Julia M. Marchingo

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. T cells follow a linear calculation when integrating costimulatory and cytokine signals. Stimulatory signals add up for T cells T cell activation is a dynamic process. T cells encounter multiple input signals such as antigens, costimulatory molecules, and cytokines at different times and anatomical locations during an infection. But how do T cells integrate this information to determine the extent to which they divide? To find out, Marchingo et al. stimulated mouse T cells in culture with different combinations of inputs and also tracked antigen-specific T cell responses in mice infected with influenza virus. They found that T cells linearly sum the various stimulatory inputs they receive to determine their “division destiny.” Science, this issue p. 1123

Stretched cell cycle model for proliferating lymphocytes

Significance Cell division is essential for an effective immune response. Estimates of rates of division are often based on DNA measurements interpreted with an appropriate model for internal cell cycle steps. Here we use time-lapse microscopy and single cell tracking of T and B lymphocytes from reporter mice to measure times spent in cell cycle phases. These data led us to a stretched cell cycle model, a novel and improved mathematical description of cell cycle progression for proliferating lymphocytes. Our model can be used to deduce cell cycle parameters for lymphocytes from DNA and BrdU labeling and will be useful when comparing the effects of different stimuli, or therapeutic treatments on immune responses, or to understand molecular pathways controlling cell division. Stochastic variation in cell cycle time is a consistent feature of otherwise similar cells within a growing population. Classic studies concluded that the bulk of the variation occurs in the G1 phase, and many mathematical models assume a constant time for traversing the S/G2/M phases. By direct observation of transgenic fluorescent fusion proteins that report the onset of S phase, we establish that dividing B and T lymphocytes spend a near-fixed proportion of total division time in S/G2/M phases, and this proportion is correlated between sibling cells. This result is inconsistent with models that assume independent times for consecutive phases. Instead, we propose a stretching model for dividing lymphocytes where all parts of the cell cycle are proportional to total division time. Data fitting based on a stretched cell cycle model can significantly improve estimates of cell cycle parameters drawn from DNA labeling data used to monitor immune cell dynamics.

T-cell stimuli independently sum to regulate an inherited clonal division fate.

In the presence of antigen and costimulation, T cells undergo a characteristic response of expansion, cessation and contraction. Previous studies have revealed that population-level reproducibility is a consequence of multiple clones exhibiting considerable disparity in burst size, highlighting the requirement for single-cell information in understanding T-cell fate regulation. Here we show that individual T-cell clones resulting from controlled stimulation in vitro are strongly lineage imprinted with highly correlated expansion fates. Progeny from clonal families cease dividing in the same or adjacent generations, with inter-clonal variation producing burst-size diversity. The effects of costimulatory signals on individual clones sum together with stochastic independence; therefore, the net effect across multiple clones produces consistent, but heterogeneous population responses. These data demonstrate that substantial clonal heterogeneity arises through differences in experience of clonal progenitors, either through stochastic antigen interaction or by differences in initial receptor sensitivities.

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 Measurement Models for Quantifying Lymphocyte Responses Using Flow Cytometry.

Adaptive immune responses are complex dynamic processes whereby B and T cells undergo division and differentiation triggered by pathogenic stimuli. Deregulation of the response can lead to severe consequences for the host organism ranging from immune deficiencies to autoimmunity. Tracking cell division and differentiation by flow cytometry using fluorescent probes is a major method for measuring progression of lymphocyte responses, both in vitro and in vivo. In turn, mathematical modeling of cell numbers derived from such measurements has led to significant biological discoveries, and plays an increasingly important role in lymphocyte research. Fitting an appropriate parameterized model to such data is the goal of these studies but significant challenges are presented by the variability in measurements. This variation results from the sum of experimental noise and intrinsic probabilistic differences in cells and is difficult to characterize analytically. Current model fitting methods adopt different simplifying assumptions to describe the distribution of such measurements and these assumptions have not been tested directly. To help inform the choice and application of appropriate methods of model fitting to such data we studied the errors associated with flow cytometry measurements from a wide variety of experiments. We found that the mean and variance of the noise were related by a power law with an exponent between 1.3 and 1.8 for different datasets. This violated the assumptions inherent to commonly used least squares, linear variance scaling and log-transformation based methods. As a result of these findings we propose a new measurement model that we justify both theoretically, from the maximum entropy standpoint, and empirically using collected data. Our evaluation suggests that the new model can be reliably used for model fitting across a variety of conditions. Our work provides a foundation for modeling measurements in flow cytometry experiments thus facilitating progress in quantitative studies of lymphocyte responses.

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.