Month: July 2023

Which technology is better, RNAi or CRISPR?

The best answer to this question, like so many others is, it depends.

If cells can adapt and compensate for loss of the gene, or you want to titrate gene levels (important in drug discovery), then RNAi will be better.

If a gene’s transcripts have lots of secondary structure and must be silenced to 99.9% in order to see an assay phenotype, then CRISPR may be better.

We have used two large datasets to attempt to answer the following question: is RNAi or CRISPR better at identifying essential genes?

The first dataset is the BROAD Institute’s Dependency Map (DepMap). It has both RNAi (shRNA) and CRISPR (Cas9) screens from over 700 human cell lines, using hundreds of thousands of reagents. Both types of reagents were used to do pooled screening for cell viability.

The second dataset, also from the BROAD Institute, is called gnomAD. It has genome and exome sequencing for over 100K humans. Based on how frequently mutations are found in the sequenced genomes/exomes (and what type of mutations are preferred), an essentiality score can be assigned to every human gene. It’s the ultimate test (within ethical limits) of whether a gene is essential to humans.

Our approach was as follows:

• for each gene, get the median DepMap viability score across the 700+ cell lines
  • done separately for RNAi and CRISPR screens
• for each gene, retrieve the gnomdAD pLI score (probability that loss-of-function not tolerated)
  • higher values means the gene is considered more essential
  • genes with pLI > 0.9 are classified by gnomAD as essential

If we look at the top 200 genes in each of the RNAi and CRISPR datasets (note: 70 genes are common to both lists), we see that the top 200 genes from RNAi screening are more essential (as measured by pLI) than are the top 200 genes from CRISPR screening. (note that the curves show the running mean for 30 genes)

Eventually the curves do converge, but for the top genes, we see that those found by RNAi are more essential.

Alternatively, if we group the genes from the CRISPR and RNAi screens into deciles for cell viability score, we again see that the results from the RNAi screens are more consistent with gnomAD.

In the following plots, we look at the number of gnomAD essential genes (defined as pLI > 0.9) in each of the deciles. Decile 1 has the top 10% of genes for reducing cell viability (most essential), whereas Decile 10 has the bottom 10% (least essential).

For CRISPR screens, we see that the top 2 deciles show markedly more gnomAD essential genes. But after that, the counts flatten out. There is little difference in the number of gnomAD essential genes in deciles 3 through 10.

The results from RNAi screening show a fairly steady decline in gnomAD essential genes in deciles 1 through 10. Which is what one would expect. Genes that increase cell count should tend to be less essential. i.e., decile 10 should have the smallest number of essential genes. That is what we see with the RNAi screens, but not with the CRISPR screens.

Conclusion

RNAi and CRISPR screens can both pull out genes found to be essential in the gnomAD dataset.

However, the top genes from RNAi screening tend to be a bit more essential in real-life experiments (i.e., the humans from the gnomAD dataset).

Furthermore, the trend for gnomAD essential gene counts through the ranked datasets makes more sense for RNAi screens than for CRISPR screens.

CRISPR may be a newer technology, but that does not necessarily make it better than RNAi.

Both have their advantages and disadvantages, which we will discuss more in future blog posts.

It should also be noted that two of the main disadvantages of RNAi screening (seed-based off-target effects, and variability in silencing between different siRNAs) have been addressed by siPOOLs.

In an upcoming blog post, we will take a closer look at genes that gave different results in the DepMap RNAi and CRISPR screens.

In the last post, we saw that chemically modified ON-TARGETplus siRNAs still have a strong seed effect.

The seed-based off-target effects (measured by correlation of reagents with the same 7mer seed) were as strong for chemically modified ON-TARGETplus (R = 0.50) and Silencer Select (R = 0.59) as what we typically see with unmodified siRNAs (Qiagen, siGENOME, or Silencer).

Chemical modification must not prevent seed-based target recognition, because RISC uses the seed to scan the transcriptome for target sites. Because of how RISC presents the guide strand seed region for target scanning, the binding energy for finding an on-target site (19-base complementarity) versus an off-target site (6/7-base complementarity) is nearly the same. It’s not like a microarray oligo, where more extensive complementarity leads to stronger binding. The seed is driving this site recognition, so any modification that eliminates its binding will make the siRNA ineffective.

The chemical modifications added by Ambion and Dharmacon do not prevent seed binding, but instead change the efficiency of different bases at certain positions, in effect changing the seed profile of off-target sites.

The following heatmap shows the cell viability scores from 9 genome-wide siRNA screens. The average viability score for all siRNAs with a specific base at a specific position was calculated (shown are guide positions 1-9). If the value is red, it means siRNAs with the base at that position tend to be more lethal.

The first 4 columns are from screens using chemically modified, Silencer Select siRNAs (S+). The next 2 columns are from screens using unmodified, Silencer siRNAs (S). And the last 3 are from screens using unmodified, Qiagen siRNAs (Q).

We see that for some bases (e.g. 2C, top row), siRNAs tend to be non-toxic regardless of whether or not they are chemically modified (S+, S, and Q all show deep blue).

But there are other positions where the chemically modified siRNAs are very different from the unmodified siRNAs.

For example, the bottom row shows that 6G tends to be very toxic in unmodified siRNAs, but is not toxic in Silencer Select (chemically modified) siRNAs. On the other hand, 6U (towards the middle row) looks to be toxic for Silencer Select siRNAs but have the opposite effect for unmodified siRNAs.

Whatever the chemical modification for Silencer Select is (has not been made public), it appears to make seed off-targets stronger when position 6 is a U, and weaker when position 6 is a G.

If we compare the effect on cell viability of Silencer Select vs ON-TARGETplus siRNAs from the Tan and Martin screen (subject of last post), we also see strong differences in the effect of having a U or a G at position 6.

The following plot shows the toxicity rank of seed bases in Silencer Select siRNAs vs ON-TARGETplus siRNAs. Bases towards the origin (e.g., 2C) tend to make siRNAs non-toxic for both types, whereas bases towards the top right (e.g., 2G) tend make make siRNAs toxic for both types. Bases that fall off the diagonal tend to be toxic for one type and non-toxic for the other.

We see that 6U is toxic for Silencer Select siRNAs (as also seen in the heat map) and ON-TARGETplus siRNAs, like the unmodified siRNAs from the heat map, tend to be non-toxic. And the effect is similar to the heat map for 6G: toxic for Silencer Select and non-toxic for ON-TARGETplus (and unmodified in heat map).

Conclusion

Chemical modification does not get rid of seed effects, as evidenced by the strong phenotypic correlation of modified siRNAs with the same seed sequence. Rather, modifications tend to change the effectiveness for specific bases in eliciting seed-based silencing.

One suggestion would be to design a chemically modified siRNA library that avoids bases that tend to be toxic (e.g., 6U for Silencer Select).

However, there are a few problems:

• The heat maps and scatterplot only show tendencies. There is still variation within those positions. While 2G tends to be non-toxic for Silencer Select, there are still lots of toxic siRNAs with that sequence.
• Bases that reduce toxicity may be doing so because they tend to reduce target recognition. For example, 2C is also associated with poorer on-target silencing. Using only 2C for siRNAs could thus result in a library that is not as efficient at on-target silencing.
• Finally, these suppliers have already produced their siRNA libraries. i.e., those bases have already been used.

The only reliable way to both reduce the off-target effect (via dilution of seeds) and maintain robust on-target silencing is by using siRNA pools (siPOOLs).