Two effective methods for correcting experimental high-throughput screening data

Using a Quantitative High-Throughput Screening Platform to Identify Molecular Targets and Compounds as Repurposing Candidates for Endometriosis

Endometriosis, defined as the growth of hormonally responsive endometrial-like tissue outside of the uterine cavity, is an estrogen-dependent, chronic, pro-inflammatory disease that affects up to 11.4% of women of reproductive age and gender-diverse people with a uterus. At present, there is no long-term cure, and the identification of new therapies that provide a high level of efficacy and favourable long-term safety profiles with rapid clinical access are a priority. In this study, quantitative high-throughput compound screens of 3517 clinically approved compounds were performed on patient-derived immortalized human endometrial stromal cell lines. Following assay optimization and compound criteria selection, a high-throughput screening protocol was developed to enable the identification of compounds that interfered with estrogen-stimulated cell growth. From these screens, 23 novel compounds were identified, in addition to their molecular targets and in silico cell-signalling pathways, which included the neuroactive ligand-receptor interaction pathway, metabolic pathways, and cancer-associated pathways. This study demonstrates for the first time the feasibility of performing large compound screens for the identification of new translatable therapeutics and the improved characterization of endometriosis molecular pathophysiology. Further investigation of the molecular targets identified herein will help uncover new mechanisms involved in the establishment, symptomology, and progression of endometriosis.

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.

Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening

High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.

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.

Screen Targeting Lung and Prostate Cancer Oncogene Identifies Novel Inhibitors of RGS17 and Problematic Chemical Substructures

Regulator of G protein signaling (RGS) proteins temporally regulate heterotrimeric G protein signaling cascades elicited by G protein-coupled receptor activation and thus are essential for cell homeostasis. The dysregulation of RGS protein expression has been linked to several pathologies, spurring discovery efforts to identify small-molecule inhibitors of these proteins. Presented here are the results of a high-throughput screening (HTS) campaign targeting RGS17, an RGS protein reported to be inappropriately upregulated in several cancers. A screen of over 60,000 small molecules led to the identification of five hit compounds that inhibit the RGS17-Gα_o protein-protein interaction. Chemical and biochemical characterization demonstrated that three of these hits inhibited the interaction through the decomposition of parent compound into reactive products under normal chemical library storage/usage conditions. Compound substructures susceptible to decomposition are reported and the decomposition process characterized, adding to the armamentarium of tools available to the screening field, allowing for the conservation of resources in follow-up efforts and more efficient identification of potentially decomposed compounds. Finally, analogues of one hit compound were tested, and the results establish the first ever structure-activity relationship (SAR) profile for a small-molecule inhibitor of RGS17.

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.

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.

Fluorescence Polarization Screening Assays for Small Molecule Allosteric Modulators of ABL Kinase Function

The ABL protein-tyrosine kinase regulates intracellular signaling pathways controlling diverse cellular processes and contributes to several forms of cancer. The kinase activity of ABL is repressed by intramolecular interactions involving its regulatory Ncap, SH3 and SH2 domains. Small molecules that allosterically regulate ABL kinase activity through its non-catalytic domains may represent selective probes of ABL function. Here we report a screening assay for chemical modulators of ABL kinase activity that target the regulatory interaction of the SH3 domain with the SH2-kinase linker. This fluorescence polarization (FP) assay is based on a purified recombinant ABL protein consisting of the N-cap, SH3 and SH2 domains plus the SH2-kinase linker (N32L protein) and a short fluorescein-labeled probe peptide that binds to the SH3 domain. In assay development experiments, we found that the probe peptide binds to the recombinant ABL N32L protein in vitro, producing a robust FP signal that can be competed with an excess of unlabeled peptide. The FP signal is not observed with control N32L proteins bearing either an inactivating mutation in the SH3 domain or enhanced SH3:linker interaction. A pilot screen of 1200 FDA-approved drugs identified four compounds that specifically reduced the FP signal by at least three standard deviations from the untreated controls. Secondary assays showed that one of these hit compounds, the antithrombotic drug dipyridamole, enhances ABL kinase activity in vitro to a greater extent than the previously described ABL agonist, DPH. Docking studies predicted that this compound binds to a pocket formed at the interface of the SH3 domain and the linker, suggesting that it activates ABL by disrupting this regulatory interaction. These results show that screening assays based on the non-catalytic domains of ABL can identify allosteric small molecule regulators of kinase function, providing a new approach to selective drug discovery for this important kinase system.

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.

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.

SbacHTS: spatial background noise correction for high-throughput RNAi screening

MOTIVATION

High-throughput cell-based phenotypic screening has become an increasingly important technology for discovering new drug targets and assigning gene functions. Such experiments use hundreds of 96-well or 384-well plates, to cover whole-genome RNAi collections and/or chemical compound files, and often collect measurements that are sensitive to spatial background noise whose patterns can vary across individual plates. Correcting these position effects can substantially improve measurement accuracy and screening success.

RESULT

We developed SbacHTS (Spatial background noise correction for High-Throughput RNAi Screening) software for visualization, estimation and correction of spatial background noise in high-throughput RNAi screens. SbacHTS is supported on the Galaxy open-source framework with a user-friendly open access web interface. We find that SbacHTS software can effectively detect and correct spatial background noise, increase signal to noise ratio and enhance statistical detection power in high-throughput RNAi screening experiments.

AVAILABILITY

http://www.galaxy.qbrc.org/