Description
Abstract Rapid antibiotic susceptibility tests (ASTs) are an increasingly important part of clinical care as antimicrobial resistance (AMR) becomes more common in bacterial infections. Here, we use the spatial distribution of fluorescently labelled ribosomes to detect intracellular changes associated with antibiotic susceptibility in single E. coli cells using a convolutional neural network (CNN). By using ribosome-targeting probes, a single fluorescence cell image provides data for cell segmentation and susceptibility phenotyping. Using 50,722 images of cells from an antibiotic-susceptible laboratory strain of E. coli, we showed that antibiotics with different mechanisms of action result in distinct ribosome phenotypes, which can be identified by a CNN with high accuracy (99%, 96%, and 91% for ciprofloxacin, gentamicin, and chloramphenicol). With 6 E. coli strains isolated from bloodstream infections, we used 34,205 images of ribosome phenotypes to train a CNN that could classify susceptible cells with 92% accuracy and resistant cells with 99% accuracy. Such accuracies correspond to the ability to differentiate susceptible and resistant samples with 99% confidence with just 2 cells, meaning that this method could eliminate lengthy sample culturing steps and could determine in vitro susceptibility with 30 minutes of antibiotic treatment. Our ribosome phenotype method should also be able to identify phenotypes in other strains and species.
Creative Commons Attribution 4.0 International License (CC-BY 4.0)
