Extending digital biology: bacterial survival and morphological heterogeneity under antibiotic stress
The morphology of bacteria is modified by antibiotic stress while also serving to survive the antibiotic. However associating morphological descriptors with quantitative measurements of cell survival remains elusive. Here we present a workflow to generate morphological signatures for the progeny of individual cells for 168 different antibiotic conditions. The workflow uses stationary microfluidic droplets, to encapsulate and grow bacteria, and confocal microscopy to image the contents of each droplet. A custom image analysis pipeline is developed to interact with the images in order to label of the morphologies within a subset of the images and train a neural network. The network yields a multidimensional morphological signature for 82000 droplets, showing the co-existence of different morphologies even for the progeny of individual cells. The morphological signatures are different for varying antibiotic type and concentration, thus providing a way to distinguish antibiotics by their mode of action. By combining these morphological signatures with the digital detection of survival within droplets, this workflow can serve to understand the emergence of antibiotic resistance or to identify antimicrobial activity of unknown substances.