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CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository with examples of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community., 17 pages, 2 figures, 1 table

Streptomyces species of bacteria have a very lively ADP-ribosylation metabolism. ADP- ribosylation is involved in the regulation of morphological differentiation and antibiotic production. More than 15 proteins that constitute ADP-ribosylation metabolic cycle have been found in Streptomyces coelicolor. We focused on the member of the macrodomain ADP-ribosylhydrolase SCO6735, whose disruption has been shown to increase the production of antibiotic actinorhodin. We structurally and biochemically characterized SCO6735 protein. SCO6735, shares a 19% similarity with DarG from toxin/antitoxin DarT/DarG bacterial system and 25% with human TARG1. Molecular dynamic simulations of SCO6735 WT and mutants in complex with substrate suggest a novel, substrate-assisted, catalytic mechanism. We showed that SCO6735 can reverse thymidine-linked ADP-ribosylation in vitro and in vivo. Experiments in vitro showed that SCO6735 is active on both protein and DNA modified substrates ; although it reverses DNA ADP-ribosylation on thymidines (modified by toxin DarT) much more efficiently. SCO6735 activity in vivo has been shown in heterologous (E. coli) and homologous (S. coelicolor) system. Both in vivo systems SCO6735 rescued from the toxic effect of DarT suggesting the SCO6735 might be a defence mechanism hydrolase, protecting the cell from genotoxic stress.

Diverse proteins that constitute ADP- ribosylation metabolic cycle have been found in bacteria from the genus Streptomyces. We focused on the macrodomain family of ADP- ribosyl hydrolases that encompass key players in the recognition, interpretation, and turnover of ADP-ribose signaling. In Streptomyces, ADP-ribosylation is involved in the regulation of morphological differentiation and antibiotic production. Our results on ADP- ribosyl hydrolase from S. coelicolor - SCO6735 showed that disruption of SCO6735 increases production of antibiotic actinorhodin. We structurally and biochemically characterized SCO6735 protein. The crystal structure of SCO6735 revealed highly conserved three- layered α-β-α sandwich macrodomain fold with a deep cleft that represents a putative ligand- binding site. Currently, we are employing different molecular modelling methods in order to pinpoint catalytic mechanism of SCO6735 protein. Experiments in vitro showed that SCO6735 is active on both protein and DNA modified substrates ; it efficiently reverses protein ADP-ribosylation on glutamate residues and DNA ADP-ribosylation on thymidines that have been modified by DarT toxin. SCO6735 activity in vivo has been shown in E. coli ; when SCO6735 and DarT are co-expressed, SCO6735 can rescue E. coli from the toxic effect of DarT.

CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerized tools, improved documentation, and added citation/reference tools to facilitate the use and contribution of the community.

Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.