CRISPR Screening: the What, Why and How
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Speaker: Benedict C. S. Cross, PhD, Team leader (Discovery Screening), Horizon Discovery CRISPR–Cas9 mediated genome editing provides a highly efficient way to probe gene function. Using this technology, thousands of genes can be knocked out and their function assessed in a single experiment. We have conducted over 150 of these complex and powerful screens and will use our experience to guide you through the process of screen design, performance and analysis. We'll be discussing: • How to use CRISPR screening for target ID and validation, understanding drug MOA and patient stratification • The screen design, quality control and how to evaluate success of your screening program • Horizon’s latest developments to the platform • Horizon’s novel approaches to target validation screening
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CRISPR Screening: the What, Why and How
- 1. HORIZON DISCOVERYHORIZON DISCOVERY CRISPR-Cas9 Screening The What, Why and How Benedict Cross | Team Leader, Discovery Screening Webinar September 13th 2016
- 2. 22 Presenter Dr. Benedict CS Cross Team Leader, Discovery Screening Ben joined Horizon in 2013 to expand and develop Horizon’s functional genomic screening capability and to lead a major research alliance in synthetic lethal target discovery. Ben now manages Horizon’s functional genomic screening platform including the CRISPR-Cas9 screening service launched in September 2015. Prior to working at Horizon, Ben completed his PhD at the University of Manchester and trained as a post- doctoral scientist at the University of Cambridge studying reverse chemical genetic screening in the unfolded protein response.
- 3. 33 Webinar Agenda 1. Intro to CRISPR and functional genomic screening 2. Horizon’s CRISPR screening platform evolution 3. How CRISPR is turbocharging functional genomics 4. Example data sets from CRISPR screening programmes 5. What does a CRISPR screen look like and how to tell if it is any good 6. The Future: where next for Horizon and CRISPR
- 4. 44 Horizon – A Cell Building Company Horizon’s mission is to facilitate Functional Genomics and Translational Medicine
- 5. 55 Functional Genomic Screening Mutagenesis Phenotype Measurement Drug discovery programme timeline Gene ID Target ID Drug resistance (patient stratification) Drug sensitivity (patient stratification) Enhancer mutations (drug-gene interaction) MOA Studies (drug-gene interaction) Target ValidationBiological Discovery De-orphan phenotypic screens
- 6. 66 The CRISPR-Cas9 Gene Editing Platform CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) takes advantage of the nuclease activity of the Cas9 protein targeted to a precise genomic locus by a short guide sequence (sgRNA) Cas9 endonuclease sgRNA Target Genomic Locus PAM siteTarget sequence Site-specific dsDNA break NHEJ and InDel editing GENE KNOCKOUT Anticipated to provide fewer off-target concerns than RNAi Robust phenotypes due to complete loss of gene function Cas9 enacts knock- out of target gene
- 7. 77 How Do CRISPR-Cas9 Screens Work? Selection of genes to target and design of a suitable sgRNA library Cell line is optimised and transduced with a pooled lentivirus library Selected transduced cells are treated with the assay conditions Deep sequencing is used to determine the abundance of each KO genotype
- 8. 88 Next Generation Sequencing for Pooled Screen Readout Lentivirus library Control sample Test Sample Deep sequencing to quantitatively identify the genotype of the cells in each sample Use sequence of sgRNA as barcode for genotype lentiviral expression cassette
- 9. 99 Positive and Negative Selection Screening Genes which confer a growth advantage in the assay conditions Resistance screening Genes which confer a growth inhibition in the assay conditions Sensitivity screening Screen Optimised cell line
- 10. 1010 Functional genomic screening with a biomarker readout Deep Sequencing for Target ID FITC Stain with anti-target [or use reporter line] FACS Screen Optimised cell line Genetically modulated cell pool
- 11. 1111 Functional genomic screening with a biomarker readout Adaptation of CRISPR-Cas9 screening to study secreted/intracellular markers • Treatment of cells using brefeldin A to block secreted cytokine (e.g. TNFα) intracellularly • Cells are then fixed, stained and subjected to FACS • Resulting cell pellets were then unfixed (proteinase K) prior to NGS NGS FITC Fixed & permeablised cells stained with αTarget FACS Genetically modulated cell pool LPS activation + BFA block Innovative approach adapts pooled screening to studies using myriad biomarkers
- 12. 1212 Screening platform optimisation … the proof and principles for successful screening
- 13. 1313 Horizon’s New CRISPR-Cas9 Screening System Evaluation of system performance using adapted tracrRNA system Essential vs. non-essential gene depletion used as metric for analysis of tracrRNA performance Adapted tracrRNA removes potential stop codon and extended hairpin to better replicate endogenous chimera A B Chen et al. (2014), Cross et al, (2016)
- 14. 1414 Horizon’s New CRISPR-Cas9 Screening System Evaluation of system performance using adapted tracrRNA system Robust overall mean drop-out in all collections of essential genes Significant improvement of screen performance with Horizon’s new optimised system Cross et al, (2016)
- 15. 1515 Library Design for CRISPR-Cas9 KO Screening Multiple competing guide design algorithms – which is best? Machine learned set (Wang et al. 2014 – 3G) appears to show best performance overall. However, in Horizon’s improved system, both guide sets performed equivalently indicating the major determinant is the vector Cross et al, (2016)
- 16. 1616 Horizon’s off-the-shelf libraries for CRISPR-Cas9 Screening CRISPR-Cas9 Screening | Libraries ID Name Genes Complexity Guides APT Apoptosis 646 10 6453 CAN Cancer Genome 593 10 5922 CTK Cytokine 613 10 6060 CYC Cell cycle 1130 10 11277 DDR DNA Damage Response 289 10 2885 DUB Deubiquitinase 142 10 1418 EPI Epigenetics 818 10 8162 GPR GPCR 309 10 3079 HWG Horizon Whole Genome 19051 6 119461 ION Ion Channel 333 10 3330 KIN Kinase 636 10 6354 MET Metabolic 390 10 3899 ODT Oncology Drug Targets 1083 10 10825 SRF Cell Surface 4503 10 44826 Note: All libraries are in Horizon’s optimised system. Custom libraries can also be prepared.
- 17. 1717 Horizon’s Tier I pre-optimised cell lines for CRISPR-Cas9 Screening CRISPR-Cas9 Screening | Fast-track Cell Lines Cell Line Lineage Cell Line Lineage Cell Line Lineage Cell Line Lineage A375 Skin TYKNU Ovary HAP1 CML SKMES-1 Lung DLD1 Colon HT29 Colon HCC95 Lung HCC827 Lung GP2D Colon NCI-H2122 Lung LK-2 Lung NCI-H1299 Lung HCT116 Colon SK-HEP-1 Liver LXF-289 Lung NCI-H460 Lung SW480 Colon NCI-H661 Lung MCF10A Breast NCI-H838 Lung HT29 colon Jurkat Blood NCI-H1975 Lung OVISE Ovary CFPAC Pancreas eHAP CML NCI-H2106 Lung TOV-21G Ovary HCC15 Lung CALU1 Lung NCI-H358 Lung NCI-H1793 Lung Ishikawa Endometrium DU145 Prostate PC14 Lung NCI-H1703 Lung LCLC103H Lung EBC1 Lung RERFLCAI Lung A375 Skin TYKNU Ovary Sk-LU-1 Lung Note: Cell lines not on this list can be evaluated as part of a custom programme
- 18. 1818 Cell line variance in response to gene deletion Comparison of phenotypic effect of depletion essential and putative neutral genes Cell line-dependent maximal drop-out of essential guides and to loss of putative neutral genes Changeinabundanceovertime(LogFoldChange) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Cell line number
- 19. 1919 Kinetics of CRISPR-Cas9 KO & Design Considerations Analysis of time-resolved samples of guides targeting essential genes In our new backbone, the loss of essential genes can occur very rapidly Particularly important for hit calling in genetic interaction studies where longitudinal samples are crucial Sample collection point Normalisedabundance(Log2counts) Sample collection point Plasmid D3 D7 D18 D30 Plasmid D3 D7 D18 D30 Cross et al, (2016)
- 20. 2020 Impact of cell ploidy on screen performance Essential gene drop-out monitored in A375 cells were compared to haploid cells Modest but significant increase in the speed of drop-out of essential genes in haploid cell line Density Log2FC Essential Genes A375 (hypotriploid) Density Log2FC Essential Genes eHAP (fully haploid) Cross et al, (2016)
- 21. 2121 Screening Case Study Just one of >150 screens conducted so far at Horizon…
- 22. 2222 Drug-gene interaction Screening: Optimisation Maximising the window for discovery is contingent upon the correct dose selection Different outcomes can be optimised through experimental design
- 23. 2323 De-orphaning Compounds and MOA analysis • Demo experiment with compound of known molecular mechanism • Compound treatment in resistance mode yielded compelling pattern of hits with high significance • Two pathways highly enriched: DDR and nucleotide metabolism • Validation of DDR hits in array-based experiment Whole genome screen Prosecco plot Validation of top hits with engineered KO cell lines
- 24. 2424 De-orphaning Compounds and MOA analysis • Demo experiment with compound of known molecular mechanism • Compound treatment in resistance mode yielded compelling pattern of hits with high significance • Two pathways highly enriched: DDR and nucleotide metabolism • Validation of DDR hits in array-based experiment Whole genome screen Prosecco plot Validation of top hits with engineered KO cell lines 6-TG DNA damaging agent Purine analogue which is incorporated into DNA during S-phase Requires metabolism for activity Dependent on HPRT1 activity
- 25. 2525 Screen Analysis & Data Processing … and is my data any good?
- 26. 2626 Process Overview | Horizon Analysis Pipeline (SG3) Screen data analysis deliverables • Data is provided as raw and analysed form • Transfer by secure FTP, Amazon cloud or hard copy • Reactive hit calling, in collaboration with client
- 27. 2727 Hit calling | Overview of approaches Potential approaches to hit finding RRA & LogFC – direct evaluation of essentiality • Staggered mapping of guides from reference library in each sample file (zero tolerance for mismatch) • Median-based normalisation (to account for NGS depth per sample) • Mean-variance modelling – provides sample variance data points • Mean change in abundance over time (Zero vs Endpoint, LogFC) – essentiality index • Guide and gene-based ranking – RRA (robust ranking aggregation), e.g. MAGeCK αRRA (p-value) • Li et al. 2014 Genome Biology MLE – Maximum likelihood estimation for essentiality • Staggered mapping of guides from reference library in each sample file (zero tolerance for mismatch) • Size factor estimation, mean-variance modelling & beta-score determination (LogFC proxy) • Wald test used to generate p-value and FDR value confidence measures • Contributes sgRNA efficiency estimations into beta-score • Li et al. 2015 Genome Biology Bagel – Bayesian modelling to determine essentiality • Staggered mapping of guides from reference library in each sample file (zero tolerance for mismatch) • Cohort-based population modelling for essential vs. nonessential genes • Log2 Bayes Factor for each gene is reported based on probability of partitioning into one group or the other • Hart et al. 2015 BioRxiv
- 28. 2828 Quality Control | NGS raw data FASTQ quality plot Individual samples mean quality Scores (Phred) Mapped reads library coverage per sample Time Zero samples End Point samples Overall MeanQ = 35 Sample ID Sample ID PercentageofreadatQCthresholdMeanNGScoverage(X) NGS quality scores • Illumina sequencing files evaluated using Phred system • Log10 based metrics: Q30 = 1/1000 chance of mis-read • Assessed per-base position in the read • Mapped reads (guide counting) also informs as to coverage • Aim for >300-fold NGS coverage in each sample • All QC conducted per FASTQ file (barcode, or sample) Mean @ Q20 Mean @ Q30
- 29. 2929 Quality Control | Mapped reads analysis Percentage of guides mapped with >100 reads • Guide mapping allows an evaluation of the number of guides mapped with >100 (arbitrary QC threshold) • High frequency at early time points indicate good QC and NGS coverage • In this example, two samples were identified with problematic characteristics • Frequency distributions also indicate potential concerns Time Zero samples Probabilitydensity Frequency distribution: QC PASS Frequency distribution: QC FLAGGED Probabilitydensity End Point samples
- 30. 3030 Quality Control | Replicate analysis Evaluation of covariance using Pearson’s correlations within replicate samples Good QC threshold set at 0.9, lower R2 can indicate problems with the samples or innate variance in cell line properties
- 31. 3131 Quality Control | Control guide behaviour Performance of screen monitored in each cell line by control guide behaviour Mean Log2-fold change in abundance over time of each classification Here there is excellent group, gene, and guide-level drop-out rates for CTRL_POS CTRL_NT accumulate in all lines, as previously observed by HZD Control group (mean of many guides plotted), per cell line Control gene level drop-out rates, all cell lines Control guide waterfall, all cell lines LogFC LogFCLogFC Group and Sample ID Group and Gene ID Guide ID
- 32. 3232 After the screen… TARGET VALIDATION: STRATEGIES AND EXPECTATIONS
- 33. 3333 Efficient validation of sgRNA hits| The Path to Drug Discovery Manageable number of compelling hits Increasing level of confidence in target TARGET VALIDATION TOOLBOX • Parallel immunoblot and qRT-PCR expression analysis of target depletion • Inducible CRISPRi, CRSIPRa siRNA or sgRNA confirmation • Extended time course analysis (e.g. by medium-throughput 3D screening) • Target essentiality assay • Use of commercially available inhibitors, where available • Functional evaluation of proximal/distal biomarkers for on-pathway activity • cDNA rescue from induced phenotype • Evaluation of drug-mimetic effect by rescue with functional hypomorph of target • Cell line engineering to support gold-standard target validation and drug discovery Whole-genome CRISPR screen Ultra-complex pooled secondary screen Orthogonal technology Arrayed siRNA or CRISPRi/a TARGET VALIDATION Initiate Drug discovery programme
- 34. 3434 Target Validation | Ultra-complex tiled libraries Ultra-complex pooled secondary screening targeting functional domains sgRNA performance in drop-out experiments is dependent on whether in-frame indels result in a functional protein Future libraries could select sgRNAs based on specificity, biochemical effectiveness and predicted biological effectiveness
- 35. 3535 Target Validation | Ultra-complex tiled libraries Horizon’s first datasets evaluating this approach now completed Screening in colon cancer lines for synthetic lethality identified a number of apparent hit clusters LogFC(Changeinabundanceovertime) Guide target locus (3’-5’) Functional domain
- 36. 3636 Target Validation | Ultra-complex tiled libraries Horizon’s first datasets evaluating this approach now completed Promising results obtained, we are now further evaluating the nature of the high activity guides LogFC(Changeinabundanceovertime) Guide target locus (3’-5’) Functional domain
- 37. 3737 Arrayed Screening | Properties of the Technology CRISPR KO One issue with Cas9-sgRNA KO screens is a lack of a unimodal phenotype in cells edited by Cas9 bound to a particular sgRNA. dCas9 CRISPR Adaptation of Cas9 to be a DNA binder rather than cutter, enables CRISPR interference, a technique yielding knockdown rather than knock-out phenotypes, but thought to be more specific than shRNA.
- 38. 3838 Arrayed Screening | CRISPR-Cas9 PoP Horizon has so far focussed on pooled-format screens for the development of its CRISPR- enabled target ID platform. Arrayed screens have a higher cost of implementation and also raise issues: Horizon sees several ways to improve the performance of arrayed CRISPR screens • CRISPR/Cas9 generates ds. breaks that are repaired by NHEJ. • Only 2/3rd or repairs lead to a frame shift mutation and early termination. • As many short in-frame indels are tolerated, target function is retained in a fraction of cells. • Where an essential gene is targeted a population growth delay is observed, rather than a sustained change in doubling time. • For non-essential genes there will be a bimodal phenotype Modelled data Data from arrayed CRISPR KO expt at Horizon
- 39. 3939 Catalytically-inactivated Cas9 (dCas9) can be fused to transcriptional repressor or transactivation domains and then targeted to gene promoters using sgRNAs • CRISPRi: Enables interrogation of hypomorphic phenotypes of essential genes • CRISPRa: Screen for gain-of-function mutations CRISPRa West Coast CRISPRa East CoastCRISPRi dCas9 Catalytically inactive dCas9-KRAB fusion Version 1 (2013) Version 2 (2014) Qi et al 2013 Gilbert et al 2013 Gilbert et al 2013 Gilbert et al 2014 Konermann et al 2015 Mali et al 2014 CRISPR-dCas9 Transcriptional Regulation Screening
- 40. 4040 Transcriptional Regulation | CRISPRi • Horizon have built and are testing multiple variants for CRISPRi • Amenable to both pooled screening and arrayed approaches • Early data with novel all-in-one vector systems showing promising results • Modest overlap between RNAi and CRISPR KO data necessitates orthologous system control1 sgRNA1 sgRNA2 sgRNA3 sgRNA4 sgRNA5 sgRNA6 sgRNA7 sgRNA8 sgRNA9 sgRNA10 scramblevirus1 -5 -4 -3 -2 -1 0 1 V P S 5 4 Log2(RQ)
- 41. 4141 Project phases 1. Library design and generation (optional) 2. Cell line optimisation 3. Screen initiation and sample collection 4. Sample preparation and NGS 5. Screen QC and analysis 6. Hit nomination by Horizon scientists Deliverables A final report containing all raw & analysed data and hit nominations Turnaround time 14-20 weeks Horizon’s CRISPR-Screening Service Mutagenesis Phenotype Measurement Gene ID
- 42. 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 Benedict Cross, PhD Team Leader | Discovery Screening b.cross@horizondiscovery.com 01223 655580
Notas do Editor
- CASE STUDY The first analysis of screening data is a survey of the quality of the NGS, similar to the Library analysis shown previously Quality scores are shown bottom left – very high quality with our collaborator, and turnaround time of 2-3 weeks (3 days sometimes!) A summary of many samples quality scores are shown bottom right, where 80 samples all return with mean Q score of 35 (high) Finally, we can also evaluate how deep the sequencing was in terms of reference to the library/experiment size. In this case, we need a sufficient number of reads to be able to identify significant changes between samples (sampling stats). Plotted on the top right graph is the NGS coverage (reads per guide) of the mapped samples – we ideally will have >300 reads per guide (300-fold coverage) for all samples. Here all except two meet this criteria ,and the mean of all is >650-fold – a good data set for hit finding.
- CASE STUDY Good coverage does not always mean that we have met requisite quality for hit calling, as reads are distributed across the samples approximately evenly within the flow cell of the sequencer. Once the FASTQ (sequence) files are mapped to the library file to provide sequence counts per guide, we can evaluate the data in more meaningful ways. This mapping process is conducted with our in-house data pipeline SG3. In the top left graph the number of counts per guide in the library is plotted. In this case, we used a threshold of 100 counts per guide, and all early time points scored highly. End point samples show greater drop-out as expected since these data include guides which have been lost from the population due to cytotoxic consequences of gene loss. Two samples were identified with poor metrics, and the distribution of this sample is shown on the right. This reveals that some guides in this sample were mapped with very high counts, suggesting a bottle neck during the screening programme, most likely from catastrophic population crash, limiting the complexity in that sample and creating this noise.
- Pearsons correlation tests can also be used to determine the variance between samples. The important measure being between the end point technical replicates Here a good example shows >0.9 Rsquared, whereas a poor sample is shown with <0.6 Rsq.
- A similar example data set with multiple cell lines evaluated simultaneously. In each case, the control guides have been able to enact cell viability effects which is tracked by the deep sequencing. Gene-level and guide level analysis is shown as previously and the waterfall plot (bottom right) shows good guide partitioning for guide derivations.