Confirming ‘hits’ from single-siRNA screens is a lot of work. For low-complexity pool screens, it’s even worse (and, as we will discuss in a later post, less likely to result in true genes of interest).
Progressively, one accumulates a nearly indigestible set of experiments and analyses.
On the in vitro side:
Screen additional siRNAs for ‘hit’ genes.
Do quantitative PCR. Single siRNAs vary significantly in their effectiveness, so look for correlation between knock-down and phenotypic strength.
Create C911 versions of hit siRNAs as off-target controls. To rule out a confounding on-target effect, do qPCR.
Screen additional siRNAs with the same seed sequence as off-target controls (as done in a recent paper).
For low-complexity pools, test the siRNAs individually.
On the in silico side:
For each hit siRNA, look at plots of phenotypic effects of siRNAs with same seed sequence.
Adjust phenotypic scores based on predicted off-target effects for seeds.
Run off-target hit selection tools (like Haystack or GESS), to see if hit genes also show up as strong off-targets.
Does it really have to be so complicated?
Wouldn’t you prefer being able to trust your phenotypic readout?
Better yet, how about hits that don’t turn out to be mostly false-positives?
There is a simpler, better way.
siPools maximize the separation between on-target signal and off-target noise, making interpretation of RNAi phenotypes as clear as possible.
This year marks the 11th anniversary of Jackson et al.‘s seminal paper on siRNA off-target effects.
The past decade of high-throughput siRNA screening is largely a deductive footnote to their observation that “…the vast majority of the transcript expression patterns were siRNA-specific rather than target-specific“.
2005, Lin et al. show that top hits from RNAi screen are due to off-target effects
2009, Bushman et al. report poor overlap between hits from HIV host factor screens
2012, Marine et al. show that correlation between siRNAs for same gene is near zero, while seed sequences (involved in off-target effects) account for ~50 times more screening variance
2013, Hasson et al. find little overlap between hits from a mitophagy assay run in parallel with different siRNA libraries
Wouldn’t it have been a minor miracle if the phenotype from the following transcriptional profile were due to knockdown of the intended gene? (intended gene: Scyl1, gene actually responsible for phenotype: Mad2L1, source)
We are not saying that siRNA screens are not useful. There is some signal amongst the off-target noise. But luck and a lot of work are required. Among the top genes from the resulting ‘hit’ list, one must hope that a story can be made (TOMM7, a major character in the Hasson paper, was relatively far down the hit list, and its known location in the mitochondrial outer membrane made it more than a lucky guess).
But there is a better way. By maximising the separation between on-target signal and off-target noise, siPools can provide clearer phenotypes, thereby reducing wasted effort and dependance on luck.
Birthday cake created using Fig 2b from Jackson et al. (heat map showing deregulation of off-target transcripts by siRNAs against IGF1R).
Calculation of variance explained by genes vs. seeds (from Marine et al.):
by-gene R = .073; by-seed R = .53; .53 ^ 2 / .073 ^ 2 = 52.71
Follow us or share this post:
Like what you see? Mouse over icons to Follow / Share