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 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.

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 G 1 phase, and many mathematical models assume a constant time for traversing the S/G 2 /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/G 2 /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.he kinetic relationship between phases of the cell cycle first came to attention with the advent of autoradiographic techniques for detecting DNA synthesis in the 1950s (1, 2). It was realized that such data could be used to resolve the dynamics of the proliferating population if combined with an appropriate cell cycle model. However, direct filming of times to divide revealed remarkable variation, even among cloned, presumed identical, cells (3-6), eliminating simple deterministic models as the basis for cell cycle control. Working toward developing a general model, Smith and Martin made the striking observation that plotting the proportion of undivided cells versus time (so-called "alpha plots"), gave curves suggestive of two distinct phases, one relatively constant and another stochastic (7). They proposed that the two phases mapped to discrete states of the cell cycle. A resting "A state," they suggested, was contained within the G 1 phase from which cells could exit with constant probability per unit time (analogous to radioactive decay). The cells then entered the "B phase," which includes that part of G 1 not included in A state, as well as the entirety of S/G 2 /M. In B phase, cells' activities were first described to be "deterministic, and directed towards replication," implying a constant B phase. However, in the same paper, this assumption was relaxed and the duration of B phase was described with a relatively constant random variable (7).Although details of the quantitative relationship and biological interpretation have been debated (7-12), the rule that the bulk of kinetic variation is in G 1 phase, and that time in S/G 2 /M is relatively fixed, is widely accepted. Furthermore, mathematical models adopting this mechanical description (so-called "transition probability" or "compartment" models) remain popular and form the basis of many studies of lymphocyte and cancer kinetics in vitro and in vivo today (13-21).More recently, a molecular description of cell cycle regulation, including the discovery of key regulatory proteins such as cyclins a...

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.

Machine learning (ML) refers to a set of automatic pattern recognition methods that have been successfully applied across various problem domains, including biomedical image analysis. This review focuses on ML applications for image analysis in light microscopy experiments with typical tasks of segmenting and tracking individual cells, and modelling of reconstructed lineage trees. After describing a typical image analysis pipeline and highlighting challenges of automatic analysis (for example, variability in cell morphology, tracking in presence of clutters) this review gives a brief historical outlook of ML, followed by basic concepts and definitions required for understanding examples. This article then presents several example applications at various image processing stages, including the use of supervised learning methods for improving cell segmentation, and the application of active learning for tracking. The review concludes with remarks on parameter setting and future directions.

SummaryMotility is a fundamental part of cellular life and survival, including for Plasmodium parasites – single‐celled protozoan pathogens responsible for human malaria. The motile life cycle forms achieve motility, called gliding, via the activity of an internal actomyosin motor. Although gliding is based on the well‐studied system of actin and myosin, its core biomechanics are not completely understood. Currently accepted models suggest it results from a specifically organized cellular motor that produces a rearward directional force. When linked to surface‐bound adhesins, this force is passaged to the cell posterior, propelling the parasite forwards. Gliding motility is observed in all three life cycle stages of Plasmodium: sporozoites, merozoites and ookinetes. However, it is only the ookinetes – formed inside the midgut of infected mosquitoes – that display continuous gliding without the necessity of host cell entry. This makes them ideal candidates for invasion‐free biomechanical analysis. Here we apply a plate‐based imaging approach to study ookinete motion in three‐dimensional (3D) space to understand Plasmodium cell motility and how movement facilitates midgut colonization. Using single‐cell tracking and numerical analysis of parasite motion in 3D, our analysis demonstrates that ookinetes move with a conserved left‐handed helical trajectory. Investigation of cell morphology suggests this trajectory may be based on the ookinete subpellicular cytoskeleton, with complementary whole and subcellular electron microscopy showing that, like their motion paths, ookinetes share a conserved left‐handed corkscrew shape and underlying twisted microtubular architecture. Through comparisons of 3D movement between wild‐type ookinetes and a cytoskeleton‐knockout mutant we demonstrate that perturbation of cell shape changes motion from helical to broadly linear. Therefore, while the precise linkages between cellular architecture and actomyosin motor organization remain unknown, our analysis suggests that the molecular basis of cell shape may, in addition to motor force, be a key adaptive strategy for malaria parasite dissemination and, as such, transmission.

How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and semantic search. In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. While a number of approaches have been developed to address this problem for general graphs, they do not fully utilize information available in KGs, or lack flexibility needed to model complex relationship between entities and their importance. To address these limitations, we explore supervised machine learning algorithms. In particular, building upon recent advancement of graph neural networks (GNNs), we develop GENI, a GNN-based method designed to deal with distinctive challenges involved with predicting node importance in KGs. Our method performs an aggregation of importance scores instead of aggregating node embeddings via predicate-aware attention mechanism and flexible centrality adjustment. In our evaluation of GENI and existing methods on predicting node importance in real-world KGs with different characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.

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.