Lee Kamentsky

An image analysis toolbox for high-throughput C. elegans assays

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.

High- and low-throughput scoring of fat mass and body fat distribution in C. elegans

Fat accumulation is a complex phenotype affected by factors such as neuroendocrine signaling, feeding, activity, and reproductive output. Accordingly, the most informative screens for genes and compounds affecting fat accumulation would be those carried out in whole living animals. Caenorhabditis elegans is a well-established and effective model organism, especially for biological processes that involve organ systems and multicellular interactions, such as metabolism. Every cell in the transparent body of C. elegans is visible under a light microscope. Consequently, an accessible and reliable method to visualize worm lipid-droplet fat depots would make C. elegans the only metazoan in which genes affecting not only fat mass but also body fat distribution could be assessed at a genome-wide scale. Here we present a radical improvement in oil red O worm staining together with high-throughput image-based phenotyping. The three-step sample preparation method is robust, formaldehyde-free, and inexpensive, and requires only 15min of hands-on time to process a 96-well plate. Together with our free and user-friendly automated image analysis package, this method enables C. elegans sample preparation and phenotype scoring at a scale that is compatible with genome-wide screens. Thus we present a feasible approach to small-scale phenotyping and large-scale screening for genetic and/or chemical perturbations that lead to alterations in fat quantity and distribution in whole animals.

Pharmacological HIF2α inhibition improves VHL disease-associated phenotypes in zebrafish model.

Patients with a germline mutation in von Hippel-Lindau (VHL) develop renal cell cancers and hypervascular tumors of the brain, adrenal glands, and pancreas as well as erythrocytosis. These phenotypes are driven by aberrant expression of HIF2α, which induces expression of genes involved in cell proliferation, angiogenesis, and red blood cell production. Currently, there are no effective treatments available for VHL disease. Here, using an animal model of VHL, we report a marked improvement of VHL-associated phenotypes following treatment with HIF2α inhibitors. Inactivation of vhl in zebrafish led to constitutive activation of HIF2α orthologs and modeled several aspects of the human disease, including erythrocytosis, pathologic angiogenesis in the brain and retina, and aberrant kidney and liver proliferation. Treatment of vhl(-/-) mutant embryos with HIF2α-specific inhibitors downregulated Hif target gene expression in a dose-dependent manner, improved abnormal hematopoiesis, and substantially suppressed erythrocytosis and angiogenic sprouting. Moreover, pharmacologic inhibition of HIF2α reversed the compromised cardiac contractility of vhl(-/-) embryos and partially rescued early lethality. This study demonstrates that small-molecule targeting of HIF2α improves VHL-related phenotypes in a vertebrate animal model and supports further exploration of this strategy for treating VHL disease.

QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents.

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening

Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning.

Highlights • Imaging flow cytometry enables potentially powerful, multiplexed single-cell analysis.• Data analysis techniques for imaging flow cytometry are largely manual and subjective.• Our machine learning workflow identifies phenotypes in imaging flow cytometry.• The workflow uses open-source software and does not require computational expertise.

The antidepressant drug paroxetine as a new lead candidate in schistosome drug discovery

Recently, our in silico repositioning-chemogenomics approach predicted paroxetine (PAR), an antidepressant drug, as a inhibitor of Schistosoma mansoni serotonin transporters (SmSERTs), and consequently, a new anti-schistosomal candidate. With the aim of determining the anti-schistosomal activity of this drug, we initially used a spectrophotometric assay to determine activity against schistosomula worms. During this investigation, we verified that PAR showed a pronounced effect on schistosomula viability (IC50 = 2.5 μM) after 72 h of incubation. Then, we performed ex vivo studies with adult S. mansoni worms using a new automated image-based assay to accurately measure worm motility. As expected from the PARs predicted mechanism of action, both male and female worms treated with low concentrations of PAR exhibited enhanced motility followed by reduction in motility as incubation time increased. PAR EC50 values for motility reduction in male and female worms were 5.1 μM and 9.9 μM after 24 h of exposure, respectively, and this effect was maintained until the end of the experiment (72 h). Lastly, homology modeling and docking studies with SmSERT-A and human SERT (hSERT) revealed insights into the chemical basis of PAR antischistosomal activity. These results provide crucial guidance for further studies to optimize PAR in terms of potency and selectivity.

Scalable Interactive Visualization for Connectomics

Connectomics has recently begun to image brain tissue at nanometer resolution, which produces petabytes of data. This data must be aligned, labeled, proofread, and formed into graphs, and each step of this process requires visualization for human verification. As such, we present the BUTTERFLY middleware, a scalable platform that can handle massive data for interactive visualization in connectomics. Our platform outputs image and geometry data suitable for hardware-accelerated rendering, and abstracts low-level data wrangling to enable faster development of new visualizations. We demonstrate scalability and extendability with a series of open source Web-based applications for every step of the typical connectomics workflow: data management and storage, informative queries, 2D and 3D visualizations, interactive editing, and graph-based analysis. We report design choices for all developed applications and describe typical scenarios of isolated and combined use in everyday connectomics research. In addition, we measure and optimize rendering throughput—from storage to display—in quantitative experiments. Finally, we share insights, experiences, and recommendations for creating an open source data management and interactive visualization platform for connectomics.

CellProfiler 3.0: Next-generation image processing for biology

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler’s infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.