Streamlining the image capture and analysis for high throughput unbiased quantitation and categorization of nuclear morphology

The morphology of cells can be closely linked to their function, particularly in sperm, where it can affect movement and behavior, thus impacting fertility. Automated image analysis can quantify shape differences between cells and has demonstrated links between fertility and sperm shape, particularly in mice. However, a bottleneck in manually capturing images limits the detection of rare (both uncommon and abnormal) shape phenotypes. There are three main aims of this thesis: to develop an unbiased automated sperm imaging system and compare this system to manual imaging, to increase the analytical capacity to identify sperm morphologies in a sample and to use the pipeline to characterise new rodent sperm morphologies. Automated imaging was implimented using a Nikon Ti flourescence microscope with a focus on unbiased, fast, low effort image capture. In this project, imaging throughput was increased from 100 to 3000 images per sample, permitting analysis of tens of thousands of nuclei from a single sample. With these image the statistical analysis portion of the pipeline can be developed to segment different phenotypes in a sample of sperm. An automated R analysis pipline was developed to characterise common morphotypes within a sample, providing biological insight into the region of the sperm head varying. Using this method, the structure of two species of hamster sperm has also been clearly categorised, demonstrating a conservation of hamster sperm structure over 1 million years of evolution. This conservation of hamster sperm shape is in stark contrast to the variety of sperm shapes in mouse subspecies. This system has potential to be used to identify clinically relevant data about morphology of human sperm in the analysis of infertility phenotypes.