Patrick J. Metz

Early transcriptional and epigenetic regulation of CD8+ T cell differentiation revealed by single-cell RNA sequencing

During microbial infection, responding CD8+ T lymphocytes differentiate into heterogeneous subsets that together provide immediate and durable protection. To elucidate the dynamic transcriptional changes that underlie this process, we applied a single-cell RNA-sequencing approach and analyzed individual CD8+ T lymphocytes sequentially throughout the course of a viral infection in vivo. Our analyses revealed a striking transcriptional divergence among cells that had undergone their first division and identified previously unknown molecular determinants that controlled the fate specification of CD8+ T lymphocytes. Our findings suggest a model for the differentiation of terminal effector cells initiated by an early burst of transcriptional activity and subsequently refined by epigenetic silencing of transcripts associated with memory lymphocytes, which highlights the power and necessity of single-cell approaches.

Asymmetric Cell Division in T Lymphocyte Fate Diversification.

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.

Regulation of Asymmetric Division and CD8+ T Lymphocyte Fate Specification by Protein Kinase Cζ and Protein Kinase Cλ/ι

During an immune response against a microbial pathogen, activated naive T lymphocytes give rise to effector cells that provide acute host defense and memory cells that provide long-lived immunity. It has been shown that T lymphocytes can undergo asymmetric division, enabling the daughter cells to inherit unequal amounts of fate-determining proteins and thereby acquire distinct fates from their inception. In this study, we show that the absence of the atypical protein kinase C (PKC) isoforms, PKCζ and PKCλ/ι, disrupts asymmetric CD8+ T lymphocyte division. These alterations were associated with aberrant acquisition of a pre-effector transcriptional program, detected by single-cell gene expression analyses, in lymphocytes that had undergone their first division in vivo and enhanced differentiation toward effector fates at the expense of memory fates. Together, these results demonstrate a role for atypical PKC in regulating asymmetric division and the specification of divergent CD8+ T lymphocyte fates early during an immune response.

Downregulation of 26S proteasome catalytic activity promotes epithelial-mesenchymal transition

The epithelial-mesenchymal transition (EMT) endows carcinoma cells with phenotypic plasticity that can facilitate the formation of cancer stem cells (CSCs) and contribute to the metastatic cascade. While there is substantial support for the role of EMT in driving cancer cell dissemination, less is known about the intracellular molecular mechanisms that govern formation of CSCs via EMT. Here we show that β2 and β5 proteasome subunit activity is downregulated during EMT in immortalized human mammary epithelial cells. Moreover, selective proteasome inhibition enabled mammary epithelial cells to acquire certain morphologic and functional characteristics reminiscent of cancer stem cells, including CD44 expression, self-renewal, and tumor formation. Transcriptomic analyses suggested that proteasome-inhibited cells share gene expression signatures with cells that have undergone EMT, in part, through modulation of the TGF-β signaling pathway. These findings suggest that selective downregulation of proteasome activity in mammary epithelial cells can initiate the EMT program and acquisition of a cancer stem cell-like phenotype. As proteasome inhibitors become increasingly used in cancer treatment, our findings highlight a potential risk of these therapeutic strategies and suggest a possible mechanism by which carcinoma cells may escape from proteasome inhibitor-based therapy.

Ubiquitylation of CD98 limits cell proliferation and clonal expansion

ABSTRACT CD98 heavy chain (SLC3A2) facilitates lymphocyte clonal expansion that enables adaptive immunity; however, increased expression of CD98 is also a feature of both lymphomas and leukemias and represents a potential therapeutic target in these diseases. CD98 is transcriptionally regulated and ectopic expression of the membrane-associated RING-CH (MARCH) E3 ubiquitin ligases MARCH1 or MARCH8 leads to ubiquitylation and lysosomal degradation of CD98. Here, we examined the potential role of ubiquitylation in regulating CD98 expression and cell proliferation. We report that blocking ubiquitylation by use of a catalytically inactive MARCH or by creating a ubiquitylation-resistant CD98 mutant, prevents MARCH-induced CD98 downregulation in HeLa cells. March1-null T cells display increased CD98 expression. Similarly, T cells expressing ubiquitylation-resistant CD98 manifest increased proliferation in vitro and clonal expansion in vivo. Thus, ubiquitylation and the resulting downregulation of CD98 can limit cell proliferation and clonal expansion. Summary: Ubiquitylation of CD98 regulates its expression, and is a mechanism to limit the clonal expansion of T lymphocytes and potentially of other normal and transformed cells.

Talin Plays a Critical Role in the Maintenance of the Regulatory T Cell Pool.

Talin, a cytoskeletal protein essential in mediating integrin activation, has been previously shown to be involved in the regulation of T cell proliferation and function. In this study, we describe a role for talin in maintaining the homeostasis and survival of the regulatory T (Treg) cell pool. T cell–specific deletion of talin in Tln1fl/flCd4Cre mice resulted in spontaneous lymphocyte activation, primarily due to numerical and functional deficiencies of Treg cells in the periphery. Peripheral talin-deficient Treg cells were unable to maintain high expression of IL-2Rα, resulting in impaired IL-2 signaling and ultimately leading to increased apoptosis through downregulation of prosurvival proteins Bcl-2 and Mcl-1. The requirement for talin in maintaining high IL-2Rα expression by Treg cells was due, in part, to integrin LFA-1–mediated interactions between Treg cells and dendritic cells. Collectively, our data suggest a critical role for talin in Treg cell–mediated maintenance of immune homeostasis.

Reply to: "CD8 + T cell diversification by asymmetric cell division"

The goal of our recent work1 was to trace the transcriptional ‘roadmap’ of individual CD8+ T lymphocytes as they differentiated in vivo from the naive state into one of three ‘fates’: terminally differentiated, short-lived effector (TSLE) cells (KLRG1hiIL-7Rlo), central memory (TCM) cells (CD44hiCD62Lhi) and effector memory (TEM) cells (CD44hiCD62Llo). We performed single-cell gene expression analyses on individual CD8+ T lymphocytes isolated at multiple time points (naive, cell division 1, day 3, day 5, day 7 TSLE, day 45 TEM, day 45 TCM) following microbial infection in vivo. This work suggested that an early divergence in lymphocyte fates may occur during an immune response to microbial infection. We disagree with the statement by Flossdorf et al. that we did not demonstrate how well the proposed early divergent model described the experimental data2. Single-cell gene expression measurements from cells representing all time points sampled were first incorporated into six linear and twelve bifurcative Hidden Markov Models (HMMs). We then tested how well each of these 18 possible models described the experimental data by calculating the cumulative distribution functions (CDF) of the log-likelihoods for each model. An alternative to calculating the CDF of the log-likelihoods for each model would have been to report the maximum log-likelihood of each model. However, the maximum log-likelihood value can be affected by the high degree of measurement noise that can occur in single-cell data. We therefore elected to use a ‘bootstrap’ technique commonly employed in modern statistics3 which resamples the dataset with replacement multiple times in order to assess the robustness of each model. Using this bootstrap technique, we calculated the CDFs of the log-likelihoods for each of the 18 models we tested, identifying an early divergent model as the one that best described the experimental data (Fig. 5b and Supplementary Fig. 4c in ref. 1). In this model, an activated CD8+ T lymphocyte gives rise to pre-TSLE or pre-memory cells; pre-TSLE cells subsequently undergo further differentiation to acquire the TSLE fate, whereas pre-memory cells further diverge to give rise to TCM or TEM cells. We further disagree with the contention by Flossdorf et al. that we did not demonstrate that the best model described the data significantly better than alternative models2. We compared the CDFs of the log-likelihoods for each of the 17 alternative models to the best model in a pairwise fashion. The significance of each of these pairwise comparisons was tested using the 2-sample Kolmogorov-Smirnov test, a commonly used, non-parametric, goodness-of-fit test. These analyses demonstrated that the best model described the data significantly better than alternative models (Supplementary Fig. 4c in ref. 1). Moreover, as a negative control, we showed that for all models tested, scrambled data performed no better than the bootstrap samples from actual data. While most of the alternative models had a higher log-likelihood on the experimental data than on the scrambled data, some did not. Notably, these alternative models, specifically sequential models that ordered memory and pre-memory states before the terminal effector and pre-effector states, were least consistent with the experimental data. Flossdorf et al. also argue that HMM is not a suitable analysis approach because it does not take into account potential differences in proliferative rates between pre-TSLE and pre-memory cells2. While the HMM does not explicitly model the different proliferative activities of the developing subsets, the training data we used to construct the HMM most certainly does. Different proliferative rates exhibited by pre-memory and pre-TSLE cell subsets would result in different frequencies of these cells within the day 3 and day 5 populations used for constructing the HMM. In other words, because pre-TSLE cells proliferate more, these cells would be found at higher frequencies within the day 3 and day 5 populations and therefore be more highly represented in the HMM. Thus, the HMM approach implicitly takes into account the different proliferative rates of the developing subsets. Flossdorf et al. suggest that an alternative computational analysis approach, Monocle4, is superior to our HMM because it does not assign cells to particular subsets and therefore makes inferences directly from their gene expression profiles. It should be clarified, however, that in our study, only cells at the beginning (naive T cells) or end (TSLE, TCM, or TEM cells) of the differentiation process were actually given ‘assignments.’ The unknown processes that the HMM was designed to reveal (i.e., the pathways by which a cell differentiates from the naive state to one of the three fates) were traced using gene expression data from cells with no labels other than the time after infection at which they were isolated (cell division 1, day 3, day 5, day 7). Thus, like Monocle, HMM can infer the path between the naive state to each one of the final cell fates on the basis of gene expression data alone without being biased by any cell labels or assignments. Flossdorf et al. re-analyzed our day 5 data using Monocle and failed to detect evidence of a bifurcation2. It is likely that Flossdorf et al. did not arrive at the same conclusion we did for several reasons. First, they used data only from a single time point, day 5 post-infection, rather than including data from all of the other intermediate time points that we used for our analysis. Second, selection of this particular time point may have been too late to discern the divergence in fate specification. In our study, both the principal component (Fig. 3 in ref. 1) and HMM (Fig. 5 in ref. 1) analyses suggested an early bifurcation in cell fate specification, which we confirmed experimentally in cells that had undergone their first division (Fig. 6 in reference 1). In a second analysis using Monocle, Flossdorf et al. analyzed data from naive, day 7 TSLE, day 45 TCM and day 45 TEM cells from our study, and failed to discern evidence of a bifurcation between memory (TCM and TEM) and terminal effector (TSLE) subsets2. However, the use of gene expression data solely from cells representing the ‘fully differentiated’ final fates, and not from cells representing the intermediate states, would seem to be inadequate to discern possible pathways of differentiation between the naive state and final fates. Lastly, Flossdorf et al. re-analyzed their published data5 and did not reach the same conclusions that we did in our study. Flossdorf et al. defined the starting state as naive and the final fates as day 8 CD27hi cells and day 8 CD27lo cells. They assayed protein expression of two markers, CD62L and CD27, by CD8+ T cells at a single time point, day 8 post-infection, and applied mathematical modeling to ascertain the differentiation pathways from a naive cell to day 8 CD27hi cells or day 8 CD27lo cells. Importantly, there were several differences in the aim and experimental design of the two studies that are likely to account for the discrepant conclusions obtained (summarized in Table 1). First, we defined the final fates as day 7 KLRG1hiIL-7Rαlo TSLE cells, day 45 CD44hiCD62Lhi TCM cells and day 45 CD44hiCD62Llo TEM cells. We then isolated cells from multiple intermediate time points without labeling them on the basis of surface markers and thus avoided making a priori assumptions about the identity of ‘pre-memory’ or ‘pre-effector’ T cells. Notably, in contrast to our study, no cells derived from intermediate states of differentiation were included in the analysis by Flossdorf et al (ref. 2). Moreover, we note that because our experimental data suggested that a divergence in cell fate occurs during the first cell division (Fig. 6 in ref. 1), evidence of a bifurcation may not have been detectable using only data obtained from cells isolated at a single late time point, day 8 post-infection, 7 days after the proposed bifurcation is thought to occur. Thus, important differences in the goals, assumptions and parameters analyzed are likely to account for the differences in the conclusions reached by each study. Table 1 Comparison of aim and experimental design of studies by Arsenio et al. and Buchholz et al. In summary, the correspondence by Flossdorf et al. contends that our recently published work1 omitted key computational and functional validations of the model put forth by our experimental data; analysis of our dataset using an alternative computational approach yielded different conclusions; and re-analysis of their previously published dataset failed to recapitulate our findings. In this response, we demonstrate that these contentions lack merit, as the requested computational and functional validations were already included in the published paper; only a selected subset of our dataset was used for their analysis, making a direct comparison impossible; and substantial differences in the goals, designs and parameters analyzed by the two studies are likely to account for the discrepant conclusions drawn. Nonetheless, the points raised serve to highlight the utility and importance of single-cell approaches and make a compelling argument for their use in future studies of T lymphocyte fate specification.

Regulation of Asymmetric Division by Atypical Protein Kinase C Influences Early Specification of CD8+ T Lymphocyte Fates

Naïve CD8+ T lymphocytes responding to microbial pathogens give rise to effector T cells that provide acute defense and memory T cells that provide long-lived immunity. Upon activation, CD8+ T lymphocytes can undergo asymmetric division, unequally distributing factors to the nascent daughter cells that influence their eventual fate towards the effector or memory lineages. Individual loss of either atypical protein kinase C (aPKC) isoform, PKCζ or PKCλ/ι, partially impairs asymmetric divisions and increases CD8+ T lymphocyte differentiation toward a long-lived effector fate at the expense of memory T cell formation. Here, we show that deletion of both aPKC isoforms resulted in a deficit in asymmetric divisions, increasing the proportion of daughter cells that inherit high amounts of effector fate-associated molecules, IL-2Rα, T-bet, IFNγR, and interferon regulatory factor 4 (IRF4). However, unlike CD8+ T cells deficient in only one aPKC isoform, complete loss of aPKC unexpectedly increased CD8+ T cell differentiation toward a short-lived, terminal effector fate, as evidenced by increased rates of apoptosis and decreased expression of Eomes and Bcl2 early during the immune response. Together, these results provide evidence for an important role for asymmetric division in CD8+ T lymphocyte fate specification by regulating the balance between effector and memory precursors at the initiation of the adaptive immune response.

Integrin Activation Controls Regulatory T Cell–Mediated Peripheral Tolerance

Maintenance of the regulatory T (Treg) cell pool is essential for peripheral tolerance and prevention of autoimmunity. Integrins, heterodimeric transmembrane proteins consisting of α and β subunits that mediate cell-to-cell and cell-to-extracellular matrix interactions, play an important role in facilitating Treg cell contact–mediated suppression. In this article, we show that integrin activation plays an essential, previously unappreciated role in maintaining murine Treg cell function. Treg cell–specific loss of talin, a β integrin–binding protein, or expression of talin(L325R), a mutant that selectively abrogates integrin activation, resulted in lethal systemic autoimmunity. This dysfunction could be attributed, in part, to a global dysregulation of the Treg cell transcriptome. Activation of integrin α4β1 led to increased suppressive capacity of the Treg cell pool, suggesting that modulating integrin activation on Treg cells may be a useful therapeutic strategy for autoimmune and inflammatory disorders. Taken together, these results reveal a critical role for integrin-mediated signals in controlling peripheral tolerance by virtue of maintaining Treg cell function.