Gene machines

Abstract:

We demonstrate the rapid capture, enrichment, and identification of bacterial pathogens using Adaptive Channel Bacterial Capture (ACBC) devices. Using controlled tuning of device backpressure in polydimethylsiloxane (PDMS) devices, we enable the controlled formation of capture regions capable of trapping bacteria from low cell density samples with near 100% capture efficiency. The technical demands to prepare such devices are much lower compared to conventional methods for bacterial trapping and can be achieved with simple benchtop fabrication methods. We demonstrate the capture and identification of seven species of bacteria with bacterial concentrations lower than 1000 cells per mL, including common Gram-negative and Gram-positive pathogens such as Escherichia coli and Staphylococcus aureus. We further demonstrate that species identification of the trapped bacteria can be undertaken in the order of one-hour using multiplexed 16S rRNA-FISH with identification accuracies of 70–98% with unsupervised classification methods across 7 species of bacteria. Finally, by using the bacterial capture capabilities of the ACBC chip with an ultra-rapid antimicrobial susceptibility testing method employing fluorescence imaging and convolutional neural network (CNN) classification, we demonstrate that we can use the ACBC chip as an imaging flow cytometer that can predict the antibiotic susceptibility of E. coli cells after identification.

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