exhibits heterogeneous and adaptive cytoprotective responses to antifungal compounds.

an opportunistic human pathogen, poses a significant threat to human health and is associated with significant socio-economic burden. Current antifungal treatments fail, at least in part, because can initiate a strong drug tolerance response that allows some cells to grow at drug concentrations above their minimal inhibitory concentration. To better characterize this cytoprotective tolerance program at the molecular single-cell level, we used a nanoliter droplet-based transcriptomics platform to profile thousands of individual fungal cells and establish their subpopulation characteristics in the absence and presence of antifungal drugs.

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albicans. Our system, entitled Candescence, automatically detects C. albicans cells from differential image contrast microscopy and labels each detected cell with one of nine morphologies.