Professor Achillefs Kapanidis

Abstract:

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain’s own minimum inhibitory concentration. We used the users’ classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.

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