Piximi - An Images to Discovery web tool for bioimages and beyond.

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

A Postdoctoral Training Program in Bioimage Analysis.

We herein describe a postdoctoral training program designed to train biologists with microscopy experience in bioimage analysis. We detail the rationale behind the program, the various components of the training program, and outcomes in terms of works produced and the career effects on past participants. We analyze the results of an anonymous survey distributed to past and present participants, indicating overall high value of all 12 rated aspects of the program, but significant heterogeneity in which aspects were most important to each participant. Finally, we propose this model as a template for other programs which may want to train experts in professional skill sets, and discuss the important considerations when running such a program. We believe that such programs can have extremely positive impact for both the trainees themselves and the broader scientific community.

Reproducible image-based profiling with Pycytominer.

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.

Assessing the performance of the Cell Painting assay across different imaging systems.

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.

The interplay of TARG1 and PARG protects against genomic instability.

The timely removal of ADP-ribosylation is crucial for efficient DNA repair. However, much remains to be discovered about ADP-ribosylhydrolases. Here, we characterize the physiological role of TARG1, an ADP-ribosylhydrolase that removes aspartate/glutamate-linked ADP-ribosylation. We reveal its function in the DNA damage response and show that the loss of TARG1 sensitizes cells to inhibitors of topoisomerase II, ATR, and PARP. Furthermore, we find a PARP1-mediated synthetic lethal interaction between TARG1 and PARG, driven by the toxic accumulation of ADP-ribosylation, that induces replication stress and genomic instability. Finally, we show that histone PARylation factor 1 (HPF1) deficiency exacerbates the toxicity and genomic instability induced by excessive ADP-ribosylation, suggesting a close crosstalk between components of the serine- and aspartate/glutamate-linked ADP-ribosylation pathways. Altogether, our data identify TARG1 as a potential biomarker for the response of cancer cells to PARP and PARG inhibition and establish that the interplay of TARG1 and PARG protects cells against genomic instability.

CellProfiler plugins - An easy image analysis platform integration for containers and Python tools.

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 containerised tools, improved documentation and added citation/reference tools to facilitate the use and contribution of the community.

CellProfiler plugins -- an easy image analysis platform integration for containers and Python tools.

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.

Assessing the performance of the Cell Painting assay across different imaging systems.

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.

Streptomyces coelicolor macrodomain hydrolase SCO6735 cleaves thymidine-linked ADP-ribosylation of DNA.

ADP-ribosylation is an ancient, highly conserved, and reversible covalent modification critical for a variety of endogenous processes in both prokaryotes and eukaryotes. ADP-ribosylation targets proteins, nucleic acids, and small molecules (including antibiotics). ADP-ribosylation signalling involves enzymes that add ADP-ribose to the target molecule, the (ADP-ribosyl)transferases; and those that remove it, the (ADP-ribosyl)hydrolases. Recently, the toxin/antitoxin pair DarT/DarG composed of a DNA ADP-ribosylating toxin, DarT, and (ADP-ribosyl)hydrolase antitoxin, DarG, was described. DarT modifies thymidine in single-stranded DNA in a sequence-specific manner while DarG reverses this modification, thereby rescuing cells from DarT toxicity. We studied the DarG homologue SCO6735 which is highly conserved in all Streptomyces species and known to be associated with antibiotic production in the bacterium S. coelicolor. SCO6735 shares a high structural similarity with the bacterial DarG and human TARG1. Like DarG and TARG1, SCO6735 can also readily reverse thymidine-linked ADP-ribosylation catalysed by DarT in vitro and in cells. SCO6735 active site analysis including molecular dynamic simulations of its complex with ADP-ribosylated thymidine suggests a novel catalytic mechanism of DNA-(ADP-ribose) hydrolysis. Moreover, a comparison of SCO6735 structure with ALC1-like homologues revealed an evolutionarily conserved feature characteristic for this subclass of macrodomain hydrolases.

TARG1 protects against toxic DNA ADP-ribosylation.

ADP-ribosylation is a modification that targets a variety of macromolecules and regulates a diverse array of important cellular processes. ADP-ribosylation is catalysed by ADP-ribosyltransferases and reversed by ADP-ribosylhydrolases. Recently, an ADP-ribosyltransferase toxin termed 'DarT' from bacteria, which is distantly related to human PARPs, was shown to modify thymidine in single-stranded DNA in a sequence specific manner. The antitoxin of DarT is the macrodomain containing ADP-ribosylhydrolase DarG, which shares striking structural homology with the human ADP-ribosylhydrolase TARG1. Here, we show that TARG1, like DarG, can reverse thymidine-linked DNA ADP-ribosylation. We find that TARG1-deficient human cells are extremely sensitive to DNA ADP-ribosylation. Furthermore, we also demonstrate the first detection of reversible ADP-ribosylation on genomic DNA in vivo from human cells. Collectively, our results elucidate the impact of DNA ADP-ribosylation in human cells and provides a molecular toolkit for future studies into this largely unknown facet of ADP-ribosylation.

Molecular basis for DarT ADP-ribosylation of a DNA base.

ADP-ribosyltransferases use NAD+ to catalyse substrate ADP-ribosylation1, and thereby regulate cellular pathways or contribute to toxin-mediated pathogenicity of bacteria2-4. Reversible ADP-ribosylation has traditionally been considered a protein-specific modification5, but recent in vitro studies have suggested nucleic acids as targets6-9. Here we present evidence that specific, reversible ADP-ribosylation of DNA on thymidine bases occurs in cellulo through the DarT-DarG toxin-antitoxin system, which is found in a variety of bacteria (including global pathogens such as Mycobacterium tuberculosis, enteropathogenic Escherichia coli and Pseudomonas aeruginosa)10. We report the structure of DarT, which identifies this protein as a diverged member of the PARP family. We provide a set of high-resolution structures of this enzyme in ligand-free and pre- and post-reaction states, which reveals a specialized mechanism of catalysis that includes a key active-site arginine that extends the canonical ADP-ribosyltransferase toolkit. Comparison with PARP-HPF1, a well-established DNA repair protein ADP-ribosylation complex, offers insights into how the DarT class of ADP-ribosyltransferases evolved into specific DNA-modifying enzymes. Together, our structural and mechanistic data provide details of this PARP family member and contribute to a fundamental understanding of the ADP-ribosylation of nucleic acids. We also show that thymine-linked ADP-ribose DNA adducts reversed by DarG antitoxin (functioning as a noncanonical DNA repair factor) are used not only for targeted DNA damage to induce toxicity, but also as a signalling strategy for cellular processes. Using M. tuberculosis as an exemplar, we show that DarT-DarG regulates growth by ADP-ribosylation of DNA at the origin of chromosome replication.