Advancing single cell microbiology and bacteriophage-bacteria interactions using microfluidics and image-based deep learning

The rapid rise of antimicrobial resistance (AMR) poses a critical global health threat, demanding innovative approaches for diagnosing infections and developing effective therapies. Conventional culture-based assays are often slow, labour-intensive, and limited in their ability to capture the heterogeneous responses of individual bacteria to antibiotics or bacteriophages. This thesis addresses these challenges by developing integrated, label-free, droplet-based microfluidic platforms coupled with advanced deep learning algorithms for studying bacteriophage-host interactions towards high-throughput single-cell microbiology. First, microfluidic devices featuring tailored droplet trapping arrays were designed and fabricated using soft lithography. These platforms enabled the long-term culture and monitoring of individual bacterial microcolonies under controlled microenvironments, supporting time-resolved quantification of bacterial growth, morphological changes, and phenotypic responses during bacteriophage–host interactions. By miniaturising cultivation volumes into picolitre droplets, bacterial–phage interactions were accelerated and parallelised, reducing assay times from days to hours. Complementary computational pipelines were established to automate droplet detection, segmentation, and single-cell morphological analysis using deep convolutional neural networks, thereby eliminating reliance on fluorescent labels and subjective manual inspection. These tools were applied to investigate clinically relevant individual and polymicrobial–phage interactions, with a focus on Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus. Morphology-based phenotyping revealed dynamic lytic and non-lytic responses at the single-cell level, including transient states such as spheroplast-like morphotypes that may contribute to phage persistence. Together, the methodological and analytical advances presented in this work establish a scalable, high-resolution framework for studying complex microbial communities and their viral predators. Beyond fundamental microbiology, these approaches hold translational potential for rapid antimicrobial susceptibility testing and personalised phage therapy, offering a path toward more effective interventions against drug-resistant infections.<p></p>

Author dc:creator

• Anuj Tiwari (21042038)

• Phages
• AMR
• Image-based deep learning
• Single-cell microbiology
• Bacteriophage–bacteria interactions
• Droplet microfluiidcs
• Polymicrobial communities

Statement dc:rights

• All rights reserved

Identifier

10779/exe.31097794.v1

OAI identifier oai:identifier

oai:figshare.com:article/31097794