High-Throughput Imaging of Arrays of Fluorescently Tagged Yeast Mutant Strains.

We describe a protocol for live-cell high-throughput (HTP) screening of yeast mutant strains carrying fluorescent protein markers for subcellular compartments of choice using automated confocal microscopy. This procedure, which combines HTP genetics and microscopy, results in the acquisition of thousands of images that can be analyzed in a systematic and quantitative way to identify morphology defects in the tagged subcellular compartments. This HTP protocol is readily adapted for screening any combination of markers and can be expanded to different growth conditions or higher order mutant genetic backgrounds.

The Candida albicans transcription factor Cas5 couples stress responses, drug resistance and cell cycle regulation.

The capacity to coordinate environmental sensing with initiation of cellular responses underpins microbial survival and is crucial for virulence and stress responses in microbial pathogens. Here we define circuitry that enables the fungal pathogen Candida albicans to couple cell cycle dynamics with responses to cell wall stress induced by echinocandins, a front-line class of antifungal drugs. We discover that the C. albicans transcription factor Cas5 is crucial for proper cell cycle dynamics and responses to echinocandins, which inhibit ß-1,3-glucan synthesis. Cas5 has distinct transcriptional targets under basal and stress conditions, is activated by the phosphatase Glc7, and can regulate the expression of target genes in concert with the transcriptional regulators Swi4 and Swi6. Thus, we illuminate a mechanism of transcriptional control that couples cell wall integrity with cell cycle regulation, and uncover circuitry governing antifungal drug resistance.Cas5 is a transcriptional regulator of responses to cell wall stress in the fungal pathogen Candida albicans. Here, Xie et al. show that Cas5 also modulates cell cycle dynamics and responses to antifungal drugs.

Automated analysis of high-content microscopy data with deep learning.

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data.

Machine learning and computer vision approaches for phenotypic profiling.

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.