Lessons learned from high throughput CRISPR targeting in human cell lines
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In just a short period of time CRISPR-Cas9 technology has revolutionized the field of genome editing, and taken the scientific community by storm. Already our understanding of how best to apply this technology has advanced significantly and almost every week new publications appear showcasing its application in basic and translational research. While CRISPR-Cas9 is applicable across many different cell types, we have found it particularly suited for genome editing in near-haploid human cell lines. This has allowed us to establish a robust pipeline for the inactivation of non-essential genes at unprecedented scale and efficiency. We have now knocked out over 1500 human genes and have generated a resource that is, to the best of our knowledge, the largest collection of human knockout cell lines available, covering comprehensive subsets of genes clustered by biological pathway (e.g. the autophagy pathway, the JAK/STAT pathway) or by phylogenetic relationship (e.g. kinases, bromodomain-containing proteins). In this talk we will discuss how, through more than 1500 genome editing experiments, we have started to unravel some of the general principles governing the use of CRISPR-Cas9 in mammalian cells. For example, we have analyzed the impact of variation in the guide RNA sequence on Cas9 cleavage efficiency and characterized the mutational signature arising from CRISPR-Cas9 cleavage. We will also highlight (with examples) how these learnings are now being applied to introduce other genomic modifications in a high throughput manner, including chromosomal deletions, translocations, point mutations and endogenous gene tags.
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Lessons learned from high throughput CRISPR targeting in human cell lines
- 1. HORIZON DISCOVERY Genome Editing Comes of Age Lessons learned from high throughput CRISPR targeting in human cell lines Chris Thorne, PhD | Commercial Marketing Manager
- 2. 2 Overview Introduction to genome editing and CRISPR-Cas9 Haploid cells – the genome editors dream (and lessons learned from 1500 experiments) High throughput genome editing – where next?
- 3. 3 The Genomic Era… Adapted from The US National Human Genome Research Institute, (2003) Nature
- 4. 4 The Genomic Era… 1. Elucidate the organisation of genetic networks and their contribution to cellular and organismal phenotypes 2. Understand the heritable variations and their association with health and disease 3. Translate genome-based knowledge into health benefits Adapted from The US National Human Genome Research Institute, (2003) Nature
- 5. 5 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)
- 6. 6 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
- 7. 7 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
- 8. 8 The Opportunity: Genome Editing
- 9. 9 The Opportunity: Genome Editing 1. Elucidate the organisation of genetic networks and their contribution to cellular and organismal phenotypes Knockouts 2. Understand the heritable variations and their association with health and disease Knock-ins 3. Translate genome-based knowledge into health benefits Gene Therapy Adapted from The US National Human Genome Research Institute, (2003) Nature
- 10. 10 CRISPR/Cas system: Adaptive immunity in bacteria
- 11. 11 The CRISPR/Cas revolution Jinek et al. (2012) in Science Cong et al. (2013) in Science Mali et al. (2013) in Science Cho et al. (2013) in Nat Biotech AGCTGGGATCAACTATAGCG CGG gRNA target sequence PAM
- 12. 12 CRISPR mediated genome editing Exon 1 Exon 2 Exon 3 Exon Exon 2 Exon 31 Cas9 nuclease-induced DNA double-strand break Non-homologous end joining Exon 1 Homology-directed repair Exon 2 Exon 2Exon 2Exon 1 Frameshift mutation Exon 1
- 13. 13 Cell Line Gene Target Guide Choice Guide Position Donor Design Screening Validation Challenges – Experimental Design Is it suitable? Is it essential/expressed/amplified? Specificity vs Efficiency Will depend on modification Donor design to maximise efficiency How many clones to find a positive? Is my engineering as expected?
- 14. 14 Challenges - Polyploid cells… e.g. Disruption of the MAPK3 gene in the A375 cell line (copy number = 3) 1 2 3
- 15. 15 Kotecki et al. (1999) in Exp Cell Res Carette et al. (2009) in Science KBM-7 is a human cell line that is haploid for all chromosomes but chromosome 8. Thijn Brummelkamp NKI/CeMM The Solution? Haploid cells...
- 16. 16 Genotyping analysis in haploid cells Exon 1 Exon 2 Exon 3 PCR with custom primers Sanger sequencing of PCR product Mutation masked by second copy Mutation leads to knockout Diploid Haploid
- 17. 17 (Near-) Haploid Human Cell Lines KBM-7 Near-haploid (diploid chr8, chr15) Isolated from CML patient Myeloid lineage Suspension cells HAP1 Near-haploid (chr15) Derived from KBM-7 Fibroblast like Adherent cells eHAP Fully haploid Derived from HAP1 Patent EP 13194940.6
- 18. 18 Advantages of haploid cells for genome editing High efficiency Unambiguous genotyping Defined copy number Knockouts >2 fold improvement Defined mutations >10 fold improvement Knowledge base RNA sequencing Predict suitability as cellular model Essentiality dataset Predict success rate for knockouts
- 19. 19 Production pipeline Shipment Packaging Quality Control Production Design Customer Custom knockouts for any human gene in 10 weeks
- 20. 20 Available gene sets Gene sets in the making • Phosphoinositide Metabolism (~50) • Phospholipases (~25) • Protein Phosphatases (~90) • TRIM Ubiquitin E3 ligases (~70) • FDA drug targets (~300) Available collection • Knockouts for >1,500 human genes • Verified by Sanger sequencing • One gRNA per gene • Two clones per gRNA www.horizondiscovery.com/cell-lines
- 21. 21 1500 gene targeting experiments later…
- 22. 22 Editing efficiency in human cells 0 20 40 60 80 100 120 140 160 180 200 #ofgRNAprojects Editing Efficiency (in %)
- 23. 23 Cas9-induced mutational pattern PAM 0 200 400 600 800 1000 1200 1400 1600 1800 2000 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 Peak at position -3
- 24. 24 Cas9-induced mutational pattern Deletions Insertions
- 25. 25 Assessment of off-target editing in clonal cell lines Off-target sites
- 26. 26 Hap1 Gene Targeting – what we‘ve learned CRISPR/Cas9 is highly efficient Mutations cluster at PAM -3 Deletions are favored over insertions Off-target editing represents a minor issue
- 27. 27 Cell line engineering in haploid cells Knockouts Deletions Translocations Point mutations Insertions Reporters
- 28. 28 Haploid Knockin Cell Lines
- 29. 29 Exon 1 Exon 2 Exon 3 Exon Exon 2 Exon 31 Cas9-induced double-strand break Exon 2 Exon 3 Homology-directed repair (precise) Exon 1 Exon 1 Introduction of point mutations by homology-directed repair
- 30. 30 Point mutation in EGFR L858R Targeting Efficiency ~8% AACGTACTGGTGAAAACACCGCAGCATGTCAAGATCACAGATTTTGGGCTGGCCAAACTG AsnValLeuValLysThrProGlnHisValLysIleThrAspPheGlyLeuAlaLysLeu AACGTACTAGTGAAAACACCGCAGCATGTCAAGATCACAGATTTTGGGCGGGCCAAACTG AsnValLeuValLysThrProGlnHisValLysIleThrAspPheGlyArgAlaLysLeu Clone 5 Wild-type SpeI PCR + SpeI
- 32. 32 Chromosomal deletions HAP1 cells are disomic for a fragment from chromosome 15
- 33. 33 Strategy for CRISPR/Cas-mediated excision of chr15 fragment
- 34. 34 Deletion of chr15 fragment is detectable by PCR 400 clones screened 5 positive clones identified ~1% targeting efficiency Essletzbichler et al Genome Research 2014
- 35. 35 Single cell clones that carry the deletion can be isolated SKY staining of clone E9
- 36. 36 Genomic MALAT1 deletion leads to loss of MALAT1 RNA RNA/ cDNAGenomic DNA Deletion PCR MALAT1 PCR MALAT1 RT-PCR GAPDH RT-PCR
- 38. 38 Translocations / Chromosomal Fusions Chin J Cancer. 2013 Nov;32(11):594-603
- 39. 39 Interchromosomal translocation leads to CD74-ROS1 fusion Chr 5 Chr 6 Chr 5 Chr 6 ROS1-CD74 CD74-ROS1 Translocation CD74 ROS1 ex6 ex7 Chr 5 Chr 6 Simultaneous cleavage with Cas9 ex33 ex34 ex7 ex6 Screen for fusion by PCR ex33 ex34
- 40. 40 PCR screening identifies two clones with CD74-ROS1 fusion CD74-ROS1 ROS1-CD74 A10 E4 E4 A10 ~1% Clones Tested are positive for fusion
- 41. 41 Clones 1C2 and 1G13 contain the CD74-ROS1 CTTACGCATACTGCTGACAGTTAAATTTAGTTGAAG-GCCTGGGGCCTCAGTTTCTGCATCAGATTCATAGAA CTTACGCATACTGCTGACAGTTAAATTTAGTTGAAG-GCCTGGGGCCTCAGTTTCTGCATCAGATTCATAGAA CTTACGCATACTGCTGACAGTTAAATTTAGTTGAAGTGCCTGGGGCCTCAGTTTCTGCATCAGATTCATAGAA CD74-ROS1 (chr 6) Predicted Clone 1C2 Clone 1G13 ROS1 CD74 CCTGAAGTAGAAGGTCAAAGGGCCACCCTCACAGGCTGGATTACTTAATCCCTCTCTGAAATACCCACAAT CCTGAAGTAGAAGGTCAAAGGGCCACCCTCACAGGCTGGATTACTTAATCCCTCTCTGAAATACCCACAAT CCTGAAGTAGAAGGTCAAAGGGCCACCCTC------TGGATTACTTAATCCCTCTCTGAAATACCCACAAT Predicted Clone 1C2 Clone 1G13 CD74 ROS1 CD74-ROS1 ROS1-CD74 CD74 ROS1
- 42. 42 CD74-ROS1 fusion is expressed in clone 1G13 RT-PCR from clone 1G13 shows the CD74-ROS1 fusion transcript CD74 exon 6 ROS1 exon 34
- 43. 43 Engineering of EML4-ALK fusion EML4 ALK EML4-ALK Inversion Genomic DNA Chr 2 Chr 2 RNA/cDNA gRNA2gRNA1 EML4 ALK
- 45. 45 The conventional approach Gene tagging by homology-directed repair Exon 7 Exon 8 Exon 9 Reporter Exon 7 Exon 8 Exon 9 Homology-directed repair Reporter Exon 9 Genome Homology donor Major shortcoming: Requires the synthesis of gene-specific donor templates
- 46. 46 Gene tagging by non-homologous end joining Developed further by Thijn Brummelkamp (NKI) and Horizon Discovery
- 47. 47 Gene tagging by non-homologous end joining Cas9 cleavage and ligation by NHEJ Exon 8 Exon 9 gRNA Gene-specific gRNA Tagtia11 tia11 tia11 gRNA tia11 gRNAU6 Exon 8 Exon 9 Tag Generic tagging plasmid Tagged gene at endogenous locus
- 48. 48 Genotyping on pools of cells after transfection Exon NanoLuc® gRNA ID1 MX2 IRF9 STAT1 TAP2 CCL2 IL6 13 out of 14 pools show integration of reporter cassette in right orientation
- 49. 49 Generation of single clones Successful recovery of single clones for 5 out of 9 cell lines Gene gRNA ID Tagged Clones/Total Clones Editing Efficiency (%) ID1 2655 2/24 8.3 ID1 2656 5/24 20.8 IRF9 2659 1/24 4% IRF9 2660 0/24 N/A TAP2 2663 0/24 N/A TAP1 2664 0/24 N/A CCL2 2665 0/24 N/A CCL2 2666 1/24 4% IL6 669 3/24 13% BUT... only one clone contained an in-frame cassette integration!
- 50. 50 Sequencing of individual clones Genomic locus GAAGTTGGAACCCCCGGGGGCCGAGGGCTGCCGGTCCGGGCTCCGCTCAGCACCCTCAACGGCGAGATCAGCGCCCTGAC Reporter cassette GGTACTTCGCGAATGCGTCGAGATGAATTCGGTATGTCGGGAACCTCTCCAGGGGCAGCGGATCCATGGTCTTCACACTC Resulting clone 2655-13 ACTCGGAATCCGAAGTTGGAACCCCCGGGGGCCGAGGGCTGCCGGTCTCCAGGGGCAGCGGATCCATGGTCTTCACACTC gRNA 2655 gRNA tia11 Exon 7 Exon 8 Exon 9 NanoLuc® tiatia Self-liberating reporter cassette
- 51. 51 Most clones show precise ligation at PAM minus 3 without Indels >2655-13 AACCCCCGGGGGCCGAGGGCTGCCGGTCTCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2655-17 AACCCCCGGGGGCCGAGGGCTGCCGGTCTCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2656-07 CCGGTCCGGGCTCCGCTCAGCACCCTCATCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2656-10 CCGGTCCGGGCTCCGCTCAGCACCCTCATCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2656-11 CCGGTCCGGGCTCCGCTCAGCACCCTCATCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2656-15 CCGGTCCGGGCTCCGCTCAGCACCCTCATCCAGGGGCAGCGGATCCATGGTCTTCACACTC >669-14 CTGACCCAACCACAAATGCCAGCCTGCTTCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2659-08 CAGATGGAGCAGGCCTTTGCCCGATACTTCCAGGGGCAGCGGATCCATGGTCTTCACACTC >2666-10 CAGAAGTGGGTTCAGGATTCCATGGACCTCCAGGGGCAGCGGATCCATGGTCTTCACACTC >669-24 CTGACCCAACCACAAATGCCAGCCTGCT-------GCAGCGGATCCATGGTCTTCACACTC >669-12 CTGACCCAACCACAAATGCCAGCCTGCT-------GCAGCGGATCCATGGTCTTCACACTC >2656-24 CGGTCCGGGCTCCGCTCAGCACCCTCAATCCAGGGGCAGCGGATCCATGGTCTTCACACTC Genomic Sequence Cassette Sequence Imprecise cleavage/ligation Indels Precise cleavage/ligation No indels
- 52. 52 Second generation tagging cassette NanoLuc®tia11 tia11 No start or stop codon: insert anywhere in the gene Three versions: one for each reading frame
- 53. 53 NanoLuc® reporter cell lines • Tag 3 cytokine-responsive genes at the 3‘ end with NanoLuc cassette • Genotyping of single clones Exon NanoLuc® Exon PCR 5’ junction PCR 3’ junction
- 54. 54 NanoLuc® reporter cell lines are functional • Stimulate tagged cell lines with cytokines • Measure change in protein levels by luciferase assay NanoLuc® NanoLuc®
- 55. 55 TurboGFP reporter cell lines • Tag endogenous genes with TurboGFP cassette • Enrichment for targeted clones by FACS • Genotyping single clones
- 56. 56 Localization of TurboGFP-tagged proteins Assess subcellular localization by microscopy TERF1LMNA TurboGFP DAPI Enlarged (merged) TERF1
- 57. 57 13 out of 14 clones contain single integration events Assessing off-target integration of reporter cassette Copy number determination using Droplet Digital PCR
- 58. 58 Summary The combination of CRISPR and a haploid background lends itself to both simple and complex genomic modifications Modification Targeting Efficiency in Hap1 Knockout >40% Point Mutation ~8% Chromosomal Deletion ~1% Chromosomal Translocation ~1% NHEJ Ligation Gene Tagging Up to 21%
- 59. 59 Acknowledgements Academic Collaborators Thijn Brummelkamp (NKI) Bill Skarnes (Sanger) Jin-Soo Kim (Seoul) Horizon Vienna Team Daniel Lackner Tilmann Bürckstümmer Paloma Guzzardo Horizon Cambridge Team Philippe Collin David Hughes Sergey Lekomtsev
- 60. 60 Available gene sets Gene sets in the making • Phosphoinositide Metabolism (~50) • Phospholipases (~25) • Protein Phosphatases (~90) • TRIM Ubiquitin E3 ligases (~70) • FDA drug targets (~300) Available collection • Knockouts for >1,500 human genes • Verified by Sanger sequencing • One gRNA per gene • Two clones per gRNA www.horizondiscovery.com/cell-lines On demand modifications • Knockouts • Deletions • Knockins • Translocations • Endogenous tags
- 61. 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 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, PhD Commercial Marketing Manager c.thorne@horizondiscovery.com +44 1223 204 799
Editor's Notes
- Pleasure to be here to today to tell you more about Horizon and our suite of technologies based around a core expertise in human genome editing and how we are applying this to better understand the human genome, find new validated targets and support targeted drug discovery with predictive, genetically-defined, in vitro models that accurately represent target patient groups.
- 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
- 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.
- 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
- 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
- Two types of genomic modifications represent the majority of the projects we undertake Using homology directed repair to generate a knockin – for example of targeted mutations, insertions or deletions at a specific site in the genome – and we can do this with rAAV or CRISPR, or combination of the two. Or using error non-homologous end joining repair to generate a knockout
- Key to this approach is confirming that a frameshift mutation has been introduced into all copies of the gene presence. In diploid or polyploid cells this requires subcloning of the PCR products such that they can be sequenced individually. Here is an example of a MAPK3 knockout in A375 cells which contain threee copies of the gene – and where we have different frameshift mutations in each allele. This need to deconvolute and verify creates a labour intensive bottleneck for cells with multiple alleles
- 10 guide RNAs, one clone each On-target site contains frameshift mutation Off-target sites: Amplify 10 closest off-target sites in each clone by PCR Submit to Sanger sequencing
- CRISPR/Cas is revolutionizing biological research Small RNA (20bp) allows the targeting of Cas9 endonuclease to any locus in the human genome (followed by PAM motif: NGG) Double-strand break inflicted by Cas9 is repaired by NHEJ NHEJ gives rise to frameshift mutations