Protocol to train a support vector machine for the automatic curation of bacterial cell detections in microscopy images.

Manual curation of bacterial cell detections in microscopy images remains a time-consuming and laborious task. This work offers a comprehensive, step-by-step tutorial on training a support vector machine to autonomously distinguish between good and bad cell detections. Jupyter notebooks are included to perform feature extraction, labeling, and training of the machine learning model. This method can readily be incorporated into profiling pipelines aimed at extracting a multitude of features across large collections of individual cells, strains, and species. For complete details on the use and execution of this protocol, please refer to Govers et al.1.

Ethanol induces replication fork stalling and membrane stress in immortalized laryngeal cells.

Although ethanol is a class I carcinogen and is linked to more than 700,000 cancer incidences, a clear understanding of the molecular mechanisms underlying ethanol-related carcinogenesis is still lacking. Further understanding of ethanol-related cell damage can contribute to reducing or treating alcohol-related cancers. Here, we investigated the effects of both short- and long-term exposure of human laryngeal epithelial cells to different ethanol concentrations. RNA sequencing shows that ethanol altered gene expression patterns in a time- and concentration-dependent way, affecting genes involved in ribosome biogenesis, cytoskeleton remodeling, Wnt signaling, and transmembrane ion transport. Additionally, ethanol induced a slower cell proliferation, a delayed cell cycle progression, and replication fork stalling. In addition, ethanol exposure resulted in morphological changes, which could be associated with membrane stress. Taken together, our data yields a comprehensive view of molecular changes associated with ethanol stress in epithelial cells of the upper aerodigestive tract.