Describing motions in biological tissues: a continuum active model and improving measurements

Motions in biological tissues strongly influence their properties and are crucial for their functions. This is true starting from the scale of single molecules, all the way up to the scale of entire tissues. One of the key properties distinguishing motions in living systems from those in dead matter is activity: using chemical energy to generate self-propulsion. Effective theoretical, physics-based models are necessary both to interpret the rich new experimental observations in the field of biological motions, and to properly account for the inherent errors of the experimental methods. In this work we study models related to motion both on the level of tissues and individual molecules. One of our models is driven by the observation that many growing tissues form multicellular protrusions at their edges. It is not fully understood how these are initiated, therefore we propose a minimal continuum physical model to suggest a possible mechanism. We apply our model to a growing circular tumour. We employ our approach to understand how activity affects the tumour’s dynamics and the tendency to form “fingers” at its boundary. This approach rests on just four key biophysical parameters and we can estimate them based on experiments described in the literature. Our modelling of a tumour is experimentally well justified and analytically solvable in many systems. It is, to the best of our knowledge, the first analytical description of tumour interface dynamics incorporating the activity of the tumour bulk. We can explain the propensity of tissues to fingering instabilities, as conditioned by the magnitude of active traction and the growth kinetics. We are also able to derive predictions for the tumour size at the onset of metastasis, and predictions for the number of subsequent invasive fingers. Microscopy-based techniques are essential for observing biological motions at all aforementioned length scales. Brownian particle videotracking is one example of such a technique. In the second part of this thesis, we apply physics-based theory to understand inherent errors and limitations of this method. Using analytic solutions and simulations, we show the effects of errors in particle videotracking on recovering energy landscapes from the distributions of Brownian particles. We point out mechanisms that result in nontrivial systematic biases in the measurements.The Cambridge Trust Cambridge Philosophical Societ

An integrated optical platform for micromanipulation of cells and tissue in live animals

Thesis (Ph.D.)--Boston UniversityThe hematopoietic stem cell niche is a specialized bone marrow (BM) microenvironment where blood-forming cells reside. Interactions between these rare cells and their niche need to be studied at the single-cell level. While live animal cell tracking with optical microscopy has proven useful for this purpose, a more thorough characterization requires novel approaches. This can be accomplished by using an integrated optical platform for cell and tissue manipulations (cell transplantation and extraction) in the skull bone of live mice. The platform integrates a non-damaging laser ablation microbeam for bone removal and tissue cutting, optical tweezers for single cell trapping, and a video-rate scanning microscope. For single cell delivery, a narrow channel is ablated through bone under imaging guidance. Cells are then transferred from a micropipette into an optical trap, which brings cells into the BM through the channel. The survival and proliferation of implanted cells can be tracked in vivo by imaging. For cell extraction after laser bone thinning, different approaches can be implemented and three of them are presented

Quantifying the Organization and Dynamics of Excitable Signaling Networks

The transmission of extracellular information through intracellular signaling networks is ubiquitous in biology---from single-celled organisms to complex multicellular systems. Via signal-transduction machinery, cells of all types can detect and respond to biological, chemical, and physical stimuli. Although studies of signaling mechanisms and pathways traditionally involve arrays of biochemical assays, detailed quantification of physical information is becoming an increasingly important tool for understanding the complexities of signaling. With the rich datasets currently being collected in biological experiments, understanding the mechanisms that govern intracellular signaling networks is becoming a multidisciplinary problem at the intersection of biology, computer science, physics, and applied mathematics. In this dissertation, I focus on understanding and characterizing the physical behavior of signaling networks. Through analysis of experimental data, statistical modeling, and computational simulations, I explore a characteristic of signaling networks called excitability, and show that an excitable-systems framework is broadly applicable for explaining the connection between intracellular behaviors and cell functions. One way to connect the physical behavior of signaling networks to cell function is through the structural and spatial analyses of signaling proteins. In the first part of this dissertation, I employ an adaptive-immune-cell model with a key activation step that is both promoted and inhibited by a microns-long, filamentous protein complex. I introduce a novel image-based bootstrap-like resampling method and demonstrate that the spatial organization of signaling proteins is an important contributor to immune-cell self regulation. Furthermore, I use the bootstrap-like resampling to demonstrate that the location of contact points between signaling proteins can provide mechanistic insight into how signal regulation is accomplished on the single-cell level. Finally, I probe the excitable dynamics of the system with a Monte Carlo simulation of nucleation-limited growth and degradation. Using the simulations, I show that careful balance between simulation parameters can elicit a tunable response dynamic. The spatiotemporal dynamics of signaling components are also important mediators of cell function. One key readout of the connection between signaling dynamics and cell function is the behavior of the cytoskeleton. In the second part of this dissertation, I use innate-immune-cell and epithelial-cell models to understand how a key cytoskeletal component, actin, is influenced by topographical features in the extracellular environment. Engineered nanotopographic substrates similar in size to typical extracellular-matrix structures have been shown to bias the flow of actin, a concept known as esotaxis. To measure this bias, I introduce a generalizable optical-flow-based-analysis suite that can robustly and systematically quantify the spatiotemporal dynamics of actin in both model systems. Interestingly, despite having wildly different migratory phenotypes and physiological functions, both cell types exhibit quantitatively similar topography-guidance dynamics which suggests that sensing and responding to extracellular textures is an evolutionarily-conserved phenomena. The signaling mechanisms that enable actin responses to the physical environment are poorly understood. Despite experimental evidence for the enhancement of actin-nucleation-promoting factors (NPFs) on extracellular features, connecting texture-induced signaling to overall cell behavior is an ongoing challenge. In the third part of this dissertation, I study the topography-induced guidance of actin in amoeboid cells on nanotopographic textures of different spacings. Using optical-flow analysis and statistical modeling, I demonstrate that topography-induced guidance is strongest when the features are similar in size to typical actin-rich protrusions. To probe this mechanism further, I employ a dendritic-growth simulation of filament assembly and disassembly with realistic biochemical rates, NPFs, and filament-network-severing dynamics. These simulations demonstrate that topography-induced guidance is more likely the result of a redistribution, rather than an enhancement, of NPF components. Overall, this dissertation introduces quantitative tools for the analysis, modeling, and simulations of excitable systems. I use these tools to demonstrate that an excitable-systems framework can provide deep, phenomenological insights into the character, organization, and dynamics of a variety of biological systems

Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework

Swim Like Your Lifecycle Depends On It. Investigating Motility of Leishmania mexicana; its Impact on Parasite Lifecycle Progression and Infectivity.

The motility of Leishmania promastigote parasites is important for survival during host transitions and lifecycle progression. An oscillating flagellum at the anterior end of the promastigotes pulls it through environmental conditions which change significantly during the lifecycle. The parasite morphologically transforms to optimise infection potential. This study adapts a unique method of high-speed, three-dimensional imaging called digital inline holographic microscopy (DIHM) allowing us to examine the movements of Leishmania mexicana promastigotes. We have tracked distinct stages of promastigote parasites over multiple frames to gain information on the swimming patterns of these cells. Quantification of the 3D trajectories reveals stage-specific differences in swimming behaviour. Using this technique we reveal that mammalian-infective metacyclic promastigotes are more capable of swimming in highly viscous solutions, a result which has interesting implications in the ability of this specific stage to transmit through promastigote secretory gel. Additionally, the DIHM technique has allowed us to investigate whether these different stages of Leishmania promastigote are capable of sensing chemicals in their environment. We reveal how distinct chemotaxic capabilities could play a role in the uptake of parasites by host cells during early infection. Mathematically quantifying the cell movements of L. mexicana within contrasting, biologically relevant environments has revealed swimming mechanisms that are essential for the parasite to remain unencumbered by environmental pressures and adapt their motility to reach preferred conditions

Intravital imaging technology guides FAK-mediated priming in pancreatic cancer precision medicine according to Merlin status

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic, chemoresistant malignancy and is characterized by a dense, desmoplastic stroma that modulates PDAC progression. Here, we visualized transient manipulation of focal adhesion kinase (FAK), which integrates bidirectional cell-environment signaling, using intravital fluorescence lifetime imaging microscopy of the FAK-based Förster resonance energy transfer biosensor in mouse and patient-derived PDAC models. Parallel real-time quantification of the FUCCI cell cycle reporter guided us to improve PDAC response to standard-of-care chemotherapy at primary and secondary sites. Critically, micropatterned pillar plates and stiffness-tunable matrices were used to pinpoint the contribution of environmental cues to chemosensitization, while fluid flow–induced shear stress assessment, patient-derived matrices, and personalized in vivo models allowed us to deconstruct how FAK inhibition can reduce PDAC spread. Last, stratification of PDAC patient samples via Merlin status revealed a patient subset with poor prognosis that are likely to respond to FAK priming before chemotherapy

Discovery of Novel Mechanisms Regulating Cancer Extravasation in the Chorioallantoic Membrane Model

Cancer metastasis is a multistep process that begins with the invasion of tumour cells into the stroma and migration towards the blood vessels. Tumour cells that have entered the bloodstream must then survive and leave by a process known as extravasation. Finally, extravasated cells proliferate and establish the secondary site in the metastatic cascade. Although extravasation encompasses key events during cancer cell invasion to aid in the development of effective treatments, an in vivo model that rapidly, reproducibly and economically recapitulates cancer cell extravasation is needed. Therefore, the objectives of my research were to 1) establish and validate an in vivo model of cancer cell extravasation, and 2) identify novel cellular and molecular events. I used the chorioallantoic membrane of chicken embryos as a model system of extravasation as it provides an accessible and highly vascularized structure. The combination of the chorioallantoic membrane of chicken embryos, nanoscale flow cytometry, and confocal microscopy-based intravital imaging allowed me to observe that extravasating prostate cancer cells exhibited significant cell volume reduction. This reduction is suggestive of an invasive cell phenotype. However, cell volume reduction at certain threshold also decreased cancer cell extravasation efficiency. I also found that cancer cell released extravascular vesicles during extravasation, and an increase in extracellular vesicle release reduced cell volume. I then tested the hypothesis that extracellular vesicle release and extravasation may be linked to modes of cell death. Real-time imaging of extravasating cancer cells that released extracellular vesicles did not show activation of caspase-3. Activation of necroptosis, however, increased extracellular vesicle release and decreased cell extravasation and secondary colony formation. These results suggest that necroptosis may be targeted to induce extracellular vesicle release, decrease extravasation, and halt cancer metastasis. Collectively, my work lays out the protocols for the use of the chorioallantoic membrane of chicken embryos as a model system to investigate cancer cell extravasation and invasion. Use of this model system allowed me to identify extracellular vesicle release during extravasation and discover that necroptosis may be a potential regulator of cancer metastasis

Intravital imaging technology guides FAK-mediated priming in pancreatic cancer precision medicine according to Merlin status

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic, chemoresistant malignancy and is characterized by a dense, desmoplastic stroma that modulates PDAC progression. Here, we visualized transient manipulation of focal adhesion kinase (FAK), which integrates bidirectional cell-environment signaling, using intravital fluorescence lifetime imaging microscopy of the FAK-based Forster resonance energy transfer biosensor in mouse and patient-derived PDAC models. Parallel real-time quantification of the FUCCI cell cycle reporter guided us to improve PDAC response to standard-of-care chemotherapy at primary and secondary sites. Critically, micro-patterned pillar plates and stiffness-tunable matrices were used to pinpoint the contribution of environmental cues to chemosensitization, while fluid flow-induced shear stress assessment, patient-derived matrices, and personalized in vivo models allowed us to deconstruct how FAK inhibition can reduce PDAC spread. Last, stratification of PDAC patient samples via Merlin status revealed a patient subset with poor prognosis that are likely to respond to FAK priming before chemotherapy