, 11Willinger T. et al.Molecular signatures distinguish human central memory from effector memory CD8 T cell subsets.J. Immunol. 2005; 175: 5895-5903Crossref PubMed Google Scholar]. While the traditional microarray approaches used by these studies have provided fundamental insights into the molecular basis and timing in gene expression changes underlying the functional differences in effector and memory CD4+ and CD8+ T cell subsets, it should be noted that these analyses were performed on bulk cell populations. As such, conclusions from these studies have been restricted to the cell population level, and cell-to-cell differences in gene expression may have been hidden as a result of averaged gene expression from seemingly homogeneous populations of cells. Recent technological advances in high-throughput single-cell gene expression profiling, such as microfluidics-based quantitative reverse transcription (qRT)-PCR analyses and RNA sequencing, have been combined with computational analyses to analyze the in vivo transcriptional changes in thousands of single cells in an array of diverse biological systems [12Buganim Y. et al.Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase.Cell. 2012; 150: 1209-1222Abstract Full Text Full Text PDF PubMed Scopus (432) Google Scholar, 13Dalerba P. et al.Single-cell dissection of transcriptional heterogeneity in human colon tumors.Nat. Biotechnol. 2011; 29: 1120-1127Crossref PubMed Scopus (307) Google Scholar, 14Guo G. et al.Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.Dev. Cell. 2010; 18: 675-685Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar, 15Lu R. et al.Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding.Nat. Biotechnol. 2011; 29: 928-933Crossref PubMed Scopus (166) Google Scholar]. Systematic modeling of temporal changes in single-cell transcription pattern dynamics has uncovered substantial heterogeneity within several diverse cell populations, including immune cells [16Arsenio J. et al.Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses.Nat. Immunol. 2014; 15: 365-372Crossref PubMed Scopus (73) Google Scholar, 17Shalek A.K. et al.Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.Nature. 2014; 510: 363-369Crossref PubMed Scopus (267) Google Scholar], murine embryonic tissue [14Guo G. et al.Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.Dev. Cell. 2010; 18: 675-685Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar], human colon tumors [13Dalerba P. et al.Single-cell dissection of transcriptional heterogeneity in human colon tumors.Nat. Biotechnol. 2011; 29: 1120-1127Crossref PubMed Scopus (307) Google Scholar], and primary glioblastomas [18Patel A.P. et al.Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.Science. 2014; 344: 1396-1401Crossref PubMed Scopus (634) Google Scholar]. Moreover, cell-intrinsic fate determinants crucial for driving the formation of cellular diversity have been identified [14Guo G. et al.Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.Dev. Cell. 2010; 18: 675-685Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar, 19Buettner F. et al.Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.Nat. Biotechnol. 2015; 33: 155-160Crossref PubMed Scopus (233) Google Scholar]. For instance, high expression of Id2 and Sox2 has been found to indicate early fate commitment into the outer and inner cell lineages, respectively, during mouse embryogenesis [14Guo G. et al.Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst.Dev. Cell. 2010; 18: 675-685Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar], thus highlighting the importance of dissecting gene expression heterogeneity at the single-cell level. Tracking individual lymphocytes as they progress through the early stages of the immune response has been difficult due to biological and technical constraints, such as the inability to sample adequate endogenous antigen-experienced cell numbers owing to low precursor frequencies of cells specific for a particular antigen (on the order of 10–100) [20Moon J.J. et al.Naive CD4+ T cell frequency varies for different epitopes and predicts repertoire diversity and response magnitude.Immunity. 2007; 27: 203-213Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 21Obar J.J. et al.Endogenous naive CD8+ T cell precursor frequency regulates primary and memory responses to infection.Immunity. 2008; 28: 859-869Abstract Full Text Full Text PDF PubMed Scopus (263) Google Scholar]. Recent advances in magnetic bead-based strategies have enabled the enrichment of antigen-specific T cells at early phases of the immune response, during which these cells are virtually undetectable [20Moon J.J. et al.Naive CD4+ T cell frequency varies for different epitopes and predicts repertoire diversity and response magnitude.Immunity. 2007; 27: 203-213Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Combining the approaches described above has recently made it possible to analyze transcriptional changes in individual T lymphocytes early after microbial infection [16Arsenio J. et al.Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses.Nat. Immunol. 2014; 15: 365-372Crossref PubMed Scopus (73) Google Scholar], thereby providing some initial insights into two fundamental questions: how is T cell diversification achieved and when does this divergence in fates occur? In the following we explore these questions as we discuss recent studies aimed at interrogating the pathways by which single activated T cells differentiate towards effector- and memory-fated lineages. We highlight how asymmetric division is exploited by T lymphocytes to yield robust immune responses, and we draw attention to several gaps in our current understanding of how asymmetric division may shape T lymphocyte diversification. A detailed understanding of how and when T lymphocyte fate specification occurs may have far-reaching implications in the design of vaccination and therapeutic approaches to enhance long-term protective immunity against infectious agents. It is well established that heterogeneity in CD8+ and CD4+ T cell responses is required for robust immunity [22Chang J.T. et al.Molecular regulation of effector and memory T cell differentiation.Nat. Immunol. 2014; 15: 1104-1115Crossref PubMed Scopus (0) Google Scholar]. For the purposes of this review, we will focus on terminal effector CD8+ T cells, long-lived central memory (TCM) and effector memory (TEM) CD8+ T cells, CD4+ T helper type 1 (TH1) cells, and CD4+ follicular helper T (TFH) cells. Pioneering in vivo cell-tracing studies provided the first experimental evidence to support the idea that heterogeneous cellular progeny can be derived from a single activated naïve T cell. Terminal effector (KLRG1hiIL-7Rlo), TEM (CD44hiCD62Llo), and TCM (CD44hiCD62Lhi) CD8+ T lymphocyte subsets were shown to arise from a single TCR transgenic OT-1 CD8+ T cell adoptively transferred into a congenic recipient infected with Listeria monocytogenes expressing ovalbumin (Lm-OVA) [23Stemberger C. et al.A single naive CD8+ T cell precursor can develop into diverse effector and memory subsets.Immunity. 2007; 27: 985-997Abstract Full Text Full Text PDF PubMed Scopus (190) Google Scholar]. The development of ‘DNA-barcode’ technologies, in which DNA sequences (barcodes) are retrovirally introduced into thymocytes, has permitted the generation of naïve T cells harboring genetic tags [24Gerlach C. et al.One naive T cell, multiple fates in CD8+ T cell differentiation.J. Exp. Med. 2010; 207: 1235-1246Crossref PubMed Scopus (88) Google Scholar]. This strategy has allowed a single barcode-labeled naïve T cell and its progeny to be traced following in vivo infection to better understand the developmental histories of individual cells [24Gerlach C. et al.One naive T cell, multiple fates in CD8+ T cell differentiation.J. Exp. Med. 2010; 207: 1235-1246Crossref PubMed Scopus (88) Google Scholar, 25Gerlach C. et al.Heterogeneous differentiation patterns of individual CD8+ T cells.Science. 2013; 340: 635-639Crossref PubMed Scopus (0) Google Scholar]. Applications of limiting-dilution strategies have shown that pathogen-induced environmental cues influence the differentiation path of single activated CD8+ T cells responding to Lm-OVA or vesicular stomatitis virus infection [26Plumlee C.R. et al.Environmental cues dictate the fate of individual CD8+ T cells responding to infection.Immunity. 2013; 39: 347-356Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar], and that diversity derived from single CD4+ T lymphocytes can also be achieved in response to several attenuated Lm strains [27Tubo N.J. et al.Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection.Cell. 2013; 153: 785-796Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar]. In the latter study, single naïve CD4+ T lymphocytes were capable of producing each of the TH1, TFH, and germinal center TFH effector subsets; however, the ratios of these subsets within the generated effector pool were found to be influenced by the peptide:MHCII (type II major histocompatibility complex) dwell times specific to unique TCRs [27Tubo N.J. et al.Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection.Cell. 2013; 153: 785-796Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar]. The aforementioned studies have undoubtedly illustrated the capacity of a single lymphocyte to give rise to differentially fated cellular progeny and have highlighted the influence of TCR avidity, pathogen-specific differences, and the cytokine milieu on the generation of cellular diversity. However, single-cell transfer experiments using limiting-dilution approaches should be interpreted with caution because the adoptive transfer of a single cell is dependent on Poisson probabilities [26Plumlee C.R. et al.Environmental cues dictate the fate of individual CD8+ T cells responding to infection.Immunity. 2013; 39: 347-356Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar], and on the assumption that ∼20% of transferred cells will survive in recipient mice [27Tubo N.J. et al.Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection.Cell. 2013; 153: 785-796Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar]; thus, it is a challenge to guarantee the transfer of an individual cell. Nonetheless, a key question remains unanswered – how do the progeny of a single activated T cell differentiate into effector and memory cells? While the analysis of antigen-specific T cell populations during the early (days 6–8 postinfection) and late (days 30 and later postinfection) phases of the immune response in these studies [23Stemberger C. et al.A single naive CD8+ T cell precursor can develop into diverse effector and memory subsets.Immunity. 2007; 27: 985-997Abstract Full Text Full Text PDF PubMed Scopus (190) Google Scholar, 24Gerlach C. et al.One naive T cell, multiple fates in CD8+ T cell differentiation.J. Exp. Med. 2010; 207: 1235-1246Crossref PubMed Scopus (88) Google Scholar, 26Plumlee C.R. et al.Environmental cues dictate the fate of individual CD8+ T cells responding to infection.Immunity. 2013; 39: 347-356Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 27Tubo N.J. et al.Single naive CD4+ T cells from a diverse repertoire produce different effector cell types during infection.Cell. 2013; 153: 785-796Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar] has informed us that single activated naïve T lymphocytes can give rise to distinct subsets during primary and secondary infections, they do not reveal how or when terminal effector and memory subsets are formed. In recent years, improvements in T cell enrichment techniques along with advances in single-cell genomics approaches and computational modeling analyses have made it possible to begin to dissect how and when single T lymphocytes differentiate into terminal effector and memory subsets. We recently investigated how an activated naïve CD8+ T lymphocyte differentiates in vivo into one of three fates: terminal effector, TCM, and TEM cell [16Arsenio J. et al.Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses.Nat. Immunol. 2014; 15: 365-372Crossref PubMed Scopus (73) Google Scholar]. Single-cell gene expression analyses of activated CD8+ T cells at sequential timepoints following Lm-OVA infection suggested that an early divergent model may be the most likely pathway that underlies T lymphocyte fate specification, at least in this experimental system [16Arsenio J. et al.Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses.Nat. Immunol. 2014; 15: 365-372Crossref PubMed Scopus (73) Google Scholar] (Figure 1A , Key Figure). Unsupervised clustering analysis revealed that single CD8+ T cells exhibit marked molecular heterogeneity during early phases of the immune response (at the first division and at day 3 postinfection), owing to the acquisition of distinct transcriptional programs conferring disparate propensities towards the terminal effector versus memory cell fates. Our findings raised the possibility that T lymphocyte fates may already begin to be specified before the onset of phenotypic differences in cell surface marker expression, such as KLRG1 (killer cell lectin-like receptor subfamily G, member 1) and IL-7R (interleukin-7 receptor), that are observed only at later stages of infection [28Joshi N.S. et al.Inflammation directs memory precursor and short-lived effector CD8+ T cell fates via the graded expression of T-bet transcription factor.Immunity. 2007; 27: 281-295Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. To test this possibility, we applied a hidden Markov model (HMM) approach to capture dynamic changes in gene expression patterns of single activated CD8+ T lymphocytes derived at multiple intermediate timepoints (first division, days 3 and 5 postinfection) between the naïve ‘state’ and a differentiated ‘fate’ (terminal effector, TCM, or TEM cell). Coordinated changes in the transcriptional profiles of antigen-experienced CD8+ T cells were associated with progression towards disparate transitional states and eventual fates. Most notably, high and low expression of Il2ra were predictive of cells that had adopted a pre-terminal effector versus pre-memory path of differentiation, respectively. In support of this observation, CD62LloIL-2Rαhi CD8+ T cells within the population of cells that had undergone their first division in vivo exhibited enhanced production of interferon (IFN)-γ and granzyme B upon ex vivo stimulation. Conversely, first-division CD62LhiIL-2Rαlo cells appeared to represent pre-memory cells because they survived long-term following adoptive transfer into infection-matched recipients and mounted a robust proliferative response to rechallenge. The observation of asymmetric IL-2Rα segregation in CD8+ T lymphocytes undergoing their first division in vivo suggested that asymmetric division might play a role in mediating this early divergence in transcriptional programming. Our recent work characterizing CD8+ T lymphocytes deficient in either isoform of the evolutionarily conserved polarity protein, atypical protein kinase C (aPKC), has provided some of the first evidence suggesting that asymmetric division is an important first step in specifying T lymphocyte fates [29Metz P.J. et al.Regulation of asymmetric division and CD8+ T lymphocyte fate specification by protein kinase Czeta and protein kinase Clambda/iota.J. Immunol. 2015; 194: 2249-2259Crossref PubMed Google Scholar]. The aPKC isoforms – PKCζ and PKCλ/ι – have been identified as important regulators of asymmetry in Drosophila and C. elegans (Box 1), and function during the first division of activated naïve CD8+ T lymphocytes to mediate asymmetric segregation of effector fate-associated factors, including IL-2Rα, IFNγR, and T-bet. Loss of either aPKC isoform increased the symmetric distribution of these effector fate-associated molecules, resulting in a striking reduction in the molecular heterogeneity exhibited by individual cells that had undergone their first division, and increased the proportion of effector-fated precursor cells. Although asymmetric division was only partially reduced, the subsequent alterations to the initial balance of effector- versus memory-fated precursor cells increased differentiation towards the effector T lymphocyte fates at the expense of memory T cell formation [29Metz P.J. et al.Regulation of asymmetric division and CD8+ T lymphocyte fate specification by protein kinase Czeta and protein kinase Clambda/iota.J. Immunol. 2015; 194: 2249-2259Crossref PubMed Google Scholar]. While complete ablation of asymmetric division would be necessary to definitively test the full extent of its role in cell fate specification, the currently available data suggest that asymmetric division, by virtue of excluding important effector fate-associated factors from one of the two nascent daughter cells, enables the simultaneous generation of effector- and memory-fated precursor cells.Box 1Asymmetric Cell Division in Drosophila and C. elegansAsymmetric division has been well studied in Drosophila neuroblasts [96Yamashita Y.M. et al.Polarity in stem cell division: asymmetric stem cell division in tissue homeostasis.Cold Spring Harb. Perspect. Biol. 2010; 2: a001313Crossref PubMed Scopus (0) Google Scholar] and C. elegans embryos, which undergo one or more rounds of asymmetric division to yield self-renewing and terminally-differentiating cell lineages [97Doe C.Q. Bowerman B. Asymmetric cell division: fly neuroblast meets worm zygote.Curr. Opin. 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• W1817768716 created "2016-06-24" @default.
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• W1817768716 date "2015-11-01" @default.
• W1817768716 modified "2023-10-06" @default.
• W1817768716 title "Asymmetric Cell Division in T Lymphocyte Fate Diversification" @default.
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