Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not only pollen included in training libraries to the species level but, depending on the sample, can classify previously unseen pollen to the likely phylogenetic order, family and even genus. Our approach offers valuable insights into the development of a widely transferable, rapid and accurate exploratory tool for pollen classification in 'real-world' environmental samples with improved accuracy over pure deep learning techniques. This work has the potential to revolutionise many aspects of palynology, allowing a more detailed spatial and temporal understanding of pollen in the environment with improved taxonomical resolution.

A multiparametric activity profiling platform for neuron disease phenotyping and drug screening.

Patient stem cell-derived models enable imaging of complex disease phenotypes and the development of scalable drug discovery platforms. Current preclinical methods for assessing cellular activity do not, however, capture the full intricacies of disease-induced disturbances and instead typically focus on a single parameter, which impairs both the understanding of disease and the discovery of effective therapeutics. Here, we describe a cloud-based image processing and analysis platform that captures the intricate activity profile revealed by GCaMP fluorescence recordings of intracellular calcium changes and enables the discovery of molecules that correct 153 parameters that define the amyotrophic lateral sclerosis motor neuron disease phenotype. In a high-throughput screen, we identified compounds that revert the multiparametric disease profile to that found in healthy cells, a novel and robust measure of therapeutic potential quite distinct from unidimensional screening. This platform can guide the development of therapeutics that counteract the multifaceted pathological features of diseased cellular activity.

Identification and local delivery of vasodilators for the reduction of ureteral contractions.

Kidney stones and ureteral stents can cause ureteral colic and pain. By decreasing contractions in the ureter, clinically prescribed oral vasodilators may improve spontaneous stone passage rates and reduce the pain caused by ureteral stenting. We hypothesized that ureteral relaxation can be improved via the local administration of vasodilators and other smooth muscle relaxants. Here, by examining 18 candidate small molecules in an automated screening assay to determine the extent of ureteral relaxation, we show that the calcium channel blocker nifedipine and the Rho-kinase inhibitor ROCKi significantly relax human ureteral smooth muscle cells. We also show, by using ex vivo porcine ureter segments and sedated pigs that, with respect to the administration of a placebo, the local delivery of a clinically deployable formulation of the two drugs reduced ureteral contraction amplitude and frequency by 90% and 50%, respectively. Finally, we show that standard oral vasodilator therapy reduced contraction amplitude by only 50% and had a minimal effect on contraction frequency. Locally delivered ureteral relaxants therefore may improve ureter-related conditions.

Capturing single-cell heterogeneity via data fusion improves image-based profiling.

Single-cell resolution technologies warrant computational methods that capture cell heterogeneity while allowing efficient comparisons of populations. Here, we summarize cell populations by adding features' dispersion and covariances to population averages, in the context of image-based profiling. We find that data fusion is critical for these metrics to improve results over the prior alternatives, providing at least ~20% better performance in predicting a compound's mechanism of action (MoA) and a gene's pathway.

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.

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.

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.

Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling.

High-throughput screening has become a mainstay of small-molecule probe and early drug discovery. The question of how to build and evolve efficient screening collections systematically for cell-based and biochemical screening is still unresolved. It is often assumed that chemical structure diversity leads to diverse biological performance of a library. Here, we confirm earlier results showing that this inference is not always valid and suggest instead using biological measurement diversity derived from multiplexed profiling in the construction of libraries with diverse assay performance patterns for cell-based screens. Rather than using results from tens or hundreds of completed assays, which is resource intensive and not easily extensible, we use high-dimensional image-based cell morphology and gene expression profiles. We piloted this approach using over 30,000 compounds. We show that small-molecule profiling can be used to select compound sets with high rates of activity and diverse biological performance.

Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment.

Quantitative microscopy has proven a versatile and powerful phenotypic screening technique. Recently, image-based profiling has shown promise as a means for broadly characterizing molecules' effects on cells in several drug-discovery applications, including target-agnostic screening and predicting a compound's mechanism of action (MOA). Several profiling methods have been proposed, but little is known about their comparative performance, impeding the wider adoption and further development of image-based profiling. We compared these methods by applying them to a widely applicable assay of cultured cells and measuring the ability of each method to predict the MOA of a compendium of drugs. A very simple method that is based on population means performed as well as methods designed to take advantage of the measurements of individual cells. This is surprising because many treatments induced a heterogeneous phenotypic response across the cell population in each sample. Another simple method, which performs factor analysis on the cellular measurements before averaging them, provided substantial improvement and was able to predict MOA correctly for 94% of the treatments in our ground-truth set. To facilitate the ready application and future development of image-based phenotypic profiling methods, we provide our complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework.

A microscale human liver platform that supports the hepatic stages of Plasmodium falciparum and vivax.

The Plasmodium liver stage is an attractive target for the development of antimalarial drugs and vaccines, as it provides an opportunity to interrupt the life cycle of the parasite at a critical early stage. However, targeting the liver stage has been difficult. Undoubtedly, a major barrier has been the lack of robust, reliable, and reproducible in vitro liver-stage cultures. Here, we establish the liver stages for both Plasmodium falciparum and Plasmodium vivax in a microscale human liver platform composed of cryopreserved, micropatterned human primary hepatocytes surrounded by supportive stromal cells. Using this system, we have successfully recapitulated the full liver stage of P. falciparum, including the release of infected merozoites and infection of overlaid erythrocytes, as well as the establishment of small forms in late liver stages of P. vivax. Finally, we validate the potential of this platform as a tool for medium-throughput antimalarial drug screening and vaccine development.

High-throughput screen for novel antimicrobials using a whole animal infection model.

The nematode Caenorhabditis elegans is a unique whole animal model system for identifying small molecules with in vivo anti-infective properties. C. elegans can be infected with a broad range of human pathogens, including Enterococcus faecalis, an important human nosocomial pathogen. Here, we describe an automated, high-throughput screen of 37,200 compounds and natural product extracts for those that enhance survival of C. elegans infected with E. faecalis. Using a robot to dispense live, infected animals into 384-well plates and automated microscopy and image analysis, we identified 28 compounds and extracts not previously reported to have antimicrobial properties, including six structural classes that cure infected C. elegans animals but do not affect the growth of the pathogen in vitro, thus acting by a mechanism of action distinct from antibiotics currently in clinical use.

Image-based chemical screening.

Technological advances have made it feasible to conduct high-throughput small-molecule screens based on visual phenotypes of individual cells, using automated imaging and analysis. These screens are rapidly moving from being small, proof-of-principle tests to robust and widespread screens of hundreds of thousands of compounds. Automated imaging screens maximize the information obtained in an initial screen and improve the ability to select high-quality leads. In this Perspective, I highlight the key steps necessary for conducting a high-throughput image-based chemical compound screen.