Adaptive immunity relies on the generation and maintenance of memory T cells to provide protection against repeated antigen exposure. It has been hypothesised that a self-renewing population of T cells, named stem cell–like memory T (TSCM) cells, are responsible for maintaining memory. However, it is not clear if the dynamics of TSCM cells in vivo are compatible with this hypothesis. To address this issue, we investigated the dynamics of TSCM cells under physiological conditions in humans in vivo using a multidisciplinary approach that combines mathematical modelling, stable isotope labelling, telomere length analysis, and cross-sectional data from vaccine recipients. We show that, unexpectedly, the average longevity of a TSCM clone is very short (half-life < 1 year, degree of self-renewal = 430 days): far too short to constitute a stem cell population. However, we also find that the TSCM population is comprised of at least 2 kinetically distinct subpopulations that turn over at different rates. Whilst one subpopulation is rapidly replaced (half-life = 5 months) and explains the rapid average turnover of the bulk TSCM population, the half-life of the other TSCM subpopulation is approximately 9 years, consistent with the longevity of the recall response. We also show that this latter population exhibited a high degree of self-renewal, with a cell residing without dying or differentiating for 15% of our lifetime. Finally, although small, the population was not subject to excessive stochasticity. We conclude that the majority of TSCM cells are not stem cell–like but that there is a subpopulation of TSCM cells whose dynamics are compatible with their putative role in the maintenance of T cell memory.
The maintenance of long-lived T cell memory is one of the hallmarks of adaptive immunity [1,2]. Multiple studies have shown that the recall response to a previously encountered antigen has a half-life of the order of decades [3,4]. It has been hypothesised that this T cell memory is dynamically maintained by differentiation of a precursor stem cell–like memory population [5]. Alternative, nonexclusive explanations include replacement by proliferation of differentiated memory T cells or the existence of a putative subpopulation of long-lived memory T cells that has not yet been identified, either because such cells are very rare or because they reside primarily outside of the peripheral blood [6–9]. Central memory T (TCM) cells (CD45RA―CCR7+ in humans) were previously thought to constitute the stem cell–like memory precursor population. Evidence supporting the ‘stemness’ of TCM cells includes their capacity to differentiate into effector memory T (TEM) cells and T effector (TEFF) cells [10,11]. This hypothesis was further strengthened by cell fate–tracking experiments in mice (using genetic barcoding and single-cell transfer), showing that TCM cells had the capacity to self-renew and that a single TCM cell could reconstitute immune protection against an otherwise lethal pathogen [12,13]. However, the concept of TCM as the stem cell population has been challenged by the identification of ‘stem cell–like’ memory T (TSCM) cells—which have enhanced stem cell–like properties compared to TCM cells—in mice [14], nonhuman primates [15], and humans [16]. In humans, like naïve cells, TSCM cells are CD45RA+CD45RO―, and they express high levels of CD27, CD28, interleukin 7 receptor alpha (IL-7Rα), CD62L, and C-C chemokine receptor 7 (CCR7). Unlike naïve cells, TSCM cells are clonally expanded and express the memory markers CD95 and CD122 [1,16]. TSCM cells exhibit enhanced proliferative capacity compared to TCM cells, the potential to differentiate into all other classically defined T cell memory subsets (including TCM), and the ability to retain their phenotype following proliferation both in vitro and in mice in vivo [1,14–16]. In light of these observations, it has been suggested that TSCM cells are the main stem cell memory population and play a key role in maintaining long-term memory in vivo [15–18]. There are 3 basic prerequisites for T cell memory stemness: multipotency, self-renewal, and clonal longevity. In this study, we focus on the related dynamic properties of self-renewal and clonal longevity. Self-renewal of human TSCM cells has been demonstrated in vitro [19], but it remains a concern that the local microenvironment, which may crucially affect the degree of self-renewal, will be different in vivo and in vitro. However, proving self-renewal of human TSCM cells in vivo has so far not been possible because of ethical and technical limitations. The second property we investigate is clonal longevity. Long-lived T cell memory requires that memory T cell clonotypes expressing the same T cell receptor (TCR) persist for several decades in vivo. For example, influenza immunity has been shown to last for several decades [20], and small pox vaccine–induced T cell memory has a half-life of 8–15 years [3,4]. For TSCM cells to constitute a potential precursor population for T cell memory in vivo, the survival of TSCM clones needs to be consistent with those estimates. A number of studies suggest that TSCM clones can survive for several years. Biasco and colleagues [17] observed that genetically engineered TSCM cells could persist for many years in patients suffering from severe combined immunodeficiency disease. Fuertes Marraco and colleagues [21] identified a yellow fever virus (YFV) -specific TSCM population up to 25 years after vaccination. Finally, in leukaemia patients who had undergone haematopoietic stem cell (HSC) transplantation, Oliveira and colleagues [22] reported that gene-modified TSCM cells could be detected in the circulation up to 14 years after treatment. These studies support the concept of TSCM longevity, albeit in scenarios of lymphocyte depletion or profound CD8+ T cell expansion. However, it has been shown that the dynamics of posttransplant haematopoiesis in mice differs significantly from normal, unperturbed haematopoiesis [23–25], and so it cannot be assumed that these transplantation studies in humans necessarily recapitulate the healthy human system. In short, TSCM longevity has not been quantified in normal, unperturbed homeostasis in humans, and the related question of the ability of TSCM to self-renew has not been addressed in any human in vivo system. In order to investigate human TSCM cells in homeostasis, we previously performed stable isotope labelling of healthy volunteers and analysed label uptake in CD4+ and CD8+, naïve T and TSCM cells. We found that the TSCM population was rapidly turning over (median 0. 02 per day, interquantile range 0. 016–0. 037 per day, half-life < 1 year) and concluded that the TSCM population is dynamically maintained [26]. However, in this previous study, only labelling data were modelled, and so it was not possible to address the central question of the ‘stemness’ of the TSCM pool. First, to constitute a stem cell population, it is not enough to have a stably maintained population of cells; stemness requires long-term clonal persistence [18]. That is, whilst the size of the TSCM population as a whole may be stably maintained, the lifespan of any given antigen-specific precursor population could be short; such limited lifespans would be difficult to reconcile with the hypothesis that TSCM cells are the repository of T cell memory. Second, the high turnover rates obtained in this labelling study [26] do not necessarily indicate that the majority of the TSCM population is replaced by the self-renewal of the TSCM pool; frequent naïve cell differentiation could also be responsible. Indeed, given the very large size of the naïve pool compared to the TSCM pool [19], a relatively low proportion of proliferating naïve cells would be sufficient to replace lost TSCM cells. In this scenario, TSCM cells would simply represent transit cells on the differentiation pathway from naïve to effector rather than self-renewing stem cells. Here, we investigate whether the dynamics of TSCM cells in healthy humans are consistent with their putative role as memory stem cells. Specifically, we investigate both the capacity of TSCM cells to self-renew and the longevity of TSCM clones. It is challenging to address these questions in humans, and they cannot be answered using stable isotope labelling alone, since different scenarios (e. g. , ‘all new TSCM cells come from naïve cell differentiation’ versus ‘all new TSCM cells come from TSCM proliferation’) can give rise to very similar levels of label in the TSCM population. To enable us to deconvolute these possibilities, we performed telomere length analysis and utilised cross-sectional TSCM cell data from YFV vaccine recipients. Deterministic and stochastic mechanistic mathematical modelling were then used to analyse all 3 datasets. This novel approach allows us to investigate human TSCM cell dynamics in vivo and to address questions previously only investigated in animal models.
Estimates of the rate of TSCM renewal and TSCM clonal lifespan will depend upon the kinetic structure of the TSCM pool. We therefore first asked whether there was evidence for kinetic heterogeneity (i. e. , existence of subpopulations with differing kinetics) within the TSCM pool by comparing the quality of fit of a homogenous and heterogeneous version of the mathematical model. In the homogeneous version of the model, we constrain the input rate (proliferation rate + rate of new entrants due to differentiation from naïve cells) of the whole TSCM population to be equal to the disappearance rate of labelled TSCM cells; this condition will be met for a kinetically homogenous population of constant size. In the heterogeneous version of the model, this constraint was relaxed to allow for the possibility of kinetic heterogeneity in the TSCM pool (Methods, [28]). We used this implicit description of kinetic heterogeneity rather than an explicit description of the subpopulations because it requires fewer parameters (2 compared with 3 for the explicit model [28–30]) and furthermore does not suffer from the parameter identifiability issue inherent in the explicit kinetic heterogeneity model, which arises due to the very strong correlation between the proliferation rate and size of a subpopulation [31]. A total of 9 datasets were included in this analysis, representing CD4+ and CD8+ T cells (naïve and TSCM) from 5 individuals (1 CD8+ TSCM cell dataset from 1 subject was not available). We found that, in 7 out of the 9 cases, constraining the TSCM population to be homogeneous resulted in a substantially worse description of the data (Fig 3); and there was strong evidence to reject the assumption of homogeneity P = 5. 8 × 10−7, P = 4. 1 × 10−6 (median of p-values calculated using Fisher’s F-test for nested models between the homogeneous and heterogeneous models for CD4+ and CD8+ TSCM, respectively), indicating considerable support for the heterogeneous description of the TSCM pool in both CD4+ and CD8+ T cell populations (S1 Table). In contrast, there was no evidence to reject the null hypothesis of homogeneity in the naïve cell pool (P = 0. 6, P = 0. 5, for CD4+ and CD8+ TN, respectively; median of p-values calculated using Fisher’s F-test for nested models between the homogeneous and heterogeneous models; S1 Table). The size of a newly generated TSCM clone will be an important determinant of clonal longevity, as this determines not just the initial magnitude of a new clone but also the rate at which an existing clone is displaced by new entrants bearing different TCRs. Unfortunately, the size of the clonal expansion accompanying the differentiation of naïve to TSCM cells (k in the model; Fig 2, Methods) was not identifiable. Different fitting runs to the same dataset (with different initial conditions or different random seeds) gave different estimates of the clonal expansion parameter k. Consistent with this, we found that if k was fixed to different constant values in the range 0–20, then, with the exception of 1 individual for which the sum of squares increases dramatically for k above 15, the sum of squares remained constant in every case for all values of k (Fig 4A and Fig 4E). Henceforth, we systematically repeat all analyses for multiple values of k in the range 0–20 to ensure that results are robust despite uncertainty in the clonal expansion parameter. Values of k above 20 were not considered biologically plausible [13,32,33]. In summary, we were not able to estimate the size of the clonal expansion k upon differentiation of naïve cells to TSCM cells and instead utilised a strategy to investigate TSCM dynamics despite uncertainty in this parameter. Next, we quantified TSCM clonal longevity. We fitted the mathematical model (implicit heterogeneous version) simultaneously to the telomere length and isotope labelling data, with k fixed sequentially at different values in the range 0–20. We found that, with the exception of 1 dataset (CD8+ T cells in DW01), the contribution of naïve cells to TSCM replacement was never less than 20% and could be as much as 90% (Fig 4B and 4F). Correspondingly, the average half-life of a TSCM clone was short: the maximum ever observed (across all values of k and across all individuals) was 4 years, but typically, it was much shorter and in the range 0–500 days (Fig 4C and 4G). These conclusions about short clonal longevity were robust to assumptions regarding the activity of telomerase. Specifically, for all values of telomerase compensation considered in the range 0–k, the estimated average clonal half-lives were never higher than those estimates reported above (in which compensation was a free parameter). Importantly, this half-life represents the duration of memory to an antigen (Methods), not the conventional population half-life. It is possible that a very small number of surviving TSCM cells is sufficient to generate a substantial recall response, and so a short clonal half-life is not necessarily incompatible with long-lived recall responses. Moreover, extinction of some clones specific for a given antigen is not necessarily problematic if other clones (bearing different TCRs) specific for the same antigen survive. To assess this possibility, we used the exact Gillespie algorithm (Methods) to quantify the time for the last cell of an antigen-specific precursor population to disappear. This is reported as the precursor lifespan in Fig 4D and 4H. Whilst this did lead to a considerable increase in longevity (stochastic estimates of total antigen-specific precursor lifespan were typically 3 times longer than the deterministic clonal half-life), maximum estimates were still only of the order of 2,000 days (about 5 years) for most individuals. In summary, although individual parameters were poorly identifiable, all parameter combinations able to describe the experimental data were associated with average clonal half-life estimates, which were much lower than the 8–15 year half-life of the recall response [3,4]. Even total precursor lifespans (times until the last cell of an antigen-specific precursor population disappears) were lower than those values in most cases. We conclude that the average TSCM population is replaced too rapidly for it to be the stem cell population responsible for maintaining memory. The model used up to this point allows for heterogeneity but nevertheless reports population averages (i. e. , the proliferation rate and clonal half-life averaged across the whole TSCM population). This averaging could be hiding a small, long-lived population within the bulk short-lived population. Expanding our model, which deals with heterogeneity implicitly (and thus averages across the population), to one that deals with heterogeneity explicitly (and thus provides estimates for the half-lives of all subpopulations) is problematic, as even the simplest version of the explicit heterogeneity model suffers from severe identifiability issues [30] and fails to deliver the parameters of interest when fitted to labelling data. We confirmed that, for our more complex system with both naïve and TSCM cells, an explicit description of heterogeneity provided no information. To address this problem, we therefore sought an alternative class of data. We analysed published data of the vaccine-induced YFV-specific TSCM response in humans from Fuertes Marraco and colleagues [21]. In brief, the magnitude of the CD8+ TSCM cell response to the HLA-A*02-restricted YFV nonstructural protein 4b (NS4b214−222) epitope was measured by HLA class I tetramer at different time points (range 0. 27–35. 02 years) postvaccination in a cross-sectional study of 37 recipients of the YF-17D YFV vaccine. We fitted the explicit heterogeneity version of the naïve T (“TN”) and TSCM model to all 3 types of CD8+ T cell data (isotope labelling, telomere length, and YFV) simultaneously (Supplementary Methods in S1 Text). The fits are shown in Fig 5. As for the implicit heterogeneity model, the fraction of new TSCM cells originating from naïve cells was high (minimum 10%, median 44%). We found evidence for at least 2 subpopulations of CD8+ TSCM cells (designated TSCM1 and TSCM2 for the purposes of this discussion). The majority of the TSCM cells generated upon clonal expansion of naïve cells differentiated into the TSCM1 subpopulation, characterised by a short half-life (≤1 year) and a high replacement rate (median 0. 02 per day, interquantile range 0. 024–0. 045 per day), slightly higher than the average rates estimated by the previous model. The remaining fraction of the generated clone was observed to enter a long-lived subpopulation with a median half-life of 9 years (Table 1, S2 Table). Surprisingly, although the fraction of naïve cells entering the TSCM2 pool was low, because of its low death/differentiation rate, the number of long-lived TSCM2 cells in the circulation at any given time could be as high as, or even higher than, the number of rapidly proliferating TSCM1 cells. Results are summarised schematically in Fig 5C. Four other weighting strategies (of the different types of data) yielded the same conclusions in all cases (S1 Fig, S3 Table). The long-lived TSCM subpopulation identified in the previous section (Subpopulation kinetics) is a potential candidate for the stem cell population responsible for the maintenance of immune memory. We therefore investigated the degree of self-renewal and clonal stability within this long-lived TSCM compartment. The degree of self-renewal of a population at steady state, 1 / (death rate + differentiation rate–proliferation rate), quantifies the upstream input necessary to maintain a population. If there is a large upstream contribution, then the degree of self-renewal will be low [34]. A perfectly self-renewing population (e. g. , HSCs) will have an infinite degree of self-renewal. We quantified the degree of self-renewal for the long-lived population and found a median of 4,600 days, with the range 2,400–7,300 days (S2 Table). This implies that, on average, a TSCM cell (or its progeny) from the long-lived subpopulation resides in the TSCM compartment without dying or differentiating for 4,600 days. The total CD8+ TSCM population is small (2%–3% of circulating CD8+ lymphocytes [19], 1%–5% of lymph node–resident CD8+ T cells [35]). If only a proportion of this already small population is responsible for maintaining memory, then this raises the issue that, although the precursor population specific for a given antigen may have a long half-life, the small size of that population could mean that its dynamics are highly stochastic. That is, there may be wide ranges in the length of memory, and some antigen-specific precursor populations would be predicted to be lost by stochastic extinction soon after generation—i. e. , memory would be erratic and fallible. To investigate the stochasticity of the length of memory within the long-lived TSCM pool, we performed Gillespie simulations of the size of the antigen-specific precursor population based on the parameter estimates derived from model fitting for each of the 4 individuals with CD8+ T cell data. Although there was stochasticity in the half-lives of antigen-specific precursors across different runs, the variation was not large (Fig 6A), and the different trajectories were tightly clustered (Fig 6B). Concerned that the YFV vaccine, which is known to generate an exceptional CD8+ T cell response, may not be representative of a typical antigen, we also sought to study TSCM dynamics independent of the YFV dataset. Guided by the concept that there may be a long-lived TSCM subpopulation, we fitted the explicit kinetic heterogeneity model to the isotope labelling and telomere length data, ignoring the YFV data but imposing a half-life greater than 5 years on the long-lived TSCM subpopulation. As expected, parameters could no longer be reliably identified, but we were able to conclude that the dynamics of CD8+ TSCM cells were compatible with long subpopulation half-lives between 5 and 15 years (S2 Fig). This approach also allowed us to study CD4+ TSCM cells (which were not measured in the YFV study). Again, we found that the dynamics of CD4+ TSCM cells were compatible with the presence of a similarly long-lived subpopulation with a half-life between 5 and 15 years (S3 Fig).
One leading explanation for the maintenance of long-term immunological memory is the existence of a stem cell–like population of memory T cells, able to both self-renew and to differentiate into all other subsets of the T cell memory pool [12,17,21]. It has been suggested that the recently discovered TSCM population is the main stem cell population responsible for maintaining T cell memory [18,36]. In a previous study, we used stable isotope labelling to investigate TSCM dynamics at equilibrium in healthy subjects [26]. This revealed unexpectedly high rates of turnover in the CD4+ and CD8+ TSCM compartments. Whilst supporting the concept that the TSCM population as a whole is stable, it did not establish whether new TSCM cells were generated from naïve cells or by TSCM proliferation. Moreover, the key parameters of clonal longevity and self-renewal, which are prerequisites for stemness, could not be deconvoluted. Given that TSCM cells were found to die and be replaced rapidly, the source of the replacing cell becomes critical. Even a small contribution from naïve cells can result in a weakly self-renewing population and loss of memory as existing clones are replaced by cells specific for a different antigen. In the present study, we overcame these limitations by simultaneously fitting telomere length and tetramer data to constrain the space of possible models and improve parameter identifiability. We report that firstly, the TSCM population is kinetically heterogeneous, with at least 2 kinetically distinct subpopulations turning over at different rates; and secondly, the dynamics of a subpopulation of TSCM cells are compatible with their hypothesised role as the main stem cell–like T cell precursor responsible for the maintenance of T cell memory. The best description of the data is one in which the kinetically heterogeneous TSCM population, despite its high average replenishment rate of approximately 2% per day, contained a fraction of long-lived TSCM cells. The half-life of this slower subpopulation was approximately 9 years, consistent with the 8–15 year half-life estimated for the recall response to a given antigen [3,4]. Furthermore, although this subpopulation was small, the dynamic behaviour of individual clones was not excessively stochastic, and the half-life of a given antigen-specific precursor population was tightly distributed. Finally, we estimated that the degree of self-renewal of the long-lived TSCM subpopulation was approximately 4,600 days. The quantification of the dynamics of TSCM cells is not easily addressed in humans, and different studies invariably involve different compromises. An advantage of our study is that it examines the natural dynamics of TSCM cells in healthy, lymphocyte-replete individuals. A disadvantage is that many of the model parameters were poorly identifiable; nevertheless, firm conclusions about clonal longevity and self-renewal can be drawn. A second disadvantage is that it utilises vaccination data generated using a vaccine (the YFV-17D vaccine) known for its ability to generate an exceptional CD8+ T cell response and which may not be representative of average immunity. To address this potential caveat, we repeated our analysis without reference to the YFV vaccination data. Whilst this reduced our ability to estimate individual parameters, it did confirm the conclusion that the TSCM population consists of subpopulations with different kinetics and that high turnover and short half-lives of the bulk TSCM population do not rule out the existence of a slowly turning over subpopulation with the dynamic properties required for TSCM cells to maintain both CD4+ and CD8+ T cell memory. Finally, it should be noted that our study was confined to circulating TSCM cells. In mice, it has been shown that memory T cell dynamics can vary across different anatomical compartments (spleen versus bone marrow) [37]. For human studies, ethical and technical considerations mean that repeated sampling of cells must be limited to the peripheral blood compartment. However, this does enable direct comparison with previously reported data, including the seminal description of human TSCM cells [16], which was based on circulating cells. Our findings of kinetic heterogeneity in the human TSCM population are reminiscent of the proliferative heterogeneity described for transplanted HSCs in lethally irradiated mice [38], in which levels of Kit receptor distinguished cell subpopulations with different expansion capacities. Similarly, studies of human CD4+ and CD8+ TSCM cells cultured in the presence of cytokines (either interluekin 15 [IL-15] or interluekin 7 [IL-7] + IL-15) in vitro have also reported that a fraction of cells proliferated rapidly, while the majority remained quiescent [39] [19]. Finally, in rhesus macaques, circulating TSCM cells had a level of Ki-67 expression that the author remarked was ‘unexpectedly large’ (mean > 10%) [36]; this is consistent with our finding that, on average, the TSCM population in peripheral blood turns over rapidly. Our proliferation rate estimates for naïve and TSCM cells can be compared with proliferation rate estimates of other T cell subsets obtained using stable isotope labelling. We found that the proliferation of naïve T cells is slowest, p = 0. 0005d−1, and comparable with previous estimates [40], though in this latter study, the gating strategy would have inadvertently included TSCM cells in the naïve cell gate. Next is the slow TSCM subpopulation with a proliferation rate an order of magnitude faster than naïve cells (p = 0. 002d−1), then the fast TSCM subpopulation with a median proliferation rate of p = 0. 015d−1 comparable with that of memory cells (0. 006d−1–0. 02d−1 [29,41]). Giving the following rank order of proliferation rates: naïve < slow TSCM < fast TSCM ≲ memory. Our estimate of the degree of self-renewal is more difficult to place in context. To the best of our knowledge, this parameter has only been quantified previously for murine HSCs. Our estimate of 4,600 days for the degree of self-renewal of the long-lived TSCM subpopulation is naturally less than the corresponding estimate for murine HSC—which is, by definition, infinite—but is greater than the degree of self-renewal of compartments immediately adjacent to HSCs in the differentiation pathway—namely, short-term HSCs (degree of self-renewal of 90–150 days in mice) and multipotent progenitors (MPPs; degree of self-renewal of 7–28 days in mice) [23,34]. If we convert to ‘animal lifespans’ (80 years for human, 2 years for mice; a scaling which appears valid for T cell kinetics [41]), then we see that the degree of self-renewal of TSCM cells is 0. 15 lives; i. e. , a long-lived TSCM cell resides without dying or differentiating for approximately 15% of our lifespan. This is similar to the degree of self-renewal of short-term HSCs (0. 12–0. 2 lives) and greater than the degree of self-renewal of MPPs (0. 01–0. 04 lives). It is remarkable that a peripheral cell population that is towards the end of the haematopoietic differentiation pathway should have a degree of self-renewal that is comparable with short-term HSCs. Unexpectedly, we found strong evidence for continual differentiation of naïve T cells into the TSCM cell pool despite the study volunteers being healthy with no symptomatic infection. For both the implicit and the explicit heterogeneity models, the contribution of naïve cells to TSCM replacement was typically about 50% and never less than 10% (Fig 4B and 4F). This may represent differentiation of naïve cells in response to continual low-level exposure to novel environmental antigen and/or to persistent antigen. Considerable recruitment of naïve cells to the memory pool in the apparent absence of novel antigen has been previously described for mice persistently infected with Polyoma virus [42] or lymphocytic choriomeningitis virus [42] and for healthy mice [43]. The role of the short-lived TSCM subpopulation that we identify is unclear. Potentially, it is activated naïve cells rapidly transitioning to effectors whilst others are retained to form the long-lived TSCM pool that is the basis of memory. This is consistent with recent evidence that TSCM cells may pass through a phase in which they express effector molecules [44]. This work suggests a number of future directions. One important direction is to establish phenotypic markers to distinguish the ‘true’ TSCM subpopulation. A second is to develop a model to predict the TCR repertoire of the true TSCM subpopulation and whether this differs from bulk TSCM cells and the functional consequences of any such difference. Finally, it is important to know whether or not murine TSCM populations are similarly heterogeneous, since this would facilitate a whole range of experiments not possible in humans. Our results show that substantial kinetic heterogeneity exists within the TSCM pool, encompassing a long-lived subpopulation with the dynamic properties required to maintain both CD4+ and CD8+ T cell memory. Further characterisation of these bona fide TSCM cells may illuminate the mechanistic basis of durable immune protection and facilitate translational efforts to develop more effective vaccines and immunotherapies.
The fit of the homogenous and heterogeneous model was compared using Fisher’s F-test for nested models. This test compares the goodness of fit of nested models to data, taking into account the different number of parameters in the models [62].
The human immune system remembers previously encountered pathogens so that, on meeting the same pathogen a second time, the response is quicker and more effective. This immune memory is the basis of all vaccinations. Immune memory persists for decades, but how memory is maintained is unclear. It has been hypothesised that there is a dedicated population of cells called stem cell–like memory T (TSCM) cells that have stem cell–like behaviour and are responsible for the persistence of T cell memory. Here, we show that a subset of TSCM cells, in healthy humans in vivo, have the dynamic properties of self-renewal and clonal longevity necessary to maintain long-lived immune memory.
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developmental biology
cytotoxic t cells
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Human TSCM cell dynamics in vivo are compatible with long-lived immunological memory and stemness