Treffer: Extending digital biology: bacterial survival and morphological heterogeneity under antibiotic stress

The morphology of bacteria is intimately linked to their biological state: It is modified by antibiotic stress while also serving to survive the antibiotic. As such knowing which morphologies are selected by the surviving cells would help explain how individual cells are able to escape the antibiotic pressure. However associating morphological descriptors with quantitative measurements of cell survival remain elusive. Here we present a workflow to generate morphological signatures for the progeny of individual cells in the presence or absence of antibiotics. The workflow uses stationary microfluidic droplets, to encapsulate and grow bacteria, and confocal microscopy to image the contents of each droplet. Experiments are performed for 168 antibiotic conditions, corresponding to 82,000 droplet images. This massive data set is treated using a custom image analysis pipeline that enables rapid labeling of the morphologies within a subset of the images. The curated images are then used to train a neural network that assigns a positive or negative label, for each of these non-exclusive features, yielding a multidimensional signature of labels within each droplet. The results show the co-existence of different morphologies, even for the progeny of individual cells. The mix of morphologies changes as a function of 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, this workflow can serve to understand the emergence of antibiotic resistance or to identify antimicrobial activity of unknown substances. This dataset contains the SQL database, network weights, and imaging data needed to recreate the interactive web viewer. More details are available at the associated GitHub repository.Note that the database in version 2 did not containt the correct concentration values.