Gene machines

Abstract:

The rise of antimicrobial resistance (AMR) is one of the most pressing global healthcare challenges, already causing an estimated 1.2 million preventable deaths annually and rising. Crucial to the management of AMR is rapid and specific diagnosis, allowing early and optimized intervention. Unfortunately, current gold-standard antimicrobial susceptibility tests are low-throughput and can take up to 48 hours to produce clinically relevant insights. In this thesis, we propose and evaluate a novel AST approach, based on the deep-learning of single-cell phenotypes directly associated with antimicrobial susceptibility. The phenotypes are revealed by widefield fluorescence microscopy and evaluated automatically by a deep-learning pipeline built on convolutional neural networks (CNNs). We demonstrate our Deep Antimicrobial Susceptibility Phenotyping (DASP) can robustly recognise susceptibility phenotypes associated with 4 representative antibiotics of major antibiotic families, in Escherichia coli, with over 80% single cell accuracy. We then deploy our models trained on susceptible lab strains, to clinical isolates of Escherichia coli treated with one of the antibiotics. Here, we demonstrate the distribution of single-cell phenotypic classification decisions is a reliable indicator of isolatesusceptibilityaroundafixedtreatmentpoint, revealingstatisticallysignificant (p<0.001) differences between untreated and treated cell populations in susceptible isolates, and no difference in resistant isolates. Further, we evaluate the limit of detection, and show this population-level output is indeed sensitive to the resistance status of single cells. Lastly, we investigate the relationship between treatment concentration, the minimum inhibitory concentration (MIC) of the isolate, and the DASP output, and compare this against the gold-standard growth assay. Here, we show that DASP has potential to produce equivalent information to the current gold-standard, but an order on magnitude faster. We conclude this thesis with an outlook on the developmental and mechanistic principles of the phenotypes by studying their time evolution

Abstract:

PIFE was first used as an acronym for protein-induced fluorescence enhancement, which refers to the increase in fluorescence observed upon the interaction of a fluorophore, such as a cyanine, with a protein. This fluorescence enhancement is due to changes in the rate of cis/trans photoisomerisation. It is clear now that this mechanism is generally applicable to interactions with any biomolecule and, in this review, we propose that PIFE is thereby renamed according to its fundamental working principle as photoisomerisation-related fluorescence enhancement, keeping the PIFE acronym intact. We discuss the photochemistry of cyanine fluorophores, the mechanism of PIFE, its advantages and limitations, and recent approaches to turn PIFE into a quantitative assay. We provide an overview of its current applications to different biomolecules and discuss potential future uses, including the study of protein-protein interactions, protein-ligand interactions and conformational changes in biomolecules.Comment: No Comment

Abstract:

Diseases caused by viruses represent a serious global health concern. In this thesis, I present novel methods for labelling, imaging and analysing virus particles. I also describe mathematical and computational models that can be used to describe biological structures, such as viruses. In studies of viral structure, pleomorphism, and articularly filamentous morphologies, are frequently overlooked.To characterise and better understand viral filaments, I developed and optimised an imaging protocol for individual virus particles that captured the heterogeneity of viral shape. I also produced analysis pipelines that took this image data to study the structure of viruses at the individual virion level in order to find an overall picture of the populations of virions that exist. These pipelines investigated virus size, patterning of surface proteins and the location of viral genome. I also demonstrated a novel virus labelling strategy that specifically labels virus that will aid in the future study of viral assembly and infection. I also used mathematical modelling to further understand the role of viral morphologies. I modelled virus particles as a pressure vessel to study engineering stress in relation to virus, bacteria, and cellular morphology. Together these advancements will further our ability to study viruses and our knowledge of virus structure