Demystified Figure 2 RNAi screening formats. Top panel: One gene per well screens target each gene separately in a multi-well format. Short-interfering RNAs (siRNAs), short-hairpin RNA (shRNA) plasmids or virally packaged shRNA constructs can be used to transfect or infect cells. Various readouts may be used to determine the effect of RNAi on the phenotype of interest; the measurement of cell viability is common, and luminescence-based plate readers are often used for this purpose. Alternatively, high-throughput microscopes may be used to measure cellular phenotypes in screens. Lower panel: In pooled screens, pools of shRNA-expressing vectors are introduced into cells by transfection or infection. Cells are then exposed to a selective agent such as a drug. In this case, shRNAs that cause drug resistance can be identified and quantified by amplifying shRNA sequences from genomic DNA in surviving cells. Microarray analysis is ideal for detecting shRNA sequences, as is Next Generation Sequencing. For more details, see the main text. start is with the standard conditions for most routinely used cell lines found on RNAi reagent suppliers’ websites. Once efficient RNAi delivery can be achieved, positive and negative RNAi reagents should be used to assess whether the screen phenotype can be robustly measured. The key issues here are scale of effect, variability and reproducibility; if the screen assay is such that the difference between positive and negative controls can only be observed rarely, the omens for the screen itself are poor. Having addressed these concerns, one may consider a pilot screen. This is especially important if one is to embark upon a genome-wide screen, and normally involves testing control RNAi reagents alongside a panel of a few hundred selected RNAi reagents. Again this pilot will assess the reproducibility of the positive and negative controls and decipher the hit rate of the screen. If the hit rate is very high, it may suggest lack of specificity of the screen. Pilot screens may also identify problems such as time and space limitations. It is also worth noting here the main reason why screens fail; the inability to precisely reproduce experimental conditions from the initial 198 determination of screen conditions through to the screen itself. Given that most screen conditions are determined empirically (ie, by titrating variables such as cell number, amount of siRNA, etc), it is perhaps wise to standardise all procedures, by using identical batches of cells, media, etc as well as carefully timing and reproducing all of the steps involved in the screen process. It is also absolutely vital to consider the analysis of screen data before embarking upon the screen itself. Perhaps one of the best methods of screen analysis has been described by Boutros et al.15 Their CellHTS software package uses a modification of the Z score/median absolute deviation method of defining screen hits. Although we will not go into this form of analysis in detail, it is key that one understands the method of statistical analysis before embarking on a screen, as this will inform how the screen is performed and also allow an assessment of the quality of the screen itself. Once the screen and data analysis are performed, it is often common to perform a secondary screen using only the control RNAi reagents and those silencing the ‘‘hits’’ identified in the J Clin Pathol 2009;62:195–200. doi:10.1136/jcp.2008.058735