A novel specific edge effect correction method for RNA interference screenings

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.

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.

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.

Temperature gradients drive radial fluid flow in Petri dishes and multiwell plates

Liquid in a Petri dish spontaneously circulates in a radial pattern, even when the dish is at rest. These fluid flows have been observed and utilized for biological research, but their origins have not been well-studied. Here we used particle-tracking to measure velocities of radial fluid flows, which are shown to be linked to evaporation. Infrared thermal imaging was used to identify thermal gradients at the air-liquid interface and at the bottom of the dish. Two-color ratiometric fluorescence confocal imaging was used to measure thermal gradients in the vertical direction within the fluid. A finite-element model of the fluid, incorporating the measured temperature profiles, shows that buoyancy forces are sufficient to produce flows consistent with the measured particle velocity results. Such flows may arise in other dish or plate formats, and may impact biological research in positive or negative ways.

A simple method for analyzing actives in random RNAi screens: introducing the "H Score" for hit nomination & gene prioritization

Due to the numerous challenges in hit identification from random RNAi screening, we have examined current practices with a discovery of a variety of methodologies employed and published in many reports; majority of them, unfortunately, do not address the minimum associated criteria for hit nomination, as this could potentially have been the cause or may well be the explanation as to the lack of confirmation and follow up studies, currently facing the RNAi field. Overall, we find that these criteria or parameters are not well defined, in most cases arbitrary in nature, and hence rendering it extremely difficult to judge the quality of and confidence in nominated hits across published studies. For this purpose, we have developed a simple method to score actives independent of assay readout; and provide, for the first time, a homogenous platform enabling cross-comparison of active gene lists resulting from different RNAi screening technologies. Here, we report on our recently developed method dedicated to RNAi data output analysis referred to as the BDA method applicable to both arrayed and pooled RNAi technologies; wherein the concerns pertaining to inconsistent hit nomination and off-target silencing in conjugation with minimal activity criteria to identify a high value target are addressed. In this report, a combined hit rate per gene, called "H score", is introduced and defined. The H score provides a very useful tool for stringent active gene nomination, gene list comparison across multiple studies, prioritization of hits, and evaluation of the quality of the nominated gene hits.

Viability assays for cells in culture

Manual cell counts on a microscope are a sensitive means of assessing cellular viability but are time-consuming and therefore expensive. Computerized viability assays are expensive in terms of equipment but can be faster and more objective than manual cell counts. The present report describes the use of three such viability assays. Two of these assays are infrared and one is luminescent. Both infrared assays rely on a 16 bit Odyssey Imager. One infrared assay uses the DRAQ5 stain for nuclei combined with the Sapphire stain for cytosol and is visualized in the 700 nm channel. The other infrared assay, an In-Cell Western, uses antibodies against cytoskeletal proteins (α-tubulin or microtubule associated protein 2) and labels them in the 800 nm channel. The third viability assay is a commonly used luminescent assay for ATP, but we use a quarter of the recommended volume to save on cost. These measurements are all linear and correlate with the number of cells plated, but vary in sensitivity. All three assays circumvent time-consuming microscopy and sample the entire well, thereby reducing sampling error. Finally, all of the assays can easily be completed within one day of the end of the experiment, allowing greater numbers of experiments to be performed within short timeframes. However, they all rely on the assumption that cell numbers remain in proportion to signal strength after treatments, an assumption that is sometimes not met, especially for cellular ATP. Furthermore, if cells increase or decrease in size after treatment, this might affect signal strength without affecting cell number. We conclude that all viability assays, including manual counts, suffer from a number of caveats, but that computerized viability assays are well worth the initial investment. Using all three assays together yields a comprehensive view of cellular structure and function.

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.

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/

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.