Determination of sample size in genome-scale RNAi screens

RNAi screening in Drosophila cells and in vivo

Here, I discuss how RNAi screening can be used effectively to uncover gene function. Specifically, I discuss the types of high-throughput assays that can be done in Drosophila cells and in vivo, RNAi reagent design and available reagent collections, automated screen pipelines, analysis of screen results, and approaches to RNAi results verification.

Peripheral nervous system genes expressed in central neurons induce growth on inhibitory substrates

Trauma to the spinal cord and brain can result in irreparable loss of function. This failure of recovery is in part due to inhibition of axon regeneration by myelin and chondroitin sulfate proteoglycans (CSPGs). Peripheral nervous system (PNS) neurons exhibit increased regenerative ability compared to central nervous system neurons, even in the presence of inhibitory environments. Previously, we identified over a thousand genes differentially expressed in PNS neurons relative to CNS neurons. These genes represent intrinsic differences that may account for the PNS's enhanced regenerative ability. Cerebellar neurons were transfected with cDNAs for each of these PNS genes to assess their ability to enhance neurite growth on inhibitory (CSPG) or permissive (laminin) substrates. Using high content analysis, we evaluated the phenotypic profile of each neuron to extract meaningful data for over 1100 genes. Several known growth associated proteins potentiated neurite growth on laminin. Most interestingly, novel genes were identified that promoted neurite growth on CSPGs (GPX3, EIF2B5, RBMX). Bioinformatic approaches also uncovered a number of novel gene families that altered neurite growth of CNS neurons.

Noise reduction in genome-wide perturbation screens using linear mixed-effect models

MOTIVATION

High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. The phenotypes measured in the screens are subject to substantial biological and technical variation. At the same time, in order to enable high throughput, it is often impossible to include a large number of replicates, and to randomize their order throughout the screens. Distinguishing true changes in the phenotype from stochastic variation in such experimental designs is extremely challenging, and requires adequate statistical methodology.

RESULTS

We propose a statistical modeling framework that is based on experimental designs with at least two controls profiled throughout the experiment, and a normalization and variance estimation procedure with linear mixed-effects models. We evaluate the framework using three comprehensive screens of Saccharomyces cerevisiae, which involve 4940 single-gene knock-out haploid mutants, 1127 single-gene knock-out diploid mutants and 5798 single-gene overexpression haploid strains. We show that the proposed approach (i) can be used in conjunction with practical experimental designs; (ii) allows extensions to alternative experimental workflows; (iii) enables a sensitive discovery of biologically meaningful changes; and (iv) strongly outperforms the existing noise reduction procedures.

AVAILABILITY

All experimental datasets are publicly available at www.ionomicshub.org. The R package HTSmix is available at http://www.stat.purdue.edu/~ovitek/HTSmix.html.

CONTACT

ovitek@stat.purdue.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Tales from an academic RNAi screening facility; FAQs

RNAi technology is now a well-established and widely employed research technique that has been adopted by many researchers for use in large-scale screening campaigns. Here, we offer our experience of genome-wide siRNA screening from the perspective of a facility providing screening as a service to a wide range of researchers with diverse interests and approaches. We have experienced the emotional rollercoaster of screening from the exuberant early promise of a screen, the messy reality of the data through to the recognition of screen data as a potential information goldmine. Here, we use some of the questions we most frequently encounter to highlight the initial concerns of many researchers embarking on a siRNA screen and conclude that an informed view of what can be reasonably expected from a screen is essential to the most effective implementation of the technology. Along the way, we suggest that for this area of research at least, either centralization of the resources or close and open collaboration between interested parties offers distinct advantages.

Illustration of SSMD, z score, SSMD*, z* score, and t statistic for hit selection in RNAi high-throughput screens

Hit selection is the ultimate goal in many high-throughput screens. Various analytic methods are available for this purpose. Some commonly used ones are z score, z* score, strictly standardized mean difference (SSMD), SSMD*, and t statistic. It is critical to know how to use them correctly because the misusage of them can readily produce misleading results. Here, the author presents basic concepts, elaborates their commonality and difference, describes some common misusage that people should avoid, and uses simulated simple examples to illustrate how to use them correctly.

False negative rates in Drosophila cell-based RNAi screens: a case study

Background

High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention.

Results

We performed a meta-analysis of several genome-wide, cell-based Drosophila RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene.

Conclusions

RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.

From single genes to gene networks: high-throughput-high-content screening for neurological disease

Neuronal development, function, and the subsequent degeneration of the brain are still an enigma in both the normal and pathologic states, and there is an urgent need to find better targets for developing therapeutic intervention. Current techniques to deconstruct the architecture of brain and disease-related pathways are best suited for following up on single genes but would take an impractical amount of time for the leads from the current wave of genetic and genomic data. New technical developments have made combined high-throughput-high-content (HT-HC) cellular screens possible, which have the potential to contextualize the information, gathered from a combination of genetic and genomic approaches, into networks and functional biology and can be utilized for the identification of therapeutic targets. Herein we discuss the potential impact of HT-HC cellular screens on medical neuroscience.

The use of SSMD-based false discovery and false nondiscovery rates in genome-scale RNAi screens

In genome-scale RNA interference (RNAi) screens, it is critical to control false positives and false negatives statistically. Traditional statistical methods for controlling false discovery and false nondiscovery rates are inappropriate for hit selection in RNAi screens because the major goal in RNAi screens is to control both the proportion of short interfering RNAs (siRNAs) with a small effect among selected hits and the proportion of siRNAs with a large effect among declared nonhits. An effective method based on strictly standardized mean difference (SSMD) has been proposed for statistically controlling false discovery rate (FDR) and false nondiscovery rate (FNDR) appropriate for RNAi screens. In this article, the authors explore the utility of the SSMD-based method for hit selection in RNAi screens. As demonstrated in 2 genome-scale RNAi screens, the SSMD-based method addresses the unmet need of controlling for the proportion of siRNAs with a small effect among selected hits, as well as controlling for the proportion of siRNAs with a large effect among declared nonhits. Furthermore, the SSMD-based method results in reasonably low FDR and FNDR for selecting inhibition or activation hits. This method works effectively and should have a broad utility for hit selection in RNAi screens with replicates.

The virus as metabolic engineer

Recent genome-wide screens of host genetic requirements for viral infection have reemphasized the critical role of host metabolism in enabling the production of viral particles. In this review, we highlight the metabolic aspects of viral infection found in these studies, and focus on the opportunities these requirements present for metabolic engineers. In particular, the objectives and approaches that metabolic engineers use are readily comparable to the behaviors exhibited by viruses during infection. As a result, metabolic engineers have a unique perspective that could lead to novel and effective methods to combat viral infection.

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.

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.

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.