CRISPR-Cas9 technology provides an efficient method to precisely edit the mammalian genome in any cell type and represents a novel means to perform genome-wide genetic screens. A detailed protocol discussing the steps required for the successful performance of pooled genome-wide CRISPR-Cas9 screens is provided here.
Genome editing using the CRISPR-Cas system has vastly advanced the ability to precisely edit the genomes of various organisms. In the context of mammalian cells, this technology represents a novel means to perform genome-wide genetic screens for functional genomics studies. Libraries of guide RNAs (sgRNA) targeting all open reading frames permit the facile generation of thousands of genetic perturbations in a single pool of cells that can be screened for specific phenotypes to implicate gene function and cellular processes in an unbiased and systematic way. CRISPR-Cas screens provide researchers with a simple, efficient, and inexpensive method to uncover the genetic blueprints for cellular phenotypes. Furthermore, differential analysis of screens performed in various cell lines and from different cancer types can identify genes that are contextually essential in tumor cells, revealing potential targets for specific anticancer therapies. Performing genome-wide screens in human cells can be daunting, as this involves the handling of tens of millions of cells and requires analysis of large sets of data. The details of these screens, such as cell line characterization, CRISPR library considerations, and understanding the limitations and capabilities of CRISPR technology during analysis, are often overlooked. Provided here is a detailed protocol for the successful performance of pooled genome-wide CRISPR-Cas9 based screens.
CRISPR-Cas, short for clustered regularly interspaced short palindromic repeats and CRISPR-associated nuclease, consists of a single nuclease protein (e.g., Cas9) in complex with a synthetic guide RNA (sgRNA). This ribonucleoprotein complex targets the Cas9 enzyme to induce double-stranded DNA breaks at a specific genomic locus1. Double-stranded breaks can be repaired via homology directed repair (HDR) or, more commonly, through non-homologous end joining (NHEJ), an error prone repair mechanism that results in insertion and/or deletions (INDELS) that frequently disrupt gene function1. The efficiency and simplicity of CRISPR enables a previously unattainable level of genomic targeting that far surpasses previous genome editing technologies [i.e., zinc finger nucleases (ZNF) or transcription activator-like effector nucleases (TALENS), both of which suffer from heightened design complexity, lower transfection efficiency, and limitations in multiplex gene editing2].
The basic research application of CRISPR single-guide RNA-based genome editing has allowed scientists to efficiently and inexpensively interrogate the functions of individual genes and topology of genetic interaction networks. The ability to perform functional genome-wide screens has been greatly enhanced by use of the CRISPR-Cas system, particularly when compared to earlier genetic perturbation technologies such as RNA interference (RNAi) and gene trap mutagenesis. In particular, RNAi suffers from high off-target effects and incomplete knockdown, resulting in lower sensitivity and specificity compared to CRISPR3,4,5, while gene trap methods are only feasible in haploid cells for loss-of-function screens, limiting the scope of cell models that can be interrogated6. The ability of CRISPR to generate complete gene knock-out provides a more biologically robust system to interrogate mutant phenotypes, with low noise, minimal off-target effects and consistent activity across reagents5. CRISPR-Cas9 sgRNA libraries that target the entire human genome are now widely available, allowing simultaneous generation of thousands of gene knock-outs in a single experiment3,7,8,9.
We have developed unique CRISPR-Cas9 genome-wide sgRNA lentiviral libraries called the Toronto Knock-out (TKO) libraries (available through Addgene) that are compact and sequence-optimized to facilitate high resolution functional genomics screens. The latest library, TKOv3, targets ~18,000 human protein-coding genes with 71,090 guides optimized for editing efficiency using empirical data10. Additionally, TKOv3 is available as a one-component library (LCV2::TKOv3, Addgene ID #90294) expressing Cas9 and sgRNAs on a single vector, alleviating the need to generate stable Cas9-expressing cells, enabling genome-wide knock-out across a broad range of mammalian cell types. TKOv3 is also available in a vector without Cas9 (pLCKO2::TKOv3, Addgene ID# 125517) and can be utilized in cells that express Cas911.
A genome-wide CRISPR-Cas9 edited cell population can be exposed to different growth conditions, with the abundance of sgRNAs over time quantified by next-generation sequencing, providing a readout to assess drop-out or enrichment of cells with traceable genetic perturbations. CRISPR knock-out libraries can be harnessed to identify genes that, upon perturbation, cause cellular fitness defects, moderate drug sensitivity (e.g., sensitive or resistant genes), regulate protein expression (e.g., reporter), or are required for a certain pathway function and cellular state12,13,14. For example, differential fitness screens in a cancer cell line can identify both depletion or reduction of oncogenes and enrichment or an increase of tumor suppressors genes3,14,15. Similarly, using intermediate doses of therapeutic drugs can reveal both drug resistance and sensitization genes16,17.
Provided here is a detailed screening protocol for genome-scale CRISPR-Cas9 loss-of-function screening using the Toronto Knock-out libraries (TKOv1 or v3) in mammalian cells from library generation, screening performance to data analysis. Although this protocol has been optimized for screening using the Toronto Knock-out libraries, it can be applied and become scalable to all CRISPR sgRNA pooled libraries.
The experiments outlined below should follow the institute’s Environmental Health and Safety Office guidelines.
1. Pooled CRISPR sgRNA lentiviral library plasmid amplification
2. Large-scale CRISPR sgRNA library lentivirus production
NOTE: All steps in this section of the protocol are performed in a BSL2+ facility in a Class II, Type A2 biosafety cabinet.
3. Cell line characterization for screening
4. Functional titration of pooled CRISPR lentivirus library for determination of MOI
5. Primary screen infection, selection, and cell passaging
6. CRISPR sample preparation and sequencing
7. Data analysis
Overview of genome-scale CRISPR screening workflow
Figure 1 illustrates an overview of the pooled CRISPR screening work flow, starting with infection of target cells with CRISPR library lentivirus at a low MOI to ensure single integration events and adequate library representation (typically 200- to 1000-fold). Following infection, cells are treated with the antibiotic puromycin to select for transduced cells. After selection, a baseline T0 cell pellet is collected to assess library distribution at the start of screening. The remaining cells, comprised of a heterogeneous population of genetic perturbations, are passaged at desired library representation every 3-4 days for 15-20 doublings to allow gene editing and the resulting effects to manifest. Screens with drug treatments are typically added at T3 or T6 after the cells have recovered from virus infection and puromycin selection. Cells are harvested at the desired library representation at every passage for genomic DNA, to determine guide abundance by next generation sequencing at desired timepoints.
It is recommended to collect multiple samples in case of any failures that may occur in the downstream sequencing library preparation steps. Pooled screens are typically viability-based assays that are designed for either positive or negative selection of essential sgRNAs. Positive screens identify genes that show resistance or increase survival under specific selection pressure (e.g., drugs or mutant cell line). In this case, most cells will die from the selection, and cells that remain will be enriched for sgRNAs targeting genes that are resistant for the drug or condition being tested. Negative selection screens or “drop-out” screens identify gene knock-outs with increased sensitivity to or loss of survival under the screen selection pressure. To identify perturbations that have a phenotypic effect such as a growth defect, guide abundance at each timepoint is quantified by next-generation sequencing and compared to T0 to assess drop-out or enrichment of guides over the course of the screen. Using analysis platforms, log-fold changes are measured for guides, and algorithms such as the BAGEL can be applied to enable ranking of gene hits.
Library amplification and maintenance of library representation in pooled CRISPR screens
Figure 2 illustrates the expected distribution of guides after amplification of the plasmid library. TKOv3 library consists of 71,090 sgRNAs with four sgRNAs per gene, targeting ~18,000 protein coding genes10. An ideal library should have every single sgRNA represented at similar quantities. Therefore, it is recommended to confirm the distribution of guides in the amplified library by next-generation sequencing. Shown here is an amplified library with very tight distribution of sgRNAs, confirming that >95% of all sgRNAs are within 4-fold distribution range (Figure 2). A wider distribution of sgRNAs will indicate that the abundance of library guides are not equally represented and can contribute to the noise in pooled screens.
Evaluation of screen performance
Figure 3 illustrates that the performance quality of a screen can be evaluated by assessing the fold change distribution of all sgRNA against a gold standard reference list of essential (684 genes) and nonessential genes (926 genes) and visualized as precision-recall curves10. Using the gold-standard reference sets, Bayes Factor (BF) scores are calculated for the screen endpoint, and precision-recall curves are plotted. BF scores are calculated by analyzing the log-fold change for all guides targeting a gene using a Bayesian framework (the BAGEL algorithm described previously19) to compare distributions of known essential and non-essential guide sets. False discovery rates (FDR) are derived empirically using the same gold standard reference sets. A high performing screen should recover a high number of essential genes at a threshold of BF >6 and FDR <5%, as evidenced by a sharp “elbow” in most curves and a straight line to the terminal point as shown by the blue line in Figure 3A. The dropout of guides targeting essential and nonessential genes should also be examined (Figure 3B). Guides targeting the reference nonessential genes should show a largely symmetric distribution of log-fold changes centered at zero, as shown by the dashed line in Figure 3B. The fold change distribution of guides targeting essential genes shows a strong negative shift relative to the distribution of guides targeting nonessential genes, as shown by the solid line in Figure 3B.
Essential genes
One of the basic applications of pooled genome-wide drop-out screens is to identify essential genes. Essential genes, a subcategory of fitness genes, are genes whose perturbation causes cell lethality, also considered loosely as proliferation genes. In the context of cancer biology, it is possible to identify context-specific essentials in order to identify dependencies for a particular tumor cell line. Figure 4, shows the gene rank of essential genes using Bayes Factor scores, derived from the BAGEL algorithm. Bayes Factor (BF) represents a confidence measure that the gene knock-out results in a fitness defect. More positive scores indicate higher confidence that the perturbation causes a decrease in fitness.
Positive selection screen
Genome-wide knock-out pools can be cultured in the presence of excess drug agent to look for suppressor/resistance genes. Shown here is an example of HCT116 cells screened in the presence of thymidine to look for suppressors of G1/S arrest3. Details of this screen can be found in a previous publication3. Briefly, 6 days after selection of CRISPR library infected cells, cells were split into replicates maintaining library coverage and treated with thymidine. Cells were passaged in the presence of drug until ample resistant cells were recovered for genomic DNA sampling. Positive selections can be sequenced (read depth) at lower coverage than negative screens since only a small fraction of guides will remain due to the strong selective pressure. In this example, sequencing was obtained with a few million reads, and 11 of 12 sgRNAs targeting thymidine kinase (TK1) were recovered and enriched as expected (Figure 5).
Amounts were determined based on molar ratio of 1:1:1 | ||
Component | Amount per 15-cm plate a | |
LCV2::TKOv3 | pLCKO2::TKOv3 | |
psPAX2 | 4.8 µg | 7.0 µg |
pMD2.G | 3.8 µg | 4.0 µg |
TKOv3b | 8.0 µg | 5.0 µg |
aAmounts determined based on most productive plasmid combination for TKO library at 1:1:1 molar ratio | ||
bAmount TKO plasmid based on CRISPR library vector backbone. LCV2 all-in-one vector =13 kb, non-Cas9 pLCKO2 vector = 7.6 kb |
Table 1: Recommended amount of plasmid for TKOv3 transfection.
Component | Amount per 15-cm plate |
Opti-MEM | 800 µL |
Transfection reagent | 48 µL |
Table 2: Lipid-based transfection reagent set-up.
Fold-coverage | Number of cells per sgRNAb | Number of cells required for infectionb |
(sgRNA library sizea × fold coverage) | (sgRNA library size × fold coverage ÷ 0.3 MOI) | |
200 | 1.5 x 107 | 5 x 107 |
500 | 3.6 x 107 | 1.2 x 108 |
1000 | 7.1 x 107 | 2.4 x 108 |
a Based on TKOv3 library size = 71,090 sgRNA | ||
b Numbers are rounded up |
Table 3: Determination of cell numbers required for TKOv3 CRISPR library infection and cell plating at various fold-coverage.
Treatment | Number of plates required for infection | |
Screening plates | Virus, + puromycin | (sgRNA library size × 200-fold) ÷ 0.3 MOI ÷ cell seeding density at infection = number of plates requireda |
Control 1 | No virus, + puromycin (0% survival control) | 1 |
Control 2 | Virus, + No puromycin (100% survival control) | 1 |
a Include extra plates to accommodate for MOI fluctuations and growth rates |
Table 4: Calculation for infection set-up.
Reagents | Amount per 1x reaction |
2x Master Mix | 25 μL |
10 mM PCR 1 LCV2 forward primer | 2.5 μL |
10 mM PCR 1 LCV2 reverse primer | 2.5 μL |
Genomic DNA | 3.5 μg |
Water | up to 50 μL |
Total | 50 μL |
Table 5: PCR 1 set-up.
Table 6: PCR primers for amplification of LCV2::TKOv3 sequencing libraries. Please click here to download this file.
Table 7: PCR primers for amplification of pLCKO2::TKOv3 sequencing libraries. Please click here to download this file.
Step | Temperature | Time | |
1 | 98°C | 30 sec | |
2 | 98°C | 10 sec | 25 cycles (step 2 – 4) |
3 | 66°C | 30 sec | |
4 | 72°C | 15 sec | |
5 | 72°C | 2 min | |
6 | 10°C | Hold |
Table 8: PCR 1 cycle parameters.
Reagents | Amount per 1x reaction |
2x Master Mix | 25 μL |
10 mM i5 forward primer | 2.5 μL |
10 mM i7 reverse primer | 2.5 μL |
PCR 1 product | 5 μL |
Water | 15 μL |
Total | 50 μL |
Table 9: PCR 2 set-up.
Step | Temperature | Time | |
1 | 98°C | 30 sec | |
2 | 98°C | 10 sec | 10 cycles (step 2 – 4) |
3 | 55°C | 30 sec | |
4 | 65°C | 15 sec | |
5 | 65°C | 5 min | |
6 | 10°C | Hold |
Table 10: PCR 2 cycle parameters.
Supplementary table S1. TKO reference gene sets Please click here to download this file.
Figure 1: Schematic overview of pooled screening workflow. (A) Target cell population is infected with CRISPR library lentivirus at low MOI to ensure that most cells receive one viral integration and that library representation is maintained. The different colors represent different sgRNAs in each viral particle. Genetically modified cell pools are selected. Once selection is complete, cells are sampled for T0 reference and serially passaged. (B) At the first passage after T0, cells have recovered from infection and drug treatments can be added, if required. Following treatment, cell populations are serially passaged for several weeks. During each passage, cells are collected for genomic DNA and reseeded at the required fold coverage of the sgRNA library. (C) Two types of screens can be performed: 1) positive selection screens, which identify mutant cells that show resistance or increased survival under the specific selection pressure (e.g., drugs or mutant cell line), as they will be enriched during the screen; or 2) negative selection screens, which identify mutant cells with increased sensitivity to or loss of survival under the screen selection pressure, as they will be lost during the screen. (D) Genomic DNA is harvested and PCR-amplified to enrich for guide regions. (E) Guide abundance is quantified by next-generation sequencing and enriched, or depleted guides are determined for “hit” identification. Please click here to view a larger version of this figure.
Figure 2: Quality of amplified CRISPR sgRNA library. Amplified library plasmids are analyzed by next-generation sequencing (recommended reads: 30 million reads, corresponding to ~400-fold representation of the library). Shown here is a library with tight distribution of sgRNAs, with >95% of all sgRNAs within a 4-fold distribution range. Please click here to view a larger version of this figure.
Figure 3: Evaluation of drop-out screen quality using gold-standard essential gene reference sets. (A) Precision recall analysis of screening results in recovering of essential genes at a threshold of BF >6 and FDR of 5%. High performing screen are represented by blue line and low performing screens are represented by red line. (B) Fold change distribution of sgRNA targeting essential genes (solid line) and nonessential genes (dotted lines). Please click here to view a larger version of this figure.
Figure 4: Determination of gene essentiality. Bayes Factor ranking of gene essentially in a particular screen. Bayes Factor (BF) represents a confidence measure that the gene knock-out results in a fitness defect. Higher Bayes Factors indicate increased confidence that gene knock-out results in fitness defect, (red dots). Lower Bayes Factor scores suggest knock-out provides growth advantage (blue dots). Please click here to view a larger version of this figure.
Figure 5: Positive selection screen for suppressor of thymidine block in HCT116 cells. Normalized read counts for all sgRNAs at T0 plotted against mean normalized read counts for thymidine treated samples. For positive selection screens (i.e., using an IC90 concentration of drug), the number of perturbations that will confer resistance to the drug is expected to be small. For this reason, read depth can be lower than what is needed for negative screens, in whch most of the library is expected to be represented. TK1 sgRNAs are circled in red. This figure has been modified from a previous publication3. Please click here to view a larger version of this figure.
Due to its simplicity of use and high pliability, CRISPR technology has been widely adopted as the tool of choice for precise genome editing. Pooled CRISPR screening provides a method to interrogate thousands of genetic perturbations in a single experiment. In pooled screens, sgRNA libraries serve as molecular barcodes, as each sequence is unique and is mapped to the targeted gene. By isolating the genomic DNA from the cell population, genes causing the phenotype of interest can be determined by quantifying sgRNA abundance by next generation sequencing. Massively parallel sequencing methods are utilized to quantify sgRNAs in samples, meaning that multiple independent cell populations can be pooled into the same sequencing lane to minimize cost.
Before embarking on a large-scale screening project, it is important to have a well-characterized and technically optimized model. Genetic background, growth rate, and transduction efficiency are important factors when choosing your cell lines for screening. For example, growth rates and editing efficiency will determine scalability and technical suitability of the model. In order to adequately represent large sgRNA libraries, tens of millions of cells are required, therefore cell number could be a limiting factor in screening feasibility for cell lines with slower doublings or ones that do not have good proliferative capacity (e.g., primary cells). Based on growth rates, cell culture conditions such as cell seeding density and plate size for screening should be selected accordingly. It is recommended to culture cells in the largest vessel that is practical and technically feasible for the screen.
Lentivirus transduction efficiencies vary between cell types, as cells differ in inherent infectivity. As a result, the volume of virus required to achieve sufficient infection in one cell type will not necessarily be the same in another. Therefore, it is critical to functionally titer each batch of lentivirus library produced in the cell line to be screened to ensure sufficient coverage of the library and mostly single transduction events per cell by transducing at lower MOIs around 0.3 (section 4). Transduction efficiencies can also be influenced by cell culture conditions; therefore, functional titers should be determined using the same cell conditions that will be used in the screen. That is, it is important to use the same tissue culture vessels, media constituents and volume, cell plating density, and virus preps without prior thaws. Measurements made in different formats or conditions will not reliably scale to the screening format.
Despite the advantage of using all-in-one CRISPR-Cas9 guide libraries such as LCV2::TKOv3, the gene encoding Cas9 is quite large, making it difficult to efficiently package into viral particles (105-106 TU/mL). Delivering lower lentiviral titers can be a limitation for cell lines that are difficult to transduce, as they will have even more difficulty with the all-one-CRISPR libraries. To mitigate this, Cas9 should be expressed in the cell line in advance, followed by delivery of CRISPR libraries only containing sgRNAs (e.g., pLCKO2::TKOv3), which can be made at much higher titers (107-108 TU/mL). The ploidy of a cell line is also important, as it determines the number of target loci that need to be modified. The ability to generate complete knock-outs in haploid cells is more efficient than in cells with multiple copies of a given gene. Therefore, screens in haploid cells may be more sensitive and yield higher quality data than screens performed in diploid or aneuploid cell lines6. Testing known genes that are linked to the phenotype will help determine the screen-ability of a cell line model. For example, for essentiality screens, guides targeting a subunit of the 26S proteasome, PSMD1 (Addgene: plasmid #74180), a core essential gene, can be used to test editing efficiency and infectibility of cell lines, as perturbation of PSMD1 will result in cell death.
The robustness of pooled screens highly depends on sgRNA representation. This is an important metric that determines library performance during a screen and the ability to identify hits. Library diversity is biased in the representation of each sgRNA; therefore, the population of cells to be screened and analyzed should be sufficiently large to ensure the capture of under-represented sgRNAs6. 200- to 1000-fold representation of each sgRNA is the typical coverage that has been used in published screens (i.e., 200-1000 cells per sgRNA)10,15. This representation should be maintained when amplifying the library plasmid (section 1) and throughout the screen by infecting and passaging the required cell number (section 5) to represent the desired library coverage and during sequencing library preparation (protocol 6), as described throughout the protocol. For example, to achieve ~200-fold coverage of the TKOv3 library requires selection and passaging of 15 million infected cells. During sequencing, assuming a diploid human genome contains ~7.2 pg of DNA and 1 sgRNA per genome, a total of 100 μg of genomic DNA is required to generate the sequencing library for 15 million sequence reads. The decision of coverage will depend on the size of the library, as coverage of larger libraries will require culturing larger number of cells that can be difficult to maintain and not technically practical. A minimum of 200-fold coverage is recommend with TKOv3 libraries, as 200-fold provides an optimal balance between the logistics of screening large number of cells and maintaining sufficient dynamic range to detect true biological sgRNA drop-outs with limited noise from random depletions22,23. Higher fold library representations will result in improved reproducibility and ensure sufficient window for detection of changes in sgRNA abundance, especially for negative selections. A limiting feature of negative screens is that the perturbation is only depleted to the extent that it was present in the starting library24. In comparison, the dynamic range of positive selection screens is much larger, as they rely on enrichment of cells, and could enrich to 100% of the final population23. Therefore, for positive selection screens (e.g. drug resistance screens), library coverage and read depth can be reduced to 50- to 100-fold representation since only a small cell population is expected to survive.
The sequencing library protocol described here is a two-step PCR optimized for TKOv3 CRISPR libraries in both vector backbones and sequenced on the Illumina sequencing platform. These sequencing libraries can also be generated using a single PCR protocol, similar to that described in Hart et al.3. For other ready-made libraries, the primers and sequencing protocols provided for those libraries should be consulted. When preparing genomic DNA and PCR samples, it is essential to be considerate of contamination precautions. For example, a dedicated area for genomic DNA purification is highly recommended. It should also be physically distinct from bacterial plasmid preps, which are common contaminants found in genomic DNA samples. PCR reactions should be set up in a dedicated PCR hood, as this will minimize contamination from plasmids and other sequencing libraries. For good practice, a no-template negative control can be included to help monitor for PCR contamination.
Data analysis to translate sequencing reads from screens is a non-trivial task, given the size and diversity of these datasets. Once the sequence reads have been aligned and normalized, several bioinformatic tools are available to assist with evaluating screen performance (Figure 3) and hit identification (Figure 4). BAGEL is described in this protocol as the key tool for data analysis. BAGEL uses a Bayesian framework to compare the distributions of known essential and non-essential gene sets to the log-fold change of all guides targeting a gene. This method is described in detail in Hart et al3. In addition to BAGEL, other algorithms designed to identify both enriched and depleted sgRNAs, such as MAGeCK25 can also be used. For drug screens, it is recommended to use the DrugZ algorithm to identify both synergistic and suppressor chemical genetic interactions. DrugZ was designed to compare the relative abundance of sgRNA in a treated population to the relative abundance of sgRNA in an untreated population at the same timepoint20.
A limitation of CRISPR screens is that Cas9 does not always lead to a knock-out, as there is always a possibility that the indels created are in-frame mutations, leaving the gene function intact13. This results in a mixed population, making the screen “noisy” and interpretation of data challenging. Using multiple independent sgRNAs targeting a gene can build-in redundancy, reducing the effect of sgRNAs with low activity. An additional caveat to CRISPR studies is the effect of the double strand breaks created by Cas9 nuclease, which can lead to cellular lethality independent of the gene being targeted. This anti-proliferative effect increases with target site copy number, leading to false positive identification of genes within highly amplified regions26. Computational methods like CERES have been developed to correct for copy number effects27. These workflows consider the copy number effect to estimate gene dependency levels in knock-out-based essentiality screens. Careful examination of genomic locations of hit genes in amplified regions can help determine false positives that are due to multiplicity of cutting effects13. Primary screens can only identify potential hits. It is important to follow-up with a secondary screen or protocol to validate the hits and distinguish on-target from off-target effects, weeding out false positives and ensuring genes that scored weakly due to ineffective perturbations are not left behind as false negatives23.
This protocol focuses on viability-based screening approaches, in which the condition of study should lead to a proliferation defect or death of cells. For processes that do not lead to a change in cellular viability, the viability-based pooled screening method can be restrictive. An alternative is to perform screens using reporter or marker-based assays and enrichment by fluorescence activated cell sorting (FACS) approaches. In marker-based selection screens, the phenotype is based on mutations that regulate marker gene expression rather than cell health13,23. Arrayed CRISPR formats are also available for one-gene per well screening. Arrayed formats are more amenable to complex or microscopy-based read outs. However, arrayed formats require automated equipment and large amounts of reagents28.
The screening protocol discussed here uses S. pyogenes Cas9 nuclease to create null alleles, which is the most widely used for genetic screens and for which many libraries are available (Addgene: Pooled Libraries). Alternative options to knock-out libraries are also available, which use a catalytically dead dCas9 tethered to chromatin modifier proteins to inhibit (CRISPRi) or activate (CRISPRa) transcription of genes. Similar to RNAi, CRISPRi offers the ability to study phenotypic effects at different gene doses and essential genes that cannot tolerate complete knock-out, while CRISPRa can be used to perform gain-of-function screens. Each of these technologies have their advantages, but in general, the CRISPR knock-out approach is the most developed. It has been proven to perform well with low noise, minimal off-target effects, and experimental consistency, especially in lethality-based essential gene screens, when compared to knock-down approaches using either CRISPRi and shRNAs5. Despite its extensive applicability to date, CRISPR screening technology remains in its early stages. New tools are continuing to be built from the basic components of CRISPR. These include combinatorial gene editing strategies that can target multiple genomic loci, optimization of orthogonal Cas enzymes, and modifications with chromatin functional domains to diversify Cas9 activities. As CRISPR technology continues to grow, its coupling to genetic screening approaches will serve as a powerful platform for functional discovery in genetics.
The authors have nothing to disclose.
This work was supported by Genome Canada, the Ontario Research Fund, and the Canadian Institutes for Health Research (MOP-142375, PJT-148802).
0.22 micron filter | |||
30°C plate incubator | |||
37°C shaking incubator | |||
37°C, 5% CO2 incubator | |||
5 M NaCl | Promega | V4221 | |
50X TAE buffer | BioShop | TAE222.4 | |
6 N Hydrochloric acid solution | BioShop | HCL666.500 | |
95% Ethanol | |||
Alamar blue | ThermoFisher Scientific | DAL1025 | |
Blue-light transilluminator | ThermoFisher Scientific | G6600 | |
Bovine Serum Albumin,Heat Shock Isolation, Fraction V. Min. 98%, Biotechnology grade | Bioshop | ALB001.250 | |
Dulbecco's Modification of Eagles Medium | Life Technologies | 11995-065 | Cel culture media |
Electroporation cuvettes | BTX | 45-0134 | |
Electroporator | BTX | 45-0651 | |
Endura electrocompetent cells | Lucigen | 90293 | |
Fetal Bovine Serum | GIBCO | 12483-020 | |
HEK293T packaging cells | ATCC | CRL-3216 | recommend passage number <15 |
Hexadimethrine Bromide (Polybrene) | Sigma | H9268 | Cationic polymer to enhance transduction efficiency |
Hexadimethrine Bromide (Polybrene) | |||
LB agar plates with carbenicillin | |||
LB medium with carbenicillin | |||
Low molecular weight DNA ladder | New England Biolabs | N3233S | |
Nanodrop spectrophotometer | ThermoFisher Scientific | ND-ONE-W | |
NEBNext Ultra II Q5 Master Mix | New England Biolabs | M0544L | |
Opti-MEM | Life Technologies | 31985-070 | Reduced serum media |
Plasmid maxi purification kit | Qiagen | 12963 | |
pMD2.G (envelope plasmid) | Addgene | Plasmid #12259 | lentiviral system |
psPAX2 (packaging plasmid) | Addgene | Plasmid #12260 | lentiviral system |
Puromycin | Wisent | 400-160-UG | |
QIAquick gel extraction kit | Qiagen | 28704 | |
Qubit dsDNA BR assay | ThermoFisher Scientific | Q32853 | |
Qubit fluorometer | ThermoFisher Scientific | Q33226 | |
RNAse A | Invitrogen | 12091021 | |
S.O.C recovery medium | Invitrogen | 15544034 | |
SYRB Safe DNA gel stain | ThermoFisher Scientific | S33102 | |
Toronto KnockOut CRIPSR library (TKOv3) – Cas9 included | Addgene | Addgene ID #90203 | Genome-wide CRISPR library , includes Cas9, 71,090 sgRNA |
Toronto KnockOut CRIPSR library (TKOv3) – non-cas9 | Addgene | Addgene ID #125517 | Genome-wide CRISPR library, non-Cas9, 71,090 sgRNA |
Tris-EDTA (TE) solution, pH8.0 | |||
UltraPure agarose | ThermoFisher Scientific | 16500500 | |
Wizard genomic DNA purification kit | Promega | A1120 | |
X-tremeGENE 9 DNA transfection reagent | Roche | 06 365 809 001 | Lipid based transfection reagent |