Andrey Kan

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.

On compressing weighted time-evolving graphs

Existing graph compression techniquesmostly focus on static graphs. However for many practical graphs such as social networks the edge weights frequently change over time. This phenomenon raises the question of how to compress dynamic graphs while maintaining most of their intrinsic structural patterns at each time snapshot. In this paper we show that the encoding cost of a dynamic graph is proportional to the heterogeneity of a three dimensional tensor that represents the dynamic graph. We propose an effective algorithm that compresses a dynamic graph by reducing the heterogeneity of its tensor representation, and at the same time also maintains a maximum lossy compression error at any time stamp of the dynamic graph. The bounded compression error benefits compressed graphs in that they retain good approximations of the original edge weights, and hence properties of the original graph (such as shortest paths) are well preserved. To the best of our knowledge, this is the first work that compresses weighted dynamic graphs with bounded lossy compression error at any time snapshot of the graph.

Automated and semi-automated cell tracking: addressing portability challenges

Cell tracking is a key task in the high‐throughput quantitative study of important biological processes, such as immune system regulation and neurogenesis. Variability in cell density and dynamics in different videos, hampers portability of existing trackers across videos. We address these potability challenges in order to develop a portable cell tracking algorithm. Our algorithm can handle noise in cell segmentation as well as divisions and deaths of cells. We also propose a parameter‐free variation of our tracker. In the tracker, we employ a novel method for recovering the distribution of cell displacements. Further, we present a mathematically justified procedure for determining the gating distance in relation to tracking performance. For the range of real videos tested, our tracker correctly recovers on average 96% of cell moves, and outperforms an advanced probabilistic tracker when the cell detection quality is high. The scalability of our tracker was tested on synthetic videos with up to 200 cells per frame. For more challenging tracking conditions, we propose a novel semi‐automated framework that can increase the ratio of correctly recovered tracks by 12%, through selective manual inspection of only 10% of all frames in a video.

Quantitative analysis of Plasmodium ookinete motion in three dimensions suggests a critical role for cell shape in the biomechanics of malaria parasite gliding motility

Motility 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.

Machine learning applications in cell image analysis

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.

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.

Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation

Blockmodelling is an important technique in social network analysis for discovering the latent structure in graphs. A blockmodel partitions the set of vertices in a graph into groups, where there are either many edges or few edges between any two groups. For example, in the reply graph of a question and answer forum, blockmodelling can identify the group of experts by their many replies to questioners, and the group of questioners by their lack of replies among themselves but many replies from experts. Non-negative matrix factorisation has been successfully applied to many problems, including blockmodelling. However, these existing approaches can fail to discover the true latent structure when the graphs have strong background noise or are sparse, which is typical of most real graphs. In this paper, we propose a new non-negative matrix factorisation approach that can discover blockmodels in sparse and noisy graphs. We use synthetic and real datasets to show that our approaches have much higher accuracy and comparable running times.

A time decoupling approach for studying forum dynamics

Online forums are rich sources of information about user communication activity over time. Finding temporal patterns in online forum communication threads can advance our understanding of the dynamics of conversations. The main challenge of temporal analysis in this context is the complexity of forum data. There can be thousands of interacting users, who can be numerically described in many different ways. Moreover, user characteristics can evolve over time. We propose an approach that decouples temporal information about users into sequences of user events and inter-event times. We develop a new feature space to represent the event sequences as paths, and we model the distribution of the inter-event times. We study over 30,000 users across four Internet forums, and discover novel patterns in user communication. We find that users tend to exhibit consistency over time. Furthermore, in our feature space, we observe regions that represent unlikely user behaviors. Finally, we show how to derive a numerical representation for each forum, and we then use this representation to derive a novel clustering of multiple forums.

Measures for ranking cell trackers without manual validation

Abstract Cell tracking is often implemented as cell detection and data association steps. For a particular detection output it is a challenge to automatically select the best association algorithm. We approach this challenge by developing novel measures for ranking the association algorithms according to their performance without the need for a ground truth. We formulate tracking as a binary classification task and develop our principal measure (ED-score) based on the definitions of precision and recall. On a range of real cell videos tested, ED-score has a strong correlation (−0.87) with F-score. However, ED-score does not require a ground truth for computation.