Automated microscopy for high-content RNAi screening

Fluorescence microscopy is one of the most powerful tools to investigate complex cellular processes such as cell division, cell motility, or intracellular trafficking. The availability of RNA interference (RNAi) technology and automated microscopy has opened the possibility to perform cellular imaging in functional genomics and other large-scale applications. Although imaging often dramatically increases the content of a screening assay, it poses new challenges to achieve accurate quantitative annotation and therefore needs to be carefully adjusted to the specific needs of individual screening applications. In this review, we discuss principles of assay design, large-scale RNAi, microscope automation, and computational data analysis. We highlight strategies for imaging-based RNAi screening adapted to different library and assay designs

A Direct Phenotypic Comparison of siRNA Pools and Multiple Individual Duplexes in a Functional Assay

Background: RNAi is a prominent tool for the identification of novel regulatory elements within complex cellular pathways. In invertebrates, RNAi is a relatively straightforward process, where large double-stranded RNA molecules initiate sequencespecific transcript destruction in target cells. In contrast, RNAi in mammalian cell culture assays requires the delivery of short interfering RNA duplexes to target cells. Due to concerns over off-target phenotypes and extreme variability in duplex efficiency, investigators typically deliver and analyze multiple duplexes per target. Currently, duplexes are delivered and analyzed either individually or as a pool of several independent duplexes. A choice between experiments based on siRNA pools or multiple individual duplexes has considerable implications for throughput, reagent requirements and data analysis in genome-wide surveys, yet there are relatively few data that directly compare the efficiency of the two approaches. Methodology/Principal Findings: To address this critical issue, we conducted a direct comparison of siRNA pools and multiple single siRNAs that target all human phosphatases in a robust functional assay. We determined the frequency with which both approaches uncover loss-of-function phenotypes and compared the phenotypic severity for siRNA pools and the constituent individual duplexes. Conclusions/Significance: Our survey indicates that screens with siRNA pools have several significant advantages over identical screens with the corresponding individual siRNA duplexes. Of note, we frequently observed greater phenotypi

iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application’s ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at (https://itrex.kitz-heidelberg.de).Peer reviewe

Integration and analysis of phenotypic data from functional screens

Motivation: Although various high-throughput technologies provide a lot of valuable information, each of them is giving an insight into different aspects of cellular activity and each has its own limitations. Thus, a complete and systematic understanding of the cellular machinery can be achieved only by a combined analysis of results coming from different approaches. However, methods and tools for integration and analysis of heterogenous biological data still have to be developed. Results: This work presents systemic analysis of basic cellular processes, i.e. cell viability and cell cycle, as well as embryonic stem cell pluripotency and differentiation. These phenomena were studied using several high-throughput technologies, whose combined results were analysed with existing and novel clustering and hit selection algorithms. This thesis also introduces two novel data management and data analysis tools. The first, called DSViewer, is a database application designed for integrating and querying results coming from various genome-wide experiments. The second, named PhenoFam, is an application performing gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Both programs are accessible through a web interface. Conclusions: Eventually, investigations presented in this work provide the research community with novel and markedly improved repertoire of computational tools and methods that facilitate the systematic analysis of accumulated information obtained from high-throughput studies into novel biological insights

Facile high-throughput forward chemical genetic screening by in situ monitoring of glucuronidase-based reporter gene expression in Arabidopsis thaliana

The use of biologically active small molecules to perturb biological functions holds enormous potential for investigating complex signaling networks. However, in contrast to animal systems, the search for and application of chemical tools for basic discovery in the plant sciences, generally referred to as ‘chemical genetics’, has only recently gained momentum. In addition to cultured cells, the well-characterized, small-sized model plant Arabidopsis thaliana is suitable for cultivation in microplates, which allows employing diverse cell- or phenotype-based chemical screens. In such screens, a chemical’s bioactivity is typically assessed either through scoring its impact on morphological traits or quantifying molecular attributes such as enzyme or reporter activities. Here, we describe a facile forward chemical screening methodology for intact Arabidopsis seedlings harboring the β-glucuronidase (GUS) reporter by directly quantifying GUS activity in situ with 4-methylumbelliferyl-β-D-glucuronide (4-MUG) as substrate. The quantitative nature of this screening assay has an obvious advantage over the also convenient histochemical GUS staining method, as it allows application of statistical procedures and unbiased hit selection based on threshold values as well as distinction between compounds with strong or weak bioactivity. At the same time, the in situ bioassay is very convenient requiring less effort and time for sample handling in comparison to the conventional quantitative in vitro GUS assay using 4-MUG, as validated with several Arabidopsis lines harboring different GUS reporter constructs. To demonstrate that the developed assays is particularly suitable for large-scale screening projects, we performed a pilot screen for chemical activators or inhibitors of salicylic acid-mediated defense signaling using the Arabidopsis PR1p::GUS line. Importantly, the screening methodology provided here can be adopted for any inducible GUS reporter line

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

A New Method with Flexible and Balanced Control of False Negatives and False Positives for Hit Selection in RNA Interference High-Throughput Screening Assays

The z-score method and its variants for testing mean difference are commonly used for hit selection in high-throughput screening (HTS) assays. Strictly standardized mean difference (SSMD) offers a way to measure and classify the short interfering RNA (siRNA) effects. In this article, based on SSMD, the authors propose a new testing method for hit selection in RNA interference (RNAi) HTS assays. This SSMD-based method allows the differentiation between siRNAs with large and small effects on the assay output and maintains flexible and balanced control of both the false-negative rate, in which the siRNAs with strong effects are not selected as hits, and the restricted false-positive rate, in which the siRNAs with weak or no effects are selected as hits. This method directly addresses the size of siRNA effects represented by the strength of difference between an siRNA and a negative reference, whereas the classic z-score method and t-test of testing no mean difference address whether the mean of an siRNA is exactly the same as the mean of a negative reference. This method can readily control the false-negative rate, whereas it is nontrivial for the classic z-score method and t-test to control the falsenegative rate. Therefore, theoretically, the SSMD-based method offers better control of the sizes of siRNA effects and the associated false-positive and false-negative rates than the commonly used z-score method and t-test for hit selection in HTS assays. The SSMD-based method should generally be applicable to any assay in which the end point is a difference in signal compared to a reference sample, including those for RNAi, receptor, enzyme, and cellular function. (Journal of Biomolecular Screening 2007:000-000