Microfluidics and AI for single-cell microbiology

Abstract: Most of the biological sciences deal with understanding the relationships between phenotypes and the underlying molecular mechanisms of organisms. This thesis is an engineering, computational, and experimental exercise in expanding the scope and scale of phenotype-genotype mapping techniques in single-cell microbiology using microscopy, microfluidics, and image processing. To this end, we use mother-machine-based microfluidic devices together with recently developed techniques in deep learning and optics. We use optical microscopes to observe cells of different genotypes, physically move cells, and image molecules inside them.We have designed a novel microfluidic device to expand the throughput of single-cell lineage tracing an order of magnitude compared to existing methods. We demonstrate the ability to isolate single cells from such a device using optical tweezers after phenotypic characterization in real time. We have developed analysis algorithms of various kinds with the prime intention of performing high-throughput real-time image processing in conjunction with experimental runs to identify interesting cells for further investigation.We have also developed an experimental protocol for bacterial species identification using fluorescence-in-situ hybridization (FISH) in microfluidic chips to complement an existing phenotype-based antibiotic-susceptibility test (AST). We apply this method together with deep-learning-based cell segmentation and tracking algorithms, and image classification methods to perform species-ID of up to 10 species in 2-3 hrs.Lastly, we have developed a 3D dot localization method to investigate how the chromosome structure changes during the E. coli cell cycle. Different loci on the E. coli chromosome were labeled using DNA-binding fluorescent proteins and imaged using an optical setup with an astigmatic point-spread-function. Mother-machine devices were used to constrain the movement of cells to the lateral plane during growth. A deep-learning-based single-molecule localization method was adapted for this application and used to map the chromosomal loci’s physical position in 3D as a function of cell size during the E. coli cell cycle.