Microscopy image processing is critical for precision oncology and immunotherapy, two approaches in cancer treatment often combined to enhance patient outcomes. Numerous scientists have studied the effects of drugs on the immune system and tumors. To quantify the impact of various drugs on different immune and cancer cell types, medical researchers conduct ex vivo assays. In these assays, patient-derived live cells are placed in an artificial microenvironment where drug responses are monitored over time. Brightfield microscopy captures one image every half hour of numerous patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to six days. These images are used to quantify the response of different cells to various drugs. However, tracking thousands of cells in low-resolution, low-frame-rate images remains challenging. Existing digital image processing algorithms require high-resolution images involving very few cells from homogeneous cell line populations. In this work, we propose three novel frameworks to track cancer cells, capture their behavior, and quantify cell viability to inform clinical decisions in a high-throughput manner.

Injury, infection and tissue malfunction are triggers of inflammation which if not regulated may acquire new characteristics that result in pathological outcomes. Since innate immunity plays a key role in the resolution of acute inflammation knowledge of the regulation of this component of the host response is relevant to understanding processes in disease progression and therefore has potential clinical benefits. In this thesis I have applied zebrafish as a model organism to investigate the response of innate immune cells to qualitatively distinct inflammatory signals in the absence of adaptive immunity. Using a zebrafish embryo wound injury model I have investigated leukocyte migration profiles by in vivo imaging. In response to wound alone leukocytes migrated to the site of injury with predominantly random walk behaviour. However, the addition of lipopolysaccharide (LPS) enhanced recruitment and influenced the directionality of leukocyte migration to the wound. I demonstrate that leukocyte dynamic behaviour is also dependent on the location of the cells. The LPS enhanced directionality and reduced the random walk behaviour of the leukocytes, and these effects were ablated in the presence of the p38 mitogenactivated protein kinase (MAPK) specific inhibitor SB203580. Cytokine gene profiling in adult zebrafish leukocytes reveals that LPS can stimulate a pro-inflammatory response via the activation of p38 MAPK characteristic of mammalian innate immune responses. It is documented in mammalian innate immune cells that LPS can modulate Notch mediated signalling and thereby cell function. Using zebrafish with null mutations in Notch, which provide an unbiased in vivo model, I have investigated the influence of Notch signalling on leukocyte recruitment and demonstrate that migration to a wound injury is reduced. However, this effect is due to decreased cell numbers and not altered function as the Notch signalling inhibitor DAPT had no effect of recruitment to wound injury. The defect in myelomonocyte numbers was also present in adult zebrafish and this was partially compensated for by an increase in lymphocytes. The experimental results that I report here highlight zebrafish as a model 2 organism for studying the function and regulation of innate immunity. The unique optical translucency, which permits in vivo imaging of host responses in real-time, facilitates the analysis of the innate immune response to different inflammatory signals and immune modulators.

Motility of unicellular algal cells, especially its ability to respond to environmental cues, is crucial in industrial and ecological contexts and has been studied extensively with the model organism Chlamydomonas. However, little is known about the relationship between motility and the cell-cycle, despite the apparent link of mitosis dependent morphological changes which involve the flagella and the dependency on light/dark conditions of the cellular reactions. In this study, the cell swimming speed, the rotational diffusivity and its swimming bias against gravity were measured with high-speed video microscopy with Chlamydomonas reinhardtii cultures highly synchronised to a diurnal cycle. A simulation of gravitactic cell trajectories was developed to assist the statistical analysis of the cell trajectories from the images, which subject to a projection effect and has not been addressed previously. Its morphological changes, including cell size, shape, mass density, and presence of flagella were also evaluated. Un unforeseen change of cell motility at a critical mitosis time point was discovered, and our analysis suggests a connection to the alternating cell energetic regimes rather than the cell morphology. As indicated by results obtained from imaging based experimental measurements and by computational methods, the motility variation has direct consequences on the collective motion of algal cells in a Hagen-Poiseuille flow, a relevant component flow for air-lift photobioreactors. The cell number density profiles were calculated by an individual-based simulation and a continuum system inclusive of the buoyancy effect imposed by the aggregated cells on their surrounding fluid. Qualitative experimental-theoretical agreement suggests that the models can be employed for optimisation problems of photobioreactor flow designs inclusive of the non-negligible cell-cycle effects, which has been commonly overlooked in previous studies.

The goal of this project was to develop a series of nano platforms for single cell analysis and drug delivery. Nanoparticles are a promising option to improve our medical therapies by controlling biodistribution and pharmacokinetics of therapeutics. Nanosystems also offer significant opportunity to improve current imaging modalities. The systems developed during this thesis work can be foundations for developing advanced therapies for obesity and improving our fundamental understandings of single cell behavior. The first of the two systems we attempt to create was a drug delivery system that could selectively target adipose tissue to deliver uncoupling agents and drive browning of adipose tissue and associated weight loss. Protonophores have a history of significant toxic side effects in cardiac and neuronal tissues a recently discovered protonophore, but BAM-15, has been shown to have reduced cytotoxicity. We hypothesized that the altered biodistribution of BAM-15 encapsulated in a nanoparticle could provide systemic weight loss with minimized side effects. The second system developed utilized quantum dots to create a fluorescent barcode that could be repeatedly identified using quantitative fluorescent emission readings. This platform would allow for the tracking of individual cells, allowing repeat interrogation across time and space in complex multicellular environments. Ultimately this work demonstrates the process and complexity involved in developing nanoparticulate systems meant to interact with incredibly complex intracellular environments.

Analysing migrating cells in microscopy time-lapse images has already helped the understanding of many biological processes and may be of importance in the development of new medical treatments. Today’s biological experiments tend to produce a huge amount of dynamic image data and tracking the individual cells by hand has become a bottleneck for the further analysis work. A number of cell tracking methods have therefore been developed over the past decades, but still many of the techniques have a limited performance. The aim of this Master Project is to develop a particle filter algorithm that automatically detects and tracks a large number of individual cells in an image sequence. The solution is based on a Rao-Blackwellized particle filter for multiple object tracking. The report also covers a review of existing automatic cell tracking techniques, a review of well-known filter techniques for single target tracking and how these techniques have been developed to handle multiple target tracking. The designed algorithm has been tested on real microscopy image data of neutrophils with 400 to 500 cells in each frame. The designed algorithm works well in areas of the images where no cells touch and can in these situations also correct for some segmentation mistakes. In areas where cells touch, the algorithm works well if the segmentation is correct, but often makes mistakes when it is not. A target effectiveness of 77 percent and a track purity of 80 percent are then achieved.

Recent breakthroughs in microscopy techniques and fluorescence probes enable the recording of mouse embryogenesis at the cellular level for days, easily generating terabyte-level 3D time-lapse data. Since millions of cells are involved, this information-rich data brings a natural demand for an automated tool for its comprehensive analysis. This tool should automatically (1) detect and segment cells at each time point and (2) track cell migration across time. Most existing cell tracking methods cannot scale to the data with such large size and high complexity. For those purposely designed for embryo data analysis, the accuracy is heavily sacrificed. Here, we present a new computational framework for the mouse embryo data analysis with high accuracy and efficiency. Our framework detects and segments cells with a fully probability-principled method, which not only has high statistical power but also helps determine the desired cell territories and increase the segmentation accuracy. With the cells detected at each time point, our framework reconstructs cell traces with a new minimum-cost circulation-based paradigm, CINDA (CIrculation Network-based DataAssociation). Compared with the widely used minimum-cost flow-based methods, CINDA guarantees the global optimal solution with the best-of-known theoretical worst-case complexity and hundreds to thousands of times practical efficiency improvement. Since the information extracted from a single time point is limited, our framework iteratively refines cell detection and segmentation results based on the cell traces which contain more information from other time points. Results show that this dramatically improves the accuracy of cell detection, segmentation, and tracking. To make our work easy to use, we designed a standalone software, MIVAQ (Microscopic Image Visualization, Annotation, and Quantification), with our framework as the backbone and a user-friendly interface. With MIVAQ, users can easily analyze their data and visually check the results. / Doctor of Philosophy / Mouse embryogenesis studies mouse embryos from fertilization to tissue and organ formation. The current microscope and fluorescent labeling technique enable the recording of the whole mouse embryo for a long time with high resolution. The generated data can be terabyte-level and contains more than one million cells. This information-rich data brings a natural demand for an automated tool for its comprehensive analysis. This tool should automatically (1) detect and segment cells at each time point to get the information of cell morphology and (2) track cell migration across time. However, the development of analytical tools lags far behind the capability of data generation. Existing tools either cannot scale to the data with such large size and high complexity or sacrifice accuracy heavily for efficiency. In this dissertation, we present a new computational framework for the mouse embryo data analysis with high accuracy and efficiency. To make our framework easy to use, we also designed a standalone software, MIVAQ, with a user-friendly interface. With MIVAQ, users can easily analyze their data and visually check the results.

Advances in time-lapse 3D in vivo fluorescence microscopic imaging techniques enables the observation and investigation into the migration of Oligodendrocyte precursor cells (OPCs) and its role in the central nervous system. However, current practice of image-based OPC motility analysis heavily relies on manual labeling and tracking on 2D max projection of the 3D data, which suffers from massive human labor, subjective biases, weak reproducibility and especially information loss and distortion. Besides, due to the lack of OPC specific genetically encoded indicator, OPCs can only be identified from other oligodendrocyte lineage cells by their observed motion patterns. Automated analytical tools are needed for the identification and tracking of OPCs. In this dissertation work, we proposed an analytical framework, MicTracker (Migrating Cell Tracker), for the integrated task of identifying, segmenting and tracking migrating cells (OPCs) from in vivo time-lapse fluorescence imaging data of high-density cell clusters composed of cells with different modes of motions. As a component of the framework, we presented a novel strategy for cell segmentation with global temporal consistency enforced, tackling the challenges caused by highly clustered cell population and temporally inconsistently blurred boundaries between touching cells. We also designed a data association algorithm to address the violation of usual assumption of small displacements. Recognizing that the violation was in the mixed cell population composed of two cell groups while the assumption held within each group, we proposed to solve the seemingly impossible mission by de-mixing the two groups of cell motion modes without known labels. We demonstrated the effectiveness of MicTracker in solving our problem on in vivo real data. / Doctor of Philosophy / Oligodendrocyte precursor cells (OPCs) are a type of motile cells in the central nervous system (CNS). OPCs' migration plays a critical role in the repair and re-distribution of myelin sheaths, a structures that helps to accelerate the transmission of electrical signals from neuron to neuron. But the mechanism behind the motility of OPCs is largely unclear. In recent years, advances in genetic fluorescence indicators and time-lapse optical microscopic imaging techniques, especially 3D in vivo imaging, enables neuroscientists to investigate into the puzzle. However, current practice of OPC motility analysis heavily relies on compressing the 3D data into 2D then manually tracking the OPCs, which suffers from not only massive human labor, subjective biases, weak reproducibility and especially information loss and distortion. Automated analytical tools are needed. Due to the limitation of current techniques in fluorescent labeling of cells in live animals, OPCs cannot be distinctively labeled. Instead, in the field of view there are also other irrelevant cells that cannot migrate but locally vibrate. Therefore, the human analyzer or the analytical software is supposed to detect OPCs from a cluster of touching cells containing multiple types of cells by their motion patterns only. In this dissertation, we presented a fully automatic machine learning based algorithm, MicTracker (Migrating Cell Tracker), to identify and track migrating OPCs. The task cannot be straightforwardly solved by existing generic-purpose cell tracking tools due to quite a few special challenges. To tackle the challenges, we also proposed novel methods for two major modules of MicTracker, segmentation and linking, respectively. We demonstrated the effectiveness of MicTracker and its components on real data and compared it with related existing works. The results of experiments showed notable superiority of MicTracker in solving our problem, compared with existing methods.

Fluorescence microscopy is a pivotal imaging technique to visualize biological processes and has been extensively utilized in live-cell morphology analysis. Despite its utility, related object detection and tracking tasks still face challenges due to large data scales, inferior data quality, and insufficient annotations, leading to reliance on adaptive thresholding. Current adaptive thresholding approaches have two significant limitations: Firstly, they cannot handle the heteroscedasticity of image data well and result in biased outputs. Secondly, they deal with frames of time-series imaging data independently and result in inconsistent detections over time. We introduce two novel optimization techniques to address these limitations and enhance detection and tracking results in live-cell imaging. The first one, ConvexVST, is a convex optimization approach to transform heteroscedastic data into homoscedastic data, making them more tractable for subsequent analysis. The second one, Joint Thresholding, is a graph-based approach to get the optimal adaptive thresholds while maintaining temporal consistency. Our methods demonstrate superior performance across various object detection and tracking tasks. Specifically, when applied to microglia imaging data, our techniques enable the acquisition of more complete cell morphology and more accurate detection of microglia tips. Furthermore, by integrating these techniques with existing frameworks, we propose an advanced pipeline for embryonic cell detection and tracking in light-sheet microscopy images, which is streets ahead of state-of-the-art peer methods and sets a new benchmark in the field. / Doctor of Philosophy / Fluorescence microscopy is an important imaging tool for observing biological processes and is widely used to study live-cell structures and activities. However, detecting and tracking objects in these images can be difficult because of the large amount of data, poor image quality, and lack of accurate annotations. It leads to the reliance on basic image segmentation approaches, which try to distinguish foreground from background by setting intensity thresholds. These methods have two main problems: they don't handle varying noise in image data well, resulting in inaccurate outputs, and they analyze each frame in a sequence of images independently, causing inconsistencies over time. To solve these issues, we developed two new techniques to improve detection performance in live-cell imaging. The first one, ConvexVST, makes the noise levels in image data more uniform, simplifying the following analysis. The second one, Joint Thresholding, can find the best intensity thresholds while maintaining consistency across frames over time. Our methods have shown significant improvements in detecting and tracking objects. For example, when applied to images of microglia (a type of brain cell), they provide more complete cell shapes and more accurate detection of cell structures. Additionally, by combining these techniques with existing frameworks, we create an advanced pipeline for detecting and tracking embryonic cells that outperforms current leading methods.

Yes / To develop an efficient nanotechnology fluorescence-based method to track cell proliferation to avoid the limitations of current cell-labeling dyes. Material & methods: Synthesis, PEGylation, bifunctionalization and labeling with a fluorophore (Cy5) of 200 nm polystyrene nanoparticles (NPs) were performed. These NPs were characterized and assessed for in vitro long-term monitoring of cell proliferation. Results: The optimization and validation of this method to track long-term cell proliferation assays have been achieved with high reproducibility, without cell cycle disruption. This method has been successfully applied in several adherent and suspension cells including hard-to-transfect cells and isolated human primary lymphocytes. Conclusion: A novel approach to track efficiently cellular proliferation by flow cytometry using fluorescence labeled NPs has been successfully developed.

A novel series of near-infrared fluorescent contrast agents was developed and characterized. Their physicochemical and optical properties were measured. By altering functional groups of cyanine fluorophores, the selective targeting of endocrine glands, exocrine glands, cartilage and bone using NIR fluorescence to visualize the targeted tissue has been reported. These agents have high specificity for tissue targeting inherent to the chemical structure of the fluorophore. After a single low-dose intravenous injection these agents have high specificity for tissue targeting inherent to the chemical structure of the fluorophore. The results lay the foundation for future improvements in optical imaging in endocrine surgery, tissue engineering, joint surgery, and cartilage-specific drug development.