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Month: April 2018

Low complexity pooling does not prevent siRNA off-targets

Low complexity pooling does not prevent siRNA off-targets

Summary: Low-complexity siRNA pooling (e.g. Dharmacon siGENOME SMARTpools) does not prevent siRNA off-targets.  It may in fact exacerbate off-target effects.  Only high-complexity pooling (siPOOLs) can reliably ensure on-target phenotypes.

Low-complexity pooling increases the number of siRNA off-targets

One of the claims often made in favour of low-complexity pooling (e.g Dharmacon siGENOME SMARTpools) is that this pooling reduces the number of seed-based off-target effects compared to single siRNAs.

If this were true, we would expect different low-complexity siRNA pools for the same gene to give similar phenotypes.  But this is not the case.

Published expression data shows that low-complexity pooling actually increases the number of off-targets.

Kittler et al. (2007) looked at the effect of combining differing number of siRNAs in low to medium complexity siRNA pools (siRNA pools sizes were: 1, 3, 5, 9, and 12).

Their work showed that the number of down-regulated genes (50% or greater silencing) actually increases when small numbers of siRNAs are combined.  Only when larger numbers of siRNAs are combined does the number of off-targets start to drop:



[The figure is based on data from GEO dataset GSE6807.  Down-regulated genes are those whose expression is reduced by 50% or more.  Note that the orange point is taken from our 2014 NAR paper, as we are not aware of other published expression datasets with this many pooled siRNAs.  A few caveats with combining these datasets are that they use different target genes, siRNA concentrations, and the data comes from a different expression platform.]

Low-complexity pooling: a bad solution for siRNA off-targets

Low-complexity pooling does not get rid of the main problem associated with single siRNAs: seed-based off-target effects.   Based the above analysis, it can make it even worse.  It also prevents use of the most effective computational measures against seed effects.

Redundant siRNA Activity (RSA) is a common on-target hit analysis method for single-siRNA screens.  It checks how over-represented the siRNAs for a gene are at the top of a ranked screening list.  If a gene has 2 or more siRNAs near the top of the list, it will score better than a gene that only has a single siRNA near the top of the list.  This is one way to reduce the influence of strong off-target siRNAs.

Correcting single siRNA values by seed medians has also been shown to be an effective way to increase the on-target signal in screens.  This correction is not effective for low-complexity pools, since each pool can contain 3-4 different seeds.

Off-target based hit detection algorithms (e.g. Haystack and GESS) are also only effective for single-siRNA screens.  The advantage of these algorithms is that it permits the detection of hit genes that were not screened with on-target siRNAs.  These algorithms are not effective for low-complexity pool screens.

Our recommendation: do not convert single siRNAs into low-complexity pools, rather use high-complexity siPOOLs to confirm hits

We do not recommend that screeners combine their single siRNA libraries into low-complexity pools (e.g. combining 3 Silencer Select siRNAs for the same target gene).  If possible, it is better to screen the siRNAs individually and then apply seed-based correction, RSA and seed-based hit-detection algorithms.

The time saved by only screening one well per target may prove illusory when the deconvolution experiments show that the individual siRNAs have divergent phenotypes.

It is probably better to deal with off-target effects up front (by screening single siRNAs) than to be surprised by them later in the screen (during pool deconvolution).

Reliable high-complexity siPOOLs, as independent on-target reagents, can then be used to confirm screening hits.

siTOOLs also now has RNAi screening libraries available.  Please contact us for more information.

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What is the probability of an siRNA off-target phenotype?

What is the probability of an siRNA off-target phenotype?

Summary:   Conventional siRNAs have a high probability of giving off-target phenotypes.  siRNA off-target effects can be reduced by using more specific reagents or narrowing the assay focus (to reduce the number of relevant genes).  Even when the assay is relatively focused, more specific reagents significantly increase the probability of observing on-target effects.

Probability of siRNA off-target phenotype depends on reagent specificity and assay biology

The probability of getting an off-target effect from an siRNA depends on several factors, the main ones being reagent specificity and assay biology.  If an siRNA down-regulates a large number of genes, or if an assay phenotype can be induced by a large number of genes, the probability of observing an off-target phenotype increases.

siRNAs can down-regulate many off-target genes

Garcia et al. (2011) compiled 164 different microarray experiments measuring gene expression following transfection with siRNAs.  The mean number of down-regulated genes in these experiments was 132 and the median was 68 (down-regulated genes were silenced by 50% or more).

As noted in earlier studies of gene expression following siRNA treatment (e.g. Jackson et al. 2003), few of the down-regulated genes are shared between siRNAs with the same target gene.  This suggests that the down-regulated genes are not the downstream result of target gene knockdown (i.e. they are mostly off-target).

High-complexity pooling of siRNAs (e.g. with siPOOLs) can reduce the number of down-regulated genes.

The following figure, based on data from Hannus et al. 2014, shows the difference between the gene expression changes caused by a single siRNA (left) and a high-complexity siRNA pool (siPOOL, right), which also includes that same single siRNA:


Estimating the probability of siRNA off-target phenotypes

Assuming different numbers of down-regulated genes (off-target) and different numbers of potent genes involved in assay pathways, we can try to estimate the probability of an siRNA giving an off-target effect.

The following plot shows the probability of getting an off-target effect when:

  • assuming RNAi reagents down-regulate varying numbers of off-target genes (5, 25, 50, 100)
    • down-regulated means that gene expression is reduced by 50% or more
    • in the Garcia paper dataset, the mean is 132 and median is 68
  • assuming different numbers of assay-potent genes
    • an assay-potent gene is one whose down-regulation by 50% or more is sufficient to produce a hit phenotype
    • for assays with more general phenotypes (e.g. cell count) we would expect more  assay-potent genes


We can see that even if there are only 20 assay-potent genes, there’s a nearly 10% chance of getting an off-target phenotype when siRNAs down-regulate 100 off-target genes (which is close to the average observed in the Garcia dataset).

In a genome-wide screen of 20,000 genes with 3 siRNAs per gene, we would thus expect 2,000 off-target siRNAs.

In contrast, a more specific reagent that only down-regulates 5 off-target genes only has a 0.5% change of producing an off-target phenotype.  For the above-mentioned genome-wide RNAi screen, we would expect only 100 off-target siRNAs (a 20-fold reduction).

The importance of RNAi reagent specificity

The above analysis demonstrates the importance of using specific siRNA reagents.

Changing an assay to make the phenotypic readout narrower (to reduce the number of genes capable of inducing a phenotype) is one way to reduce the risk of off-target phenotypes.  But this may be a lot of work and is not necessarily desirable or even possible.

A more ideal solution is the use of a specific RNAi reagent, like siPOOLs.


As the number of assay-potent genes increases, the probability of getting an off-target phenotype approaches one.

The following plot (same format as the one above) shows the distribution


The p-values were calculated using the hypergeometric distribution, assuming a population size of 20,000 (the approximate number of protein-coding genes in the human genome).

Note that one of the major simplifying assumptions of the above analysis is that all siRNAs have the same number of down-regulated off-target genes.

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5 factors to consider in multi-gene targeting RNAi screens

5 factors to consider in multi-gene targeting RNAi screens

Summary: Effective functional genomic screening depends on a variety of factors that need to be simultaneously addressed to obtain meaningful results. A recent Cell Reports paper demonstrates this by taking a holistic approach to siRNA screening with the use of multi-isoform/multi-gene targeting to address redundant paralogs and pathways in cancer cells.

The case for multi-gene targeting

Many RNAi screens use arrayed single gene knockdowns to find genes that play an important role in a biological process. The idea is that a single bullet is enough to take down its target leaving a gaping hole that one cannot fail to notice. In some cases, this is true, and is certainly relied upon by drug developers seeking to create specific mono-target drugs.  However, in complex diseases like cancer, cells have evolved fail-safe mechanisms to make them more resistant to external assaults. A single bullet is simply not enough.

Take for example oncogenic protein RAF or Rapidly Accelerated Fibrosarcoma, a tyrosine kinase effector that is a component of the MAPK signalling pathway (Ras-Raf-MEK-ERK). RAF has three isoforms – ARAF, BRAF and RAF1 (also called CRAF). Studies in mouse embryonic development show they all share some form of functional redundancy as knocking out two isoforms produces more severe effects than knocking out each isoform alone.

Screens that target single genes/isoforms therefore tends to bias results towards genes that have no paralogs or only have single isoforms. This was indeed the reason why classical Ras effectors were not identified in previous screens.

Factors to consider in a multi-gene targeting RNAi screen

Determining gene combinations that make sense

The authors of the study did a focussed siRNA screen on 41 RAS effector nodes represented by 84 genes. Out of the 41 nodes, 25 of them had 2-4 functional paralogs where combinatorial gene silencing was carried out with multiple siRNAs. 5 nodes knocked down multiple members of a protein complex. 5 nodes had siRNAs targeting multiple steps within a pathway. Only 6 nodes silenced single genes (highlighted).

Multi-gene targeting screen design

The only caveat with designing such a screen is the requirement for prior knowledge to perform meaningful gene silencing combinations. In this instance, many of the Ras effector pathways are characterized sufficiently to do this well however in other less studied fields, this could be a challenge. Useful tools that would help in designing gene knockdown combinations would include pathway or phenotype databases such as KEGG, REACTOME or Wikipathways. The Phenovault which siTOOLs Biotech is developing, is yet another potentially useful tool.. more details to come!

Number and types of phenotypes

The authors also highlight how a screen that reads only one phenotype might miss other important gene functions. Many RNAi screens sadly still stick to measuring cell proliferation as their only read-out which is greatly influenced by siRNA off-target effects. Here, 5 different phenotypes were measured (cell size, proliferation, apoptosis, reactive oxygen species [ROS], and viability). It was noted that silencing of Cdc42 had little effect on cell viability yet a prominent effect on ROS levels.

To take this up a notch, analysis was also performed at the single-cell level in cells expressing uniform levels of GFP and co-transfected with GFP siRNA. This allowed authors to correlate phenotypes with levels of gene knockdown, generating dose-response curves. How clever!

A lot more work, but adds to data robustness especially when using single siRNAs that are known to be rather variable.

Heterogeneity of cell lines

Many reports and our own observations attest to the heterogenous response of different cell lines to the same treatment. In cancer especially, the large heterogeneity necessitates the use of multiple cell lines. Not doing so would be failing to account for the large genetic diversity observed in the clinic. The authors screened 92 cell lines derived from lung, pancreas and colorectal tissue.

Despite seeing heterogenous responses to node knockdowns, phenotypic responses could be distinguished into  several groups based on effector engagement.  A major group dependended on RAF through direct binding with KRAS, a second major group worked via RSK p90 S6 kinases to drive RSK-mTOR signalling. And a third minor group was dependent on RalGDS. They went on to focus on the first two major groups, naming them KRAS-type and RSK-type respectively.

Reagents – choosing siRNAs and siRNA concentrations

The authors used previously characterized siRNAs to select for more potent siRNAs. This involved an RNAi sensor reporter-based assay that required the generation of 20,000 clones. The reporter was also shRNA-based. Due to heterogeneity in Dicer-mediated cleavage of shRNA, its uncertain if knockdown potency is accurately reflected when translated to siRNAs (read about the difference between shRNAs and siRNAs).

siRNA off-target effects are concentration-dependent

In any case, its a lot of work to characterize all siRNAs to be used in a screen. Furthermore, off-target effects are not addressed.

The authors stuck to a maximal concentration of 12 nM where 2 nM of siRNA was applied per gene. At 2 nM per siRNA, one still risks deregulating other genes. One of the first papers by Aimee Jackson et al., demonstrated an siRNA targeting MAPK14 deregulated many other genes even at concentrations of 1-4 nM.

An important consideration is to ensure total siRNA concentrations are kept constant. In which case, a negative control siRNA has to match or follow the maximal siRNA concentration used. Using different levels of siRNAs runs the risk of biasing off-target effects towards sequences present at higher concentrations.

To learn what the causes, extent and consequences of siRNA off-target effects are, read siTOOLs Technote 1)

Validating results

As with all scientific hypothesis, it helps to arrive at the same conclusion with different approaches.

The two different effector response subgroups identified also responded differently to small molecules. The KRAS-type lines being more sensitive to EGFR and ERK inhibition while the RSK-type lines more sensitive to inhibitors of PDK1, RSK, MTOR, S6K1 and DNA repair enzymes. This was attributed to the latter’s higher basal metabolic activity manifested in larger investments towards oxidative phosphorylation and mitochondrial ribosome maintenance.

By also projecting signatures obtained from cell lines into patient samples (in The Cancer Genome Atlas, TCGA), the subtypes were also effective at predicting differential sensitivity to multiple drug treatments. This highlights the importance in designing effective drug combinations in cancer.

Interestingly, the authors also performed CRISPR pooled screens in parallel. However, due to the restraints of being only able to knockout 1 gene at a time, smaller effects were seen due to gene redundancy. However, they did go on to use CRISPR as well to mutate key genes to affirm the pathway relationships established.

siPOOLs have been used successfully for multi-gene targeting for up to 4 genes, and potentially more. They also safely address off-target effects by high complexity pooling, enabling each siRNA to be applied at picomolar concentrations. For more articles on multi-gene targeting, read an older blogpost:

Understanding gene networks with combinatorial gene knockdown


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