Genome Editing Comes Of Age
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Recent breakthroughs in genome editing technology have led to a rapid adoption that parallels that seen with RNAi. And like RNAi, these methods are taking the scientific world by storm, with high profile publications in fields as diverse as HIV treatment, stem cell therapy, food crop modification and drug development to name but a few. Critically, the endogenous modification of genes enables the study of their function in a physiological context. It also overcomes some of the artefacts that can result from established techniques such as transgenesis and RNAi, which have mislead researchers with false positives or negatives. Until recently however genome editing required considerable technical expertise, and consequently was a relatively niche pursuit. In this talk we will look at how the latest developments in genome editing tools have changed this, with improvements in both ease-of-use and targeting efficiency, as well as a concomitant reduction in costs opening up these approaches to the wider scientific community. Rapid adoption of the CRISPR/Cas9 system has for example led to a long list of organisms and tissues in which genetic changes have been made with high efficiency. Other technologies such as recombinant adeno-associated virus (rAAV) offer further precision, stimulating the cell’s high-fidelity DNA repair pathways to insert exogenous sequence with unrivalled specificity. Targeting efficiency can be improved still further by using the technologies in combination – genome cutting induced by CRISPR can significantly enhance homologous recombination mediated by rAAV. Despite these rapid advances, some pitfalls remain, and so we’ll discuss some of the key considerations for avoiding these, ranging from simply picking the right tool for the job to designing an experiment that maximises chances of success. Finally we’ll look at how genome editing is being applied to both basic and translational research, and in both a gene-specific and genome wide manner. For the study of disease associated genes and mutations scientists can now complement wide panels of tumour cells with genetically defined isogenic cell pairs identical in all but precise modifications in their gene of interest. The ease-of-design and efficiency of the CRISPR system is also being exploited for genome wide synthetic lethality screens, facilitating rapid drug target identification with significantly reduced risk of false negatives and off-target false positives. And again, further synergies are achieved when these approaches are combined to look for potential synthetic lethal targets in specific genomic contexts.
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Genome Editing Comes Of Age
- 1. HORIZON DISCOVERY Genome Editing Comes of Age CRISPR, rAAV and the new landscape of molecular cell biology Chris Thorne | Gene Editing Specialist
- 2. 2 Horizon Discovery Powering Genomic Research and Translational Medicine, …from Sequence to Treatment
- 4. 4 INDUSTRY ACADEMIA Our Customers: Over 1,000 customers in more than 50 countries
- 5. 5 Overview • The case for Genome Editing • Genome Editing Tools • rAAV • CRISPR/Cas9 • Key Considerations for Gene Editing • Genome Editing at scale • High through Knock-out Cell Line Generation • Genome Wide sgRNA Synthetic Lethality Screening 5
- 6. 6 The Opportunity: The challenge has shifted from obtaining genomic information to understanding what it means
- 7. 7 Gene function analysis - Patient-derived cell lines Human cell lines contain pre-existing mutations are derived directly from human tumors Immense genetic diversity However Lack of wild type controls Availability of rare mutation models Cell line diversity makes it very hard link observations to specific genetics (Domke et al Nat. Comms 2013)
- 8. 8 Gene function analysis - RNAi Problems with RNAi can result in false positives or negatives Loss of function analysis using RNAi is inexpensive and widely applicable Incomplete knockdown However Lack of reproducibility Off-target effects Brass et al. Science 273 genes Total overlap only 3 genes Shalem et al Science 2014 HIV Host Factors
- 9. 9 Gene function analysis - Overexpression Overexpression of oncogenes can over represent their role in disease biology Gain of function analysis using overexpression is inexpensive and widely applicable Result may be artefact of overexpression However Difficult to achieve long- term overexpression • Large growth induction phenotype • Transforming alone • Milder growth induction phenotype • Non-transforming alone
- 10. 10 Gene function analysis – Gene Editing Gene editing represents the most biologically relevant means to explore gene function in cells Genetically defined mutant cell line Matched isogenic wild type control Complete loss of function possible Endogenous gain of function possible
- 11. 11 Genome Editing: The Right Tool For The Right Outcome Horizon is ‘technology agnostic’, using the right technology to generate virtually any genomic modification in a cell
- 12. 12 The Right Tool For The Right Outcome rAAV • High precision / low thru-put • Any locus, wide cell tropism • Well validated, KI focus • Exclusive to HD Zinc Fingers • Med precision / med thru-put • Good genome coverage • Well validated / KO Focus • Licensed from Sigma CRISPR • New but high potential • Capable of multi-gene targeting • Simple RNA-directed cleavage • Combinable with AAV • Extensive IP position Great for knock-outsGreat for heterozygous knock-ins
- 13. 13 rAAV: How Does It Work? Nature Genetics 18, 325 - 330 (1998) AAV = Adeno Associated Virus (ssDNA)
- 14. 14 Crispr (cr) RNA + trans-activating (tra) crRNA combined = single guide (sg) RNA CRISPR/Cas9: How Does It Work?
- 15. 15 AGCTGGGATCAACTATAGCG CGG gRNA target sequence PAM CRISPR/Cas9: How Does It Work?
- 16. 16 Cas9 Wild type Cas9 Nickase (Cas9n) Induces double strand break Only “nicks” a single strand Only requires single gRNA Requires two guide RNAs for reasonable activity Concerns about off-target specificity Reduced likelihood of off-target events High efficiency of cleavage Especially good for random indels (= KO) Guide efficiency dictated by efficiency of the weakest gRNA CRISPR/Cas9: How Does It Work? Nishimasu et al Cell
- 17. 17 CRISPR/Cas9: What can you do? Hsu et al. Cell. 2014
- 18. 18 Genome Editing: As Simple As… ... HOWEVER … Cell Line Screen for clones Engineered cells! Genome Editing Vector
- 19. 19 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation
- 20. 20 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Normal human karyotype HeLa cell karyotype Gene copy number Effect of modification on growth
- 21. 21 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Transfection/electroporation Single-cell dilution
- 22. 22 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Sequence source Off-target potential Guide proximity Wild-type Cas9 or mutant nickase
- 23. 23 Ran et al Cell 2014
- 24. 24 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Number of gRNAs gRNA activity measurement NT Cas9 wt only 4uncut 1 52 3 gRNA 200 300 400 500 100 600 +ve 700 200 300 400 500 100 600 700
- 25. 25 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Donor sequence modifications Modification effects on expression or splicing Size and type of donor (AAV, oligo, plasmid) Selection based strategies Cas9 Cut Site Genomic Sequence Donor Sequence containing mutation
- 26. 26 Technology Development at Horizon: Systematic improvements Donor lengths: sODNs ranging from 50-200nt, with single phosthothioate modifications at both outer nucleotides 100nt ssODN is optimal 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 0 .0 0 .5 1 .0 1 .5 H R e ffic ie n c y u s in g s s O D N s o f d iffe r e n t le n g th s O lig o le n g th (N T ) Efficiency(%) 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 0 5 1 0 1 5 T ra n s fe c tio n e ffic ie n c y u s in g 1 0 p m o l s s O D N O ligo length (N T ) Transfection%(RFP) Size Oligo Sequence 50 C*ACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCC*C 70 T*CCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTG*C 90 T*GATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACC*A 110 A*CAGTTATGTTGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAG*C 130 T*TTTTGCTCTACAGTTATGTTGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCC*G 150 G*TATCTGGTATTTTTGCTCTACAGTTATGTTGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCCCTGAGCA*A 170 T*AAGCCTGCAGTATCTGGTATTTTTGCTCTACAGTTATGTTGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCCCTGAGCAAAGACCCCAA*C 200 A*AATGTCTTTATAAATAAGCCTGCAGTATCTGGTATTTTTGCTCTACAGTTATGTTGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCCCTGAGCAAAGACCCCAACGAGAAGCGCGATCA*C GFP Mutation, PAM mutation
- 27. 27 Technology Development at Horizon: Systematic improvements Donor modifications: number and position of phosphothioate medications Only 3’ PTO modifications required for ssODNs tested Oligo Sequence None TGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCA 5' PTO T*GATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCA 3' PTO TGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACC*A 5+3 PTO T*GATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACC*A Mut Flank TGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACT*A*C*C*AGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCA Mut Flank + 5+3 PTO T*GATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACT*A*C*C*AGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACC*A 3x5' PTO T*G*A*TGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCA 3x3' PTO TGATGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAA*C*C*A 3x5'+3' PTO T*G*A*TGGTTCTTCCATCTTCCCACAGCTGGCCGACCACTACCAGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCA Mut Flank + 3x5'+3' PTO T*G*A*TGGTTCTTCCATCTTCCCACAGCTGGCCGACCACT*A*C*C*AGCAGAACACACCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAA*C*C*A GFP Mutation, PAM mutation N o n e 5 'P T O 3 'P T O5 + 3 P T O M u t F la n k M u t F la n k + 5 + 3 P T O3 x 5 'P T O3 x 3 'P T O 3 x 5 '+ 3 'P T O M u t F la n k + 3 x 5 '+ 3 'P T O 0 .0 0 .5 1 .0 Targetingfrequency(GFP%) H R e ffic ie n c y u s in g s s O D N s w ith v a r y in g n u m b e r s a n d p o s ito n s o f p h o s p h t io la t e p r o t e c t e d n u c le o t id e s 2 5 ) T r a n s fe c tio n e ffic ie n c y u s in g s s O D N s w ith v a r y in g n u m b e r s a n d p o s it o n s o f p h o s p h t io la t e p r o t e c t e d n u c le o t id e s N o n e 5 'P T O 3 'P T O5 + 3 P T O M u t F la n k M u t F la n k + 5 + 3 P T O3 x 5 'P T O3 x 3 'P T O 3 x 5 '+ 3 'P T O M u t F la n k + 3 x 5 '+ 3 'P T O 0 .0 0 .5 Targetingfrequency(GFP N o n e 5 'P T O 3 'P T O5 + 3 P T O M u t F la n k M u t F la n k + 5 + 3 P T O3 x 5 'P T O3 x 3 'P T O 3 x 5 + 3 P T O M u t F la n k + 3 x 5 + 3 P T O 0 5 1 0 1 5 2 0 2 5 Transfection%(RFP) T r a n s fe c tio n e ffic ie n c y u s in g s s O D N s w ith v a r y in g n u m b e r s a n d p o s it o n s o f p h o s p h t io la t e p r o t e c t e d n u c le o t id e s
- 28. 28 Introducing gUIDEBook™ Supports all Cas9 nuclease variants Advanced tools for knock-in design Comprehensive gRNA scoring • Off target • Activity Full integration with annotated reference genomes Flexible and easy to use
- 29. 29 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Donor sequence modifications Modification effects on expression or splicing Size and type of donor (AAV, oligo, plasmid) Selection based strategies (+/+) (+/-) (-/-) (KI/-) (KI/+) (KI/KI)
- 30. 30 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Number of cells to screen Screening strategy Modifications on different alleles Homozygous or heterozygous modifications versus mixed cultures % cells targeted
- 31. 31 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation Confirmatory genotyping strategies Off-target site analysis Modification expression Contamination Heterozygous knock-in Wild type
- 32. 32 Key Considerations For CRISPR Gene Editing Gene Target Specifics Cell Line gRNA Design gRNA Activity Donor Design Screening Validation How many copies? Is it suitable? What’s my goal? (Precision vs Efficiency) Does my guide cut? Have I minimised re-cutting? How many clones to find a positive? Is my engineering as expected?
- 33. 33 High throughput knock-out cell line generation (Near) Haploid human cell lines • Near-haploid (diploid for chr8, and chr15) • Isolated from CML patient • Myeloid lineage • Suspension cells KBM-7 HAP1 • Near-haploid (diploid for chr15) • Derived from KBM-7 • Fibroblast like • Adherent cells
- 34. 34 Unambiguous genotyping Defined copy number Knowledge base RNA sequencing - Predict suitability as cellular model Essentiality dataset - Predict success rate for knockouts Haploid High efficiency Diploid - Knockouts - Defined mutations High throughput knock-out cell line generation Advantages of Haploid Cells
- 35. 35 Wildtype TCCTTTGCGGAGAGCTGCAAGCCGGTGCAGCAG ||||||||||| |||||||||||||| Knockout TCCTTTGCGGA--------AGCCGGTGCAGCAG Wildtype SerPheAlaGluSerCysLysProValGlnGln Knockout SerPheAlaGlu AlaGlyAlaAla Exon 1 DNA sequencing Exon 2 Cas9 cleavage High throughput knock-out cell line generation CRISPR/Cas9 allows rapid and high efficiency targeting
- 36. 36 Customer Design ProductionQuality control Packaging Shipment Custom knockouts for any human gene in 10 weeks High throughput knock-out cell line generation
- 37. 37 Exon 7 Exon 8 Exon 9 Exon 8 Cas9-induced double-strand break Non-homologous end joining (imprecise) Exon 9Exon 7 Exon 9Exon 8Exon 7 Insertion of DNA cassettes by NHEJ
- 38. 38 EEA1-GFP DAPI Merge LAMP1-GFP DAPI Merge KDM1A-GFP DAPI Merge Nucleus Lysosomes Early Endosomes
- 39. 39 Genome Wide sgRNA Screening Lentivirally delivered sgRNA can drive efficient cleavage of target genomic sequences for use in whole genome screens Use massively-parallel next-gen sequencing to assess results Possible addition/replacement to RNAi screens
- 40. 40 Genome Wide sgRNA Screening Shalem et al Science 2014
- 41. 41 We are combining CRISPR and isogenic cell lines to perform CRISPR-based Synthetic Lethality Screens sgRNA technology will be transformational for both Target ID and early-stage Validation Synthetic lethal target ID via sgRNA screening
- 42. 42 Ready-made knock-out X-MAN® cell lines X-MAN® - gene X Mutant And Normal cell line Advantages: • Genetically verified • More than 3,000 available clones already available, in a variety of cell line backgrounds • Quick and easy way to get first data on gene of interest • Available with validated gRNAs to use with your own human cell line of choice. More Information: www.horizon-genomics.com/ Bromodomain 40 genes Autophagy 15 genes mTOR pathway 50 genes Kinases 350 genes HATs/HDACs 15 genes DNA damage 50 genes RAB GTPases 15 genes Deubiquitinases 80 genes
- 43. 43 Genome Editing Tools and Services • Wild type and nickase • Separate or all-in one vectors • gRNA design and validation service • Pre-validated guides available • Custom donor design and synthesis • Multiple formats inc. rAAV available • >1000 ready-modified cell lines • Custom cell line generation service • Viral encapsulation of rAAV donor • Project design support
- 44. Your Horizon Contact: t + 44 (0)1223 655580 f + 44 (0)1223 655581 e info@horizondiscovery.com w www.horizondiscovery.com Horizon Discovery, 7100 Cambridge Research Park, Waterbeach, Cambridge, CB25 9TL, United Kingdom Chris Thorne Gene Editing Specialist c.thorne@horizondiscovery.com +44 1223 204 742
- 45. Useful Resources From Horizon Technical manuals for working with CRISPR - http://www.horizondiscovery.com/talk-to- us/technical-manuals In the Literature Exploring the importance of offset and overhand for nickase - http://www.cell.com/cell/abstract/S0092-8674(13)01015-5 sgRNA whole genome screening: • Shalem et al - http://www.sciencemag.org/content/343/6166/84.short • Wang et al - http://www.sciencemag.org/content/343/6166/80.abstract On the web Feng Zhang on Game Changing Therapeutic Technology (Link to Feng’s Video) Guide design - http://crispr.mit.edu/ CRISPR Google Group - https://groups.google.com/forum/#!forum/crispr
Editor's Notes
- The case for Genome Editing Why you should be using genome editing in your research – what are the potential advantages vs existing approaches. Genome Editing tools and how they’re used How genome editing is done, why Horizon have chosen to build a toolbox of tools, and how we apply them Genome Editing applications What are the kind of advanced biological tools you can create with genome editing, and the potential for these Summary – a quick wrap up, and I’ll also at this point explain why I truly believe Horizon is un matched in gene editing capability.
- As researchers working in the era of the human genome, with unparalleled access to genetic information obtained from healthy and diseased individuals the challenge has fundamentally shifted from obtaining that information to understanding what it means. And the reason for this, is that by understanding the genetic drivers of disease we can identify individuals with these genetics and tailor specific therapies to treat their diseases – the era of personalised medicine. And A better understanding of the genetics of disease stands to impact not just patient prognosis, but also drug development outcomes, as targets can be identified and rationalised more rapidly, and suitable clinical and patient populations identified – allowing companies to fail ineffective drugs faster, and get effective drugs to market quicker. In the past researchers had (broadly speaking) three options open to them to explore gene function which are: Using patient derived cell lines with preexisting disease-associated mutations to study gene function Using RNAi based loss of function study the effect of removing a gene from the system Using overexpression based gain of function experiments
- The first of these is to find a pre-existing human cell line with the same genetic aberation and use this as a model system to study your gene. Ideally this would be compared to a cell line that lacks the mutation. However with leaps forward in sequencing technology we have come to appreciate the genetic diversity of human cell line models And so this can make attributing any phenotypic observations to a specific genetic observations challenging, with each cell line potentially containing tens of mutations that might be drivers or simply passengers.
- Another approach to studying a gene is to remove it from the system using RNAi, and look for phenotypic effects. RNAi is not without it’s weaknesses however. Whilst easy to use it is often challenging to achieve a complete knockdown of expression, and residual 5-10% of a transcript can result in masked phenotypes. Further to this as this study demonstrates RNAi screens are often difficult to reproduce – here we see three screens looking for HIV host factors, with an overlap of just 3 genes between the three. Hence there is a very real risk of fast positives or negatives inherent to the technology.
- So rather than removing the gene from the system the next approach is to overexpress it as a transgene – either in wild type or mutant form and again look for effects on phenotype. Overexpression can itself however be the cause of phenotypic changes – a huge over abundance of protein can lead to miscompartmentalisation and mistrafficking of proteins which in turn can lead to non-biological functional consequences Over expression of the oncogenic form of PI3 Kinase in a normal epithelial cell line results in a large growth induction phenotype and transformation of those cells. If you knock-in the same mutation at the endogenous locus the phenotype is much milder – with the mutation not being transforming by itself, In other words over expression of oncogenes can over represent their role in disease biology.
- So given the limitations of these three approaches what is ideally required to best characterise the genetics of a disease is a genetically defined mutant cell line, with an isogenic wild type equivalent. For loss of function analyses the functional gene should be completely removed from the system, and for gain of function a mutant should be expressed at levels consistent with those seen physiologically. And with gene editing we are now able to rapidly generate such biological models. It is for this reason that Horizon have developed its catalogue of over 1000 X-MAN isogenic cells lines, with over 300 different genotypes relevant to disease Perfect model for oncology research Isogenic cell line pairs as disease models 350 different genotypes relevant in oncology research 30 different cell line backgrounds (HCT116; DLD-1; SW48; RKO; MCF10A) Deep genetic validation STR profiling Gene copy number Selection cassette copy number SNP profiling Gene expression (wt and mut) via digital PCR and qPCR Insertion validated by genomic PCR
- At Horizon we have access to a toolbox of genome editing technologies – which allows us to pick the right tool for any particular genomic outcome.
- Each of these tools has strengths and weakness. rAAV for example is a viral system that efficiently delivers a ssDNA-vectors that use a natural high-fidelity DNA-repair pathway (HR) in cells to alter genomic sequences. As No DNA-breaks created or required, off target modifications can be easily monitored and controlled. We’ve found this system Highly flexible and widely applicable across mammalian cell-types – it is our tool of choice for hard to transfect cell-lines But it does not have the efficiency of nucleases meaning it is hard do multi-allelic KO’s quickly …..ideal for ‘deep-biology’ & disease model generation ZFNs and CRISPR on the other hand are nuclease based technologies – relying on the recruitment of an endonuclease to a target site in the genome to elicit changes. This makes them rapid and highly efficient for doing knock-outs, but comes with an inherent risk of off-target modifications being made elsewhere in the genome.
- AAV is a single-stranded, linear DNA virus with a a 4.7 kb genome which for the purpose of genome editing is replaced almost in entirety with the targeting vector sequence (except for the iTRs) It is in effect a highly effective DNA delivery mechanism After entry of the vector into the cell, target-specific homologous DNA is believed to activate and recruit HR-dependent repair factors can induce HR at rates approximately 1,000 times greater than plasmid based double stranded DNA vectors, but the mechanism by which it achieves this is still largely unknown By including a selection cassette can select for cells that have integrated the targeting vector, and then screen for clones which have undergone targeted insetion rather than random integration, which will generally be around 1%.
- CRISPR/Cas9 gene editing is based on a microbial restriction system, which ordinarily functions effectively as an immune system in these organisms, but that has been harnessed for genome targeting. The beauty of the system is that unlike protein binding based technologies such as Zinc Fingers and TALENs which require complex protein engineering, the design rules are very simple, and it is this fact that is allowing CRISPR to take genome engineering from a relatively niche persuit to the mainstream scientific community. The system itself is comprised of three key components the Cas9 protein, which cuts/cleaves the DNA and Two RNAs - a crispr RNA contains a sequence homologous to the target site and a trans-activating crisprRNA (or TracrRNA) which recruits the nuclease/crispr complex For genome editing, the crisperRNA and TraceRNA are generally now constructed together into a single guideRNA or sgRNA Genome editing is elicited through hybridization of the sgRNA with its matching genomic sequence, and the recruitment of the Cas9, which cleaves at the target site. This then cuts the dsDNA, triggering repair by either the low fidelity NHEJ pathway, or by HDR in the presence of an exogenous donor sequence.
- The design rules for CRISPR are straightforward, as you require only a 23 nucleotide sequence that ends in an NGG motif – known as the protospacer associated motif (or PAM site). Of this 23bases, only the first 20 are included in the guide target sequence which is appended to the tracrRNA “fixed scaffold” and together comprise the gRNA. So as the only requirement is this NGG, on average eligible PAM sites can be found every 12bp, although this will depends on sequence Several tools for gRNA design – HD has our own. One of the key considerations is what is the off target potential of my guide – very often even a 23 base pair sequence will be found elsewhere in the enormity of the genome, and even if an exact match isn’t observed there may be instances of homology with a few mismatches. In fact the specificity of the CRISPR system remains a concern for researchers, especially where minimising off target modifications is critical, such as those working in the field of gene therapy. Various approaches are being taken improve specificity - Interestingly recent work by Keith Joung’s lab has shown using a 17bp target region can reduce some of the off target potential that guides have
- Another approach has been to mutate the nuclease such that it will only nick one strand of the dsDNA, a nickase form of the protein. Nicks will in general be repaired by the base excision repair pathway which is significantly higher fidelity than NHEJ. Targeting strategies using the nickase are designed with two gRNAs, one to recruit the nickase to each strand of the DNA, only after which a DSB will be introduced. This increase in specificity is unfortunately at the expense of some efficiency at you’re at the mercy of your weakest guide in the pair
- The design of CRISPR system is simple and Genome editing might appear as simple as: Identifying a gRNA target sequence Ordering an oligo with the target sequence and cloning it into a gRNA vector Transfecting cells with the gRNA + Cas9 + Voila! well… is not always that simple, there are things you need to consider
- And as we are running gene editing projects every day at Horizon we’ve learnt from experience that there are various ways that things can go wrong if you don’t consider the following, and I want to briefly run through each of these one at a time.
- The first group of considerations regard the quirks of your specific target gene How many copies of your gene exist in your cell line? Many of us use transformed human cell lines – there aren’t many that actually come with a normal copy number of 2. For many, many, years people using HeLa cells - quadroploid – see pic = mess -Their karyotype is very different from wild type and they might have multiple copies of an allele It is important to understand YOUR cell line. Do you need to modify all alleles present? Would KO of one allele and modification of the other be viable/acceptable? This is something that happens frequently with CRISPR. When you make the modification do you expect it affect the growth of the cells? When the gene alteration we are trying to make don’t seem to be able to be isolated and then they tell us expts with shRNAs show viability of cells affected by modification
- The second category, and this is probable the one that causes us here at Horizon the most trouble/has required a lot of hard-won expertise - the suitability of the cell line. For example: Need to get DNA into cells..Does your cell transfect/electroporate well? Should you transduce instead? Can the cell line be single cell dilute (SCD)? (Single cell or as in Top panel = triplicate stuck together – takes a long time to separate artefacts) And have it come back at reasonable growth levels? Even if the cell line grows very well, it might not tolerate single cell dilution and you need to find the most suitable conditions to let the cell line grow What is the doubling time? Optimal growth conditions? Looked at whole panel of media formulations, additives, diff seeding densities..(see panel on bottom gives example of taking 1 particularly hard cell line and trying a range of conditions to find the conditions most suitable for SCD
- Now let’s get to the most interesting part the guide RNA design gRNA design: A very important consideration is What source you are using for your genomic sequence? Even a single base discrepancy can be the difference between success and failure with CRISPR and most of these other technologies. It is important that you know what the target looks like IN YOUR cell line. We therefore highly recommend that you sequence all alleles in your cell line so you know what you are targeting. 1bp or 2bp difference will have a big effect on whether you are going to be able to make an effective change. Imperative that you make sure you understand your target region so that your guides are appropriately designed to your real sequence. That’s one of the strengths of the gUIDEbook guide design system that we have developed jointly with Desktop Genetics – see example of output in slide – need to use it to identify potential guides and figure out how close are they to the modification want to make? Distance does matter! How close is the guide to the desired mutation? Distance is important, particularly for something like a point mutation. The closer the cut is to the change you want to make the most effective will be the guide. What are the potential off-target considerations? We pay a lot of attention to this at Horizon, the design algorithm takes into account all the sites that are obviously a perfect match and up to 1, 2, 3, or 4bp mismatched potential off effects elsewhere in the genome. Sometimes, can’t avoid; common to have some mismatches but need to know if in coding or non-coding. If non-coding is it in a regulatory region?
- Data suggests that two nicks that result in a 5’ overhang are most efficient at being modified It has also been shown that the distance or “offset” between the two guides is important for efficiency.
- Once you have guide designs it can pay dividends to validate their activity before hand. The accepted wisdom is to design 5 guides (or 10 if using pairs). Activity can be assessed semi quantitiatively using the Surveyor assay. This done, you can then take just one or two forward for gene targeting in your cell line of choice.
- Donor must be sufficiently different to prevent re-cutting Include silent mutations However, consider effects on expression on splicing Also to consider is nature of donor – selection? Delivery method? If you can imagine, even if you dsb is repaired by the HDR pathway, unless your donor is sufficiently different from you guide target sequence, you’re at high risk of having your modified allele further modified with CRISPR cuts and indels. It is highly recommended therefore to include with your donor silent mutations in the guide target sequence. If you alter the donor, in a coding region like our hypothetical example, how will codon substitutions affect the outcome? Will expression or splicing be affected? How big a donor do you need, how many changes are you introducing – where do you derive donor from? For targeting, every single bp important…also impt. on the donor side of things - want to introduce as few changes as possible in donor in order to achieve highest efficiencies. Can use selection but need to be careful as introducing another ORF. So you can see, there’s a lot to consider and a lot of value in working with a company that can do much more than just providing plasmids Between our personal expertise and through our design tool we have implemented a donor design module that helps us to design the perfect donor for your project, and which will be stable and in-frame.
- Historically Horizon’s tool of choice for gene editing was rAAV – for reasons not yet fully understood rAAV is very good at stimulating HDR, and so the system is perfect for introducing very precise modifications into the genome. However, in general these modifications will only be introduced onto a single allele, which means that whilst it’s a great system for knock-in’s it lacks the efficiency of nucleases when it comes to knock-outs. What we’re able to do now however is combined our experience with rAAV with CRISPR – evidence in the literature suggests that DSBs can stimulate HDR by rAAV, and we’ve found that by combining a cut with CRISPR and rAAV delivery of the donor we can improve efficiency of HDR by 50 fold. In this case we have an in hosue testing system, a cell line containing a mutant (non-fluoresecent) GFP, and we can measure efficiency of targeting by rescue of fluoresence in FACS.
- Historically Horizon’s tool of choice for gene editing was rAAV – for reasons not yet fully understood rAAV is very good at stimulating HDR, and so the system is perfect for introducing very precise modifications into the genome. However, in general these modifications will only be introduced onto a single allele, which means that whilst it’s a great system for knock-in’s it lacks the efficiency of nucleases when it comes to knock-outs. What we’re able to do now however is combined our experience with rAAV with CRISPR – evidence in the literature suggests that DSBs can stimulate HDR by rAAV, and we’ve found that by combining a cut with CRISPR and rAAV delivery of the donor we can improve efficiency of HDR by 50 fold. In this case we have an in hosue testing system, a cell line containing a mutant (non-fluoresecent) GFP, and we can measure efficiency of targeting by rescue of fluoresence in FACS.
- Desktop Genetics, in partnership with Horizon, has developed the premiere software platform tailored for CRISPR/Cas9 experiments Complete guide design capabilities for all common nucleases, including Fok1 fused dCas9 Advanced tools for knock-in design (donors and optimal guide designs specifically for KIs) Comprehensive multiple scoring dimensions to iform experimental prediction Full integration with annotated reference genomes for accurate guide design Stores experimental outcomes to continuously improve gRNA design Flexible and simple user interface with collaboration tools and vendor integration
- What is your like probability of your targeting desired event?
- Armed with all the above information on the nature of the cell line, your transfection efficiency and your guide activity, you next consideration will be how many cells do you need to screen to have a chance of finding a positive. In our case, as we’re looking for a single modified allele which at best will be ¼ of those cells that have been targeted we will need to scale up our screen accordingly. The method you use to screen will depend largely on the modification your introducting – if for example you’re inserting a tag, you can screen using PCR at that locus. If you’re introducing a framshift that will disrupt a restriction site, this can be used. Finally, in many knock-outs the modification on each allele will be different, and so the surveyor assay can be used.
- Finally, once you have identified a clone or clones that you believe contain your desired mutation the ultimate step is the validate these. Of most interest is certainly going to be the nature of the modifications introduced at your target site – whether for example insertion deletions are present on all of your alleles, and if so, whether they result in frameshifts. You may also wish to assess the off-target cutting in your clones, whether mutations have been introduced at your prediced off target sites, and you can do this using sequencing. Finally, there are various other factors that will be critical to the utility of your cell line. Does the modification express (if it’s a knock-in) or not (if it’s a knock-out). Does your cell line remain genomically stable over multiple passages. And finally, given the length of time cells must remain in culture, and also the degree of handling we always test our engineered lines for contamination with mycoplasma and other microbes.
- In summary If we however, try to consider them in the context of a hypothetical experimental goal this might help, and so for the sake of this example lets say we want to introduce a point mutation the coding sequence of a single allele of a gene, and we want to use CRISPR to make this happen Further to this we….
- Recent data from Feng Zhang’s and Eric Lander/David Sabatini’s laboratories indicate lentivirally delivered sgRNA can drive efficient enough cleavage of target genomic sequences that the technology can be used for whole genome screens Results can then be assessed using next generation sequencing, with each sgRNA effectively acting as a bar code This style of screen will complement or replace si or shRNA screens of a similar nature
- SO the principal is that you synthesis a guide or guides against every gene in the genome, with the aim being that that guide is capable of disrupting the coding sequence of the gene and knocking it out. These guides are cloned into a lentiviral delivery system to generate what has become known as a lentiCRISPR library You can use this library to transduce cells, and by using the guides themselves as barcodes ask the question which guides are enriched when treated with a drug for example vs my control cell. This for example would tell you which knock-outs promotes resistance to this drug In the case of the two papers on the previous slide, the proof of concept was to look for those genes that are essential.
- Cancer is a genetic disease Genome sequencing has generated 100’s of potential targets for cancer therapy However, most targets are rare and poorly characterised Most of them can’t be drugged directly e.g., tumour suppressors If tumour suppressor loss is the prime cancer initiating event, agents exploiting this loss may assist with overcoming tumour heterogeneity Systematic de-orphaning required to find key druggable downstream targets - exploiting co-dependence or synthetic lethality
- Vectors gRNA design services Donor services and expertise in knock-ins Services – viral encapsulation, project design support, expert support