An efficient method for the detection and elimination of systematic error in high-throughput screening

Enhanced motivated behavior mediated by pharmacological targeting of the FGF14/Nav1.6 complex in nucleus accumbens neurons

Protein/protein interactions (PPI) play crucial roles in neuronal functions. Yet, their potential as drug targets for brain disorders remains underexplored. The fibroblast growth factor 14 (FGF14)/voltage-gated Na+ channel 1.6 (Nav1.6) complex regulates excitability of medium spiny neurons (MSN) of the nucleus accumbens (NAc), a central hub of reward circuitry that controls motivated behaviors. Here, we identified compound 1028 (IUPAC: ethyl 3-(2-(3-(hydroxymethyl)-1H-indol-1-yl)acetamido)benzoate), a brain-permeable small molecule that targets FGF14R117, a critical residue located within a druggable pocket at the FGF14/Nav1.6 PPI interface. We found that 1028 modulates FGF14/Nav1.6 complex assembly and depolarizes the voltage-dependence of Nav1.6 channel inactivation with nanomolar potency by modulating the intramolecular interaction between the III-IV linker and C-terminal domain of the Nav1.6 channel. Consistent with the compound's effects on Nav1.6 channel inactivation, 1028 enhances MSN excitability ex vivo and accumbal neuron firing rate in vivo in murine models. Systemic administration of 1028 maintains behavioral motivation preferentially during motivationally deficient conditions in murine models. These behavioral effects were abrogated by in vivo gene silencing of Fgf14 in the NAc and were accompanied by a selective reduction in accumbal dopamine levels during reward consumption in murine models. These findings underscore the potential to selectively regulate complex behaviors associated with neuropsychiatric disorders through targeting of PPIs in neurons.

Whole Cell Luminescence-Based Screen for Inhibitors of the Bacterial Sec Machinery

There is a pressing need for new antibiotics to combat rising resistance to those already in use. The bacterial general secretion (Sec) system has long been considered a good target for novel antimicrobials thanks to its irreplacable role in maintaining cell envelope integrity, yet the lack of a robust, high-throughput method to screen for Sec inhibition has so far hampered efforts to realize this potential. Here, we have adapted our recently developed in vitro assay for Sec activity─based on the split NanoLuc luciferase─to work at scale and in living cells. A simple counterscreen allows compounds that specifically target Sec to be distinguished from those with other effects on cellular function. As proof of principle, we have applied this assay to a library of 5000 compounds and identified a handful of moderately effective in vivo inhibitors of Sec. Although these hits are unlikely to be potent enough to use as a basis for drug development, they demonstrate the efficacy of the screen. We therefore anticipate that the methods presented here will be scalable to larger compound libraries, in the ultimate quest for Sec inhibitors with clinically relevant properties.

Hit screening with multivariate robust outlier detection

Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme.

High throughput and targeted screens for prepilin peptidase inhibitors do not identify common inhibitors of eukaryotic gamma-secretase

INTRODUCTION

Prepilin peptidases (PPP) are essential enzymes for the biogenesis of important virulence factors, such as type IV pili (T4P), type II secretion systems, and other T4P-related systems of bacteria and archaea. PPP inhibitors could be valuable pharmaceuticals, but only a few have been reported. Interestingly, PPP share similarities with presenilin enzymes from the gamma-secretase protease complex, which are linked to Alzheimer's disease. Numerous gamma-secretase inhibitors have been reported, and some have entered clinical trials, but none has been tested against PPP.

OBJECTIVE

The objective of this study is to develop a high-throughput screening (HTS) method to search for inhibitors of PPP from various chemical libraries and reported gamma-secretase inhibitors.

METHOD

More than 15,000 diverse compounds, including 13 reported gamma-secretase inhibitors and other reported peptidase inhibitors, were screened to identify potential PPP inhibitors.

RESULTS

The authors developed a novel screening method and screened 15,869 compounds. However, the screening did not identify a PPP inhibitor. Nevertheless, the study suggests that gamma-secretase is sufficiently different from PPP that specific inhibitors may exist in a larger chemical space.

CONCLUSION

The authors believe that the HTS method that they describe has numerous advantages and encourage others to consider its application in the search for PPP inhibitors.

A statistical framework for high-content phenotypic profiling using cellular feature distributions

High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.

Major Improvements in Robustness and Efficiency during the Screening of Novel Enzyme Effectors by the 3-Point Kinetics Assay

The throughput level currently reached by automatic liquid handling and assay monitoring techniques is expected to facilitate the discovery of new modulators of enzyme activity. Judicious and dependable ways to interpret vast amounts of information are, however, required to effectively answer this challenge. Here, the 3-point method of kinetic analysis is proposed as a means to significantly increase the hit success rates and decrease the number of falsely identified compounds (false positives). In this post-Michaelis-Menten approach, each screened reaction is probed in three different occasions, none of which necessarily coincide with the initial period of constant velocity. Enzymology principles rather than subjective criteria are applied to identify unwanted outliers such as assay artifacts, and then to accurately distinguish true enzyme modulation effects from false positives. The exclusion and selection criteria are defined based on the 3-point reaction coordinates, whose relative positions along the time-courses may change from well to well or from plate to plate, if necessary. The robustness and efficiency of the new method is illustrated during a small drug repurposing screening of potential modulators of the deubiquinating activity of ataxin-3, a protein implicated in Machado-Joseph disease. Apparently, intractable Z factors are drastically enhanced after (1) eliminating spurious results, (2) improving the normalization method, and (3) increasing the assay resilience to systematic and random variability. Numerical simulations further demonstrate that the 3-point analysis is highly sensitive to specific, catalytic, and slow-onset modulation effects that are particularly difficult to detect by typical endpoint assays.

Structurally unique PARP-1 inhibitors for the treatment of prostate cancer

The prognosis for metastatic castration-resistant prostate cancer is unfavorable, and although Poly(ADP)-ribose polymerase-1 (PARP-1) inhibitors have shown efficacy in the treatment of androgen-receptor dependent malignancies, the limited number of options present obstacles for patients that are not responsive to these treatments. Here we utilize an integrated screening strategy that combines cellular screening assays, informatics, in silico computational approaches, and dose-response testing for reducing a compound library of confirmed PARP-1 inhibitors. Six hundred and sixty-four validated PARP-1 inhibitors were reduced to 9 small molecules with favorable physicochemical/ADME properties, unique chemical fingerprints, high dissimilarity to existing drugs, few off-target effects, and dose-responsivity in the 1 µmol/L - 20 µmol/L range. The top 9 unique molecules identified by our integrated screening strategy will be selected for further preclinical development including cytotoxicity testing, effects on mitosis, structure-activity relationship, physicochemical/ADME studies, and in vivo testing.

High Content, Phenotypic Assays and Screens for Compounds Modulating Cellular Processes in Primary Neurons

High content, phenotypic screens offer a powerful approach to systems biology at the cellular level. The approach employs cells carrying fluorescently labeled molecules or organelles in 384- or 1536-well microplates, and an automated confocal screening microscope for capturing images from each well. Although some specifics vary according to the assay type, each will apply some degree of image processing and feature extraction followed by a data analysis pipeline to identify the perturbations (small molecules, etc.) of interest. We describe and discuss the advantages and limitations of high content assays and screens using the specific example of assaying mitochondrial dynamics in primary neurons. We provide a detailed description of our culturing methods, imaging and data analysis techniques and provide an open source, ready to use CellProfiler pipeline for high-throughput image segmentation and quantification tool for mitochondrial parameters.

Detecting and removing multiplicative spatial bias in high-throughput screening technologies

Motivation

Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias.

Results

We introduce three new statistical methods meant to reduce multiplicative spatial bias in screening technologies. We assess the performance of the methods with synthetic and real data affected by multiplicative spatial bias, including comparisons with current bias correction methods. We also describe a wider data correction protocol that integrates methods for removing both assay and plate-specific spatial biases, which can be either additive or multiplicative.

Conclusions

The methods for removing multiplicative spatial bias and the data correction protocol are effective in detecting and cleaning experimental data generated by screening technologies. As our protocol is of a general nature, it can be used by researchers analyzing current or next-generation high-throughput screens.

Availability and implementation

The AssayCorrector program, implemented in R, is available on CRAN.

Contact

makarenkov.vladimir@uqam.ca.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening

Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias. The measurements in these wells depend on the nature of interaction between the involved biases. Here, we propose two novel additive and two novel multiplicative spatial bias models accounting for different types of bias interactions. We describe a statistical procedure that allows for detecting and removing different types of additive and multiplicative spatial biases from multiwell plates. We show how this procedure can be applied by analyzing data generated by the four HTS technologies (homogeneous, microorganism, cell-based, and gene expression HTS), the three high-content screening (HCS) technologies (area, intensity, and cell-count HCS), and the only small-molecule microarray technology available in the ChemBank small-molecule screening database. The proposed methods are included in the AssayCorrector program, implemented in R, and available on CRAN.

Utilization of Multidimensional Data in the Analysis of Ultra-High-Throughput High Content Phenotypic Screens

High Content Screening (HCS) platforms can generate large amounts of multidimensional data. To take full advantage of all the rich contextual information provided by these screens, a combination of traditional as well as nontraditional hit identification and prioritization strategies is required. Here, we describe the workflow and analytics of multidimensional high content data to differentiate, group, and prioritize hits.

MightyScreen: An Open-Source Visualization Application for Screening Data Analysis

Screening is a methodology widely used in biological and biomedical research. There are numerous visualization methods to validate screening data quality but very few visualization applications capable of hit selection. Here, we present MightyScreen ( mightyscreen.net ), a novel web-based application designed for visual data evaluation as well as visual hit selection. We believe MightyScreen is an intuitive and interactive addition to conventional hit selection methods. We also provide study cases showing how MightyScreen is used to visually explore screening data and make hit selections.

Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies

Spatial bias continues to be a major challenge in high-throughput screening technologies. Its successful detection and elimination are critical for identifying the most promising drug candidates. Here, we examine experimental small molecule assays from the popular ChemBank database and show that screening data are widely affected by both assay-specific and plate-specific spatial biases. Importantly, the bias affecting screening data can fit an additive or multiplicative model. We show that the use of appropriate statistical methods is essential for improving the quality of experimental screening data. The presented methodology can be recommended for the analysis of current and next-generation screening data.

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Transcriptomic and Proteomic Profiling Provides Insight into Mesangial Cell Function in IgA Nephropathy

IgA nephropathy (IgAN), the most common GN worldwide, is characterized by circulating galactose-deficient IgA (gd-IgA) that forms immune complexes. The immune complexes are deposited in the glomerular mesangium, leading to inflammation and loss of renal function, but the complete pathophysiology of the disease is not understood. Using an integrated global transcriptomic and proteomic profiling approach, we investigated the role of the mesangium in the onset and progression of IgAN. Global gene expression was investigated by microarray analysis of the glomerular compartment of renal biopsy specimens from patients with IgAN (n=19) and controls (n=22). Using curated glomerular cell type-specific genes from the published literature, we found differential expression of a much higher percentage of mesangial cell-positive standard genes than podocyte-positive standard genes in IgAN. Principal coordinate analysis of expression data revealed clear separation of patient and control samples on the basis of mesangial but not podocyte cell-positive standard genes. Additionally, patient clinical parameters (serum creatinine values and eGFRs) significantly correlated with Z scores derived from the expression profile of mesangial cell-positive standard genes. Among patients grouped according to Oxford MEST score, patients with segmental glomerulosclerosis had a significantly higher mesangial cell-positive standard gene Z score than patients without segmental glomerulosclerosis. By investigating mesangial cell proteomics and glomerular transcriptomics, we identified 22 common pathways induced in mesangial cells by gd-IgA, most of which mediate inflammation. The genes, proteins, and corresponding pathways identified provide novel insights into the pathophysiologic mechanisms leading to IgAN.

Using Clustering Techniques to Improve Hit Selection in High-Throughput Screening

A typical modern high-throughput screening (HTS) operation consists of testing thousands of chemical compounds to select active ones for future detailed examination. The authors describe 3 clustering techniques that can be used to improve the selection of active compounds (i.e., hits). They are designed to identify quality hits in the observed HTS measurements. The considered clustering techniques were first tested on simulated data and then applied to analyze the assay inhibiting Escherichia coli dihydrofo-late reductase produced at the HTS laboratory of McMaster University.

Reduction of dilution error in ELISAs using an internal standard

Background: Dilution bias is a major cause of immunoassay variability due to the lack of an internal standard to determine the true versus the expected dilution value. Methodology: We used an internal control to measure dilution bias in an ELISA. Acridine-orange was added at the first dilution step and monitored throughout dilutions. Assay results were corrected using the fluorescent signal ratio between samples and reference. Acridine dilution correlated with analyte-specific assay measurements (R2 = 0.987). Correction of assay results with the measured dilution factor improved both accuracy and precision resulting in a reduction of >50% %CV reduction. Conclusion: Dilution correction can significantly improve accuracy and precision of immunoassays. Additional control strategies may further mitigate other sources of variability.

An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification

The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community.

Effect of vitrification using the Cryotop method on the gene expression profile of in vitro-produced bovine embryos

The present study analyzed the changes in gene expression induced by the Cryotop vitrification technique in bovine blastocyst-stage embryos, using Agilent EmbryoGENE microarray slides. Bovine in vitro-produced embryos were vitrified and compared with nonvitrified (control) embryos. After vitrification, embryos were warmed and cultured for an additional 4 hours. Survived embryos were used for microarray analysis and quantitative polymerase chain reaction (qPCR) quantification. Survival rates were higher (P < 0.05) in the control embryos (100%) than in the vitrified embryos (87%). Global gene expression analysis revealed that only 43 out of 21,139 genes exhibited significantly altered expression in the vitrified embryos compared to the control embryos, with a very limited fold change (P < 0.05). A total of 10 genes were assessed by qPCR. Only the FOS-like antigen 1 (FOSL1) gene presented differential expression (P < 0.05) according to both the array and qPCR methods, and it was overexpressed in vitrified embryos. Although, the major consequence of vitrification seems to be the activation of the apoptosis pathway in some cells. Indeed, FOSL1 is part of the activating protein 1 transcription factor complex and is implicated in a variety of cellular processes, including proliferation, differentiation, and apoptosis. Therefore, our results suggest that a limited increase in the rate of apoptosis was the only detectable response of the embryos to vitrification stress.

Impact of normalization methods on high-throughput screening data with high hit rates and drug testing with dose-response data

Motivation: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose–response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B-score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration.

Results: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B-score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose–response curves.

Contact:john.mpindi@helsinki.fi

Availability and implementation, Supplementary information: R code and Supplementary data are available at Bioinformatics online.

HTS/HCS to screen molecules able to maintain embryonic stem cell self-renewal or to induce differentiation: overview of protocols

Embryonic stem (ES) cells, combining self-renewal ability with wide range tissue-specific cell differentiation, represent one of the most powerful model systems in basic research, drug discovery and biomedical applications. In the field of drug development, ES cells are instrumental in high-throughput/content screening (HTS/HCS) for the evaluation of large compound libraries to test biological activity and toxic properties. Since it is a high priority to test new compounds in vitro, before starting animal and human treatments, there is an increasing demand for new in vitro models that can be used in HTS/HCS to facilitate drug development. In order to achieve this objective, several methods for ES cell self-renewal or differentiation have been evaluated to assess their compatibility with HTS/HCS. This review describes protocols used to screen molecules able to maintain self-renewal or to induce differentiation in ectodermal, mesodermal, endodermal, and their derivative cell lines.

The Bacterial Sec Pathway of Protein Export: Screening and Follow-Up

Most noncytoplasmic bacterial proteins are exported through the SecYEG channel in the cytoplasmic membrane. This channel and its associated proteins, collectively referred to as the Sec pathway, have strong appeal as a possible antibiotic drug target, yet progress toward new drugs targeting this pathway has been slow, perhaps due partly to many researchers' focus on a single component, the SecA ATPase. Here we report on a pathway-based screen in which beta-galactosidase (β-gal) activity is trapped in the cytoplasm of Escherichia coli cells if translocation through SecYEG is impaired. Several hit compounds passed a counterscreen distinguishing between β-gal overexpression and impaired β-gal export. However, the most extensively characterized hit gave limited E. coli growth inhibition (EC(50) ≥ 400 µM), and growth inhibition could not be unambiguously linked to the compound's effect on the Sec pathway. Our study and others underscore the challenges of finding potent druglike hits against this otherwise promising drug target.

[High content screening in chemical biology: overview and main challenges]

The last two decades have seen the development of high content screening (HCS) methodology and its adaptation for the evaluation of small molecules as drug candidates or their use as chemical tools for research purpose. HCS was initially set-up for the understanding of the mechanism of action of compounds by testing them on cell based-assays for pharmacological and toxicological studies. Since the last decade, the use of HCS has been extended to academic research laboratories and this technology has become the starting point for numerous projects aiming at the identification of molecular targets and cellular pathways for a given disease on which novel type of drugs could act. This screening approach relies on image capture of fluorescently labeled cells therefore generating a large amount of data that must be handled by appropriate automated image analysis methods and storage instrumentation. These latter in addition to the integration and data sharing are current challenges that HCS must still tackle.

A cell-based high-throughput screen addressing 3'UTR-dependent regulation of the MYCN gene

Neuroblastoma is the most common extracranial solid tumor of infancy. Amplification of MYCN oncogene is found in approximately 20 % of all neuroblastoma patients and correlates with advanced disease stages, rapid tumor progression, and poor prognosis, making this gene an obvious therapeutic target. However, being a transcriptional factor MYCN is difficult for pharmacological targeting, and there are currently no clinical trials aiming MYCN protein directly. Here we describe an alternative approach to address deregulated MYCN expression. In particular, we focus on the role of a 3′ untranslated region (3′UTR) of the MYCN gene in the modulation of its mRNA fate and identification of compounds able to affect it. The luciferase reporter construct with the full length MYCN 3′UTR was generated and subsequently integrated in the CHP134 neuroblastoma cell line. After validation, the assay was used to screen a 2000 compound library. Molecules affecting luciferase activity were checked for reproducibility and counter-screened for promoter effects and cytotoxic activity resulting in selection of four hits. We propose this cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms. Identification of such compounds could potentially result in development of clinically relevant therapeutics for various diseases including neuroblastoma.

Unveiling new biological relationships using shared hits of chemical screening assay pairs

Motivation: Although the integration and analysis of the activity of small molecules across multiple chemical screens is a common approach to determine the specificity and toxicity of hits, the suitability of these approaches to reveal novel biological information is less explored. Here, we test the hypothesis that assays sharing selective hits are biologically related.

Results: We annotated the biological activities (i.e. biological processes or molecular activities) measured in assays and constructed chemical hit profiles with sets of compounds differing on their selectivity level for 1640 assays of ChemBank repository. We compared the similarity of chemical hit profiles of pairs of assays with their biological relationships and observed that assay pairs sharing non-promiscuous chemical hits tend to be biologically related. A detailed analysis of a network containing assay pairs with the highest hit similarity confirmed biological meaningful relationships. Furthermore, the biological roles of predicted molecular targets of the shared hits reinforced the biological associations between assay pairs.

Contact:monica.campillos@helmholtz-muenchen.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Accelerated formulation development of monoclonal antibodies (mAbs) and mAb-based modalities: review of methods and tools

More therapeutic monoclonal antibodies and antibody-based modalities are in development today than ever before, and a faster and more accurate drug discovery process will ensure that the number of candidates coming to the biopharmaceutical pipeline will increase in the future. The process of drug product development and, specifically, formulation development is a critical bottleneck on the way from candidate selection to fully commercialized medicines. This article reviews the latest advances in methods of formulation screening, which allow not only the high-throughput selection of the most suitable formulation but also the prediction of stability properties under manufacturing and long-term storage conditions. We describe how the combination of automation technologies and high-throughput assays creates the opportunity to streamline the formulation development process starting from early preformulation screening through to commercial formulation development. The application of quality by design (QbD) concepts and modern statistical tools are also shown here to be very effective in accelerated formulation development of both typical antibodies and complex modalities derived from them.

Bigger data, collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

Improving Detection of Rare Biological Events in High-Throughput Screens

The success of high-throughput screening (HTS) strategies depends on the effectiveness of both normalization methods and study design. We report comparisons among normalization methods in two titration series experiments. We also extend the results in a third experiment with two differently designed but otherwise identical screens: compounds in replicate plates were either placed in the same well locations or were randomly assigned to different locations. Best results were obtained when randomization was combined with normalization methods that corrected for within-plate spatial bias. We conclude that potent, reliable, and accurate HTS requires replication, randomization design strategies, and more extensive normalization than is typically done and that formal statistical testing is desirable. The Statistics and dIagnostic Graphs for HTS (SIGHTS) Microsoft Excel Add-In software is available to conduct most analyses reported here.

A robust and adaptable high throughput screening method to study host-microbiota interactions in the human intestine

The intestinal microbiota has many beneficial roles for its host. However, the precise mechanisms developed by the microbiota to influence the host intestinal cell responses are only partially known. The complexity of the ecosystem and our inability to culture most of these micro-organisms have led to the development of molecular approaches such as functional metagenomics, i.e. the heterologous expression of a metagenome in order to identify functions. This elegant strategy coupled to high throughput screening allowed to identify novel enzymes from different ecosystems where culture methods have not yet been adapted to isolate the candidate microorganisms. We have proposed to use this functional metagenomic approach in order to model the microbiota's interaction with the host by combining this heterologous expression with intestinal reporter cell lines. The addition of the cellular component to this functional metagenomic approach introduced a second important source of variability resulting in a novel challenge for high throughput screening. First attempts of high throughput screening with various reporter cell-lines showed a high distribution of the response and consequent difficulties to reproduce the response, impairing an easy and clear identification of confirmed hits. In this study, we developed a robust and reproducible methodology to combine these two biological systems for high throughput application. We optimized experimental setups and completed them by appropriate statistical analysis tools allowing the use this innovative approach in a high throughput manner and on a broad range of reporter assays. We herewith present a methodology allowing a high throughput screening combining two biological systems. Therefore ideal conditions for homogeneity, sensitivity and reproducibility of both metagenomic clones as well as reporter cell lines have been identified and validated. We believe that this innovative method will allow the identification of new bioactive microbial molecules and, subsequently, will promote understanding of host-microbiota interactions.

Rank ordering plate data facilitates data visualization and normalization in high-throughput screening

High-throughput screening (HTS) of chemical and microbial strain collections is an indispensable tool for modern chemical and systems biology; however, HTS data sets have inherent systematic and random error, which may lead to false-positive or false-negative results. Several methods of normalization of data exist; nevertheless, due to the limitations of each, no single method has been universally adopted. Here, we present a method of data visualization and normalization that is effective, intuitive, and easy to implement in a spreadsheet program. For each plate, the data are ordered by ascending values and a plot thereof yields a curve that is a signature of the plate data. Curve shape characteristics provide intuitive visualization of the frequency and strength of inhibitors, activators, and noise on the plate, allowing potentially problematic plates to be flagged. To reduce plate-to-plate variation, the data can be normalized by the mean of the middle 50% of ordered values, also called the interquartile mean (IQM) or the 50% trimmed mean of the plate. Positional effects due to bias in columns, rows, or wells can be corrected using the interquartile mean of each well position across all plates (IQMW) as a second level of normalization. We illustrate the utility of this method using data sets from biochemical and phenotypic screens.

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR’s performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR’s performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.

Sharing chemical relationships does not reveal structures

In this study, we propose a new, secure method of sharing useful chemical information from small-molecule libraries, without revealing the structures of the libraries' molecules. Our method shares the relationship between molecules rather than structural descriptors. This is an important advance because, over the past few years, several groups have developed and published new methods of analyzing small-molecule screening data. These methods include advanced hit-picking protocols, promiscuous active filters, economic optimization algorithms, and screening visualizations, which can identify patterns in the data that might otherwise be overlooked. Application of these methods to private data requires finding strategies for sharing useful chemical data without revealing chemical structures. This problem has been examined in the context of ADME prediction models, with results from information theory suggesting it is impossible to share useful chemical information without revealing structures. In contrast, we present a new strategy for encoding the relationships between molecules instead of their structures, based on anonymized scaffold networks and trees, that safely shares enough chemical information to be useful in analyzing chemical data, while also sufficiently blinding structures from discovery. We present the details of this encoding, an analysis of the usefulness of the information it conveys, and the security of the structures it encodes. This approach makes it possible to share data across institutions, and may securely enable collaborative analysis that can yield insight into both specific projects and screening technology as a whole.

Development and validation of a high-content bimolecular fluorescence complementation assay for small-molecule inhibitors of HIV-1 Nef dimerization

Nef is a human immunodeficiency virus 1 (HIV-1) accessory factor essential for viral pathogenesis and AIDS progression. Many Nef functions require dimerization, and small molecules that block Nef dimerization may represent antiretroviral drug leads. Here we describe a cell-based assay for Nef dimerization inhibitors based on bimolecular fluorescence complementation (BiFC). Nef was fused to nonfluorescent, complementary fragments of yellow fluorescent protein (YFP) and coexpressed in the same cell population. Dimerization of Nef resulted in juxtaposition of the YFP fragments and reconstitution of the fluorophore. For automation, the Nef-YFP fusion proteins plus a monomeric red fluorescent protein (mRFP) reporter were expressed from a single vector, separated by picornavirus "2A" linker peptides to permit equivalent translation of all three proteins. Validation studies revealed a critical role for gating on the mRFP-positive subpopulation of transfected cells, as well as use of the mRFP signal to normalize the Nef-BiFC signal. Nef-BiFC/mRFP ratios resulting from cells expressing wild-type versus dimerization-defective Nef were very clearly separated, with Z factors consistently in the 0.6 to 0.7 range. A fully automated pilot screen of the National Cancer Institute Diversity Set III identified several hit compounds that reproducibly blocked Nef dimerization in the low micromolar range. This BiFC-based assay has the potential to identify cell-active small molecules that directly interfere with Nef dimerization and function.

Introducing Bayesian thinking to high-throughput screening for false-negative rate estimation

High-throughput screening (HTS) has been widely used to identify active compounds (hits) that bind to biological targets. Because of cost concerns, the comprehensive screening of millions of compounds is typically conducted without replication. Real hits that fail to exhibit measurable activity in the primary screen due to random experimental errors will be lost as false-negatives. Conceivably, the projected false-negative rate is a parameter that reflects screening quality. Furthermore, it can be used to guide the selection of optimal numbers of compounds for hit confirmation. Therefore, a method that predicts false-negative rates from the primary screening data is extremely valuable. In this article, we describe the implementation of a pilot screen on a representative fraction (1%) of the screening library in order to obtain information about assay variability as well as a preliminary hit activity distribution profile. Using this training data set, we then developed an algorithm based on Bayesian logic and Monte Carlo simulation to estimate the number of true active compounds and potential missed hits from the full library screen. We have applied this strategy to five screening projects. The results demonstrate that this method produces useful predictions on the numbers of false negatives.

GUItars: a GUI tool for analysis of high-throughput RNA interference screening data

Background

High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.

Results

The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.

Conclusion

With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.

Two effective methods for correcting experimental high-throughput screening data

MOTIVATION

Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003; Carralot et al., 2012; Kevorkov and Makarenkov, 2005; Makarenkov et al., 2007; Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the χ(2) goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates.

Utility-aware screening with clique-oriented prioritization

Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testing--to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than 3-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.

A novel specific edge effect correction method for RNA interference screenings

MOTIVATION

High-throughput screening (HTS) is an important method in drug discovery in which the activities of a large number of candidate chemicals or genetic materials are rapidly evaluated. Data are usually obtained by measurements on samples in microwell plates and are often subjected to artefacts that can bias the result selection. We report here a novel edge effect correction algorithm suitable for RNA interference (RNAi) screening, because its normalization does not rely on the entire dataset and takes into account the specificities of such a screening process. The proposed method is able to estimate the edge effects for each assay plate individually using the data from a single control column based on diffusion model, and thus targeting a specific but recurrent well-known HTS artefact. This method was first developed and validated using control plates and was then applied to the correction of experimental data generated during a genome-wide siRNA screen aimed at studying HIV-host interactions. The proposed algorithm was able to correct the edge effect biasing the control data and thus improve assay quality and, consequently, the hit-selection step.

Optimization and application of median filter corrections to relieve diverse spatial patterns in microtiter plate data

The standard (STD) 5 × 5 hybrid median filter (HMF) was previously described as a nonparametric local backestimator of spatially arrayed microtiter plate (MTP) data. As such, the HMF is a useful tool for mitigating global and sporadic systematic error in MTP data arrays. Presented here is the first known HMF correction of a primary screen suffering from systematic error best described as gradient vectors. Application of the STD 5 × 5 HMF to the primary screen raw data reduced background signal deviation, thereby improving the assay dynamic range and hit confirmation rate. While this HMF can correct gradient vectors, it does not properly correct periodic patterns that may present in other screening campaigns. To address this issue, 1 × 7 median and a row/column 5 × 5 hybrid median filter kernels (1 × 7 MF and RC 5 × 5 HMF) were designed ad hoc, to better fit periodic error patterns. The correction data show periodic error in simulated MTP data arrays is reduced by these alternative filter designs and that multiple corrective filters can be combined in serial operations for progressive reduction of complex error patterns in a MTP data array.

The Complete Automation of Cell Culture

Genomic approaches provide enormous amounts of raw data with regard to genetic variation, the diversity of RNA species, and protein complement. High-throughput (HT) and high-content (HC) cellular screens are ideally suited to contextualize the information gathered from other “omic” approaches into networks and can be used for the identification of therapeutic targets. Current methods used for HT–HC screens are laborious, time-consuming, and prone to human error. The authors thus developed an automated high-throughput system with an integrated fluorescent imager for HC screens called the AI.CELLHOST. The implementation of user-defined culturing and assay plate setup parameters allows parallel operation of multiple screens in diverse mammalian cell types. The authors demonstrate that such a system is able to successfully maintain different cell lines in culture for extended periods of time as well as significantly increasing throughput, accuracy, and reproducibility of HT and HC screens.

Mining small-molecule screens to repurpose drugs

Repurposing and repositioning drugs--discovering new uses for existing and experimental medicines-is an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small molecules--the type of experiments once exclusively conducted in industry--and making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods' limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.