Anuj Tiwari

Abstract:

Evaluating the impact of bacteriophages on bacterial communities is required to assess the future utility of phage therapy. Methods able to study bacterial polycultures in the presence of phages are useful to mimic evolutionary pressures found in natural environments and recapitulate complex ecological contexts. Bacteriophages can drive rapid genetic and phenotypic changes in host cells. However, the presence of other bacteria can also impact bacterial densities and community structure and classical methods remain lengthy and resource intensive. Here we introduce a microdroplet-based encapsulation method in which bacterial co-cultures are imaged using Z-stack brightfield microscopy. The method relies on automated droplet imaging using an AI-based autofocus function, coupled with morphology-based deep learning models for accurate identification of two morphologically distinct bacterial species. We monitor the interactions between bacterial mono- or co-cultures of P. aeruginosa and S. aureus in the presence of a P. aeruginosa phage growing in 11 picolitre droplets for up to 20 h. We demonstrate quantification of growth rates, bacterial densities and lysis dynamics of the two species without the need for plating. We show that a potent lytic phage of P. aeruginosa can keep its density low long-term when in the presence of S. aureus.

Abstract:

The study of dose-response relationships underpins analytical biosciences. Droplet microfluidics platforms can automate the generation of microreactors encapsulating varying concentrations of an assay component, providing datasets across a large chemical space in a single experiment. A classical method consists in varying the flow rate of multiple solutions co-flowing into a single microchannel (producing different volume fractions) before encapsulating the contents into water-in-oil droplets. This process can be automated through controlling the pumping elements but lacks the ability to adapt to unpredictable experimental scenarios, often requiring constant human supervision. In this paper, we introduce an image-based, closed-loop control system for assessing and adjusting volume fractions, thereby generating unsupervised, uniform concentration gradients. We trained a shallow convolutional neural network to assess the position of the laminar flow interface between two co-flowing fluids and used this model to adjust flow rates in real-time. We apply the method to generate alginate microbeads in which HEK293FT cells could grow in three dimensions. The stiffnesses ranged from 50 Pa to close to 1 kPa in Young modulus and were encoded with a fluorescent marker. We trained deep learning models based on the YOLOv4 object detector to efficiently detect both microbeads and multicellular spheroids from high-content screening images. This allowed us to map relationships between hydrogel stiffness and multicellular spheroid growth

Abstract:

An alarming rise in antimicrobial resistance worldwide has spurred efforts into the search for alternatives to antibiotic treatments. The use of bacteriophages, bacterial viruses harmless to humans, represents a promising approach with potential to treat bacterial infections (phage therapy). Recent advances in microscopy-based single-cell techniques have allowed researchers to develop new quantitative methodologies for assessing the interactions between bacteria and phages, especially the ability of phages to eradicate bacterial pathogen populations and to modulate growth of both commensal and pathogen populations. Here we combine droplet microfluidics with fluorescence time-lapse microscopy to characterize the growth and lysis dynamics of the bacterium Escherichia coli confined in droplets when challenged with phage. We investigated phages that promote lysis of infected E. coli cells, specifically, a phage species with DNA genome, T7 (Escherichia virus T7) and two phage species with RNA genomes, MS2 (Emesvirus zinderi) and Qβ (Qubevirus durum). Our microfluidic trapping device generated and immobilized picoliter-sized droplets, enabling stable imaging of bacterial growth and lysis in a temperature-controlled setup. Temporal information on bacterial population size was recorded for up to 25 h, allowing us to determine growth rates of bacterial populations and helping us uncover the extent and speed of phage infection. In the long-term, the development of novel microfluidic single-cell and population-level approaches will expedite research towards fundamental understanding of the genetic and molecular basis of rapid phage-induced lysis and eco-evolutionary aspects of bacteria-phage dynamics, and ultimately help identify key factors influencing the success of phage therapy