Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens

Phenotypical screening on metastatic PRCC-TFE3 fusion translocation renal cell carcinoma organoids reveals potential therapeutic agents

PURPOSE

Translocation renal cell carcinoma (tRCC) is a subtype that occurs predominantly in children and young individuals. Metastatic tRCC occurring in young patients is more aggressive than that occurring in older patients, and there are still no effective therapies. Organoids can mimic original tissues and be assessed by high-throughput screening (HTS). We aimed to utilize patient-derived organoids and HTS to screen drugs that can be repurposed for metastatic tRCC with PRCC-TFE3 fusion.

METHODS

Tumor tissues were obtained from treatment-naïve metastatic tRCC patients who underwent surgery. Histopathology and fluorescence in situ hybridization (FISH) confirmed the tRCC. Organoids derived from the dissected tissues were cultured and verified by FISH and RNA-seq. HTS was performed to seek promising drugs, and potential mechanisms were explored by RNA-seq and cell-based studies.

RESULTS

We successfully established a metastatic tRCC organoid with PRCC-TFE3 fusion, a common fusion subtype, and its characteristics were verified by histopathology, FISH, and RNA-seq. An HTS assay was developed, and the robustness was confirmed. A compound library of 1816 drugs was screened. Eventually, axitinib, crizotinib, and JQ-1 were selected for further validation and were found to induce cell cycle arrest and apoptosis. RNA-seq analyses of posttreatment organoids indicated that crizotinib induced significant changes in autophagy-related genes, consistent with the potential pathogenesis of tRCC.

CONCLUSIONS

We established and validated organoids derived from tissues dissected from a patient with metastatic tRCC with PRCC-TFE3 fusion and achieved the HTS process for the first time. Crizotinib might be a targeted therapy worthy of exploration in the clinic for metastatic tRCC with PRCC-TFE3 fusion. Such organoid and HTS assays may represent a promising model system in translational research assisting in the development of clinical strategies.

Issues Of Z-factor and an approach to avoid them for quality control in high-throughput screening studies

MOTIVATION

High throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that z-factor has major issues.

RESULTS

In this article, the following four major issues are explored and demonstrated so that researchers may use the z-factor appropriately. First, the z-factor violates the Pythagorean Theorem of Statistics. Second, there is no adjustment of sampling error in the application of the z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based z-factor does not exist. Fourth, the thresholds in the z-factor based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies.

AVAILABILITY

The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Advances in high-throughput methods for the identification of virus receptors

Viruses have evolved many mechanisms to invade host cells and establish successful infections. The interaction between viral attachment proteins and host cell receptors is the first and decisive step in establishing such infections, initiating virus entry into the host cells. Therefore, the identification of host receptors is fundamental in understanding pathogenesis and tissue tropism. Furthermore, receptor identification can inform the development of antivirals, vaccines, and diagnostic technologies, which have a substantial impact on human health. Nevertheless, due to the complex nature of virus entry, the redundancy in receptor usage, and the limitations in current identification methods, many host receptors remain elusive. Recent advances in targeted gene perturbation, high-throughput screening, and mass spectrometry have facilitated the discovery of virus receptors in recent years. In this review, we compare the current methods used within the field to identify virus receptors, focussing on genomic- and interactome-based approaches.

ARQiv-HTS, a versatile whole-organism screening platform enabling in vivo drug discovery at high-throughput rates

The zebrafish has emerged as an important model for whole-organism small-molecule screening. However, most zebrafish-based chemical screens have achieved only mid-throughput rates. Here we describe a versatile whole-organism drug discovery platform that can achieve true high-throughput screening (HTS) capacities. This system combines our automated reporter quantification in vivo (ARQiv) system with customized robotics, and is termed 'ARQiv-HTS'. We detail the process of establishing and implementing ARQiv-HTS: (i) assay design and optimization, (ii) calculation of sample size and hit criteria, (iii) large-scale egg production, (iv) automated compound titration, (v) dispensing of embryos into microtiter plates, and (vi) reporter quantification. We also outline what we see as best practice strategies for leveraging the power of ARQiv-HTS for zebrafish-based drug discovery, and address technical challenges of applying zebrafish to large-scale chemical screens. Finally, we provide a detailed protocol for a recently completed inaugural ARQiv-HTS effort, which involved the identification of compounds that elevate insulin reporter activity. Compounds that increased the number of insulin-producing pancreatic beta cells represent potential new therapeutics for diabetic patients. For this effort, individual screening sessions took 1 week to conclude, and sessions were performed iteratively approximately every other day to increase throughput. At the conclusion of the screen, more than a half million drug-treated larvae had been evaluated. Beyond this initial example, however, the ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives.

Comprehensive analysis of high-throughput screens with HiTSeekR

High-throughput screening (HTS) is an indispensable tool for drug (target) discovery that currently lacks user-friendly software tools for the robust identification of putative hits from HTS experiments and for the interpretation of these findings in the context of systems biology. We developed HiTSeekR as a one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. We chose three use cases that demonstrate the potential of HiTSeekR to fully exploit HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNFα, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is publicly available at http://hitseekr.compbio.sdu.dk It is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.

A parallel genome-wide RNAi screening strategy to identify host proteins important for entry of Marburg virus and H5N1 influenza virus

BACKGROUND

Genome-wide RNAi screening has been widely used to identify host proteins involved in replication and infection of different viruses, and numerous host factors are implicated in the replication cycles of these viruses, demonstrating the power of this approach. However, discrepancies on target identification of the same viruses by different groups suggest that high throughput RNAi screening strategies need to be carefully designed, developed and optimized prior to the large scale screening.

METHODS

Two genome-wide RNAi screens were performed in parallel against the entry of pseudotyped Marburg viruses and avian influenza virus H5N1 utilizing an HIV-1 based surrogate system, to identify host factors which are important for virus entry. A comparative analysis approach was employed in data analysis, which alleviated systematic positional effects and reduced the false positive number of virus-specific hits.

RESULTS

The parallel nature of the strategy allows us to easily identify the host factors for a specific virus with a greatly reduced number of false positives in the initial screen, which is one of the major problems with high throughput screening. The power of this strategy is illustrated by a genome-wide RNAi screen for identifying the host factors important for Marburg virus and/or avian influenza virus H5N1 as described in this study.

CONCLUSIONS

This strategy is particularly useful for highly pathogenic viruses since pseudotyping allows us to perform high throughput screens in the biosafety level 2 (BSL-2) containment instead of the BSL-3 or BSL-4 for the infectious viruses, with alleviated safety concerns. The screening strategy together with the unique comparative analysis approach makes the data more suitable for hit selection and enables us to identify virus-specific hits with a much lower false positive rate.

Functional coupling of a nematode chemoreceptor to the yeast pheromone response pathway

Sequencing of the Caenorhabditis elegans genome revealed sequences encoding more than 1,000 G-protein coupled receptors, hundreds of which may respond to volatile organic ligands. To understand how the worm's simple olfactory system can sense its chemical environment there is a need to characterise a representative selection of these receptors but only very few receptors have been linked to a specific volatile ligand. We therefore set out to design a yeast expression system for assigning ligands to nematode chemoreceptors. We showed that while a model receptor ODR-10 binds to C. elegans Gα subunits ODR-3 and GPA-3 it cannot bind to yeast Gα. However, chimaeras between the nematode and yeast Gα subunits bound to both ODR-10 and the yeast Gβγ subunits. FIG2 was shown to be a superior MAP-dependent promoter for reporter expression. We replaced the endogenous Gα subunit (GPA1) of the Saccharomyces cerevisiae (ste2Δ sst2Δ far1Δ) triple mutant ("Cyb") with a Gpa1/ODR-3 chimaera and introduced ODR-10 as a model nematode GPCR. This strain showed concentration-dependent activation of the yeast MAP kinase pathway in the presence of diacetyl, the first time that the native form of a nematode chemoreceptor has been functionally expressed in yeast. This is an important step towards en masse de-orphaning of C. elegans chemoreceptors.

Discovery of a novel inhibitor of NAD(P)(+)-dependent malic enzyme (ME2) by high-throughput screening

AIM

Malic enzymes are oxidative decarboxylases with NAD(+) or NAD(P)(+) as cofactor that catalyze the conversion of L-malate to pyruvate and CO2. The aim of this study was to discover and characterize a potent inhibitor of human NAD(P)(+)-dependent malic enzyme 2 (ME2).

METHODS

Recombinant human ME2-His-Tag fusion protein was overexpressed in E coli and purified with Ni-NTA resin. A high-throughput screening (HTS) assay was developed to find ME2 inhibitors. Detergent Brij-35 was used to exclude false positives. The characteristics of the inhibitor were analyzed with enzyme kinetics analysis. A thermal shift assay for ME2 was carried out to verify the binding of the inhibitor with the enzyme.

RESULTS

An HTS system for discovering ME2 inhibitors was established with a Z' factor value of 0.775 and a signal-to-noise ratio (S/N) of 9.80. A library containing 12 683 natural products was screened. From 47 hits, NPD387 was identified as an inhibitor of ME2. The primary structure-activity relationship study on NPD387 derivatives showed that one derivative NPD389 was more potent than the parent compound NPD387 (the IC50 of NPD389 was 4.63 ± 0.36 μmol/L or 5.59 ± 0.38 μmol/L, respectively, in the absence or presence of 0.01% Brij-35 in the assay system). The enzyme kinetics analysis showed that NPD389 was a fast-binding uncompetitive inhibitor with respect to the substrate NAD(+) and a mixed-type inhibitor with respect to the substrate L-malate.

CONCLUSION

NPD389 is a potent ME2 inhibitor that binds to the enzyme in a fast-binding mode, acting as an uncompetitive inhibitor with respect to the substrate NAD(+) and a mixed-type inhibitor with respect to the substrate L-malate.

Limited agreement of independent RNAi screens for virus-required host genes owes more to false-negative than false-positive factors

Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, ∼19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be ∼2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis.

Genome-wide RNA interference assays of gene functions offer the potential for systematic, global analysis of biological processes. A pressing challenge is to develop meta-analysis methods that effectively combine information from multiple studies. One puzzle is that implicated gene lists from independent studies of the same process often show relatively low overlap. This disagreement might arise from false-positive factors, such as imperfect gene targeting (off-target effects), or from false negatives if separate studies access different components of large, complex systems. We present new methods to examine the relations between individual genome-wide RNAi studies, using studies of host genes in influenza virus replication as a test case. We find that cross-study agreement is greater than suggested by overlap of reported gene lists. This better agreement is evidenced by the strong relation of independent gene lists in functional pathways and protein interaction networks, and by a statistical model that relates multi-study, gene-level findings to factors driving correct, false-negative, and false-positive gene identification. Our analysis of multiple genome-wide studies predicts that there are many undetected host genes important for influenza virus infection, and that false negatives are the major concerns for genome-wide studies.

RNA interference in mammals: behind the screen

The discovery of RNA interference (RNAi) and the development of technologies exploiting its biology have enabled scientists to rapidly examine the consequences of depleting a particular gene product in a cell or an animal. The availability of genome-wide RNAi libraries targeting the mouse and human genomes has made it possible to carry out large scale, phenotype-based screens, which have yielded seminal information on diverse cellular processes ranging from virology to cancer biology. Today, several strategies are available to perform RNAi screens, each with their own technical and monetary considerations. Special care and budgeting must be taken into account during the design of these screens in order to obtain reliable results. In this review, we discuss a number of critical aspects to consider when planning an effective RNAi screening strategy, including selecting the right biological system, designing an appropriate selection scheme, optimizing technical aspects of the screen, and validating and verifying the hits. Similar to an artistic production, what happens behind the screen has a direct impact on its success.

A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research

AIM

To develop a new analytical method to address the issues of traditional contrast analysis for comparing gene effects in RNAi and expression-profiling research.

METHODS & RESULTS

I propose a new method consisting of contrast variable, standardized mean of contrast (SMC) and c(+)-probability analysis for comparing gene effects in multiple conditions. Compared with traditional contrast analysis, this new method has the following major advantages: it directly addresses the primary question of interest, namely the assessment of the strength of comparison; SMC and c(+)-probability capture data variability and are robust to sample size. The simulation and application studies show that traditional contrast analysis produces misleading results and erroneous conclusions whereas the new method produces reasonable results and sensible conclusions.

CONCLUSIONS

The new method may have a broad utility in comparing gene effects in multiple conditions including selecting hits in RNAi research and identifying differential expression in microarray experiments.

A 1,536-well ultra-high-throughput siRNA screen to identify regulators of the Wnt/beta-catenin pathway

High-throughput siRNA screens are now widely used for identifying novel drug targets and mapping disease pathways. Despite their popularity, there remain challenges related to data variability, primarily due to measurement errors, biological variance, uneven transfection efficiency, the efficacy of siRNA sequences, or off-target effects, and consequent high false discovery rates. Data variability can be reduced if siRNA screens are performed in replicate. Running a large-scale siRNA screen in replicate is difficult, however, because of the technical challenges related to automating complicated steps of siRNA transfection, often with multiplexed assay readouts, and controlling environmental humidity during long incubation periods. Small-molecule screens have greatly benefited in the past decade from assay miniaturization to high-density plates such that 1,536-well nanoplate screenings are now a routine process, allowing fast, efficient, and affordable operations without compromising underlying biology or important assay characteristics. Here, we describe the development of a 1,536-well nanoplate siRNA transfection protocol that utilizes the instruments commonly found in small-molecule high throughput screening laboratories. This protocol was then successfully demonstrated in a triplicate large-scale siRNA screen for the identification of regulators of the Wnt/beta-catenin pathway.

Factors affecting reproducibility between genome-scale siRNA-based screens

RNA interference-based screening is a powerful new genomic technology that addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, the authors performed 2 identically designed small interfering RNA (siRNA)-based, whole-genome screens for host factors supporting yellow fever virus infection. These screens represent 2 separate experiments completed 5 months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole-genome screens. Application of these analysis methodologies within a single screening data set using a fixed threshold equivalent to a p-value < or = 0.001 resulted in hit lists ranging from 82 to 1140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and interscreen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least 2 independent siRNAs for each gene product in primary screens. To facilitate validation, the authors conclude by suggesting methods to reduce false discovery at the primary screening stage. In this study, they present the first comprehensive comparison of multiple analysis strategies and demonstrate the impact of the analysis methodology on the composition of the "hit list." Therefore, they propose that the entire data set derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies.

Genomic screening with RNAi: results and challenges

RNA interference (RNAi) is an effective tool for genome-scale, high-throughput analysis of gene function. In the past five years, a number of genome-scale RNAi high-throughput screens (HTSs) have been done in both Drosophila and mammalian cultured cells to study diverse biological processes, including signal transduction, cancer biology, and host cell responses to infection. Results from these screens have led to the identification of new components of these processes and, importantly, have also provided insights into the complexity of biological systems, forcing new and innovative approaches to understanding functional networks in cells. Here, we review the main findings that have emerged from RNAi HTS and discuss technical issues that remain to be improved, in particular the verification of RNAi results and validation of their biological relevance. Furthermore, we discuss the importance of multiplexed and integrated experimental data analysis pipelines to RNAi HTS.

Assessing the size of gene or RNAi effects in multifactor high-throughput experiments

AIMS

To expand the recently proposed contrast variable and associated concepts to assess the size of gene effects or siRNA effects in multifactor high-throughput experiments, as well as to serve the need to consider both mean and standardized mean of contrast (SMC).

METHODS & RESULTS

The recently proposed concepts of contrast variable and SMC are expanded in the context of multifactor analysis of variance. Based on this expansion, SMC is explored as a tool for analyzing multifactor high-throughput data, a novel plot termed a dual-flashlight plot is proposed, and the incompatibility of false-discovery rates across experiments is demonstrated. The applications show that the results reached using expanded SMC and the dual-flashlight plot are more reasonable than those reached using p-value-based or false-discovery rate-based volcano plot for assessing differential expression, genetic dominance and linear/quadratic time-course changes.

CONCLUSION

Compared with traditional contrast analysis, the expanded contrast variable and SMC may serve as an alternative that can address the real need of assessing the size of gene or siRNA effects in multifactor high-throughput experiments.

A lentivirus-mediated genetic screen identifies dihydrofolate reductase (DHFR) as a modulator of beta-catenin/GSK3 signaling

The multi-protein beta-catenin destruction complex tightly regulates beta-catenin protein levels by shuttling beta-catenin to the proteasome. Glycogen synthase kinase 3beta (GSK3beta), a key serine/threonine kinase in the destruction complex, is responsible for several phosphorylation events that mark beta-catenin for ubiquitination and subsequent degradation. Because modulation of both beta-catenin and GSK3beta activity may have important implications for treating disease, a complete understanding of the mechanisms that regulate the beta-catenin/GSK3beta interaction is warranted. We screened an arrayed lentivirus library expressing small hairpin RNAs (shRNAs) targeting 5,201 human druggable genes for silencing events that activate a beta-catenin pathway reporter (BAR) in synergy with 6-bromoindirubin-3'oxime (BIO), a specific inhibitor of GSK3beta. Top screen hits included shRNAs targeting dihydrofolate reductase (DHFR), the target of the anti-inflammatory compound methotrexate. Exposure of cells to BIO plus methotrexate resulted in potent synergistic activation of BAR activity, reduction of beta-catenin phosphorylation at GSK3-specific sites, and accumulation of nuclear beta-catenin. Furthermore, the observed synergy correlated with inhibitory phosphorylation of GSK3beta and was neutralized upon inhibition of phosphatidyl inositol 3-kinase (PI3K). Linking these observations to inflammation, we also observed synergistic inhibition of lipopolysaccharide (LPS)-induced production of pro-inflammatory cytokines (TNFalpha, IL-6, and IL-12), and increased production of the anti-inflammatory cytokine IL-10 in peripheral blood mononuclear cells exposed to GSK3 inhibitors and methotrexate. Our data establish DHFR as a novel modulator of beta-catenin and GSK3 signaling and raise several implications for clinical use of combined methotrexate and GSK3 inhibitors as treatment for inflammatory disease.

Statistical methods for analysis of high-throughput RNA interference screens

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Determination of sample size in genome-scale RNAi screens

MOTIVATION

For genome-scale RNAi research, it is critical to investigate sample size required for the achievement of reasonably low false negative rate (FNR) and false positive rate.

RESULTS

The analysis in this article reveals that current design of sample size contributes to the occurrence of low signal-to-noise ratio in genome-scale RNAi projects. The analysis suggests that (i) an arrangement of 16 wells per plate is acceptable and an arrangement of 20-24 wells per plate is preferable for a negative control to be used for hit selection in a primary screen without replicates; (ii) in a confirmatory screen or a primary screen with replicates, a sample size of 3 is not large enough, and there is a large reduction in FNRs when sample size increases from 3 to 4. To search a tradeoff between benefit and cost, any sample size between 4 and 11 is a reasonable choice. If the main focus is the selection of siRNAs with strong effects, a sample size of 4 or 5 is a good choice. If we want to have enough power to detect siRNAs with moderate effects, sample size needs to be 8, 9, 10 or 11. These discoveries about sample size bring insight to the design of a genome-scale RNAi screen experiment.

Error Rates and Powers in Genome-Scale RNAi Screens

For hit selection in genome-scale RNAi research, we do not want to miss small interfering RNAs (siRNAs) with large effects; meanwhile, we do not want to include siRNAs with small or no effects in the list of selected hits. There is a strong need to control both the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with no or small effects are selected as hits. An error control method based on strictly standardized mean difference (SSMD) has been proposed to maintain a flexible and balanced control of FNR and RFPR. In this article, the authors illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD as well as how to keep high powers using the plot of power versus SSMD in RNAi high-throughput screening experiments. There are relationships among FNR, RFPR, Type I and II errors, and power. Understanding the differences and links among these concepts is essential for people to use statistical terminology correctly and effectively for data analysis in genome-scale RNAi screens. Here the authors explore these differences and links. (Journal of Biomolecular Screening 2009:230-238)

Process Validation and Screen Reproducibility in High-Throughput Screening

The use of large-scale compound screening has become a key component of drug discovery projects in both the pharmaceutical and the biotechnological industries. More recently, these activities have also been embraced by the academic community as a major tool for chemical genomic activities. High-throughput screening (HTS) activities constitute a major step in the initial drug discovery efforts and involve the use of large quantities of biological reagents, hundreds of thousands to millions of compounds, and the utilization of expensive equipment. All these factors make it very important to evaluate in advance of the HTS campaign any potential issues related to reproducibility of the experimentation and the quality of the results obtained at the end of these very costly activities. In this article, the authors describe how GlaxoSmithKline (GSK) has addressed the need of a true validation of the HTS process before embarking in full HTS campaigns. They present 2 different aspects of the so-called validation process: (1) optimization of the HTS workflow and its validation as a quality process and (2) the statistical evaluation of the HTS, focusing on the reproducibility of results and the ability to distinguish active from nonactive compounds in a vast collection of samples. The authors describe a variety of reproducibility indexes that are either innovative or have been adapted from generic medical diagnostic screening strategies. In addition, they exemplify how these validation tools have been implemented in a number of case studies at GSK. ( Journal of Biomolecular Screening 2009:66-76)

Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens

One of the most fundamental challenges in genome-wide RNA interference (RNAi) screens is to glean biological significance from mounds of data, which relies on the development and adoption of appropriate analytic methods and designs for quality control (QC) and hit selection. Currently, a Z-factor-based QC criterion is widely used to evaluate data quality. However, this criterion cannot take into account the fact that different positive controls may have different effect sizes and leads to inconsistent QC results in experiments with 2 or more positive controls with different effect sizes. In this study, based on a recently proposed parameter, strictly standardized mean difference (SSMD), novel QC criteria are constructed for evaluating data quality in genome-wide RNAi screens. Two good features of these novel criteria are: (1) SSMD has both clear original and probability meanings for evaluating the differentiation between positive and negative controls and hence the SSMD-based QC criteria have a solid probabilistic and statistical basis, and (2) these QC criteria obtain consistent QC results for multiple positive controls with different effect sizes. In addition, I propose multiple plate designs and the guidelines for using them in genome-wide RNAi screens. Finally, I provide strategies for using the SSMD-based QC criteria and effective plate design together to improve data quality. The novel SSMD-based QC criteria, effective plate designs, and related guidelines and strategies may greatly help to obtain high quality of data in genome-wide RNAi screens.

Hit selection with false discovery rate control in genome-scale RNAi screens

RNA interference (RNAi) is a modality in which small double-stranded RNA molecules (siRNAs) designed to lead to the degradation of specific mRNAs are introduced into cells or organisms. siRNA libraries have been developed in which siRNAs targeting virtually every gene in the human genome are designed, synthesized and are presented for introduction into cells by transfection in a microtiter plate array. These siRNAs can then be transfected into cells using high-throughput screening (HTS) methodologies. The goal of RNAi HTS is to identify a set of siRNAs that inhibit or activate defined cellular phenotypes. The commonly used analysis methods including median +/- kMAD have issues about error rates in multiple hypothesis testing and plate-wise versus experiment-wise analysis. We propose a methodology based on a Bayesian framework to address these issues. Our approach allows for sharing of information across plates in a plate-wise analysis, which obviates the need for choosing either a plate-wise or experimental-wise analysis. The proposed approach incorporates information from reliable controls to achieve a higher power and a balance between the contribution from the samples and control wells. Our approach provides false discovery rate (FDR) control to address multiple testing issues and it is robust to outliers.

Genome-wide screens for effective siRNAs through assessing the size of siRNA effects

Background

RNA interference (RNAi) has been seen as a revolution in functional genomics and system biology. Genome-wide RNAi research relies on the development of RNAi high-throughput screening (HTS) assays. One of the most fundamental challenges in RNAi HTS is to glean biological significance from mounds of data, which relies on the development of effective analytic methods for selecting effective small interfering RNAs (siRNAs).

Findings

Based on a recently proposed parameter, strictly standardized mean difference (SSMD), I propose an analytic method for genome-wide screens of effective siRNAs through assessing and testing the size of siRNA effects. Central to this method is the capability of SSMD in quantifying siRNA effects. This method has relied on normal approximation, which works only in the primary screens but not in the confirmatory screens. In this paper, I explore the non-central t-distribution property of SSMD estimates and use this property to extend the SSMD-based method so that it works effectively in either primary or confirmatory screens as well as in any HTS screens with or without replicates. The SSMD-based method maintains a balanced control of false positives and false negatives.

Conclusion

The central interest in genome-wide RNAi research is the selection of effective siRNAs which relies on the development of analytic methods to measure the size of siRNA effects. The new analytic method for hit selection provided in this paper offers a good analytic tool for selecting effective siRNAs, better than current analytic methods, and thus may have broad utility in genome-wide RNAi research.