Comparison of multivariate data analysis strategies for high-content screening

High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z' factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.     

Development of a high-content screening assay panel to accelerate mechanism of action studies for oncology research

Efficient elucidation of the biological mechanism of action of novel compounds remains a major bottleneck in the drug discovery process. To address this need in the area of oncology, we report the development of a multiparametric high-content screening assay panel at the level of single cells to dramatically accelerate understanding the mechanism of action of cell growth-inhibiting compounds on a large scale. Our approach is based on measuring 10 established end points associated with mitochondrial apoptosis, cell cycle disruption, DNA damage, and cellular morphological changes in the same experiment, across three multiparametric assays. The data from all of the measurements taken together are expected to help increase our current understanding of target protein functions, constrain the list of possible targets for compounds identified using phenotypic screens, and identify off-target effects. We have also developed novel data visualization and phenotypic classification approaches for detailed interpretation of individual compound effects and navigation of large collections of multiparametric cellular responses. We expect this general approach to be valuable for drug discovery across multiple therapeutic areas.     

Pyridine-pyrimidine amides that prevent HGF-induced epithelial scattering by two distinct mechanisms

Stimulation of cultured epithelial cells with scatter factor/hepatocyte growth factor (HGF) results in individual cells detaching and assuming a migratory and invasive phenotype. Epithelial scattering recapitulates cancer progression and studies have implicated HGF signaling as a driver of cancer metastasis. Inhibitors of HGF signaling have been proposed to act as anti-cancer agents. We previously screened a small molecule library for compounds that block HGF-induced epithelial scattering. Most hits identified in this screen exhibit anti-mitotic properties. Here we assess the biological mechanism of a compound that blocks HGF-induced scattering with limited anti-mitotic activity. Analogs of this compound have one of two distinct activities: inhibiting either cell migration or cell proliferation with cell cycle arrest in G2/M. Each activity bears unique structure–activity relationships. The mechanism of action of anti-mitotic compounds is by inhibition of microtubule polymerization; these compounds entropically and enthalpically bind tubulin in the colchicine binding site, generating a conformational change in the tubulin dimer.     

Forward chemical genetics: progress and obstacles on the path to a new pharmacopoeia

Forward chemical genetics is a new method to systematize the discovery and use of small molecules as tools for basic biological research. This approach requires three basic components: a library of compounds; an assay, in which the library is screened for a cellular or organismal phenotype; and a method to trace an active compound to its biological target. Bioactive compounds have traditionally been isolated from natural product extracts, although 'diversity-oriented synthesis' and commercial compound collections are gaining in prominence. New techniques, such as image-based screening and the cytoblot method, have increased the throughput of phenotypic assays. Strategies are also being developed to streamline target identification using molecular biological approaches.     

Identification of a small molecule that induces mitotic arrest using a simplified high-content screening assay and data analysis method

High-content screening has emerged as a new and powerful technique for identifying small-molecule modulators of mammalian cell biology. The authors describe the development and execution of a high-content screen to identify small molecules that induce mitotic arrest in mammalian cancer cells. Many widely used chemotherapeutics, such as Taxol and vinblastine, induce mitotic arrest, and the creation of new drugs that also induce mitotic arrest may have tremendous therapeutic value. In their screen, the authors employed a simple DNA stain (DAPI) and a sensitive nonparametric statistical test to identify compounds from an internal collection of approximately 13,000 high-quality lead-like small molecules. Subsequent analysis of 1 active compound indicated that it induces mitotic arrest, assessed using a high-content phosphohistone H3 detection assay, and caused cell proliferation defects in multiple cancer cell lines. The active compound, a quinazolinone originating from a natural product-like subset of the screened compounds, is active in cells at approximately 500 nM and appears to act by inhibiting the polymerization of tubulin.     

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds

Background

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target.

Methodology

We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery.

Conclusions

This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.     

Phaedra, a protocol-driven system for analysis and validation of high-content imaging and flow cytometry

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.     

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

BackgroundChagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity.

Methodology/Principal Findings

In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC₅₀ < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi.

Conclusions/ Significance

We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.     

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.     

Discovery of benzimidazole derivatives as novel multi-target EGFR, VEGFR-2 and PDGFR kinase inhibitors

Multi-target EGFR, VEGFR-2 and PDGFR inhibitors are highly useful anticancer agents with improved therapeutic efficacies. In this work, we used two virtual screening methods, support vector machines (SVM) and molecular docking, to identify a novel series of benzimidazole derivatives, 2-aryl benzimidazole compounds, as multi-target EGFR, VEGFR-2 and PDGFR inhibitors. 2-Aryl benzimidazole compounds were synthesized and their biological activities against a tumor cell line HepG-2 and specific kinases were evaluated. Among these compounds, compounds 5a and 5e exhibited high cytotoxicity against HepG-2 cells with IC?? values at ～2 μM. Further kinase assay study showed that compound 5a have good EGFR inhibitory activity and moderate VEGFR-2 and PDGFR inhibitory activities, while 5e have moderate EGFR inhibitory activity and slightly weaker VEGFR-2 and PDGFR inhibitory activities. Molecular docking analysis suggested that compound 5a more tightly interacts with EGFR and PDGFR than compound 5e. Our study discovered a novel series of benzimidazole derivatives as multi-target EGFR, VEGFR-2 and PDGFR kinases inhibitors.     

Time-Resolved Human Kinome RNAi Screen Identifies a Network Regulating Mitotic-Events as Early Regulators of Cell Proliferation

Analysis of biological processes is frequently performed with the help of phenotypic assays where data is mostly acquired in single end-point analysis. Alternative phenotypic profiling techniques are desired where time-series information is essential to the biological question, for instance to differentiate early and late regulators of cell proliferation in loss-of-function studies. So far there is no study addressing this question despite of high unmet interests, mostly due to the limitation of conventional end-point assaying technologies. We present the first human kinome screen with a real-time cell analysis system (RTCA) to capture dynamic RNAi phenotypes, employing time-resolved monitoring of cell proliferation via electrical impedance. RTCA allowed us to investigate the dynamics of phenotypes of cell proliferation instead of using conventional end-point analysis. By introducing data transformation with first-order derivative, i.e. the cell-index growth rate, we demonstrate this system suitable for high-throughput screenings (HTS). The screen validated previously identified inhibitor genes and, additionally, identified activators of cell proliferation. With the information of time kinetics available, we could establish a network of mitotic-event related genes to be among the first displaying inhibiting effects after RNAi knockdown. The time-resolved screen captured kinetics of cell proliferation caused by RNAi targeting human kinome, serving as a resource for researchers. Our work establishes RTCA technology as a novel robust tool with biological and pharmacological relevance amenable for high-throughput screening.     

Chemotography for multi-target SAR analysis in the context of biological pathways

The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.     

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.     

The Development and Application of the Glucose Uptake Stimulating Cell Model

The plants are great resources for digging leading compounds, and many current anti diabetic drugs are derived from plants. The key to discover compounds with anti diabetic activities from plants relies on the application of anti diabetic drug screening models. In order to establish a more stable and reliable anti diabetic drug screening model, we optimized the screening model based on the glucose uptake of adipocytes. In the previous models, insulin was used as the only positive control, while in our model both insulin and rosiglitazone were used as positive controls, which made the model more stable and reliable. Furthermore, we expanded the application of the model to screen the insulin signaling pathway inhibitors, and Akt1/2 inhibitor which was an inhibitor of insulin signaling pathway was used as positive control. In the end, we screened 16 compounds isolated from plants using this model and identified three active compounds with glucose uptake stimulating activities. We also performed the dose response experiments of compound X15 and X16. Both showed significant dose responses. These activities were first reported at the cell level, providing fundamental data for their mechanisms study of the activities and for the potential development of the drugs in future.     

Multiple genetic interaction experiments provide complementary information useful for gene function prediction

Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.     

A combination of ultrahigh throughput PathHunter and cytokine secretion assays to identify glucocorticoid receptor agonists

The use of ultrahigh throughput screens (uHTS) is a well-accepted mechanism to identify agonists and antagonists of target receptors. We used the Path Hunter [Path Hunter technology is a registered trademark of DiscoveRx Corporation.] technology from DiscoveRx to screen the entire Merck compound library for glucocorticoid receptor (GR) agonists in a 2.2-μl total reaction volume assayed in a 3456-well plate format. This single addition, homogenous assay which utilizes the principle of enzyme fragment complementation (EFC) to detect nuclear translocation of GR, an initial step of receptor activation, was used to successfully screen a large library of small molecules as indicated by an average signal to background ratio of approximately 4-fold and an average Z-factor value of 0.45. Hits from the HTS campaign were studied in a cytokine secretion assay in primary human monocytes to gain functional information regarding these compounds in a phenotypic and physiologically relevant setting. Our data indicate that using the PathHunter assay, we successfully identified compounds that showed agonism for the GR receptor in primary human monocytes and due to their performance in a physiologically relevant model they likely will have a better chance to evoke clinical efficacy.     

web cellHTS2: A web-application for the analysis of high-throughput screening data

Background  

The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.     

Image-based multivariate profiling of drug responses from single cells

Quantitative analytical approaches for discovering new compound mechanisms are required for summarizing high-throughput, image-based drug screening data. Here we present a multivariate method for classifying untreated and treated human cancer cells based on approximately 300 single-cell phenotypic measurements. This classification provides a score, measuring the magnitude of the drug effect, and a vector, indicating the simultaneous phenotypic changes induced by the drug. These two quantities were used to characterize compound activities and identify dose-dependent multiphasic responses. A systematic survey of profiles extracted from a 100-compound compendium of image data revealed that only 10-15% of the original features were required to detect a compound effect. We report the most informative image features for each compound and fluorescence marker set using a method that will be useful for determining minimal collections of readouts for drug screens. Our approach provides human-interpretable profiles and automatic determination of on- and off-target effects.