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Category: siRNA off target

Classic Papers Series: Lin et al. show RNAi screen dominated by seed effects

Classic Papers Series: Lin et al. show RNAi screen dominated by seed effects

Over the coming months, we will highlight a number of seminal papers in the RNAi field.

The first such paper is from 2005 by Lin et al. of Abbott Laboratories, who showed that the top hits from their RNAi screen were due to seed-based off-target effects, rather than the intended (and at that time, rather expected) on-target effect.

The authors screened 507 human kinases with 1 siRNA per gene, using a HIF-1 reporter assay to identify genes regulating hypoxia-induced HIF-1 response.

In the validation phase of their screen, they tested new siRNAs for hit genes, but found that they failed to reproduce the observed effect, even when using siRNAs that had a better on-target knock down than the pass 1 siRNAs.

Figure 1A.  Left panel shows on-target knock down of pass 1 siRNA for GRK4 (O) and the new design (N).  Centre panel shows Western blot of protein  levels  Right panel shows HIF-1 reporter activity for positive control (HIF1A) and the original (O) and new (N) siRNAs.

The on-target knock down is much-improved for the new design, yet its reporter activity is indistinguishable from negative control.  Yet the pass 1 siRNA with poor knock down gives almost as strong a result as HIF1A (positive control).

By qPCR, they then showed that GRK4(O) and another one of the top 3 siRNAs silence HIF1A (the positive control gene).  Using a number of different target constructs they also nicely show that it was due to seed-based targeting in the 3′ UTR.

Although the authors screened at a high initial concentration (100 nM), the observed off-targets persisted at 5 nM, suggesting that just screening at lower concentrations would not have improved their results.

The authors conclude:

In addition, due to the large percentage of the off- target hits generated in the screening, using a redundant library without pooling in the primary screen could significantly reduce the efforts required to eliminate off-target false positives and therefore, will be a more efficient design than using a pooled library.

This is true for low-complexity pools, but high-complexity pools can overcome this problem by providing a single reliable result for each screened gene.

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Russian roulette

Russian roulette

Which bases should you choose for the seed region of a single siRNA?

It’s like Russian Roulette on full-automatic, where a specific seed will result in dozens or hundreds of down-regulated genes.

If you’re lucky, none of the off-targets results in a false-positive phenotype.   But odds are that you won’t be so lucky.

A recent paper suggests that taking single siRNA drugs may be closer to real Russian Roulette than anyone would hope.

The authors show that an siRNA designed to knock down human Huntington protein (HTT), and which had shown no adverse effects in Rhesus monkeys, was toxic when administered to mice.  The siRNA had been designed to produce minimal off-target effects in human, macaques, and mice, confirming the extreme difficulty in predicting off-target effects.

By modifying a single nucleotide in the siRNA seed region, the authors were able to create a version not toxic for mice.  Whether it will also be non-toxic for macaques?  That experiment must be repeated.  Single siRNAs create a lot of work.

Using miRNA target identification algorithms, the authors identify a number of potential off-target genes that may be responsible for toxicity.  For 3 of 4 genes, they show by qPCR using brain tissue that there is significant off-target silencing by the HTT siRNA (HDS1) compared to control (Ctl):



But when they try to confirm these results in an immortalised mouse neuronal striatal cell line, only one gene (Bcl2) is still being silenced (U6 is an additional vector control):


The authors comment:

In contrast, Sdf4 and Map2k6 expression was not reduced by overexpression of miHDS1 (Figure 2D and E), suggesting that these genes may not be direct off-targets in vivo, and may reflect indirect effects of Htt suppression over time or off-target suppression in non-neuronal cells, although AAV2/1 transduces primarily neurons.

Another possibility is that Sdf4 and Map2k6 are regulated by one or more genes that are off-targets of HDS1, but which are differentially expressed in live animal cells and the immortalised cell line.

The pool of accessible off-targets (and the resulting off-target footprint) differs with each transcriptional profile.

One should not assume that single siRNAs used in different cell types, or even under different experimental conditions, will behave the same way.

Using single siRNAs as normalisation controls may be especially problematic.

Why take risks with your RNAi experiments?  Use siPools for consistent, robust phenotypes.


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The beauty of the siPool

The beauty of the siPool

The siPool strategy is beautifully simple:

By having many on-target siRNAs, each with a different seed sequence, you maintain on-target efficiency while diluting out off-target effects.

One analogy is the beauty of composite faces.

Which of these faces do you find most attractive?


If you’re like most people, you will have chosen the last face, which is actually a composite of the other 5 faces (source).

Each individual face has its flaw(s).  Spock ears, mildly everted lips, incongruous eyebrows, etc.  No face is ideal.  But when combined, these flaws are evened out.

Same thing with siRNAs.  Each individual siRNA has its own ugly off-target signature.  But combined in a high-complexity siPool, the off-target warts are removed, and you’re left with a beautiful on-target phenotype.

And not all facial averages are equal.  Perrett et al. found that a composite face created from faces pre-selected as being more attractive was preferred  over a composite created from all available faces.

Likewise, siPools are composed of the best available individual siRNAs.

Being selective about what goes into a siPool is important, as we will discuss more in future posts.

This amusing anectdote from the autobiography of Francis Galton is à propos.  (Galton pioneered composite facial analysis, in addition to finger print analysis, as discussed previously)

I could not make good composites of lunatics ; 
their features are apt to be so irregular in different 
ways that it was impossible to blend them. I took a 
photographer with me to Hanwell, where it was 
arranged that the patients should sit two at a time on 
a bench. One of them was to be led forward and 
posted in front of the camera, while his place on the 
bench was filled by the second patient moving up 
into it, whose previous place was to be occupied by a 
third patient It happened that the second of the 
pair who were the first to occupy the bench considered 
himself to be a very mighty man, I forget whom, but 
let us say Alexander the Great. He boiled with 
internal fury at not being given precedence, and when 
the photographer had his head well under the velvet 
cloth, with his body bent, in the familiar attitude of 
photographers while focusing, Alexander the Great 
slid swiftly to his rear and administered a really good 
bite to the unprotected hinder end of the poor 
photographer, whose scared face emerging from 
under the velvet cloth rises vividly in my memory as 
I write this. The photographer guarded his rear 
afterwards by posting himself in a corner of the 

Memories of My Life, pp 262-263

Don’t get bitten by bad RNAi reagents.

Please see our website for more information on siPools technology.



See for lots of information and interesting tools for facial analysis.

See this article for composite faces from different nations.

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The nasty, ugly fact of off-target effects

The nasty, ugly fact of off-target effects

Once upon a time, it was imagined that siRNAs specifically knock down the intended target gene.

Unfortunately, this turned out to be wrong.

The disappointing results from siRNA screening following the initial high hopes brings to mind T.H. Huxley‘s famous quote about a beautiful theory being killed by an ugly, nasty little fact.

As pointed out by S.J. Gould in Eight Little Pigs, the origin of the quote is given in the autobiography of Sir Francis Galton.

Galton’s autobiography is inspirational.  As one reviewer put it, “there is a feeling of calmness and awe that comes from knowing that a person of his genius, wisdom and versatility actually existed.”  He was 70, and had already made significant contributions to genetics, statistics, meteorology, and geography, when he published his first major work on fingerprints, which would become the basis for modern forensic fingerprint analysis.

His work on fingerprints also provides the context for the famous Huxley quote:

Much has been written, but the last word has not 
been said, on the rationale of these curious papillary 
ridges ; why in one man and in one finger they form 
whorls and in another loops. I may mention a 
characteristic anecdote of Herbert Spencer in con- 
nection with this. He asked me to show him my 
Laboratory and to take his prints, which I did. Then 
I spoke of the failure to discover the origin of these 
patterns, and how the fingers of unborn children had 
been dissected to ascertain their earliest stages, and so 
forth. Spencer remarked that this was beginning in 
the wrong way ; that I ought to consider the purpose 
the ridges had to fulfil, and to work backwards. 
Here, he said, it was obvious that the delicate mouths 
of the sudorific glands required the protection given 
to them by the ridges on either side of them, and 
therefrom he elaborated a consistent and ingenious 
hypothesis at great length. 

I replied that his arguments were beautiful and 
deserved to be true, but it happened that the mouths 
of the ducts did not run in the valleys between the crests, 
but along the crests of the ridges themselves. He 
burst into a good-humoured and uproarious laugh, and 
told me the famous story which I have heard from 
each of the other two who were present on the 
occurrence. Huxley was one of them. Spencer, 
during a pause in conversation at dinner at the 
Athenaeum, said, "You would little think it, but I 
once wrote a tragedy." Huxley answered promptly, 
" I know the catastrophe." Spencer declared it was 
impossible, for he had never spoken about it before 
then. Huxley insisted. Spencer asked what it was. 
Huxley replied, "A beautiful theory, killed by a 
nasty, ugly little fact."  

Memories of My Life, pp 257-258
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Don’t swallow the fly

Don’t swallow the fly

There was a PI who screened one s i [RNA],

Oh, I don’t know why they screened one s i …

siRNA screens have a high false positive rate, due to pervasive off-target effects.

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.

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Celebrating 11 years of off-target effects

Celebrating 11 years of off-target effects



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


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