Scalable Inference for Multi-Target Tracking of Proliferating Cells

With the continuous advancements in microscopy techniques such as improved image quality, faster acquisition and reduced photo-toxicity, the amount of data recorded in the life sciences is rapidly growing. Clearly, the size of the data renders manual analysis intractable, calling for automated cell tracking methods. Cell tracking – in contrast to other tracking scenarios – exhibits several difficulties: low signal to noise ratio in the images, high cell density and sometimes cell clusters, radical morphology changes, but most importantly cells divide – which is often the focus of the experiment. These peculiarities have been targeted by tracking-byassignment methods that first extract a set of detection hypotheses and then track those over time. Improving the general quality of these cell tracking methods is difficult, because every cell type, surrounding medium, and microscopy setting leads to recordings with specific properties and problems. This unfortunately implies that automated approaches will not become perfect any time soon but manual proof reading by experts will remain necessary for the time being. In this thesis we focus on two different aspects, firstly on scaling previous and developing new solvers to deal with longer videos and more cells, and secondly on developing a specialized pipeline for detecting and tracking tuberculosis bacteria. The most powerful tracking-by-assignment methods are formulated as probabilistic graphical models and solved as integer linear programs. Because those integer linear programs are in general NP-hard, increasing the problem size will lead to an explosion of computational cost. We begin by reformulating one of these models in terms of a constrained network flow, and show that it can be solved more efficiently. Building on the successful application of network flow algorithms in the pedestrian tracking literature, we develop a heuristic to integrate constraints – here for divisions – into such a network flow method. This allows us to obtain high quality approximations to the tracking solution while providing a polynomial runtime guarantee. Our experiments confirm this much better scaling behavior to larger problems. However, this approach is single threaded and does not utilize available resources of multi-core machines yet. To parallelize the tracking problem we present a simple yet effective way of splitting long videos into intervals that can be tracked independently, followed by a sparse global stitching step that resolves disagreements at the cuts. Going one step further, we propose a microservices based software design for ilastik that allows to distribute all required computation for segmentation, object feature extraction, object classification and tracking across the nodes of a cluster or in the cloud. Finally, we discuss the use case of detecting and tracking tuberculosis bacteria in more detail, because no satisfying automated method to this important problem existed before. One peculiarity of these elongated cells is that they build dense clusters in which it is hard to outline individuals. To cope with that we employ a tracking-by-assignment model that allows competing detection hypotheses and selects the best set of detections while considering the temporal context during tracking. To obtain these hypotheses, we develop a novel algorithm that finds diverseM- best solutions of tree-shaped graphical models by dynamic programming. First experiments with the pipeline indicate that it can greatly reduce the required amount of human intervention for analyzing tuberculosis treatment

Modeling flow cytometry data for cancer vaccine immune monitoring

Flow cytometry (FCM) is widely used in cancer research for diagnosis, detection of minimal residual disease, as well as immune monitoring and profiling following immunotherapy. In all these applications, the challenge is to detect extremely rare cell subsets while avoiding spurious positive events. To achieve this objective, it helps to be able to analyze FCM data using multiple markers simultaneously, since the additional information provided often helps to minimize the number of false positive and false negative events, hence increasing both sensitivity and specificity. However, with manual gating, at most two markers can be examined in a single dot plot, and a sequential strategy is often used. As the sequential strategy discards events that fall outside preceding gates at each stage, the effectiveness of the strategy is difficult to evaluate without laborious and painstaking back-gating. Model-based analysis is a promising computational technique that works using information from all marker dimensions simultaneously, and offers an alternative approach to flow analysis that can usefully complement manual gating in the design of optimal gating strategies. Results from model-based analysis will be illustrated with examples from FCM assays commonly used in cancer immunotherapy laboratories

Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings

Comparing unpaired samples of a distribution or population taken at different points in time is a fundamental task in many application domains where measuring populations is destructive and cannot be done repeatedly on the same sample, such as in single-cell biology. Optimal transport (OT) can solve this challenge by learning an optimal coupling of samples across distributions from unpaired data. However, the usual formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes (e.g., cell proliferation or death) between measurements. In this work, we introduce NubOT, a neural unbalanced OT formulation that relies on the formalism of semi-couplings to account for creation and destruction of mass. To estimate such semi-couplings and generalize out-of-sample, we derive an efficient parameterization based on neural optimal transport maps and propose a novel algorithmic scheme through a cycle-consistent training procedure. We apply our method to the challenging task of forecasting heterogeneous responses of multiple cancer cell lines to various drugs, where we observe that by accurately modeling cell proliferation and death, our method yields notable improvements over previous neural optimal transport methods

Neuronal differentiation influences progenitor arrangement in the vertebrate neuroepithelium

Cell division, movement and differentiation contribute to pattern formation in developing tissues. This is the case in the vertebrate neural tube, in which neurons differentiate in a characteristic pattern from a highly dynamic proliferating pseudostratified epithelium. To investigate how progenitor proliferation and differentiation affect cell arrangement and growth of the neural tube, we used experimental measurements to develop a mechanical model of the apical surface of the neuroepithelium that incorporates the effect of interkinetic nuclear movement and spatially varying rates of neuronal differentiation. Simulations predict that tissue growth and the shape of lineage-related clones of cells differ with the rate of differentiation. Growth is isotropic in regions of high differentiation, but dorsoventrally biased in regions of low differentiation. This is consistent with experimental observations. The absence of directional signalling in the simulations indicates that global mechanical constraints are sufficient to explain the observed differences in anisotropy. This provides insight into how the tissue growth rate affects cell dynamics and growth anisotropy and opens up possibilities to study the coupling between mechanics, pattern formation and growth in the neural tube

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarise the scope, drawbacks, and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. Finally, by providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks.Comment: 30 pages, 3 figures, 3 table

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem’s complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques