Month: September 2023

Complex siRNA pooling is especially important for silencing lncRNAs

Complex siRNA pooling is especially important for silencing lncRNAs

One advantage of pooling siRNAs is that the pool tends to silence about as well as any single siRNA. That is why independent complex siRNA pools (siPOOLs) for the same gene have much more similar and better average silencing than single siRNAs or mini-pools (Dharmacon), as discussed in our last blog post.

Another advantage of complex siRNA pools is that you can cover more regions of the target gene. This may be especially important when silencing lncRNAs, which can be very long and whose structure may cause certain regions of the transcript to be inaccessible to RISC.


A good exemplar of this phenomenon is MALAT1, whose transcripts are nearly 9 kb and whose secondary and tertiary structure is important for its cellular function.

Two groups who used single siRNAs or mini-pools (Dharmacon) found poor silencing for MALAT1.

Stojic et al. (2018) used a Lincode mini-pool (Dharmacon) and saw almost no silencing:

That was despite using a very high siRNA concentration (50 nM) in a cell line where RNAi normally works very well (HeLa).

Lennox and Behlke (2016) used several single siRNAs at two concentrations (1 nM and 10 nM), again in HeLa cells. They found highly variable silencing, where most siRNAs had poor silencing, and a small number worked fairly well at the higher concentration (10 nM):

Nuclear IncRNA: MALAT1

If one were to randomly choose an siRNA, there is only a 25% chance (3 / 12) that it would give decent silencing at 10 nM (using 30% remaining RNA as cutoff for decent silencing). And none would be considered decent at 1 nM.

Of note, this paper is often cited as a reason for not using RNAi for lncRNAs (the authors, from IDT, recommend using ASOs).

The results from Stojic et al. were quite poor (the Lincode mini-pool hardly silenced), and could be due to reagent quality.

siPOOLs effectively silence MALAT1

The results from Lennox and Behlke are more in line with what we’ve observed when researching the silencing of individual siRNAs versus complex siRNA pools (siPOOLs). Some siRNAs are much better than others. And, as mentioned earlier, a complex pool of siRNAs tends to silence like the best constituent siRNAs.

Given the best-silencing-siRNA selection from complex siRNA pooling and the increased transcript coverage, we would expect siPOOLs to give better silencing than what these authors found.

Our 2 independent siPOOLs for MALAT1 give silencing of 16.5% and 24.4% remaining RNA, respectively, at 1 nM:

MALAT1 Silencing by siPOOLs

If we compare our reagents to those used by Lennox and Behlke, we see that there is much broader transcript coverage. As mentioned, this could be especially important for lncRNAs like MALAT1 that have extensive secondary and tertiary structure.

Lennox and Behlke siRNAs (used individually):

siPOOL #1 siRNAs (used together):


Two factors make complex siRNA pools (siPOOLs) especially well suited for silencing lncRNAs:

  1. Complex siRNA pools tend to silence like their best constituent siRNAs. A single siPOOL silences about as well as the best single siRNA. For targets with high silencing variability, siPOOLs are far superior to single siRNAs or mini-pools (Dharmacon).
  2. By using a complex siRNA pool, more of the gene can be covered. For targets with lots of secondary and tertiary structure, siPOOLs give you the best chance of targeting an accessible region.
siPOOLs: robust reagents for gene silencing

siPOOLs: robust reagents for gene silencing

Although we talk a lot about off-targets, one of the main advantages of siPOOLs (complex siRNA pools) compared to single siRNAs or mini-pools (Dharmacon) is that they provide near optimum silencing of target genes. Two siPOOLs for the same gene give very similar knock down levels, and their silencing is around the best of any single siRNA. Given how many candidate siRNAs there are for a gene, and how difficult it is to accurately predict silencing levels, this makes siPOOLs the best choice for gene silencing.

The following plot, comparing independent siPOOLs and siRNAs for the same target gene, shows that siPOOLs for the same gene give more similar silencing than do siRNAs (these are Ambion Silencer Select siRNAs).

We see that the correlation for independent siPOOLs is nearly twice that for independent siRNAs.

(Note that for siRNAs we are doing all pairwise comparisons for 3 siRNAs per target gene. Randomly selecting 2 siRNAs per gene gives similar R values.)

In the above plot, we removed 3 siRNAs that did not work, for the gene TRIB1. TRIB1 has some association with the nucleus and has a short mRNA half life, both of which are factors associated with poor gene silencing.

The following plot shows the TRIB1 siPOOLs and siRNAs.

Note that including these non-functional siRNAs actually improves the reagent correlation, though not for a good reason!

We also see that independent TRIB1 siPOOLs give very similar silencing and it’s much better than for the siRNAs. In our experience, if a siPOOL does not work well for a gene, designing a second siPOOL does not substantially improve things, as the poor silencing is normally a feature of the target gene itself. ~50% silencing is probably about the best one can expect for this gene.

Just because siRNAs do not give any on-target silencing, this does not mean they can’t show up as hits in screening assays. Because most of the downregulation is in off-target genes (due to the seed effect), each of those TRIB1 siRNAs may silence nearly 100 genes.

We looked at a genome-wide RNAi screen that included these 3 Silencer Select siRNAs. We see that one of them gives a fairly strong phenotype (Z-score < -2 for cell count), even though the siRNAs do not silence their on-target gene.

Screening with siPOOLs is the smarter alternative, as you can be confident that they provide near optimal on-target silencing and have less off-target effects.

Cutting the Gordian Knot of RNAi off-targets

Cutting the Gordian Knot of RNAi off-targets

The C911 siRNA control generated a lot of excitement in the RNAi world when it emerged ~11 years ago. A former colleague, who was a pioneer in the commercialisation of RNAi, described it then as the biggest breakthrough in the last 10 years of RNAi research.

The idea of the C911 control is to get rid of the on-target effect of the siRNA by using the complement of bases 9-11, while retaining any off-target (seed-based) effects of the siRNA, which are mostly dictated by the bases in positions 2-8.

If the observed phenotype of the siRNA is due to an off-target effect (rather than silencing of the on-target gene), the C911 version will show the same phenotype. i.e., because it is not silencing the target gene, the phenotype must come from an off-target effect.

Despite the initial excitement, the C911 approach did not become that widely used. There are a number of drawbacks to the strategy, perhaps foremost being that new reagents must be ordered and the assay set up to run again. We’ve compared the validation of low-complexity RNAi reagents to the old lady who swallowed a fly.

The best strategy is to avoid getting entangled in off-targets in the first place. And that seems to be the approach preferred by the research community.

The following plot shows Google Scholar citations for siTOOLs (i.e., papers using our reagents) and the C911 method paper.

We see that after an initial adoption period, use of C911s tapered off and it has levelled out in recent years.

None of this suggests that C911s are bad. For single siRNAs or Dharmacon pools, they are indeed an effective control. But the inconvenience of the method has probably hindered its adoption.

The convenience and robustness of the siPOOL are its great advantages. The siPOOL approach ensures maximum on-target silencing and a minimum of off-target effects. We look forward to supporting more great research in the coming years.


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