Stochastic model of T cell proliferation: a calculus revealing IL-2 regulation of precursor frequencies, cell cycle time, and survival

Relevance of lymphocyte proliferation to PHA in severe combined immunodeficiency (SCID) and T cell lymphopenia

Severe combined immunodeficiency (SCID) is characterized by a severe deficiency in T cell numbers. We analyzed data collected (n = 307) for PHA-based T cell proliferation from the PIDTC SCID protocol 6901, using either a radioactive or flow cytometry method. In comparing the two groups, a smaller number of the patients tested by flow cytometry had <10% of the lower limit of normal proliferation as compared to the radioactive method (p = 0.02). Further, in patients with CD3+ T cell counts between 51 and 300 cells/μL, there was a higher proliferative response with the PHA flow assay compared to the 3H-T assay (p < 0.0001), suggesting that the method of analysis influences the resolution and interpretation of PHA results. Importantly, we observed many SCID patients with profound T cell lymphopenia having normal T cell proliferation when assessed by flow cytometry. We recommend this test be considered only as supportive in the diagnosis of typical SCID.

Programmed death receptor 1 (PD-1) ligand Fc fusion proteins reduce T-cell proliferation in vitro independently of PD-1

Programmed death receptor 1 (PD-1) is an inhibitory receptor on T cells shown to restrain T-cell proliferation. PD-1 immune checkpoint blockade has emerged as a highly promising approach in cancer treatment. Much of our understanding of the function of PD-1 is derived from in vitro T-cell activation assays. Here we set out to further investigate how T cells integrate inhibitory signals such as PD-1 in vitro using the PD-1 agonist, PD-1 ligand 1 (PD-L1) fusion protein (PD-L1.Fc), coimmobilized alongside anti-CD3 agonist monoclonal antibody (mAb) on plates to deliver PD-1 signals to wild-type and PD-1-/- CD8+ T cells. Surprisingly, we found that the PD-L1.Fc fusion protein inhibited T-cell proliferation independently of PD-1. This PD-L1.Fc inhibition was observed in the presence and absence of CD28 and interleukin-2 signaling. Binding of PD-L1.Fc was restricted to PD-1-expressing T cells and thus inhibition was not mediated by the interaction of PD-L1.Fc with CD80 or other yet unknown binding partners. Furthermore, a similar PD-1-independent reduction of T-cell proliferation was observed with plate-bound PD-L2.Fc. Hence, our results suggest that the coimmobilization of PD-1 ligand fusion proteins with anti-CD3 mAb leads to a reduction of T-cell engagement with plate-bound anti-CD3 mAb. This study demonstrates a nonspecific mechanism of T-cell inhibition when PD-L1.Fc or PD-L2.Fc fusion proteins are delivered in a plate-bound coimmobilization assay and highlights the importance of careful optimization of assay systems and reagents when interpreting their influence on T-cell proliferation.

Survival and division fate programs are preserved but retuned during the naïve to memory CD8+ T-cell transition

Memory T cells are generated from naïve precursors undergoing proliferation during the initial immune response. Both naïve and memory T cells are maintained in a resting, quiescent state and respond to activation with a controlled proliferative burst and differentiation into effector cells. This similarity in the maintenance and response dynamics points to the preservation of key cellular fate programs; however, whether memory T cells have acquired intrinsic changes in these programs that may contribute to the enhanced immune protection in a recall response is not fully understood. Here we used a quantitative model-based analysis of proliferation and survival kinetics of in vitro-stimulated murine naïve and memory CD8+ T cells in response to homeostatic and activating signals to establish intrinsic similarities or differences within these cell types. We show that resting memory T cells display heightened sensitivity to homeostatic cytokines, responding to interleukin (IL)-2 in addition to IL-7 and IL-15. The proliferative response to αCD3 was equal in size and kinetics, demonstrating that memory T cells undergo the same controlled division burst and automated return to quiescence as naïve T cells. However, perhaps surprisingly, we observed reduced expansion of αCD3-stimulated memory T cells in response to activating signals αCD28 and IL-2 compared with naïve T cells. Overall, we demonstrate that although sensitivities to cytokine and costimulatory signals have shifted, fate programs regulating the scale of the division burst are conserved in memory T cells.

Modeling T Cell Fate

Many of the pathways that underlie the diversification of naive T cells into effector and memory subsets, and the maintenance of these populations, remain controversial. In recent years a variety of experimental tools have been developed that allow us to follow the fates of cells and their descendants. In this review we describe how mathematical models provide a natural language for describing the growth, loss, and differentiation of cell populations. By encoding mechanistic descriptions of cell behavior, models can help us interpret these new datasets and reveal the rules underpinning T cell fate decisions, both at steady state and during immune responses.

Survey of activation-induced genome architecture reveals a novel enhancer of Myc

The transcription factor Myc is critically important in driving cell proliferation, a function that is frequently dysregulated in cancer. To avoid this dysregulation Myc is tightly controlled by numerous layers of regulation. One such layer is the use of distal regulatory enhancers to drive Myc expression. Here, using chromosome conformation capture to examine B cells of the immune system in the first hours after their activation, we reveal a previously unidentified enhancer of Myc. The interactivity of this enhancer coincides with a dramatic, but discrete, spike in Myc expression 3 h post-activation. However, genetic deletion of this region, has little impact on Myc expression, Myc protein level or in vitro and in vivo cell proliferation. Examination of the enhancer deleted regulatory landscape suggests that enhancer redundancy likely sustains Myc expression. This work highlights not only the importance of temporally examining enhancers, but also the complexity and dynamics of the regulation of critical genes such as Myc.

Specific Loss of ABCA1 (ATP-Binding Cassette Transporter A1) Suppresses TCR (T-Cell Receptor) Signaling and Provides Protection Against Atherosclerosis

BACKGROUND

ABCA1 (ATP-binding cassette transporter A1) mediates cholesterol efflux to apo AI to maintain cellular cholesterol homeostasis. The current study aims to investigate whether T-cell-specific deletion of ABCA1 modulates the phenotype/function of T cells and the development of atherosclerosis.

METHODS

Mice with T-cell-specific deletion of ABCA1 on low-density lipoprotein receptor knockout (Ldlr^-/-) background (Abca1^CD4-/CD4-Ldlr^-/-) were generated by multiple steps of (cross)-breedings among Abca1^flox/flox, CD4-Cre, and Ldlr^-/- mice.

RESULTS

Deletions of ABCA1 greatly suppressed cholesterol efflux to apo AI but slightly reduced membrane lipid rafts on T cells probably due to the upregulation of ABCG1. Moreover, ABCA1 deficiency impaired TCR (T-cell receptor) signaling and inhibited the survival and proliferation of T cells as well as the formation of effector memory T cells. Despite the comparable levels of plasma total cholesterol after Western-type diet feeding, Abca1^CD4-/CD4-Ldlr^-/- mice showed significantly attenuated arterial accumulations of T cells and smaller atherosclerotic lesions than Abca1^+/+Ldlr^-/-controls, which were associated with reduced surface CCR5 (CC motif chemokine receptor 5) and CXCR3 (CXC motif chemokine receptor 3), decreased antiapoptotic Bcl-2 (B-cell lymphoma 2) and Bcl-xL (B-cell lymphoma extra-large), and hampered abilities to produce IL (interleukin)-2 and IFN (interferon)-γ by ABCA1-deficient T cells.

CONCLUSIONS

ABCA1 is essential for T-cell cholesterol homeostasis. Deletion of ABCA1 in T cells impairs TCR signaling, suppresses the survival, proliferation, differentiation, and function of T cells, thereby providing atheroprotection in vivo.

Mapping CAR T-Cell Design Space Using Agent-Based Models

Chimeric antigen receptor (CAR) T-cell therapy shows promise for treating liquid cancers and increasingly for solid tumors as well. While potential design strategies exist to address translational challenges, including the lack of unique tumor antigens and the presence of an immunosuppressive tumor microenvironment, testing all possible design choices in vitro and in vivo is prohibitively expensive, time consuming, and laborious. To address this gap, we extended the modeling framework ARCADE (Agent-based Representation of Cells And Dynamic Environments) to include CAR T-cell agents (CAR T-cell ARCADE, or CARCADE). We conducted in silico experiments to investigate how clinically relevant design choices and inherent tumor features—CAR T-cell dose, CD4⁺:CD8⁺ CAR T-cell ratio, CAR-antigen affinity, cancer and healthy cell antigen expression—individually and collectively impact treatment outcomes. Our analysis revealed that tuning CAR affinity modulates IL-2 production by balancing CAR T-cell proliferation and effector function. It also identified a novel multi-feature tuned treatment strategy for balancing selectivity and efficacy and provided insights into how spatial effects can impact relative treatment performance in different contexts. CARCADE facilitates deeper biological understanding of treatment design and could ultimately enable identification of promising treatment strategies to accelerate solid tumor CAR T-cell design-build-test cycles.

Counting generations in birth and death processes with competing Erlang and exponential waiting times

Lymphocyte populations, stimulated in vitro or in vivo, grow as cells divide. Stochastic models are appropriate because some cells undergo multiple rounds of division, some die, and others of the same type in the same conditions do not divide at all. If individual cells behave independently, then each cell can be imagined as sampling from a probability density of times to division and death. The exponential density is the most mathematically and computationally convenient choice. It has the advantage of satisfying the memoryless property, consistent with a Markov process, but it overestimates the probability of short division times. With the aim of preserving the advantages of a Markovian framework while improving the representation of experimentally-observed division times, we consider a multi-stage model of cellular division and death. We use Erlang-distributed (or, more generally, phase-type distributed) times to division, and exponentially distributed times to death. We classify cells into generations, using the rule that the daughters of cells in generation n are in generation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n+1$$\end{document}n+1. In some circumstances, our representation is equivalent to established models of lymphocyte dynamics. We find the growth rate of the cell population by calculating the proportions of cells by stage and generation. The exponent describing the late-time cell population growth, and the criterion for extinction of the population, differs from what would be expected if N steps with rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}λ were equivalent to a single step of rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda /N$$\end{document}λ/N. We link with a published experimental dataset, where cell counts were reported after T cells were transferred to lymphopenic mice, using Approximate Bayesian Computation. In the comparison, the death rate is assumed to be proportional to the generation and the Erlang time to division for generation 0 is allowed to differ from that of subsequent generations. The multi-stage representation is preferred to a simple exponential in posterior distributions, and the mean time to first division is estimated to be longer than the mean time to subsequent divisions.

The perception and response of T cells to a changing environment are based on the law of initial value

αβ T cells are critical components of the adaptive immune system and are capable of inducing sterilizing immunity after pathogen infection and eliminating transformed tumor cells. The development and function of T cells are controlled through the T cell antigen receptor, which recognizes peptides displayed on major histocompatibility complex (MHC) molecules. Here, we review how T cells generate the ability to recognize self-peptide-bound MHC molecules and use signals derived from these interactions to instruct cellular development, activation thresholds, and functional specialization in the steady state and during immune responses. We argue that the basic tenants of T cell development and function follow Weber-Fetcher's law of just noticeable differences and Wilder's law of initial value. Together, these laws argue that the ability of a system to respond and the quality of that response are scalable to the basal state of that system. Manifestation of these laws in T cells generates clone-specific activation thresholds that are based on perceivable differences between homeostasis and pathogen encounter (self versus nonself discrimination), as well as poised states for subsequent differentiation into specific effector cell lineages.

An updated view into the cell cycle kinetics of human T lymphocytes and the impact of irradiation

Even though a detailed understanding of the proliferative characteristics of T lymphocytes is imperative in many research fields, prior studies have never reached a consensus on these characteristics, and on the corresponding cell cycle kinetics specifically. In this study, the general proliferative response of human T lymphocytes to phytohaemagglutinin (PHA) stimulation was characterized using a carboxyfluorescein succinimidyl ester-based flow cytometric assay. We were able to determine when PHA-stimulated T lymphocytes complete their first division, the proportion of cells that initiate proliferation, the subsequent division rate of the cells, and the impact of irradiation on these proliferative properties. Next, we accurately visualized the cell cycle progression of dividing T lymphocytes cultured in whole blood using an adapted 5-ethynyl-2'-deoxyuridine pulse-chase method. Furthermore, through multiple downstream analysis methods, we were able to make an estimation of the corresponding cell cycle kinetics. We also visualized the impact of X-rays on the progression of the cells through the cell cycle. Our results showed dose-dependent G2 arrest after exposure to irradiation, and a corresponding delay in G1 phase-entry of the cells. In conclusion, utilizing various flow cytometric assays, we provided valuable information on T lymphocyte proliferation characteristics starting from first division to fully dividing cells.

The concerted change in the distribution of cell cycle phases and zone composition in germinal centers is regulated by IL-21

Humoral immune responses require germinal centres (GC) for antibody affinity maturation. Within GC, B cell proliferation and mutation are segregated from affinity-based positive selection in the dark zone (DZ) and light zone (LZ) substructures, respectively. While IL-21 is known to be important in affinity maturation and GC maintenance, here we show it is required for both establishing normal zone representation and preventing the accumulation of cells in the G1 cell cycle stage in the GC LZ. Cell cycle progression of DZ B cells is unaffected by IL-21 availability, as is the zone phenotype of the most highly proliferative GC B cells. Collectively, this study characterises the development of GC zones as a function of time and B cell proliferation and identifies IL-21 as an important regulator of these processes. These data help explain the requirement for IL-21 in normal antibody affinity maturation.

Cyton2: A Model of Immune Cell Population Dynamics That Includes Familial Instructional Inheritance

Lymphocytes are the central actors in adaptive immune responses. When challenged with antigen, a small number of B and T cells have a cognate receptor capable of recognising and responding to the insult. These cells proliferate, building an exponentially growing, differentiating clone army to fight off the threat, before ceasing to divide and dying over a period of weeks, leaving in their wake memory cells that are primed to rapidly respond to any repeated infection. Due to the non-linearity of lymphocyte population dynamics, mathematical models are needed to interrogate data from experimental studies. Due to lack of evidence to the contrary and appealing to arguments based on Occam’s Razor, in these models newly born progeny are typically assumed to behave independently of their predecessors. Recent experimental studies, however, challenge that assumption, making clear that there is substantial inheritance of timed fate changes from each cell by its offspring, calling for a revision to the existing mathematical modelling paradigms used for information extraction. By assessing long-term live-cell imaging of stimulated murine B and T cells in vitro, we distilled the key phenomena of these within-family inheritances and used them to develop a new mathematical model, Cyton2, that encapsulates them. We establish the model’s consistency with these newly observed fine-grained features. Two natural concerns for any model that includes familial correlations would be that it is overparameterised or computationally inefficient in data fitting, but neither is the case for Cyton2. We demonstrate Cyton2’s utility by challenging it with high-throughput flow cytometry data, which confirms the robustness of its parameter estimation as well as its ability to extract biological meaning from complex mixed stimulation experiments. Cyton2, therefore, offers an alternate mathematical model, one that is, more aligned to experimental observation, for drawing inferences on lymphocyte population dynamics.

A multi-approach and multi-scale platform to model CD4+ T cells responding to infections

Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology.

CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.

Mixing and oxygen transfer characteristics of a microplate bioreactor with surface-attached microposts

Bioprocess optimization for cell-based therapies is a resource heavy activity. To reduce the associated cost and time, process development may be carried out in small volume systems, with the caveat that such systems be predictive for process scale-up. The transport of oxygen from the gas phase into the culture medium, characterized using the volumetric mass transfer coefficient, k_L a, has been identified as a critical parameter for predictive process scale-up. Here, we describe the development of a 96-well microplate with integrated Redbud Posts to provide mixing and enhanced k_L a. Mixing in the microplate is characterized by observation of dyes and analyzed using the relative mixing index (RMI). The k_L a is measured via dynamic gassing out method. Actuating Redbud Posts are shown to increase rate of planar homogeneity (2 min) verse diffusion alone (120 min) and increase oxygenation, with increasing stirrer speed (3500-9000 rpm) and decreasing fill volume (150-350 μL) leading to an increase in k_L a (4-88 h^-1 ). Significant increase in Chinese Hamster Ovary growth in Redbud Labs vessel (580,000 cells mL^-1 ) versus the control (420,000 cells mL^-1 ); t(12.814) = 8.3678, p ≤ .001), and CD4⁺ Naïve cell growth in the microbioreactor indicates the potential for this technology in early stage bioprocess development and optimization.

Partitioning stable and unstable expression level variation in cell populations: A theoretical framework and its application to the T cell receptor

Phenotypic variation in the copy number of gene products expressed by cells or tissues has been the focus of intense investigation. To what extent the observed differences in cellular expression levels are persistent or transient is an intriguing question. Here, we develop a quantitative framework that resolves the expression variation into stable and unstable components. The difference between the expression means in two cohorts isolated from any cell population is shown to converge to an asymptotic value, with a characteristic time, τ_T, that measures the timescale of the unstable dynamics. The asymptotic difference in the means, relative to the initial value, measures the stable proportion of the original population variance Rα2. Empowered by this insight, we analysed the T-cell receptor (TCR) expression variation in CD4 T cells. About 70% of TCR expression variance is stable in a diverse polyclonal population, while over 80% of the variance in an isogenic TCR transgenic population is volatile. In both populations the TCR levels fluctuate with a characteristic time of 32 hours. This systematic characterisation of the expression variation dynamics, relying on time series of cohorts’ means, can be combined with technologies that measure gene or protein expression in single cells or in bulk.

No two cells are identical. Even isogenic cells, living in the same environment and expressing the same set of genes display measurable differences or variation in the expression level of any of these genes. How much of the differences in expression levels are permanent and how much of these differences vanish in time has intrigued us for generations. We develop a theoretical framework based on a stochastic model and put it to work in the analysis of T cell receptor expression level in CD4 T cells. We show that T cell populations with genetically diverse receptors display stable variation in receptor expression but, surprisingly, we detect persistent differences in receptor levels among uniform transgenic T cells. The analysis, being based on the mean cohort expression levels logarithm, can be applied to techniques that measure expression at single-cell level and also to the myriad of genomics and proteomics techniques that measure expression in bulk populations.

Regulation of T Helper Cell Fate by TCR Signal Strength

T cells are critical in orchestrating protective immune responses to cancer and an array of pathogens. The interaction between a peptide MHC (pMHC) complex on antigen presenting cells (APCs) and T cell receptors (TCRs) on T cells initiates T cell activation, division, and clonal expansion in secondary lymphoid organs. T cells must also integrate multiple T cell-intrinsic and extrinsic signals to acquire the effector functions essential for the defense against invading microbes. In the case of T helper cell differentiation, while innate cytokines have been demonstrated to shape effector CD4⁺ T lymphocyte function, the contribution of TCR signaling strength to T helper cell differentiation is less understood. In this review, we summarize the signaling cascades regulated by the strength of TCR stimulation. Various mechanisms in which TCR signal strength controls T helper cell expansion and differentiation are also discussed.

The Rules of Human T Cell Fate in vivo

The processes governing lymphocyte fate (division, differentiation, and death), are typically assumed to be independent of cell age. This assumption has been challenged by a series of elegant studies which clearly show that, for murine cells in vitro, lymphocyte fate is age-dependent and that younger cells (i.e., cells which have recently divided) are less likely to divide or die. Here we investigate whether the same rules determine human T cell fate in vivo. We combined data from in vivo stable isotope labeling in healthy humans with stochastic, agent-based mathematical modeling. We show firstly that the choice of model paradigm has a large impact on parameter estimates obtained using stable isotope labeling i.e., different models fitted to the same data can yield very different estimates of T cell lifespan. Secondly, we found no evidence in humans in vivo to support the model in which younger T cells are less likely to divide or die. This age-dependent model never provided the best description of isotope labeling; this was true for naïve and memory, CD4⁺ and CD8⁺ T cells. Furthermore, this age-dependent model also failed to predict an independent data set in which the link between division and death was explored using Annexin V and deuterated glucose. In contrast, the age-independent model provided the best description of both naïve and memory T cell dynamics and was also able to predict the independent dataset.

Quantifying the Role of Stochasticity in the Development of Autoimmune Disease

In this paper, we propose and analyse a mathematical model for the onset and development of autoimmune disease, with particular attention to stochastic effects in the dynamics. Stability analysis yields parameter regions associated with normal cell homeostasis, or sustained periodic oscillations. Variance of these oscillations and the effects of stochastic amplification are also explored. Theoretical results are complemented by experiments, in which experimental autoimmune uveoretinitis (EAU) was induced in B10.RIII and C57BL/6 mice. For both cases, we discuss peculiarities of disease development, the levels of variation in T cell populations in a population of genetically identical organisms, as well as a comparison with model outputs.

Stochastic Inheritance of Division and Death Times Determines the Size and Phenotype of CD8+ T Cell Families

After antigen stimulation cognate naïve CD8⁺ T cells undergo rapid proliferation and ultimately their progeny differentiates into short-lived effectors and longer-lived memory T cells. Although the expansion of individual cells is very heterogeneous, the kinetics are reproducible at the level of the total population of cognate cells. After the expansion phase, the population contracts, and if antigen is cleared, a population of memory T cells remains behind. Different markers like CD62L, CD27, and KLRG1 have been used to define several T cell subsets (or cell fates) developing from individual naïve CD8⁺ T cells during the expansion phase. Growing evidence from high-throughput experiments, like single cell RNA sequencing, epigenetic profiling, and lineage tracing, highlights the need to model this differentiation process at the level of single cells. We model CD8⁺ T cell proliferation and differentiation as a competitive process between the division and death probabilities of individual cells (like in the Cyton model). We use an extended form of the Cyton model in which daughter cells inherit the division and death times from their mother cell in a stochastic manner (using lognormal distributions). We show that this stochastic model reproduces the dynamics of CD8⁺ T cells both at the population and at the single cell level. Modeling the expression of the CD62L, CD27, and KLRG1 markers of each individual cell, we find agreement with the changing phenotypic distributions of these markers in single cell RNA sequencing data. Retrospectively re-defining conventional T-cell subsets by “gating” on these markers, we find agreement with published population data, without having to assume that these subsets have different properties, i.e., correspond to different fates.

Regulatory T Cells Suppress Effector T Cell Proliferation by Limiting Division Destiny

Understanding how the strength of an effector T cell response is regulated is a fundamental problem in immunology with implications for immunity to pathogens, autoimmunity, and immunotherapy. The initial magnitude of the T cell response is determined by the sum of independent signals from antigen, co-stimulation and cytokines. By applying quantitative methods, the contribution of each signal to the number of divisions T cells undergo (division destiny) can be measured, and the resultant exponential increase in response magnitude accurately calculated. CD4⁺CD25⁺Foxp3⁺ regulatory T cells suppress self-reactive T cell responses and limit pathogen-directed immune responses before bystander damage occurs. Using a quantitative modeling framework to measure T cell signal integration and response, we show that Tregs modulate division destiny, rather than directly increasing the rate of death or delaying interdivision times. The quantitative effect of Tregs could be mimicked by modulating the availability of stimulatory co-stimuli and cytokines or through the addition of inhibitory signals. Thus, our analysis illustrates the primary effect of Tregs on the magnitude of effector T cell responses is mediated by modifying division destiny of responding cell populations.

Modifying clonal selection theory with a probabilistic cell

Problem-solving strategies in immunology currently utilize a series of ad hoc, qualitative variations on a foundation of Burnet's formulation of clonal selection theory. These modifications, including versions of two-signal theory, describe how signals regulate lymphocytes to make important decisions governing self-tolerance and changes to their effector and memory states. These theories are useful but are proving inadequate to explain the observable genesis and control of heterogeneity in cell types, the nonlinear passage of cell fate trajectories and how the input from multiple environmental signals can be integrated at different times and strengths. Here, I argue for a paradigm change to place immune theory on a firmer philosophical and quantitative foundation to resolve these difficulties. This change rejects the notion of identical cell subsets and substitutes the concept of a cell as comprised of autonomous functional mechanical components subject to stochastic variations in construction and operation. The theory aims to explain immunity in terms of cell population dynamics, dictated by the operation of cell machinery, such as randomizing elements, division counters, and fate timers. The effect of communicating signals alone and in combination within this system is determined with a cellular calculus. A series of models developed with these principles can resolve logical cell fate and signaling paradoxes and offer a reinterpretation for how self-non-self discrimination and immune response class are controlled.

Multiplexed Division Tracking Dyes for Proliferation-Based Clonal Lineage Tracing

The generation of cellular heterogeneity is an essential feature of immune responses. Understanding the heritability and asymmetry of phenotypic changes throughout this process requires determination of clonal-level contributions to fate selection. Evaluating intraclonal and interclonal heterogeneity and the influence of distinct fate determinants in large numbers of cell lineages, however, is usually laborious, requiring familial tracing and fate mapping. In this study, we introduce a novel, accessible, high-throughput method for measuring familial fate changes with accompanying statistical tools for testing hypotheses. The method combines multiplexing of division tracking dyes with detection of phenotypic markers to reveal clonal lineage properties. We illustrate the method by studying in vitro-activated mouse CD8⁺ T cell cultures, reporting division and phenotypic changes at the level of families. This approach has broad utility as it is flexible and adaptable to many cell types and to modifications of in vitro, and potentially in vivo, fate monitoring systems.

Nongenetic origins of cell-to-cell variability in B lymphocyte proliferation

Rapid antibody production in response to invading pathogens requires the dramatic expansion of pathogen-derived antigen-specific B lymphocyte populations. Whether B cell population dynamics are based on stochastic competition between competing cell fates, as in the development of competence by the bacterium Bacillus subtilis, or on deterministic cell fate decisions that execute a predictable program, as during the development of the worm Caenorhabditis elegans, remains unclear. Here, we developed long-term live-cell microscopy of B cell population expansion and multiscale mechanistic computational modeling to characterize the role of molecular noise in determining phenotype heterogeneity. We show that the cell lineage trees underlying B cell population dynamics are mediated by a largely predictable decision-making process where the heterogeneity of cell proliferation and death decisions at any given timepoint largely derives from nongenetic heterogeneity in the founder cells. This means that contrary to previous models, only a minority of genetically identical founder cells contribute the majority to the population response. We computationally predict and experimentally confirm nongenetic molecular determinants that are predictive of founder cells' proliferative capacity. While founder cell heterogeneity may arise from different exposure histories, we show that it may also be due to the gradual accumulation of small amounts of intrinsic noise during the lineage differentiation process of hematopoietic stem cells to mature B cells. Our finding of the largely deterministic nature of B lymphocyte responses may provide opportunities for diagnostic and therapeutic development.

The regulation of lymphocyte activation and proliferation

Activation induced proliferation and clonal expansion of antigen specific lymphocytes is a hallmark of the adaptive immune response to pathogens. Recent studies identify two distinct control phases. In the first T and B lymphocytes integrate antigen and additional costimuli to motivate a programmed proliferative burst that ceases with a return to cell quiescence and eventual death. This proliferative burst is autonomously timed, ensuring an appropriate response magnitude whilst preventing uncontrolled expansion. This initial response is subject to further modification and extension by a range of signals that modify, expand and direct the emergence of a rich array of new cell types. Thus, both robust clonal expansion of a small number of antigen specific T cells, and the concurrent emergence of extensive cellular diversity, confers immunity to a vast array of different pathogens. The in vivo response to a given pathogen is made up by the sum of all responding clones and is reproducible and pathogen specific. Thus, a precise description of the regulatory principles governing lymphocyte proliferation, differentiation and survival is essential to a unified understanding of the immune system.

Stochastic Effects in Autoimmune Dynamics

Among various possible causes of autoimmune disease, an important role is played by infections that can result in a breakdown of immune tolerance, primarily through the mechanism of “molecular mimicry”. In this paper we propose and analyse a stochastic model of immune response to a viral infection and subsequent autoimmunity, with account for the populations of T cells with different activation thresholds, regulatory T cells, and cytokines. We show analytically and numerically how stochasticity can result in sustained oscillations around deterministically stable steady states, and we also investigate stochastic dynamics in the regime of bi-stability. These results provide a possible explanation for experimentally observed variations in the progression of autoimmune disease. Computations of the variance of stochastic fluctuations provide practically important insights into how the size of these fluctuations depends on various biological parameters, and this also gives a headway for comparison with experimental data on variation in the observed numbers of T cells and organ cells affected by infection.

Estimates and impact of lymphocyte division parameters from CFSE data using mathematical modelling

Carboxyfluorescein diacetate succinimidyl ester (CFSE) labelling has been widely used to track and study cell proliferation. Here we use mathematical modelling to describe the kinetics of immune cell proliferation after an in vitro polyclonal stimulation tracked with CFSE. This approach allows us to estimate a set of key parameters, including ones related to cell death and proliferation. We develop a three-phase model that distinguishes a latency phase, accounting for non-divided cell behaviour, a resting phase and the active phase of the division process. Parameter estimates are derived from model results, and numerical simulations are then compared to the dynamics of in vitro experiments, with different biological assumptions tested. Our model allows us to compare the dynamics of CD4+ and CD8+ cells, and to highlight their kinetic differences. Finally we perform a sensitivity analysis to quantify the impact of each parameter on proliferation kinetics. Interestingly, we find that parameter sensitivity varies with time and with cell generation. Our approach can help biologists to understand cell proliferation mechanisms and to identify potential pathological division processes.

In vitro tracking and intracellular protein distribution in immunology

New imaging techniques have enabled major advances in understanding how immune reactions are initiated, coordinated and controlled. Imaging methods, which were previously mostly descriptive and supplementary to more quantitative approaches, have now reached sufficient precision and throughput that they are becoming integral to almost all aspects of immunology research. Imaging methodologies that increase the resolution and sensitivity of detection, alongside an ever-expanding range of fluorescent reporters of molecular and cellular activity, and vastly improved analysis methods, have all facilitated this transformation. In this review, we will discuss how advances in imaging are changing the way we view immune activation and control using T cells as the model immune system. We will describe how imaging has transformed our knowledge of molecular and signalling events in T-cell activation, and the impact of these molecular events on the behaviour of T cells.

Characterisation of mice lacking all functional isoforms of the pro-survival BCL-2 family member A1 reveals minor defects in the haematopoietic compartment

The pro-survival proteins of the BCL-2 family regulate the survival of all cells, and genetic deletion models for these proteins have revealed which specific BCL-2 family member(s) is/are critical for the survival of particular cell types. A1 is a pro-survival BCL-2-like protein that is expressed predominantly in haematopoietic cells, and here we describe the characterisation of a novel mouse strain that lacks all three functional isoforms of A1 (A1-a, A1-b and A1-d). Surprisingly, complete loss of A1 caused only minor defects, with significant, although relatively small, decreases in γδTCR T cells, antigen-experienced conventional as well as regulatory CD4 T cells and conventional dendritic cells (cDCs). When examining these cell types in tissue culture, only cDC survival was significantly impaired by the loss of A1. Therefore, A1 appears to be a surprisingly redundant pro-survival protein in the haematopoietic system and other tissues, suggesting that its targeting in cancer may be readily tolerated.

A Myc-dependent division timer complements a cell-death timer to regulate T cell and B cell responses

T lymphocytes and B lymphocytes integrate activating signals to control the size of their proliferative response. Here we report that such control was achieved by timed changes in the production rate of cell-cycle-regulating proto-oncoprotein Myc, with division cessation occurring when Myc levels fell below a critical threshold. The changing pattern of the level of Myc was not affected by cell division, which identified the regulating mechanism as a cell-intrinsic, heritable temporal controller. Overexpression of Myc in stimulated T cells and B cells did not sustain cell proliferation indefinitely, as a separate 'time-to-die' mechanism, also heritable, was programmed after lymphocyte activation and led to eventual cell loss. Together the two competing cell-intrinsic timed fates created the canonical T cell and B cell immune-response pattern of rapid growth followed by loss of most cells. Furthermore, small changes in these timed processes by regulatory signals, or by oncogenic transformation, acted in synergy to greatly enhance cell numbers over time.

Constitutive Lck Activity Drives Sensitivity Differences between CD8+ Memory T Cell Subsets

CD8(+) T cells develop increased sensitivity following Ag experience, and differences in sensitivity exist between T cell memory subsets. How differential TCR signaling between memory subsets contributes to sensitivity differences is unclear. We show in mouse effector memory T cells (TEM) that >50% of lymphocyte-specific protein tyrosine kinase (Lck) exists in a constitutively active conformation, compared with <20% in central memory T cells (TCM). Immediately proximal to Lck signaling, we observed enhanced Zap-70 phosphorylation in TEM following TCR ligation compared with TCM Furthermore, we observed superior cytotoxic effector function in TEM compared with TCM, and we provide evidence that this results from a lower probability of TCM reaching threshold signaling owing to the decreased magnitude of TCR-proximal signaling. We provide evidence that the differences in Lck constitutive activity between CD8(+) TCM and TEM are due to differential regulation by SH2 domain-containing phosphatase-1 (Shp-1) and C-terminal Src kinase, and we use modeling of early TCR signaling to reveal the significance of these differences. We show that inhibition of Shp-1 results in increased constitutive Lck activity in TCM to levels similar to TEM, as well as increased cytotoxic effector function in TCM Collectively, this work demonstrates a role for constitutive Lck activity in controlling Ag sensitivity, and it suggests that differential activities of TCR-proximal signaling components may contribute to establishing the divergent effector properties of TCM and TEM. This work also identifies Shp-1 as a potential target to improve the cytotoxic effector functions of TCM for adoptive cell therapy applications.

Transcriptional profiling of interleukin-2-primed human adipose derived mesenchymal stem cells revealed dramatic changes in stem cells response imposed by replicative senescence

Inflammation is a double-edged sword with both detrimental and beneficial consequences. Understanding of the mechanisms of crosstalk between the inflammatory milieu and human adult mesenchymal stem cells is an important basis for clinical efforts. Here, we investigate changes in the transcriptional response of human adipose-derived stem cells to physiologically relevant levels of IL-2 (IL-2 priming) upon replicative senescence. Our data suggest that replicative senescence might dramatically impede human mesenchymal stem cell (MSC) function via global transcriptional deregulation in response to IL-2. We uncovered a novel senescence-associated transcriptional signature in human adipose-derived MSCs hADSCs after exposure to pro-inflammatory environment: significant enhancement of the expression of the genes encoding potent growth factors and cytokines with anti-inflammatory and migration-promoting properties, as well as genes encoding angiogenic and anti-apoptotic promoting factors, all of which could participate in the establishment of a unique microenvironment. We observed transcriptional up-regulation of critical components of the nitric oxide synthase pathway (iNOS) in hADSCs upon replicative senescence suggesting, that senescent stem cells can acquire metastasis-promoting properties via stem cell-mediated immunosuppression. Our study highlights the importance of age as a factor when designing cell-based or pharmacological therapies for older patients and predicts measurable biomarkers characteristic of an environment that is conducive to cancer cells invasiveness and metastasis.

T Cells Integrate Local and Global Cues to Discriminate between Structurally Similar Antigens

T lymphocytes’ ability to discriminate between structurally related antigens has been attributed to the unique signaling properties of the T cell receptor. However, recent studies have suggested that the output of this discrimination process is conditioned by environmental cues. Here, we demonstrate how the IL-2 cytokine, collectively generated by strongly activated T cell clones, can induce weaker T cell clones to proliferate. We identify the PI3K pathway as being critical for integrating the antigen and cytokine responses and for controlling cell-cycle entry. We build a hybrid stochastic/deterministic computational model that accounts for such signal synergism and demonstrates quantitatively how T cells tune their cell-cycle entry according to environmental cytokine cues. Our findings indicate that antigen discrimination by T cells is not solely an intrinsic cellular property but rather a product of integration of multiple cues, including local cues such as antigen quality and quantity, to global ones like the extracellular concentration of inflammatory cytokines.

Allo-transplantation of mesenchymal stem cells attenuates hepatic injury through IL1Ra dependent macrophage switch in a mouse model of liver disease

BACKGROUND & AIMS

Autologous transplantation of mesenchymal stem cells (MSCs) reduces concanavalin A (Con A)-induced hepatic injury in mice. However, the mechanism is unclear and the therapeutic effect of allo-transplantation remains unknown. Our aim was to investigate the effects and mechanisms related to allo-transplantation of MSCs when used to treat Con A hepatic injury.

METHODS

After Con A-induced liver injury was created in C57BL/6J mice, MSCs derived from BALB/c mice or a vehicle control was administered.

RESULTS

Allo-transplantation of MSCs derived from BALB/c mice attenuated hepatic apoptosis in C57BL/6J mice that had undergone Con A-induced liver injury. MSCs increased the level of serum interleukin (IL)-10 and the phosphorylation of hepatic STAT3, but decreased the level of hepatic IFN-γ and phospho-STAT1. Notably, the administered MSCs were trapped mostly in the lungs and promoted the macrophage M2 switch, which contributed to the increased IL10 levels in the lungs and serum. Loss of the therapeutic effect was observed after knock-down of the expression of interleukin 1 receptor antagonist (IL1Ra) in the MSCs. In vitro investigation supported the hypothesis that MSCs are able to switch Con A-stimulated macrophages to the M2 phenotype, which results in an increase in IL10 production.

CONCLUSIONS

Allo-transplantation of MSCs reduces Con A liver injury by increasing IL10 production through an IL1Ra dependent macrophage switch.

Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels

A widespread feature of extracellular signaling in cell circuits is paradoxical pleiotropy: the same secreted signaling molecule can induce opposite effects in the responding cells. For example, the cytokine IL-2 can promote proliferation and death of T cells. The role of such paradoxical signaling remains unclear. To address this, we studied CD4(+) T cell expansion in culture. We found that cells with a 30-fold difference in initial concentrations reached a homeostatic concentration nearly independent of initial cell levels. Below an initial threshold, cell density decayed to extinction (OFF-state). We show that these dynamics relate to the paradoxical effect of IL-2, which increases the proliferation rate cooperatively and the death rate linearly. Mathematical modeling explained the observed cell and cytokine dynamics and predicted conditions that shifted cell fate from homeostasis to the OFF-state. We suggest that paradoxical signaling provides cell circuits with specific dynamical features that are robust to environmental perturbations.

T cell signaling. Antigen affinity, costimulation, and cytokine inputs sum linearly to amplify T cell expansion

T cell responses are initiated by antigen and promoted by a range of costimulatory signals. Understanding how T cells integrate alternative signal combinations and make decisions affecting immune response strength or tolerance poses a considerable theoretical challenge. Here, we report that T cell receptor (TCR) and costimulatory signals imprint an early, cell-intrinsic, division fate, whereby cells effectively count through generations before returning automatically to a quiescent state. This autonomous program can be extended by cytokines. Signals from the TCR, costimulatory receptors, and cytokines add together using a linear division calculus, allowing the strength of a T cell response to be predicted from the sum of the underlying signal components. These data resolve a long-standing costimulation paradox and provide a quantitative paradigm for therapeutically manipulating immune response strength.

Synergistic effects of ethanol and isopentenyl pyrophosphate on expansion of γδ T cells in synovial fluid from patients with arthritis

Low to moderate ethanol consumption has been associated with protective effects in autoimmune diseases such as rheumatoid arthritis, RA. An expansion of γδ T cells induced by isopentenyl pyrophosphate, IPP, likewise seems to have a protective role in arthritis. The aim of this project was to test the hypothesis that low doses of ethanol can enhance IPP-induced expansion of synovial fluid γδ T cells from patients with arthritis and may thereby potentially account for the beneficial effects of ethanol on symptoms of the arthritic process. Thus, mononuclear cells from synovial fluid (SF) from 15 patients with arthritis and from peripheral blood (PB) from 15 healthy donors were stimulated with low concentrations of ethanol and IPP for 7 days in vitro. IPP in combination with ethanol 0.015%, 2.5 mM, equivalent to the decrease per hour in blood ethanol concentration due to metabolism, gave a significantly higher fractional expansion of SF γδ T cells compared with IPP alone after 7 days (ratio 10.1+/-4.0, p<0.0008, n = 12) in patients with arthritis. Similar results were obtained for PB γδ T cells from healthy controls (ratio 2.0+/-0.4, p<0.011, n = 15). The augmented expansion of γδ T cells in SF is explained by a higher proliferation (p = 0.0034, n = 11) and an increased survival (p<0.005, n = 11) in SF cultures stimulated with IPP plus ethanol compared to IPP alone. The synergistic effects of IPP and ethanol indicate a possible allosteric effect of ethanol. Similar effects could be seen when stimulating PB with ethanol in presence of risedronate, which has the ability to increase endogenous levels of IPP. We conclude that expansion of γδ T cells by combinatorial drug effects, possibly in fixed-dose combination, FDC, of ethanol in the presence of IPP might give a protective role in diseases such as arthritis.

T cells translate individual, quantal activation into collective, analog cytokine responses via time-integrated feedbacks

Variability within isogenic T cell populations yields heterogeneous ‘local’ signaling responses to shared antigenic stimuli, but responding clones may communicate ‘global’ antigen load through paracrine messengers, such as cytokines. Such coordination of individual cell responses within multicellular populations is critical for accurate collective reactions to shared environmental cues. However, cytokine production may saturate as a function of antigen input, or be dominated by the precursor frequency of antigen-specific T cells. Surprisingly, we found that T cells scale their collective output of IL-2 to total antigen input over a large dynamic range, independently of population size. Through experimental quantitation and computational modeling, we demonstrate that this scaling is enforced by an inhibitory cross-talk between antigen and IL-2 signaling, and a nonlinear acceleration of IL-2 secretion per cell. Our study reveals how time-integration of these regulatory loops within individual cell signaling generates scaled collective responses and can be leveraged for immune monitoring.

DOI:http://dx.doi.org/10.7554/eLife.01944.001

The cells of the immune system face the challenge of removing viruses and other pathogens without endangering healthy tissues. Cells called T cells plays a variety of roles in the immune response: some T cells directly destroy infected cells, some recruit other cells called phagocytes to the site of infection, and some release small proteins called cytokines. These cytokines help cells to communicate with other cells and, therefore, to tailor the overall immune responses to deal with a particular pathogen.

It is known that mammals are capable of adjusting the T cell response to match the overall severity of an infection. However, it is not clear how individual T cells coordinate their seemingly binary response—they are either activated when they recognize a pathogen, or they are not activated—into a response at the collective cell level that can be varied continuously over a wide range of values.

Here, Tkach et al. show that T cell populations match their production of the cytokine interleukin 2 (IL-2) to the abundance of antigens—molecules released by the pathogen—over an unexpectedly large range of concentrations. Through a combination of experimental and computational analyses, Tkach et al. identified two novel IL-2 feedback loops that help to generate the correct quantity of cytokine, irrespective of the total number of T cells. Furthermore, this model can be used to estimate antigen quantities within diseased tissues. The work of Tkach et al. illustrates the potential of feedback integration in cell signalling and gene regulation as a mechanism to allow cellular populations to respond to environmental stimuli in a graded, collective fashion.

DOI:http://dx.doi.org/10.7554/eLife.01944.002

Quantal and graded stimulation of B lymphocytes as alternative strategies for regulating adaptive immune responses

Lymphocytes undergo a typical response pattern following stimulation in vivo: they proliferate, differentiate to effector cells, cease dividing and predominantly die, leaving a small proportion of long-lived memory and effector cells. This pattern results from cell-intrinsic processes following activation and the influence of external regulation. Here we apply quantitative methods to study B-cell responses in vitro. Our results reveal that B cells stimulated through two Toll-like receptors (TLRs) require minimal external direction to undergo the basic pattern typical of immunity. Altering the stimulus strength regulates the outcome in a quantal manner by varying the number of cells that participate in the response. In contrast, the T-cell-dependent CD40 activation signal induces a response where division times and differentiation rates vary in relation to stimulus strength. These studies offer insight into how the adaptive antibody response may have evolved from simple autonomous response patterns to the highly regulable state that is now observed in mammals.

Parameter identification for model of T cell proliferation in lymphopenia conditions

The number of T Lymphocytes (T cells) in the body is under homeostatic control. At equilibrium, the majority of naive T cells are non-dividing and express low levels of the surface protein CD44. In conditions of T cell deficiency (lymphopenia), naive T cells enter into a proliferative phase, undergoing cell division accompanied by a subtle change in their surface expression of CD44. In this study, we use a mathematical modelling approach to analyse the proliferative response of transgenic T cells in lymphopenic conditions. Our nonlinear model is composed of ordinary differential equations and partial differential equations structured by age (maturity of cell) and CD44 expression. To better understand the evolution of CD44 expression on the surface of T cells during cell division, we present a numerical analysis to solve a parameter identification problem. Finally, we show the parameters and the simulations that we obtain from the model and compare them to experimental data.

High-resolution temporal response patterns to influenza vaccine reveal a distinct human plasma cell gene signature

To identify sources of inter-subject variation in vaccine responses, we performed high-frequency sampling of human peripheral blood cells post-vaccination, followed by a novel systems biology analysis. Functional principal component analysis was used to examine time varying B cell vaccine responses. In subjects vaccinated within the previous three years, 90% of transcriptome variation was explained by a single subject-specific mathematical pattern. Within individual vaccine response patterns, a common subset of 742 genes was strongly correlated with migrating plasma cells. Of these, 366 genes were associated with human plasmablasts differentiating in vitro. Additionally, subject-specific temporal transcriptome patterns in peripheral blood mononuclear cells identified migration of myeloid/dendritic cell lineage cells one day after vaccination. Upstream analyses of transcriptome changes suggested both shared and subject-specific transcription groups underlying larger patterns. With robust statistical methods, time-varying response characteristics of individual subjects were effectively captured along with a shared plasma cell gene signature.

Fluctuation analysis: can estimates be trusted?

The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on the estimation of the relative fitness. The model is extended here to any division time distribution. Mutant counts follow a generalization of the Luria-Delbrück distribution, which depends on the mean number of mutations, the relative fitness of normal cells compared to mutants, and the division time distribution of mutant cells. Empirical probability generating function techniques yield precise estimates both of the mean number of mutations and the relative fitness of normal cells compared to mutants. In the case where no information is available on the division time distribution, it is shown that the estimation procedure using constant division times yields more reliable results. Numerical results both on observed and simulated data are reported.

Coupled IL-2-dependent extracellular feedbacks govern two distinct consecutive phases of CD4 T cell activation

T cells integrate cell-specific Ag receptor signaling with shared signals mediated by secreted cytokines, which often involve regulatory feedback loops. IL-2 signaling, for example, reduces the synthesis of IL-2 and increases the synthesis of IL-2Rα-chain, whereas both genes require TCR signaling for their activation. The ways by which T cells dynamically integrate these private and public signals during activation are not well understood. We combined robotics, multiparameter flow cytometry, and real-time quantitative PCR to analyze T cell activation at high temporal resolution over several days. Two distinct temporal phases of T cell activation were evident. First, Ag-dependent signals activated low IL-2Rα and high IL-2 production, independent of IL-2 signaling. Subsequently, secreted IL-2 acted as a shared resource driving high IL-2Rα expression, reduced IL-2 synthesis, and cell proliferation. This transition was independent of continued TCR signaling. Our data allowed the determination of the parameters of the IL-2-mediated extracellular positive and negative feedback circuits and demonstrated that the two loops are coupled and become activated at a similar level of IL-2 signaling. We propose that temporal separation of private and shared signals allows T cells to first integrate Ag-specific responses and subsequently share information leading to collective decision making.

Mutually exclusive regulation of T cell survival by IL-7R and antigen receptor-induced signals

Two major processes govern T cell proliferation and survival: interleukin-7-mediated homeostasis and antigen-induced selection. How cells transit between the two states is unknown. Here we show that T cell receptor ligation actively inhibits homeostatic survival signals while initiating a new, dominant survival programme. This switch is mediated by a change in the expression of pro- and anti-apoptosis proteins through the downregulation of Bcl-2 and the induction of Bim, A1 and Bcl-xL. Calcineurin inhibitors prevent the initiation of the new survival programme, while permitting the dominant repression of Bcl-2. Thus, in the presence of these drugs the response to antigen receptor ligation is cell death. Our results identify a molecular switch that can serve as an attractive target for inducing antigen-specific tolerance in treating autoimmune disease patients and transplant recipients.

Before antigen exposure, T cell survival is dependent on signalling stimulated by IL-7. Koenen et al. show that upon encountering specific antigen, T cell receptor signalling initiates a different set of survival pathways, which actively suppress those that sustain naive T cells.

The fate of CD4+ T cells under tolerance-inducing stimulation: a modeling perspective

Non-depleting anti-CD4 monoclonal antibodies (MAbs) induce long-term dominant tolerance mediated by regulatory T cells in several animal models of transplantation, allergy and autoimmunity. However, despite many studies on tolerance induction following CD4 blockade, the consequences of this intervention on T-cell kinetics are still unknown. Mathematical models have been useful to understand lymphocyte dynamics, estimating rates of proliferation and cell death following an intervention. Using the same strategy, we found that CD4(+) T cells activated in vitro in the presence of non-depleting anti-CD4 MAbs are prevented from undergoing optimal proliferation and show a higher frequency of apoptosis. Although the changes are small, during the course of a proliferative response, they lead to very distinct final levels of cell numbers. The importance of these mechanisms, predicted by the mathematical model, was validated by showing that lck-driven Bcl-x(L) transgenic mice, bearing T cells resistant to apoptosis, fail to become tolerant to skin grafts following CD4-blockade. Our data show that, in addition to induction of regulatory T cells, CD4 blockade has a marked effect in the effector T-cell pool by the combined action of hindering proliferation while favoring apoptosis. It is, therefore, the combination of all those mechanisms that leads to stable tolerance.

Asymmetry of Cell Division in CFSE-Based Lymphocyte Proliferation Analysis

Flow cytometry-based analysis of lymphocyte division using carboxyfluorescein succinimidyl ester (CFSE) dye dilution permits acquisition of data describing cellular proliferation and differentiation. For example, CFSE histogram data enable quantitative insight into cellular turnover rates by applying mathematical models and parameter estimation techniques. Several mathematical models have been developed using different types of deterministic or stochastic approaches. However, analysis of CFSE proliferation assays is based on the premise that the label is halved in the two daughter cells. Importantly, asymmetry of protein distribution in lymphocyte division is a basic biological feature of cell division with the degree of the asymmetry depending on various factors. Here, we review the recent literature on asymmetric lymphocyte division and CFSE-based lymphocyte proliferation analysis. We suggest that division- and label-structured mathematical models describing CFSE-based cell proliferation should take into account asymmetry and time-lag in cell proliferation. Utilization of improved modeling algorithms will permit straightforward quantification of essential parameters describing the performance of activated lymphocytes.

Quantifying T lymphocyte turnover

Peripheral T cell populations are maintained by production of naive T cells in the thymus, clonal expansion of activated cells, cellular self-renewal (or homeostatic proliferation), and density dependent cell life spans. A variety of experimental techniques have been employed to quantify the relative contributions of these processes. In modern studies lymphocytes are typically labeled with 5-bromo-2'-deoxyuridine (BrdU), deuterium, or the fluorescent dye carboxy-fluorescein diacetate succinimidyl ester (CFSE), their division history has been studied by monitoring telomere shortening and the dilution of T cell receptor excision circles (TRECs) or the dye CFSE, and clonal expansion has been documented by recording changes in the population densities of antigen specific cells. Proper interpretation of such data in terms of the underlying rates of T cell production, division, and death has proven to be notoriously difficult and involves mathematical modeling. We review the various models that have been developed for each of these techniques, discuss which models seem most appropriate for what type of data, reveal open problems that require better models, and pinpoint how the assumptions underlying a mathematical model may influence the interpretation of data. Elaborating various successful cases where modeling has delivered new insights in T cell population dynamics, this review provides quantitative estimates of several processes involved in the maintenance of naive and memory, CD4(+) and CD8(+) T cell pools in mice and men.

Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays

Methods that allow monitoring of individual cells over time, using live cell imaging, are essential for studying dynamical cellular processes in heterogeneous cell populations such as primary T lymphocytes. However, applying single cell time-lapse microscopy to study activation and differentiation of these cells was limited due to a number of reasons. First, primary naïve T cells are non-adherent and become highly motile upon activation through their antigen receptor. Second, CD4(+) T cell differentiation is a relatively slow process which takes 3-4 days. As a result, long-term dynamic monitoring of individual cells during the course of activation and differentiation is challenging as cells rapidly escape out of the microscope field of view. Here we present and characterize a platform which enables capture and growth of primary T lymphocytes with minimal perturbation, allowing for long-term monitoring of cell activation and differentiation. We use standard cell culture plates combined with PDMS based arrays containing thousands of deep microwells in which primary CD4(+) T cells are trapped and activated by antigen coated microbeads. We demonstrate that this system allows for live cell imaging of individual T cells for up to 72 h, providing quantitative data on cell proliferation and death times. In addition, we continuously monitor dynamics of gene expression in those cells, of either intracellular proteins using cells from transgenic mice expressing fluorescent reporter proteins, or cell surface proteins using fluorescently labeled antibodies. Finally, we show how intercellular interactions between different cell types can be investigated using our device. This system provides a new platform in which dynamical processes and intercellular interactions within heterogeneous populations of primary T cells can be studied at the single cell level.

Analysis and simulation of division- and label-structured population models : a new tool to analyze proliferation assays

In most biological studies and processes, cell proliferation and population dynamics play an essential role. Due to this ubiquity, a multitude of mathematical models has been developed to describe these processes. While the simplest models only consider the size of the overall populations, others take division numbers and labeling of the cells into account. In this work, we present a modeling and computational framework for proliferating cell populations undergoing symmetric cell division, which incorporates both the discrete division number and continuous label dynamics. Thus, it allows for the consideration of division number-dependent parameters as well as the direct comparison of the model prediction with labeling experiments, e.g., performed with Carboxyfluorescein succinimidyl ester (CFSE), and can be shown to be a generalization of most existing models used to describe these data. We prove that under mild assumptions the resulting system of coupled partial differential equations (PDEs) can be decomposed into a system of ordinary differential equations (ODEs) and a set of decoupled PDEs, which drastically reduces the computational effort for simulating the model. Furthermore, the PDEs are solved analytically and the ODE system is truncated, which allows for the prediction of the label distribution of complex systems using a low-dimensional system of ODEs. In addition to modeling the label dynamics, we link the label-induced fluorescence to the measure fluorescence which includes autofluorescence. Furthermore, we provide an analytical approximation for the resulting numerically challenging convolution integral. This is illustrated by modeling and simulating a proliferating population with division number-dependent proliferation rate.