Good experimental design is important when validating hits from RNAi screens. Off-target effects from single siRNAs and low-complexity siRNA pools (e.g. Dharmacon siGENOME) result in high false-positive rates that must be sorted out in validation experiments.
Dharmacon siGENOME pools (SMARTpools) have 4 siRNAs, and the most common form of validation is to test the pool siRNAs individually (deconvolution).
Unfortunately, the results of such deconvolution screening rounds are difficult to interpret.
The pool phenotype could be due to the off-target effects of any single siRNA, or even synthetic off-target effects from pooled siRNAs.
Rather than deconvoluting the pool, a better approach is to test with independent reagents. Should the phenotype be due to the seed effects of an siRNA in the siGENOME pool, the new designs (with presumably different seed sequences) should not show them. (Note that because they have their own potential complicating off-targets, an even better option would be to use a reagent like siPOOLs that minimises the likelihood of off-target effects).
Independent validation reagents was the approach used by Li et al. in a screen looking for enhancers of antiviral protein ZAP activity.
They first did a genome-wide (18,200 genes) screen with siGENOME pools, looking for pools that increased viral infection rate.
The biggest effect was with the positive control, ZAP (aka ZC3HAV1). Several other pools also stood out as giving large increases in viral infection (Fig 1B):
They identified 90 non-control genes with reproducible Z-scores above 3 in their replicate experiments (~0.5% of screened genes).
These 90 genes were then tested with 3 Ambion Silencer siRNAs. (They also included a few genes in the validation round based on pathway information and off-target analysis– more on this below.)
Of the 90 candidate hit genes, only 11 could be confirmed (Fig 2B, note that ZC3HAV1/ZAP is the positive control and JAK1 was added to the validation round based on pathway info. A gene was considered confirmed if 2 of 3 siRNAs had a Z-score > 3.):
We also see that only 1 of the 7 top hits from the first round (blue genes in the first figure) was confirmed. This is a common observation in RNAi screens: the strongest phenotypes are mostly due to off-target effects.
Off-target effects are difficult to interpret, even using advanced analysis programs like Haystack or GESS. The authors tested 4 genes identified by Haystack as targets for seed-based off-targeting. None of those genes could be confirmed in the validation round.