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Clearly compensating

Clearly compensating

Genetic compensation by transcriptional adaptation is a process whereby knocking out a gene (e.g by CRISPR or TALEN) results in the deregulation of genes that make up for the loss of gene function.

A 2015 study by Rossi et al. (discussed previously) alerted researchers that CRISPR/TALEN knock-out experiments may be subject to such effects.

Genetic adaption or compensation had been well known to mouse researchers creating knock-out lines.  In fact, one of our company founders also ran into this when trying to confirm an RNAi phenotype in a knock-out mouse line.  The knock-out mice, though not completely healthy, did not confirm the RNAi phenotype.

A paper published a couple years before the Rossi paper also showed clearly that knock-outs can create off-target effects via transcriptional adaptation.

Hall et al. showed with an siRNA screen that the centrosomal protein Azi1 was required for ciliogenesis in mouse fibroblasts, confirming previous work in zebrafish and fly.

Their Azi1 siRNA targeted the 3′ UTR, and they were able to rescue the phenotype with a plasmid expressing just the CDS (bar at far right), confirming that their phenotype was due to on-target knockdown:

However, knock-out mouse embryonic fibroblast cells (created by gene trapping) did not show any differences in in the number of cilia, centrosomes, or centrioles compared to wildtype (+/+ is wild type, Gt/Gt is the homozygous knock-out):

The one phenotypic difference they observed was that male knock-out mice were infertile, due to defective formation of sperm flagella.  Female mice had normal fertility.  Both were compensating, but only one showed a visible phenotype.

The authors note the benefits of RNAi in comparison to knock-out screening:

Discrepancies between the phenotypic severity observed with siRNA knock-down versus genetic deletion has previously been attributed to the acute nature of knock-down, allowing less time for compensation to occur

The excitement surrounding CRISPR should not diminish the continued value of RNAi screening.

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Orthogonal design in software and RNAi screening

Orthogonal design in software and RNAi screening

The software engineering classic The Pragmatic Progammer popularised the benefits of orthogonality in software design.  They introduce the concept by describing a decidedly non-orthogonal system:

You’re on a helicopter tour of the Grand Canyon when the pilot, who made the obvious mistake of eating fish for lunch , suddenly groans and faints. Fortunately, he left you hovering 100 feet above the ground. You rationalize that the collective pitch lever [2] controls overall lift, so lowering it slightly will start a gentle descent to the ground. However, when you try it, you discover that life isn’t that simple. The helicopter’s nose drops , and you start to spiral down to the left. Suddenly you discover that you’re flying a system where every control input has secondary effects. Lower the left-hand lever and you need to add compensating backward movement to the right-hand stick and push the right pedal. But then each of these changes affects all of the other controls again. Suddenly you’re juggling an unbelievably complex system, where every change impacts all the other inputs. Your workload is phenomenal: your hands and feet are constantly moving, trying to balance all the interacting forces.

[2] Helicopters have four basic controls. The cyclic is the stick you hold in your right hand. Move it, and the helicopter moves in the corresponding direction. Your left hand holds the collective pitch lever. Pull up on this and you increase the pitch on all the blades, generating lift. At the end of the pitch lever is the throttle . Finally you have two foot pedals, which vary the amount of tail rotor thrust and so help turn the helicopter.

As the authors explain:

The basic idea of orthogonality is that things that are not related conceptually should not be related in the system. Parts of the architecture that really have nothing to do with the other, such as the database and the UI [user interface], should not need to be changed together. A change to one should not cause a change to the other.

This applies to many types of design, not just for computer systems.  The plumber should not have to depend on the electrician to fix a broken pipe.

The principle has also been used in RNAi screening, notably by Perreira et al. who introduce the MORR (Multiple Orthologous RNAi Reagent) method to increase confidence in screening hits.  Comparing the results of siRNAs from different manufacturers  is important, but because they operate by the same mechanism (including the off-target effect), they are not really orthologous.  More orthologous would be the comparison between RNAi and CRISPR experiments, which sometimes show discrepancies that point to interesting biology.

To confirm RNAi screening hits, ‘partial orthogonality’ may be preferable.  If screening hits are due to either on-target or off-target effects, confirmation with RNAi reagents that only have one or the other would be better than using CRISPR, where it is difficult to interpret the reason for discrepancies (e.g. is there no phenotype  because of genetic compensation?).

One could use C911s to create a version of the siRNA that, in theory, maintains off-target effects but eliminates on-target effects.  We have observed, however, that C911s often give substantial knockdown of the original target gene (in some ways, C911s are like very good microRNAs).  To be sure that a positive effect with C911s is not due to partial knockdown, one would also need to test that via qPCR.  C911s can create a lot of work.

Far better would be to confirm screening results with siPOOLs, which provide robust knockdown and minimal off-target effects.

One place RNAi practitioners would hope not to find orthogonality is the relationship between on-target knockdown and phenotypic strength.

Since the early days of RNAi, positive correlation between knockdown and phenotypic strength has been suggested as a means to confirms screening results.  Reagents with a better knockdown should give a stronger phenotype.

To test this, we obtained qPCR data for over 2000 siRNAs (Neumann et al.) and checked the performance of those siRNAs against the designated hit genes from an endocytosis screen (Collinet et al.).

If the siRNAs work as expected, those siRNAs with better knockdown should give stronger phenotypes than those with weaker knockdown.

There were 100 genes from the Collinet hits for which there were 3 siRNAs with qPCR data.

For those 100 siRNAs triplets, we compared the phenotypic ranks with the knockdown ranks.  (We were agnostic about the direction of phenotypic strength, and checked whether knockdown and phenotype were consistent when phenotype scores were ranked in either ascending or descending order).  For example, if siRNAs A, B, and C have phenotypic scores of 100, 90, 70 and knockdown of 15%, 20%, 30% remaining mRNA, we would say that phenotypic strength is consistent with knockdown (and because we were agnostic about phenotypic direction, we would also say it was consistent if siRNAs A, B, and C had scores of 70, 90, 100).

The observed number of cases where knockdown rank was consistent with phenotypic rank was then compared to an empirical null distribution, obtained by first randomising the knockdown data for the siRNA triplets before comparison to phenotypic strength.  This randomisation was performed 300 times.  This provides an estimate of what level of agreement between knockdown and phenotype would be expected by chance.  The standard deviation (SD) from this null distribution was then used to convert the difference between observed and expected counts into SD units.

The Collinet dataset provides data for 40 different features.  The above procedure was carried out for each of the 40 features.

To take one feature (Number vesicles EGF) as an example, we observed 34 cases where knockdown was consistent with phenotypic strength.  By chance, we would expect 33.4 (with a standard deviation of 4.9).  The difference in SD units is (34-33.4)/4.9 = 0.1.

As can be seen in the following box plot, the number of SD units between observed and expected counts of knockdown/phenotype agreement for the 40 features is centered near zero (median is 0.1 SD units):

This suggests that there is very little, if any, enrichment in cases where siRNA knockdown strength is correlated with phenotypic strength.  The orthogonality between knockdown and phenotype, given the poor correlation between siRNAs with the same on-target gene, is unfortunately not unexpected.

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“Phenoville” – RNAi & CRISPR Screening Strategies

“Phenoville” – RNAi & CRISPR Screening Strategies

Pleasantville is a movie based on an interesting idea: two teenagers are magically transported through their TV to a town called Pleasantville set in the 1950s where everything is perfect (and also black-and-white).  As they discover the complex, imperfect emotions hidden below the idyllic surface, the black-and-white characters and objects start to gain colour.

In loss-of-function genetic screening, some reagents and screening formats may also give rise to a narrow, black-and-white view of a biological process.  A sort of “Phenoville”.  This was illustrated nicely in a recent review of screening strategies for human-virus interactions by Perreira et al. (2016).

The authors performed screens for human rhinovirus (HRV) infection using arrayed RNAi reagents (siRNAs) and pooled CRISPR reagents (sgRNAs), and then compared the resulting hit lists.

The arrayed RNAi screen produced over 160 high-confidence candidate genes, whereas the CRISPR screen only found 2.  The authors comment:

“The comparison of these two screening approaches side-by-side, using the same cells and virus, raises an interesting point. The number of host factors found for HRV14 was far greater using the MORR/RIGER approach [i.e. RNAi performed with multiple orthologous RNAi reagents and analysed by RNAi gene enrichment ranking method] and is approaching a systems level understanding based on bioinformatic analyses and the near saturation of, or enrichment for, multiple complexes and pathways (Fig. 4) (Perreira et al., 2015). By comparison our matched pooled CRISPR/Cas9 screen for HRV-HFs yielded two high-confidence candidates based on reagent redundancy, ICAM1, the known receptor for HRV14, and EXOC4, a gene involved in exocyst targeting and vesicular transport (He & Guo, 2009). Given the known role of ICAM1 as the host receptor for most HRVs, these results point to entry as the major viral lifecycle stage interrogated by a pooled functional genomic screening approach using a population of randomly biallelic null cells infected by a cytopathic virus.”

In simple terms, RNAi screening produced a richer data set that revealed system level interactions whereas CRISPR screening yielded a small number of specific hits that only affected an early-stage pathway. The ‘systems level understanding’ is nicely shown in the following diagram of the RNAi hits.  The red box at the top left is the only gene (ICAM1) that was common to the RNAi and CRISPR screens.

Perreira et al. conclude that arrayed siRNA screens permit the detection of a larger number of viral dependency factors, albeit with a significant tradeoff in a greater number of false positive hits (mainly due to off-target effects).  In contrast, pooled screens with CRISPR sgRNAs using cell survival as a readout, as also seen with most haploid cell screens, display limited sensitivity but excellent specificity in finding host genes that act early on in viral replication (e.g. ICAM1).

In Perreira et al.‘s words:

“… given the currently available functional genomic strategies if the goal is to find viral entry factors (e.g., host receptors) with high specificity its best to use a pooled survival screen, but alternatively if the aim is to obtain with relative ease a more comprehensive set of host factors, albeit with more prevalent false positives, than an arrayed siRNA screen would be the preferred method.”

Summarizing two options for genetic screeners:

  1. Arrayed RNAi screens
    • provide a richer view of the underlying biology
    • produce more false positives from OTEs
    • produce false negatives from OTEs
  2. Pooled CRISPR screens
    • provide a narrower view of the underlying biology
    • produce fewer false positives
    • produce false negatives because of genetic compensation

Off-target effects (OTEs) are the primary cause of false positives, and the resultant higher assay noise also increases the number of false negatives in arrayed RNAi screens. Reagents like siPOOLs minimize the risk of off-target effects and reduce assay noise.

One key factor not mentioned by Perreira et al. is the presence of genetic compensation in gene knockout approaches.

Putting genetic compensation in terms of human actors, imagine that you are investigating the function of bus drivers in Pleasantville.  To induce loss-of-function, assume that aliens will be abducting the bus drivers.  If the bus drivers are abducted in their sleep (equivalent to a CRISPR knock-out), you may not get a good idea of their function when you film the next day.  People may be compensating by driving, biking or staying home.  Alternatively, the bus company may have found emergency replacement drivers.

Now suppose the bus drivers are abducted in the middle of the day while driving their routes (equivalent to an RNAi knock-down).  The film will show buses crashing (hopefully without any serious injuries, since this is just a TV show!) and the public transportation system will suddenly come to a halt.

RNAi gene knockdown screens with siPOOLs  can provide a significant advantage over CRISPR gene knockout screens in obtaining a system level understanding in biological models.

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CRISPR/Cas9 Screening – The “Copy-Number Effect”

CRISPR/Cas9 Screening – The “Copy-Number Effect”

Several CRISPR/Cas9 screens identifying essential genes in cancer cell lines have been performed to date (Shalem et al., 2014, Hart et al., 2015, Kiessling et al., 2016). These typically take the form of pooled screens where sgRNA libraries targeting all genes or subsets of genes are introduced in parallel into Cas9-expressing cells, at a single sgRNA per cell. The sgRNAs exert a negative or positive selection pressure on cells based on their impact on cell viability and proliferation. The most depleted or enriched sgRNA sequences are determined by next-generation sequencing, revealing relevant gene ‘hits’. Very similar to how pooled shRNA screens are performed.

From these screens, several groups have observed a worrying phenomenon: CRISPR gRNAs targeting genomic regions of high copy number amplification showed a striking reduction in cell proliferation/survival. Dr William Hahn’s group at the Dana Farber Institute was one of the first to characterize this in a publication last year involving a CRISPR/Cas9 screen on 33 cancer cell lines looking for essential genes. In total, 123411 unique sgRNAs were used targeting 19050 genes (6 sgRNAs/gene), 1864 miRNAs and 1000 non-targeting negative control sgRNAs.

What they discovered is a little worrying to say the least.

The figure shows two genomic regions in two different cell lines (SU86.86 and HT29). At genomic coordinates highlighted by the red box, 3 tracks are shown. Top, copy number from the Cancer Cell Line Encyclopaedia (CCLE) SNP arrays, red indicating above average ploidy and blue showing below; middle, CRISPR/Cas9 guide scores with purple trend line indicating the mean CRISPR guide score for each CN segment defined from the above track; bottom, RNAi gene-dependency scores. AKT2 and MYC, known driver oncogenes at these loci, respectively, are highlighted in orange. For RNAi data, shRNAs targeting AKT2 used in Project Achilles were not effective in suppressing AKT2 (hence the negative result).


Key findings:

  • A striking enrichment of negative CRISPR guide scores (i.e. sgRNAs that reduced cell proliferation/survival) for genes that reside in genomic regions of high copy-number amplification.


  • Genes identified in CRISPR that reduced survival, did not have the same effect when disrupted by RNAi in the same cell lines (this RNAi screen was done by the same group but published 2 years before).


  • This enrichment was seen also for unexpressed genes, i.e. genes not transcribed. Meaning the reduced survival was not due to loss-of-function of the targeted gene.


  • Even for regions with low absolute copy numbers, a significant reduction in survival was observed compared to non-targeting control sgRNAs. Furthermore, the effect was dose-dependent with greater copy number amplifications producing larger negative CRISPR guide scores.

Notably, the correlation between copy number and genes that were scored high on essentiality was also observed when looking at data from other studies (Hart et al., 2015). The “copy number effect” would therefore produce a high number of false positives in CRISPR screens for essential genes in cancer cell lines. The graph above shows just how big an effect this is. Comparing genes identified as essential in a CRISPR screen vs RNAi screen, increasingly essential CRISPR-identified genes were more likely to reside on copy number amplifications (defined as having average sample ploidy > 2). This effect was notably absent for RNAi-derived essential genes.

Aside from false positives, the increased noise due to “copy number effects” also increases false negatives. MET, a gene identified by shRNA screens, for example, failed to be picked out by CRISPR screens as it is located on a chromosome 7 amplicon (7q31) in MKN45 cells (gastric cancer cell line) where all other gRNAs within that amplicon also scored as essential.

The authors go on to explore mechanisms behind the “copy number effect”. They found it was attributed to a DNA damage response stimulated by excessive cutting by Cas9. This response appeared p53-dependent and induced cell cycle arrest at the G2 phase, explaining the anti-proliferative effect. A similar response was seen for promiscuous sgRNAs that cut at multiple sites, with effects being more pronounced when cuts were spread over several chromosomes as opposed to a single chromosome.

How to manage this?

So far, most simply avoid analysing hits where sgRNAs lie at amplified regions or target multiple sites (Wang et al., 2017). However, these regions of copy number amplifications have been implicated in cancer and may contain relevant hits. Several computational methods have therefore recently been developed to correct for “the copy number effect”. Hahn’s group developed a computational algorithm called CERES based on data obtained from CRISPR sgRNA screens in 342 cancer cell lines representing 27 cell lineages.

Novartis also developed a Local Drop Out (LDO) algorithm that corrects obtained data based on examining gRNAs scores at direct genomic neighbours. When multiple neighbouring genes show similar drop out scores, effects are assumed to be due to “copy number effects”. This method has the advantage of not requiring prior knowledge of copy number, however it does require a sufficient density of gRNAs to accurately capture “copy number effects”.  They also had an alternative method, Generalized Additive Model (GAM) where copy number was taken into account.


How the CERES Model Works

The Results – copy number dependency is reduced while preserving essentiality of cancer-specific genes such as KRAS


A step towards the right direction but the penetrance of this effect still raises some concerns:

  • Although false positives are reduced with these computational methods, it is difficult to recapture false negatives. This is dependent on the gRNA having a stronger phenotype compared to neighbouring gRNAs on the amplicon which is not always the case. The LDO method for example still failed to recapture MET.


  • Guide scores can vary with cell line, sgRNA and experimental conditions, making it difficult to apply the same counter-measures to every experiment.


  • Given multiple cut sites trigger the same effect, how do we ensure multiple sgRNAs when introduced into a cell are not inducing a similar response? This is difficult to control in pooled screens, and poses a limitation in multiplex screens. Synthetic lethality screens for example with sgRNAs targeting multiple genes, might be subject to a higher false positive rate.


  • With even diploid genes (copy number = 2) having statistically significant growth reduction compared to haploid gene loci, the challenge still remains to delineate a true loss-of-function over a non-specific cellular response.


  • Negative sgRNA controls have to be carefully selected. From the study, non-targeting controls had little impact on viability compared to most other sgRNAs. Controls targeting non-expressed genes or non-essential loci have been recommended as better controls.


  • Lastly, although this effect seems to apply mostly to cancer cell lines that undergo a high rate of gene amplifications, similar effects may extend to polyploid tissues such as the liver.

Hence as always gene function should be determined by a variety of methods. Using RNAi for example to affirm a CRISPR-knockout phenotype would add greater confidence to a hit. To avoid those RNAi-related false positives however, its probably best to use siPOOLs.


Source of figures:

Aguirre, A. J., Meyers, R. M., Weir, B. A., Vazquez, F., Zhang, C.-Z., Ben-David, U., … Hahn, W. C. (2016). Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting. Cancer Discovery, 6(8), 914 LP-929.

Meyers, R. M., Bryan, J. G., McFarland, J. M., Weir, B. A., Sizemore, A. E., Xu, H., … Tsherniak, A. (2017). Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. bioRxiv. Retrieved from

Other relevant sources:

Munoz, D. M., Cassiani, P. J., Li, L., Billy, E., Korn, J. M., Jones, M. D., … Schlabach, M. R. (2016). CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions. Cancer Discovery, 6(8), 900 LP-913. Retrieved from

de Weck, A., Golji, J., Jones, M. D., Korn, J. M., Billy, E., McDonald, E. R., … Kauffmann, A. (2017). Correction of copy number induced false positives in CRISPR screens. bioRxiv. Retrieved from


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Unexpected Mutations after CRISPR in vivo editing – post-commentary

Unexpected Mutations after CRISPR in vivo editing – post-commentary

You might have heard or participated in the global discussion over the recently published Nature Commentary that described >1000 off-target mutations in CRISPR-edited mice.

The paper reported a small study involving three mice but gained enough virality online to trigger a significant drop in share prices of companies founded based on CRISPR gene-editing – Editas Medicine, CRISPR Therapeutics and Intellia Therapeutics.

Here is a summary of the study, with respective concerns raised by the scientific community regarding the validity of the findings. These are highlighted *in blue with further explanations below:

  • FVB/NJ mice were used in the study.These mice are a highly inbred strain (F87 on Dec 2002) originating from the NIH but transferred to The Jackson Laboratory for maintenance and sale. They are homozygous for the Pde6brd1 allele, subjecting them to early onset retinal degeneration.


  • The same authors previously published a pretty decent paper where they functionally characterized a rescue of the retinal degeneration by correcting what was thought to be a nonsense mutation (Y347X, C>A) at exon7 of the Pde6β subunit. The same “rescued” mice, edited by CRISPR (F03 and F05), along with the control co-housed mouse that did not undergo editing, were used in this subsequent sequencing study. *Concern 1


  • The CRISPR mutation was performed by introducing the sgRNA via a pX335 plasmid (which would co-express Cas9D10A nickase) into FVB/NJ zygotes, alongside a single-stranded oligo which acts as a donor to introduce a controlled mutation at the Pde6b. WT Cas9 protein was also introduced. *Concern 2


  • DNA was isolated from spleen of the mice and whole genome sequencing was performed with an Illumina HiSeq 2500 sequencer with a 50X coverage for CRISPR-treated mice and 30X coverage for the control mouse.


  • The authors used three different algorithms to detect variants – Mutect, Lofreq and Strelka. The number of single nucleotide variants (SNVs) and insertion deletions (indels) detected that were absent in the control mouse are shown below for the two CRISPR-edited mice.


Overlap of SNV/indels detected in two CRISPR-edited mice – F03 mouse (blue), F05 mouse (green).


  • Each of the variants were filtered against the FVB/NJ genome in the mouse dbSNP database (v138) and also against 36 other mouse strains from the Mouse Genome Project (v3). As none of the variants detected were found in these database genomes, the authors concluded they had to arise through CRISPR-editing. *Concern 3


  • Interestingly, the top 50 predicted off-target sites showed no mutations. And in sites where mutations were detected, there was no significant sequence homology against the sgRNA used. The authors conclude in silico modelling fails to predict off-target sites. *Concern 4.

A number of criticisms have been raised regarding the study and the four main concerns highlighted are explained below:

Concern 1: The study only involved three mice, hence is too underpowered to draw any statistically significant conclusions. Further, the choice of control mouse simply being a co-housed mouse (no mention of its background) may fail to capture any genetic alterations induced by the experimental procedure or by genetic drift within a colony.

More appropriate controls may have included a mouse produced with a sham-injected zygote, a mouse where only Cas9 was introduced without an sgRNA, and a mouse with only sgRNA and ssDNA donor.

Parent mice should also have been sequenced to check if variants detected were already in the existing strain.

Concern 2: Cas9 was introduced both as a protein and in a plasmid. Talk about overkill! Though the plasmid form of Cas9 is the nickase version, where 2 sgRNAs are required to produce a double-strand break, having high levels of active Cas9 floating about has been demonstrated to increase the incidence of off-target effects.

Concern 3: Even though the authors filtered the variants found against mouse genome databases, this may not be sufficient to capture the extent of genetic drift that occurs over multiple generations of in-breeding.

Gaetan Burgio wrote that from his experience, the reference genomes found in databases often fail to capture the amount of variants that are specific to every breeding facility. Often large numbers of reference mice (1oo mouse exomes from > 50 founders) have to be sequenced to determine if SNPs were specific to the mouse strain and not induced by the test condition.

Editas and George Church’s group from Harvard also highlighted the high amount of overlap in SNVs/indels between the two CRISPR-edited mice which..

“strongly suggests the vast majority of these mutations were present in the animals of origin. The odds of  the exact nucleotide changes occurring in the exact same position of the exact same gene at the exact same ratios in almost every case are effectively zero.”

Concern 4: Apart from the flaw that only one sgRNA was studied, Church’s group also claim the sgRNA studied had a high off-target profile. This sgRNA would apparently have failed their criteria for use as a therapeutic candidate. The table below shows the number of predicted off-target sites when allowing for 1-3 mismatches from the sgRNA sequence.

Predicted off-target profile of sgRNA used in study
Off-target sites with 1 mismatch 1
Off-target sites with 2 mismatches 1
Off-target sites with 3 mismatches 24


What was surprising from the study however, was that despite the high off-targeting potential, mutations were not seen at predicted off-target sites.

The consensus therefore, by both Church’s group and the authors of the study was that one cannot rely on in silico prediction alone to account for off-target effects.

Calls are now being made to validate the study using the appropriate controls, or to compare the variants obtained with other more updated mouse genome SNP databases. I expect we will not hear the last of this study.

The study however, does re-enforce our message in a previous blogpost of validating CRISPR experiments with other techniques to establish gene function. It also highlights the extensive genetic heterogeneity seen now not only between cell lines, but between mouse strains. As always we recommend not being swept up in the hype, but to remain scientifically skeptical.

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CRISPR – what can go wrong and how to deal with it

CRISPR – what can go wrong and how to deal with it

CRISPR is a gene editing technique based on tools and principles learnt from the bacterial immune system. Gaining immense popularity world-wide, many are trying to establish CRISPR in their favourite model systems to study gene function. Here, we highlight issues to be aware of when using CRISPR and what one can do to counter or manage them.

To simplify matters, we have classified what could go wrong while performing CRISPR into three main categories, accompanied by associated exclamations one may hear in the process:

  1. “Hmm… I don’t see anything.” – Absence of phenotype
  2. “This is taking wayyy too long.” – Inefficient editing
  3. “What the *@#?!” – Unexpected phenotypes

First, some key terms…

Cas9: The bacterial RNA-guided endonuclease that mediates cutting of the DNA. The most commonly used Cas9 ortholog is from Streptococcus Pyogenes and can be introduced into cells in the form of DNA, mRNA, or protein.

sgRNA: single guide RNA composed of a 17-20 base long guide RNA (gRNA) which hybridizes to its complementary DNA sequence on the genome, defining  the target site. This is often joined to a ~70-80 base long transactivating crRNA (tracrRNA), a constant region that mediates recruitment of Cas9. sgRNAs can be introduced as one unit or in its separate components – gRNA and tracRNA – as DNA or RNA.

PAM: protospacer adjacent motif, a trinucleotide sequence 3’ adjacent to the gene editing site required for Cas9 to bind and mediate cleavage. Sequence is NGG for Cas9 from Streptococcus Pyogenes though NAG is often recognized as well. PAM sequences differ between various forms of Cas enzymes.


  1. “Hmm… I don’t see anything.” – Absence of phenotype

The anti-climax of a null result may stem from adaptation where the cell or organism alters other gene pathways to compensate for the loss-of-function of the target gene.

This problem is most visible to those maintaining Drosophila stocks as strength of phenotype typically decreases over multiple generations. The phenomenon is also well-documented in other models such as yeast (Teng X et al., 2013), zebrafish (Rossi et al., 2016, covered in a previous blogpost) and mice (Babaric et al., 2007). A notable Developmental Cell paper recently reported adaptation in cells (Cerikan et al., 2016) where prolonged knock-down (KD) or knock-out (KO) yielded no visible phenotype as opposed to acute KD by RNAi.

Multiple cell passages increase genetic drift, providing opportunities for the system to adapt to counter the disruptive effects of a gene knock-out. It is therefore prudent to preserve early passages of clones during clonal selection and limit multiple passages prior to assay measurement.

Besides adaptation, redundancy may also account for an absence of phenotype. Paralogous genes (i.e. genes closely related in structure or function) often exist in model systems that can fully or partially compensate for the loss-of-function of the target gene. About 50% of mouse genes and at least 17% of human genes have paralogues that may mask loss-of-function phenotypes.

One can find paralogous genes arising from gene duplication with this database and by checking existing literature. If they do exist, a co-knock-out/knock-down approach may be necessary.


  1. “This is taking wayyy too long.” – Inefficient editing

Despite the high efficiency of Cas9-mediated cleavage, obtaining the desired gene knock-out can still be a tedious and time-consuming process, with wide-ranging overall efficiencies of 1-79% (Unniyampurath et al., 2016).

These challenges often stem from issues associated with the cell line of choice. Due to many standard cell lines being polyploid (containing multiple copies of chromosomes), every copy of the gene has to be disrupted to ensure a complete knock-out. A process aggravated by the need for a homozygous knock-out. Transfection efficiencies, how well the cell line tolerates clonal selection and the impact of the gene modification on cell viability can also impact outcomes. If performing homology directed repair (HDR) to introduce a new sequence at the cut site, clone screening efforts have to be amplified due to the lower frequency of HDR events compared to indels.

Understanding the characteristics of your cell line and ensuring sufficient numbers of clones are screened is essential to avoid mindless weeks repeating experiments!

Editing efficiency may also be hindered by genomic accessibility. gRNAs targeting transcriptional start sites or promoters were found to be more efficient than intergenic sites due to the open chromatin structure in these areas (Liu X et al., 2016). Numerous design criteria have been recommended to ensure high cutting efficiency but performance of gRNAs may still vary. Therefore it is advisable to use at least 3 sgRNAs per gene to increase chances of success.

Sidenote: Looking for someone who can design CRISPR sgRNAs for you? siTOOLs Biotech’s CRISPR sgRNA design service couples our long-standing experience in off-target filtering with published gRNA design criterion to generate reliable gRNA sequences. Send us your enquiry and we will get back to you.


  1. “What the *@#?!” – Unexpected phenotypes

Unexpected results can stem from off-target effects or in some cases, may be a real effect that requires some brain rattling to make sense of.

Off-target effects are still a cause of concern for CRISPR and vary widely with different gRNA sequences ranging from 0 to up to 150 in one report (Tsai et al., 2015). In another study, ~10 to > 1000 off-target binding sites were found that varied with sgRNA sequence (Kuscu et al., 2014).

Toxicity correlated with increased off-targeting (Morgens et al., 2017) and the use of safe-targeting controls (i.e. where gRNAs are directed towards sites where cleavage is predicted to have minimal impact) was recommended. This served as a more appropriate measure of nuclease-induced toxicity as opposed to non-targeting controls that might not lead to cleavage.

Some other strategies to minimize off-targets:

  • Use the Cas9 recombinant protein/mRNA rather than a plasmid or keep DNA transfection amounts low (plasmid-driven prolonged Cas9 expression increased off-targeting events as reported by Liang et al., 2015)
  • Use truncated gRNAs of 17-18 nucleotides
  • Use D10A Cas9 nickase and paired gRNAs
  • Use a Cas9 ortholog with a longer PAM requirement

Despite our efforts to predict off-target effects, two reported sources of potential off-targets make prediction challenging:

a) Single nucleotide variants from clonal heterogeneity

b) Cas9 effects on mRNA translation


a) Single nucleotide variants from clonal heterogeneity

Table 1: Spontaneous SNVs and indels generated over clonal selection in human pluripotent stem cells.

Two studies (Smith et al., 2014Veres et al., 2014) carried out in pluripotent stem cells to detect off-targets saw a higher specificity of Cas9 in these cells compared to cancer cell lines but shockingly, rather large clonal heterogeneity (Table 1).  Each clone generated from the parental cell line had on average 100 unique SNVs per clone and 2-5 indels not induced by the gene modification but arising spontaneously during cell culture.

Target and off-target indel frequencies
Number of mismatches Number of genomic sites Cas9 targeting efficiency
0 1 53.9%
1 0
2 0 → 1 36.7%
3 32 ~0.15% per site

Table 2: Editing efficiencies at off-target sites with 0-3 mismatches. Condition of SNV enhancing editing efficiency shown in bold.

Yang et al., 2014 then goes on to demonstrate how an SNV at the wrong place at the wrong time can produce a high-efficiency off-target site. The said SNV corrected a mismatch at an off-target site, reducing mismatch number from 3 to 2, which increased Cas9 –mediated indel frequency to ~37%!

To manage clonal heterogeneity, we recommend performing deep sequencing to fully characterize the knock-out clone and its parental wild-type cell line. Once the locations of SNVs are identified, these can be aligned with potential off-target gRNA binding sites to check for interference. Check locations of identified unique SNVs or indels to see if they are impacting genes that may play a relevant role in your studied phenotype.

b) Cas9 effects on mRNA translation

A Scientific Reports study (Liu Y et al., 2016) reported a worrying finding that Cas9 could be recruited by gRNAs to mRNAs and block their translation. Neither PAM sequences nor Cas9 enzyme activity was required for this and the effect varied with gRNA sequence. Cas9-mediated mRNA translation suppression produced a 30-60% decrease in protein levels, sufficient to impact downstream phenotypes. For example, a gRNA targeting VEGFA with an off-target binding site to the mRNA of oncogene, B3GNT8, produced a nearly 50% drop in B3GNT8 protein levels with a corresponding drop in cell viability. This was partially rescued by overexpressing B3GNT8 with a vector.

It is still unclear to what extent this phenomenon occurs. There have been limited reports on this mechanism so far, but if true, would have a far-ranging impact. The study found gRNAs with single base mismatches at position 8-20 were still able to carry out Cas9-mediated translation repression. This low hybridization stringency requirement would make off-targets impossible to predict.

CRISPR is no doubt a powerful technology, but it still brings many unknowns. After its discovery in the 1990s, RNAi experienced a similar exponential uptake and use by the scientific community. It took several years for the problem of siRNA off-targets to become visible. Unfortunately by that time, enormous resources and energy had been sunk into large RNAi screens, which yielded numerous false hits and difficult-to-interpret data.

Figure 1. Pubmed Citations (1999-2015) with CRISPR or RNAi in Title/Abstract/Summary

Thankfully we now have  siPOOLs, or high-complexity defined siRNA pools (from siTOOLs Biotech). These custom-designed pools of 30 unique siRNAs counter the off-target effects often seen with single siRNAs or low complexity siRNA pools of 3-4 siRNAs (Marine et al., 2012, Hannus et al., 2014). Efficient at 1 nM in standard cell lines, it is the optimal RNAi reagent for highly specific, efficient and robust gene knock-down.

In order not to repeat past mistakes, it is imperative to proceed with caution and use multiple methods to establish gene function.

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Liu, Y. et al. Targeting cellular mRNAs translation by CRISPR-Cas9. Nat. Publ. Gr. 2–10 (2016). doi:10.1038/srep29652

Marine, S., Bahl, A., Ferrer, M. & Buehler, E. Common seed analysis to identify off-target effects in siRNA screens. J. Biomol. Screen. 17, 370–8 (2012).

Rossi, A. et al. Genetic compensation induced by deleterious mutations but not gene knockdowns. Nature 524, 230–233 (2015).

Smith, C. et al. Whole-Genome Sequencing Analysis Reveals High Specificity of CRISPR/Cas9 and TALEN-Based Genome Editing in Human iPSCs. doi:10.1016/j.stem.2014.06.011

Teng, X. et al. Genome-wide Consequences of Deleting Any Single Gene. Mol. Cell 52, 485–494 (2017).

Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat Biotech 33, 187–197 (2015).

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Veres, A. et al. Low incidence of Off-target mutations in individual CRISPR-Cas9 and TALEN targeted human stem cell clones detected by whole-genome sequencing. Cell Stem Cell 15, 27–30 (2014).

Yang, L. et al. Targeted and genome-wide sequencing reveal single nucleotide variations impacting specificity of Cas9 in human stem cells. Nat. Commun. 5, 1–6 (2014).

Further helpful reading:

Housden, B. E. et al. Loss-of-function genetic tools for animal models: cross-species and cross-platform differences. Nat. Publ. Gr. (2016). doi:10.1038/nrg.2016.118


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