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Gene silencing by RNA interference has become a powerful tool to help identify genes that regulate biological processes. However, the complexity of the biology probed and the incomplete validation of the reagents used make it difficult to interpret the results of genome-wide siRNA screens. To address this challenge and maximize the return on the efforts required for validating genomic screen hits, the screening strategy must be designed to increase the robustness of the primary screening hits and include assays that inform on the mechanism of action of the knocked-down transcripts. Here, we describe the implementation of a small interfering RNA (siRNA) screen to identify genes that sensitize the effect of poly-(ADP ribose)-polymerase (PARP) inhibitor on cell survival. In the strategy we designed for the primary screen, two biological activities, apoptosis and cell viability, were measured simultaneously at different time points in the presence and absence of a PARP inhibitor (PARPi). The multiplexed assay allowed us to identify PARPi sensitizers induced by both caspase-dependent and independent mechanisms. The multiplexed screening strategy yielded robust primary hits with significant enrichment for DNA repair genes, which were further validated using relevant high-content imaging assays and confirmation of transcript knockdown by real-time PCR (rtPCR).

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)-based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results.

RNA interference (RNAi), combined with the availability of genome sequences, provides an unprecedented opportunity for the massive and parallel investigations of gene function. Small interfering RNA (siRNA) represents a popular and quick approach of RNAi for in vitro loss-of-function genetic screens. Efficient transfection of siRNA is critical for unambiguous interpretation of screen results and thus overall success of any siRNA screen. A high-throughput, lipid-based transfection method for siRNA was developed that can process eighty 384-well microplates in triplicate (for a total of 30,720 unique transfections) in 8 h. Transfection throughput was limited only by the speed of robotics, whereas the cost of screening was reduced. As a proof of principle, a genome-scale screen with a library of 22,108 siRNAs was performed to identify the genes sensitizing cells to mitomycin C at concentrations of 0, 20, and 60 nM. Transfection efficiency, performances of control siRNAs, and other quality metrics were monitored and demonstrated that the new, optimized transfection protocol produced high-quality results throughout the screen. (Journal of Biomolecular Screening XXXX:xx-xx)

Gene silencing by RNA interference is a powerful genetic tool to identify genes involved in specific biological processes in model organisms and human cells (3, 14, 18, 38, 42, 56). It is now possible to perform unbiased genetic screens using cultured human cells. Recently, small-scale siRNA (small interfering RNA) and shRNA (small hairpin RNA) screens in human cells have successfully identified genes that modulate cell growth, apoptosis, chemoresistance, and chemosensitivity (4, 44, 52). While establishing proof-of-concept, most of these early screens tested gene sets too limited in size to provide a comprehensive view of the genes involved in the cellular phenotypes being tested. Several groups have employed nearly genome-wide screens of shRNAs expressed from vectors to identify genes that modify certain phenotypes (6, 40, 49, 59). To date, these shRNA screens have been limited to phenotypes amenable to positive selection, but other approaches like bar code screening have shown promise (6). Systematic well-by-well testing of genomic-scale siRNA or shRNA libraries for modulation of nonselectable phenotypes in human cells has not been widely attempted. Cancer cells undergo numerous genetic changes that drive cellular transformation from normal cell progenitors. Anticancer drug target discovery is now frequently directed toward understanding and exploiting genetic alterations that exist in tumor cells. Knowledge of genetic alterations in tumors may lead to better use of conventional therapeutics or development of new therapeutics that offer better therapeutic windows. Loss-of-function genetic screens can identify genes whose loss of function enhances cytotoxicity of chemotherapeutics in cells with defined genetic lesions. The TP53 transcription factor is a central mediator of the cellular response to DNA damage and a variety of other stresses and is one of the most frequently mutated genes in human cancers (19, 57). TP53 status can also effect the chemosensitivity of tumor cells (15). The identification of genes that, when silenced, selectively enhance the chemosensitivity of TP53 mutant cancer cells but not TP53 wild-type cells would make attractive drug targets. Drugs developed to these genes have the potential to selectively increase the toxicity of the chemotherapeutic in the cancer cell. Genetic screens in model organisms have led to identification of drug enhancers whose loss increases sensitivity to the drug (43, 54, 56, 61). We hypothesized that identification of enhancers for commonly used cancer therapeutics would lead to new strategies for therapeutic application of these drugs and/or identification of new targets for the development of combination chemotherapies. Here we used small- and genome-scale siRNA screens to identify genes that enhance the sensitivity of human tumor cells to the cancer chemotherapeutic cisplatin. The screen hits included known genes that function in DNA repair, cell cycle checkpoints, and survival signaling, as well as genes with no annotated functions. Selected hits from these screens were validated and then tested on matched TP53-positive and TP53-deficient cells to identify enhancers selective for tumor cells that have lost TP53 function. We show that silencing of BRCA1, BRCA2, and certain genes whose products interact with BRCA1/2, selectively enhances cisplatin cytotoxicity in TP53-deficient cells.