Quantifying bacterial responses to antibiotics at the single-cell level

The emergence of pathogen resistance to antimicrobials is putting modern medicine at risk. One of the main challenges in the treatment of bacterial infections is that current antibiotics often fail to eradicate the whole bacterial population, driven by the constant misuse and overuse of these compounds. Even genetically identical cells can take on highly heterogeneous physiological states resulting in bacterial subpopulations being less susceptible to the treatment. In particular, some cells are in physiological states that allow them to survive antibiotic treatment without any resistance mutations. For instance, slow-growing cells have been reported to be more tolerant to some antibiotics, and this increased tolerance can facilitate the subsequent fixation of resistance mutations. Most methods for the discovery and study of antimicrobial compounds are based on liquid cultures which focus on the antibiotic response at the population level, being blind to single-cell dynamics. Consequently, the determinants of sensitivity to antibiotics are only poorly understood at the single-cell level due to the lack of quantitative data.
In recent years, powerful methods have been developed to quantitatively measure behaviour and responses in single bacterial cells. By combining microfluidics with time-lapse microscopy, it is possible to track growth, gene expression, division, and death within lineages of single cells. An especially attractive microfluidic design is the Mother Machine, a device where bacteria grow within narrow growth channels that are perpendicularly connected to a main flow channel, which supplies nutrients and washes away cells growing out of the growth channels. In this work, we investigate the response of bacterial cells to antibiotics using an integrated microfluidic and computational setup: the dual-input Mother Machine (DIMM). The DIMM allows arbitrary time-varying mixtures of two input media, such that cells can be exposed to a controlled set of varying external conditions. Using the companion image analysis software Mother Machine Analyser (MoMA) we can segment and track cell lineages from phase-contrast images with high throughput and accuracy.
In light of this work, we developed new multiplexed microfluidic designs using the new PyMicrofluidics tool, which facilitates the drawing and handling of complex circuits. These new designs, enable the study of multiple conditions in parallel. Furthermore, we introduced filtering structures in the Mother Machine channels. The media carrying nutrients or antibiotics flows from the main channel through the channels where the cells are trapped, without letting the cells escape. This grants the ability to load cells inside the Mother Machine faster, as well as reduce the nutrient-gradient effect caused by the delivery of media only through diffusion in classical dead-end channels.
We use this integrated setup to quantify how the antibiotic response of individual bacteria depends on their physiological state at the time the treatment commences. The time resolution achieved with such technology allows to track how the bacteria evolve during treatment and after the treatment to assess survival. For this, we focus on treating Escherichia coli (E. coli) with a variety of clinically relevant antibiotics at different concentrations. The methods we present in this work will allow the identification of antimicrobial compounds that specifically target these resistant subpopulations, which could in the future complement existing treatment strategies and have a potential impact on antimicrobial drug discovery and treatment design.