Heterogeneous growth and death of small bacterial populations in microfluidic droplets

Antibiotic resistance is a major global health challenge, and there is still much to learn about how antibiotics work to inhibit the growth of bacterial populations. In many real infections, bacteria grow in small populations where stochastic effects can be important, especially because even a single surviving bacterium can lead to regrowth of an infection. Microfluidic droplets offer an opportunity to study this heterogeneity under well-controlled experimental conditions. Creating numerous, monodisperse microenvironments from the same initial bacterial suspension gives multiple micro-experiments which run in parallel, allowing the study of individual bacterial growth and response to stress (for example, antibiotics). This approach results in a rich data set which can be compared with predictions from both deterministic and probabilistic theoretical models, producing insight into the growth dynamics and antibiotic response of small populations, which are often hidden in conventional large-scale experiments. In this thesis I present a study of small populations of bacteria using microfluidic droplets and theoretical modelling. Chapter 1 provides motivation for the study of small bacterial populations and background on ß-lactam antibiotics (the class of antibiotics investigated in Chapters 5{6) and ß-lactamase enzymes. Chapter 2 outlines the experimental methodology and the image analysis procedure. Principally, this involves encapsulating bacteria into picolitre volumes of growth media and imaging using fluorescence and bright field microscopy for 4{7 hours. A Matlab work flow is used to count the number of bacteria in each droplet over the course of an experiment. Chapter 3 explores the heterogeneous growth dynamics by comparing hundreds to thousands of growth trajectories of clonal populations of E. coli. Deterministic and probabilistic models were developed to understand the response of small populations of ß-lactam resistant bacteria to ß-lactam antibiotics, as described in Chapter 4. The effect of stochastic bacterial loading into droplets as well as stochastic growth are compared to the deterministic case in Chapter 5, in which the survival of bacterial populations under a range of antibiotic concentrations, with different initial numbers of bacteria, is explored. These simulations predict a range of concentrations of antibiotic where stochastic effects lead to the survival of a proportion of the population, while a deterministic mean-field theory would predict success of the antibiotic treatment. In Chapter 6 these predictions are tested experimentally and it is found that, in droplets, some populations of E. coli survive at concentrations of ampicillin beyond the bulk MIC determined by equivalent plate reader experiments. Dormant cells are visible in droplets but not in plate reader experiments, and we propose that some of the growth observed in bulk plate reader experiments might be biomass ( lamentous) growth rather than (healthy) division. This implies that bulk experiments may not reveal the whole picture and need to be interpreted with care. Finally, in Chapter 7 a model is used to investigate the possibility of cooperative behaviour in mixtures of resistant and sensitive bacteria in droplets. This explores the extent to which bacteria with no intrinsic resistance could survive exposure to antibiotics when in the presence of bacteria which produce ß -lactamase enzymes, a phenomenon which is of rising ecological interest and clinical concern

Tracking the stochastic growth of bacterial populations in microfluidic droplets

Bacterial growth in microfluidic droplets is relevant in biotechnology, in microbial ecology, and in understanding stochastic population dynamics in small populations. However, it has proved challenging to automate measurement of absolute bacterial numbers within droplets, forcing the use of proxy measures for population size. Here we present a microfluidic device and imaging protocol that allows high-resolution imaging of thousands of droplets, such that individual bacteria stay in the focal plane and can be counted automatically. Using this approach, we track the stochastic growth of hundreds of replicate Escherichia coli populations within droplets. We find that, for early times, the statistics of the growth trajectories obey the predictions of the Bellman-Harris model, in which there is no inheritance of division time. Our approach should allow further testing of models for stochastic growth dynamics, as well as contributing to broader applications of droplet-based bacterial culture