Flexible, comprehensive frameworks for quantitative analysis of cell shape and subcellular organization in the context of cell motility
Keywords: Biochemistry.

At the cellular level, morphology arises from the adaptation to important physiological functions and reflects the global organization and functional state of the cell in crucial processes such as cell growth and division, differentiation, and migration. At the molecular level, morphology is the expression of cellular processes that controls the interactions between the cytoskeleton, the cell membrane, and the extracellular environment, resulting in intracellular and extracellular forces that act on the cell membrane and manifested as cell shape and other morphological changes. The study of cell morphology involves the description of a cell's properties through its shape, size, and subcellular organization (such as intracellular structures and protein distribution) by deriving one or more mathematical representations that maximally capture biologically-relevant information from these measured properties, followed by unbiased quantitative analyses that help relate variations in morphology to changes in cellular function or behavioral states. Whole cell motility plays a central role within the multitudes of complex physiological functions in multicellular organisms, ranging from embryogenesis and development to immune response and wound healing. Tumor cell migration has also been implicated as one of the first steps by which cancer metastasis commences. Human neutrophils are highly migratory cells that use a spectrum of strategies to enter and efficiently maneuver through various tissues and organs, representing a wide range of complex physiological environments. With their complex and dynamic shape change during migration, human neutrophils serve as a good model system to study cell motility through variations in morphology. In this thesis, we used human polymorphonuclear leukocytes (PMNs) and HL60 cells, a neutrophil-like model cell line for studying neutrophil motility behavior. By providing a comprehensive set of flexible frameworks that can be used to quantitatively analyze the motility behavior of migratory cells, we were able to represent cell shapes as discretized contours. Through principal component analysis (PCA) and machine learning using a convolutional neural network, we found that the complex and dynamic cell shapes of migratory neutrophils can be decomposed into five major biologically-interpretable shape modes that accounts for ~85% of the variations in the entire data set. In addition, machine learning allows us to integrate different types of motility data, such as cell speed, into the neural network training process, allowing us to connect cell speed to cell shape change. Next, by representing both the shape and the cell and the nucleus as signed distance maps, we were able to show that, in addition to the slight shape differences between PMN and HL60 cells, the nuclei of PMNs are in general positioned towards the back of the cell, while HL60 nuclei are mostly positioned towards the center or the front of the cell. Finally, by representing cytoskeletal protein signals with a contour-driven dot assignment strategy and using Earth Mover's Distance (EMD) as a distance metric, we were able to visualize the rearrangements of protein distribution of migrating neutrophils over time, thus providing us with a pathway for a unified understand on how cytoskeletal proteins are coordinated at a global level during cell migration.