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Mar 2017
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A CRISPR Competition Assay to Identify Cancer Genetic Dependencies
基于CRISPR竞争法鉴定癌症的遗传依赖性   

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Abstract

The CRISPR/Cas9 system is a powerful tool for genome editing, wherein the RNA-guided nuclease Cas9 can be directed to introduce double-stranded breaks (DSBs) at a targeted locus. In mammalian cells, these DSBs are typically repaired through error-prone processes, resulting in insertions or deletions (indels) at the targeted locus. Researchers can use these Cas9-mediated lesions to probe the consequences of loss-of-function perturbations in genes of interest. Here, we describe an optimized protocol to identify specific genes required for cancer cell fitness through a CRISPR-mediated cellular competition assay. Identifying these genetic dependencies is of utmost importance, as they provide potential targets for anti-cancer drug development. This protocol provides researchers with a robust and scalable approach to investigate gene dependencies in a variety of cell lines and cancer types and to validate the results of high-throughput or whole-genome screens.

Keywords: Cancer (癌症), CRISPR (CRISPR), Genetic dependency (遗传依赖), Cell fitness (细胞适应性), Essential genes (必需基因), Cell competition (细胞竞争)

Background

The CRISPR/Cas9 system is believed to have evolved as an adaptive prokaryotic viral defense (Mojica et al., 2005; Makarova et al., 2006). Soon after its discovery, it was co-opted by researchers and modified for laboratory use in genome editing (Doudna and Charpentier, 2014; Hsu et al., 2014). By transgenically expressing the Cas9 nuclease along with a short guide RNA (sgRNA) complementary to the target sequence, double-stranded breaks (DSBs) can be introduced at a locus of interest in a variety of cells and organisms (Cong et al., 2013). Mammalian cells typically repair these DSBs through the error-prone nonhomologous end-joining (NHEJ) pathway, resulting in insertions or deletions (indels) at the targeted locus (Bothmer et al., 2017). These indels can disrupt coding sequences and result in the generation of null alleles.

A key goal of preclinical cancer research is to identify and characterize cancer “genetic dependencies”, or genes that are required for cancer cell proliferation or fitness. These genes, sometimes called “cancer addictions”, are attractive targets for anti-cancer drug development. Historically, the first set of cancer dependencies to be identified were recurrently-mutated oncogenes, and inhibitors of these driver genes like BRAF and EGFR have proven to be highly successful clinical agents (Paez et al., 2004; Luo et al., 2009; Chapman et al., 2011). The advent of systemic screening approaches with RNA interference (RNAi) provided researchers with a powerful new methodology to identify other potential genetic dependencies in cancer cells. Through the use of short hairpin and small interfering RNA constructs complementary to target transcripts, researchers could knock-down the expression of a gene of interest to test whether its loss blocked cancer growth (Hannon and Rossi, 2004). While numerous potential drug targets have been identified with these methodologies, RNAi-based perturbations have been found to cause significant off-target effects, and often silence the expression of many other genes that were not intentionally targeted (Jackson et al., 2003; Birmingham et al., 2006). In particular, experiments conducted by our lab and several others have demonstrated that cancer cells can tolerate the loss of many putative dependencies previously discovered using RNAi and have implicated off-target gene knockdown as one potential cause of these discrepant results (Lin et al., 2017 and 2019; Huang et al., 2017; Giuliano et al., 2018; Thomenius et al., 2018). In contrast, head-to-head comparisons have demonstrated that CRISPR/Cas9 constructs exhibit significantly fewer off-target effects compared to RNAi-based approaches, underscoring the significant potential that this technology harbors to identify true cancer dependencies (Morgens et al., 2016; Smith et al., 2017).

As with RNAi, the highly programable nature of CRISPR facilitates targeted screening approaches to uncover genetic addictions in cancer cell lines (Shalem et al., 2014; Wang et al., 2014). Through rational guide design, researchers can disrupt protein function and interrogate potential drug targets. In this protocol, we describe an optimized screening methodology first described by Shi et al. (2015) in which cells that constitutively express the Cas9 nuclease are transduced with a vector that co-expressed a sgRNA and GFP. The resulting population is comprised of both transduced (GFP+) and untransduced cells (GFP-), and the relative abundance of each population is tracked via flow cytometry over the course of several passages (Figure 1). Guides that target the functional protein domains of genes essential to cellular fitness are consistently outcompeted by untransduced cancer cells, which is easily detectable as a decreasing abundance of GFP+ cells over time.


Figure 1. Overview of the competition assay. A. Cas9-expressing cells are transduced with a sgRNA vector at a low multiplicity-of-infection and the relative abundance of the transduced population is measured over the course of five passages via flow cytometry. B. Timeline of the competition assay.

Here we describe a straightforward protocol to determine whether a gene of interest is required for the viability or fitness of a particular cancer cell line. This protocol can be easily scaled-up and offers robust results in as little as three weeks. We have extensively validated this approach through the use of control gRNAs targeting known essential genes and non-essential loci. Researchers can investigate potential dependencies through this GFP competition assay by utilizing guides targeting their gene of interest in parallel with appropriate positive and negative controls. We have found that the results of these GFP competition assays are consistent with other in vitro assays measuring cellular fitness, including 2-D proliferation assays and soft-agar assays to measure anchorage-independent growth (Lin et al., 2017 and 2019). This protocol also represents a straightforward approach to validate individual hits recovered in high-throughput or whole-genome CRISPR screens.

Materials and Reagents

  1. 5 ml Polystyrene Round-Bottom Tubes (Falcon, catalog number: 352054 )
  2. 5 ml Polystyrene Round-Bottom Tubes, with Cell Strainer Snap Cap (Falcon, catalog number: 352235 )
  3. BD 10 ml Syringe Luer-Lok Tip (BD, catalog number: 309604 )
  4. Millex-HV Syringe Filter Unit, 0.45-µm (Millipore, catalog number: SLHVM33RS )
  5. 100 x 15 mm LB + 100 µg/ml ampicillin culture plates (VWR, catalog number 25384-342)
  6. Standard tissue culture plates (6 and 12-well plates recommended)
  7. One Shot Stbl3 Chemically Competent E. coli (Invitrogen, catalog number: C737303 )
  8. HEK293T cells (ATCC, catalog number: CRL-3216 )
  9. Recipient cancer cell line (from ATCC or other sources)
  10. LentiV-Cas9-Puro plasmid (Addgene, catalog number: 108100
  11. psPAX2.0 plasmid (Addgene, catalog number: 12260 )
  12. VSVG plasmid (Addgene, catalog number: 12259)
  13. Lenti-XTM qRT-PCR Titration Kit (Takara, catalog number: 631235 )
  14. gRNA plasmid backbone (see Table 1 for suggested plasmids and their respective Addgene numbers)

    Table 1. Suggested CRISPR Plasmids


  15. LB + 100 µg/ml ampicillin medium (Sigma-Aldrich, catalog number: A9518 )
  16. DMEM medium supplemented with 10% FBS, 1% Pen/Strep, and glutamine (Life Technologies, catalog number: 11995-073 )
  17. Appropriate cell culture medium for the cancer cell line of interest
  18. 50% (v/v) glycerol
  19. Anti-FLAG antibody (Sigma-Aldrich, catalog number: F1804 )
  20. 2 M CaCl2
  21. 2x HEPES-buffered saline
  22. 100 mM chloroquine (Cayman Chemical, catalog number: 14194 )
  23. Polybrene (Santa Cruz Biotechnology, catalog number: sc-134220 )
  24. Puromycin (Gibco, catalog number: A11138-03 )
  25. BsmBI restriction endonuclease with NEB buffer 3.1 (NEB, catalog number: R0580 )
  26. Alkaline phosphatase, calf intestinal (CIP) stock (10,000 U/ml; NEB, catalog number: M0290 )
  27. 100 µM gRNA oligos (IDT or preferred oligonucleotide synthesis provider)
  28. T4 Polynucleotide Kinase (NEB, catalog number: M0201 )
  29. T4 DNA Ligase with T4 DNA Ligase Buffer (NEB, catalog number: M0202 )

Equipment

  1. Pipettes
    1. Pipetman P10 (Gilson, catalog number: F144802 )
    2. Pipetman P20 (Gilson, catalog number: F123600 )
    3. Pipetman P200 (Gilson, catalog number: F123601 )
    4. Pipetman P1000 (Gilson, catalog number: F123602 )
  2. Mammalian cell culture equipment
    1. CO2 incubator
    2. Laminar flow cabinet
  3. Fluorescent Microscope
  4. Thermocycler
  5. -20 °C freezer
  6. -80 °C freezer
  7. Miltenyi Biotec MACSquant VYB Flow Cytometer (Miltenyi Biotech, catalog number: 130-096-116 )
    Alternative: Luminex Guava easyCyte or comparable flow cytometer capable of measuring GFP fluorescence

Software

  1. Benchling (https://www.benchling.com/)
  2. Microsoft Excel
  3. Prism 8

Procedure

Note: In this section, we will introduce the Cas9 transgene into the cell line of interest. If researchers already have stable Cas9-expressing cell lines, they can proceed directly to Procedure B.

  1. Generation of Cas9-expressing cell line
    1. Select a Cas9 expression vector
      We suggest using LentiV-Cas9-Puro (Addgene) to introduce Cas9 into the cell line of interest, although any constitutive SpCas9 expression vector with a mammalian selection marker should suffice.
    2. Produce Cas9 virus
      1. We suggest using the lentiviral packaging plasmids psPAX2 and VSVG (Addgene).
      2. We provide a brief overview of lentivirus production below. Our lab has made available a more detailed protocol for this process, which can be found in Giuliano et al. (2019).
        1. Calcium phosphate transfect HEK293T packaging cells with LentiV-Cas9-Puro, psPAX2, and VSVG.
        2. Replace media on the packaging cells approximately 8-14 h post transfection with fresh media.
        3. Harvest virus 24 h post-transfection. We recommend filtering the supernatant with a 0.45-µM filter to separate cell debris. Virus can be collected up to three times in the 72-h period post-transfection.
        4. Virus can be used immediately or stored at -80 °C.
    3. Transduce cell line of interest
      1. Plate cells at approximately 50% confluence.
      2. Mix viral supernatant with fresh media in a 1:1 ratio.
      3. Add polybrene to the diluted viral supernatant to a final concentration of 8 µg/ml. We find this concentration improves transduction efficiency for nearly all cell lines commonly used in our lab. However, this concentration can be adjusted if needed.
      4. Replace media on cells to be transduced with the polybrene-containing viral supernatant.
      5. Change media on the transduced cells with fresh media 24 h post-transduction.
    4. Select for Cas9-expressing cells
      1. The Cas9 expression vector used in our lab (LentiV-Cas9-Puro) contains a puromycin-resistance marker. The ideal concentration of puromycin for selection is cell-line dependent and should be optimized prior to the start of the experiment.
        1. To optimize the concentration of puromycin for selection, we suggest plating wild-type cells at approximately 50% confluence on 6 or 12-well plates.
        2. Replace media on the cells 24 h later with media containing puromycin at a range of 1-4 µg/ml. We generally test in increments of 0.5 μg/ml, with each well containing a different concentration of puromycin.
        3. Assess cell survival 3-5 days post drug selection. The optimal concentration of puromycin is the lowest concentration at which there are no surviving cells. For adherent cell lines, dead/dying cells generally detach from the plate, which facilitates identifying surviving cells. Suspension cell lines may require a viability dye for this purpose.
        4. While this range of puromycin concentrations is sufficient for most cell lines, some may require higher working concentrations. In that case, test additional concentrations up to 10 µg/ml.
      2. Replace media on the transduced cells 48 h post-transduction with media containing puromycin at a concentration of 1-4 µg/ml or as determined to be optimal for the cell line of interest.
      3. Maintain selection until Cas9 is stably expressed. We find that for most cell lines, only one round of transduction, followed by 3 to 5 days of puromycin selection, is sufficient for generating stable Cas9 expression. However, some cell lines may require more than one round of transduction and/or a longer selection period.
    5. Verify Cas9 expression
      1. Researchers can utilize a variety of approaches to verify expression of Cas9, including:
        1. Quantitive PCR (qPCR). Through the use of PCR primers targeting the Cas9 mRNA transcript, expression of the Cas9 nuclease can be confirmed. The qPCR primers our lab utilizes for detecting Cas9 expression are forward: 5′ GGCCTACCACGAGAAGTACC 3′ and reverse: 5′ CTGGCGTTGATGGGGTTTTC 3′.
        2. Western Blot. The Cas9 expression vector used in our lab (LentiV-Cas9-Puro) contains a FLAG-epitope tag fused to the N-terminus of Cas9. As such, researchers can make use of an anti-FLAG antibody in place of a Cas9 specific antibody if needed. The anti-FLAG antibody our lab utilizes can be found in the Materials and Reagents section (Sigma-Aldrich).
        3. Transduction with guides targeting essential genes. While this may not be suitable for the first GFP competition assay you conduct, our lab routinely performs control competition assays with several positive and negative control guides (as described later in this protocol) to verify Cas9 functionality in a cell line of interest.
      2. It is of note to mention that qPCR and western blotting for Cas9 can confirm Cas9 expression but not functionality. Transduction with guides with known biological activity can serve to confirm Cas9 functionality. For these reasons, we recommend using a combination of these approaches to verify Cas9 expression and functionality in any Cas9-expressing cell lines you generate.

Note: In this section, we will design guides targeting the gene of interest, and clone these guides, alongside appropriate positive and negative control guides, into guide plasmids for sgRNA and fluorescent protein expression. We discuss considerations for ideal guide design and provide control guides extensively validated by our lab and our methods for plasmid cloning.

  1. Design of sgRNAs and guide plasmids
    1. Identify target sites within the gene of interest
      1. Use an online tool such as Benchling (https://www.benchling.com/) to design suitable guides targeting the gene-of-interest.
        1. For this protocol, we use single guides with 20 base pairs of homology to the target sequence, as this is best-suited for CRISPR-knockout.
        2. The PAM sequence should be NGG on the 3′ side of the guide sequence, as the Cas9 strain we use is SpCas9. The PAM sequence is necessary for Cas9-mediated DNA cleavage and is found 3-4 base pairs downstream of the cut site.
      2. Guides targeting functional protein domains produce the most-efficient knockout of the target gene. For this reason, we highly recommend cross-referencing exons with protein domains to identify which guides are best-suited to disrupt protein function, as guides targeting the first exon alone can lead to false negatives (Figure 2A) (Shi et al., 2015).
      3. Additionally, we recommend designing 3 or more guides per gene, ideally targeting different exons.
      4. Order oligonucleotides through IDT or your preferred commercial source for DNA synthesis.


        Figure 2. Example experimental design and data analysis for the competition assay. A. Example guide RNA design. AURKB (Aurora B) encodes a kinase with key roles in mitosis. Guides are shown targeting multiple exons within Aurora B’s kinase domain. B. Example dropout data. The relative abundance of the transduced population is indicated by % GFP and the calculated fold dropout is shown over the course of five passages. C. Example dropout figure. The fold dropout over the course of five passages is shown for each guide. It can be seen that the negative control guides targeting Rosa26 exhibit minimal depletion and the positive control guides targeting RPA3 display over ten-fold dropout at the final timepoint. Consistent with prior knowledge, these results verify that AURKB is a cancer cell dependency.

    2. Identify suitable positive and negative control guides
      1. Negative control guides should target noncoding loci or genes dispensable for cellular fitness such as Rosa26 or AAVS1. These loci are considered canonical safe-harbor sites, and thus introducing indels at these loci will have minimal effects on cancer cell fitness.
      2. Positive control guides should target essential genes, such as PCNA or RPA3. These genes are critical for DNA replication, and thus introducing indels at these loci will be detrimental to cancer cell fitness.
      3. We have provided our control guides in Table 2.
        Note: The sequence provided is the 20 bp spacer sequence and does not include the 4-5 bp adapter sequence for cloning into the guide vector. The adapter for our guide vector is CACC, and a G must be added if the first nucleotide of the spacer sequence is not a guanine. For example, the complete guide sequence for Rosa26 g1 listed below with the adapter added is: 5’- CACCGACAGCAAGTTGTCTAACCCG (adapter in bold).

        Table 2. Suggested Control Guides


    3. Clone sgRNAs into guide vector
      1. We recommend the use of a lentiviral GFP sgRNA expression vector (Addgene), although any comparable sgRNA expression vector with a fluorescent marker is suitable for this assay. Additionally, our lab has developed and validated several derivatives of this sgRNA expression vector with different fluorescent protein markers, which can be used if needed.
      2. We provide a brief overview of the guide cloning process below. Our lab has made available a more detailed protocol for this process, which can be found in Giuliano et al. (2019).
        1. Linearize the guide vector backbone with a BsmBI digest.
        2. Anneal and phosphorylate guide oligos with T4 PNK.
        3. Ligate annealed and phosphorylated guides into guide vector backbone.
        4. Transform ligation product into competent cells.
        5. Sequence-verify the guide plasmid using the U6 primer (5′- GAGGGCCTATTTCCCATGATTCC).

Note: In this section, we will introduce the guide plasmids encoding the sgRNA and fluorescent protein into the Cas9-expressing cell line. We discuss methods of lentiviral production, viral titration, and some considerations for the competition assay set-up.

  1. Transduction of Cas9-expressing cell line with guide plasmids
    1. Produce lentivirus with guide plasmids
      See Step A2 for a brief overview of the virus production process, or our more comprehensive virus production protocol in Giuliano et al. (2019).
    2. Determine viral titer
      1. As the guide plasmids used in this assay encode fluorescent proteins, viral titer can be approximated by transducing cells with differing amounts of virus and measuring the percentage of cells exhibiting fluorescence three days post-transduction.
      2. Researchers can also utilize a qPCR-based approach to determine viral titer if they prefer (e.g., Lenti-XTM qRT-PCR Titration Kit [Takara], see manufacturer’s protocol for more information). While such an approach may provide a more definitive answer as to the number of virus particles per microliter of supernatant, we find that the approximation provided by measuring the percentage of cells exhibiting fluorescence is sufficient for this assay.
    3. Transduce Cas9-expressing cell line with titrated viral supernatant.
      1. The ideal transduction efficiency is 50%. However, this assay is flexible in its requirements such that transduction efficiencies ranging from 10-80% are suitable for this assay.
      2. See Step A3 for a brief overview of the viral transduction process.
      3. We recommend conducting this assay on 12 or 24-well plates. This assay can be scaled up or down if needed. Generally, conducting this assay on larger plates reduces background variation, but if there are constraining factors at play (i.e., not enough virus), this assay can be conducted on a 48 or 96-well plate as well.

Note: In this section, we will take our first time-point of the competition assay. We discuss methods for measuring the percentage of cells expressing the GFP marker through flow cytometry, and considerations for conducting the competition assay.

  1. Start of competition assay
    1. Harvest cells
      Generally, we set our first time-point for three days post-transduction to provide ample time for transgene expression.
    2. Seed cells at appropriate confluency for the next passage
      1. Perform an appropriate cell split (i.e., 1:2 to 1:10) depending on the cell line’s proliferative capacity.
      2. Depending on the choice of cell line and its doubling time, researchers may choose to passage the cells every three or four days.
      3. For example, we generally split the breast cancer cell line MDA-MB-231 1:4 every three days.
    3. Measure GFP expression by flow cytometry
      1. After plating cells for the next passage, prepare leftover samples for flow cytometry. We suggest passing cells through a single cell strainer (Falcon) prior to flow analysis. It is not necessary to utilize any flow cytometry specific resuspension buffer, as cell culture medium should be sufficient for this experiment.
      2. Run control GFP- cells to set appropriate voltages for forward scatter and side scatter lasers.
      3. Run control GFP- cells and GFP+ cells to adjust flow parameters and determine a gate for GFP-expressing cells.
      4. Run experimental samples. Researchers should aim to run at least 10% of the sample volume (i.e., if you harvested 1 ml of cells from a 12-well plate, run at least 100 µl). If possible, we recommend running about 25% of the sample, as a larger sample will yield a more accurate measurement of GFP expression.
      5. Record % GFP for all samples.
        To ensure experimental consistency, we suggest saving a program with your laser voltages and analysis templates to use for successive time-points.

Note: In this section, we will continue the competition assay through its completion.
  1. Successive timepoints of competition assay
    1. Harvest cells
    2. Seed cells at appropriate confluency for next passage.
      At the fifth timepoint, the assay is complete, and it is not necessary to re-plate the cells for the next passage.
    3. Measure GFP expression by flow cytometry.

Data analysis

Note: In this section, we will transform our measurements of % GFP expressing cells from each of the timepoints into fold change values. These fold change values from individual guides can then be compared to one another, and to the positive and negative controls, to draw conclusions as to the essentiality of the gene of interest in the cell lines tested.

  1. Determine fold change
    1. Fold change in the context of this assay is defined as (% GFP at passage 1)/(% GFP at passage X).
    2. Therefore, the fold change at passage 1 should be 1. An example of these calculations can be seen in Figure 2B.

  2. Prepare grouped graphs
    We suggest using Prism, but any software that can graph numerical data (i.e., Excel) will suffice (Figure 2C).

  3. Set threshold for dropout
    1. The threshold should be greater than the fold change of the negative controls, as these guides target loci dispensable for cancer cell fitness.
    2. Our lab uses a threshold of 2.5-fold change for determining dependencies, as we have found that this threshold is always above the level of depletion observed with negative control guides.
    3. This can be adjusted based on the cell line as needed, as the dynamic range for fold change is dependent on a variety of factors including Cas9 expression, cell type differences, and GFP % at passage 1.
    4. If multiple guides targeting a gene exhibit minimal dropout above background levels, this gene is unlikely to be a genetic dependency in the cell line tested.
    5. If multiple guides targeting a gene exhibit consistent dropout above background levels, this is evidence for the gene being a dependency in that cell line.

Notes

  1. This protocol provides researchers with a streamlined approach to probe individual genetic dependencies in cancer cell lines. It has been variously used to identify novel dependencies in leukemia, small-cell lung cancer, pancreas cancer, and to interrogate several drug targets in clinical trials (Shi et al., 2015; Lin et al., 2017 and 2019; Huang et al., 2018; Somerville et al., 2018; Tarumoto et al., 2018). Additionally, our lab regularly deploys variations of this approach to probe for genetic dependencies in other contexts. For instance, this assay can be modified to probe for synthetically lethal relationships between two genes (i.e., gene A and gene B). If guides targeting each gene individually exhibit minimal dropout, but the researcher has evidence to suggest these genes have some degree of functional redundancy, then a dual-competition assay can be conducted instead. In this experiment, researchers can co-transduce target cells with two guides that express different fluorescent protein markers (Table 1), with guide 1 targeting gene A and guide 2 targeting gene B. By measuring the individual fluorescent protein expression, as well as the percentage of cells expressing both fluorescent proteins at each passage, researchers can gain insight into potential synthetically lethal relationships. If the double-positive population exhibits consistent depletion, but the single-positive populations do not, this is evidence for a potential synthetically lethal relationship between genes A and B.
  2. The development of additional CRISPR tools for transcriptional modulation provides researchers with other possible approaches for probing genetic dependencies. The CRISPR-interference (CRISPRi) system, wherein catalytically-inactive Cas9 (dCas9) is fused to a Krüppel-associated box (KRAB) domain, provides researchers the ability to suppress target gene expression without double-strand break formation (Gilbert et al., 2013). The resulting partial loss-of-function phenotype provides yet another context for researchers to investigate potential drug targets. The use of such an orthogonal approach complements the total loss-of-function phenotype generated by standard CRISPR/Cas9. Partial loss-of-function approaches, such as CRISPRi provide models of genetic perturbation more closely resembling the effects of targeted therapies, as pharmacological inhibition of any potential genetic dependency is unlikely to be absolute. This competition assay can be readily tailored to a CRISPRi approach through the use of a dCas9-KRAB expression vector in place of a Cas9 expression vector (Addgene #85969), and through the use of guides targeting promoters rather than functional exons. Our lab regularly conducts such assays, and we have found our results from this modified approach to be largely consistent with traditional Cas9 screening (Lin et al., 2019).
  3. While the CRISPR/Cas9 system offers numerous advantages over RNAi for investigating genetic dependencies, there are some limitations to consider. Generally, the Cas9 nuclease is utilized to produce an assortment of indels at the target site, and it is thought that the resulting premature stop codon leads to nonsense-mediated decay (NMD) of the mutant mRNA. However, the production of indels does not necessarily result in the ablation of the target gene product. CRISPR-induced genetic lesions can be bypassed by alternative splicing and downstream transcriptional initiation (Lalonde et al., 2017; Sharpe and Cooper, 2017). Additionally, an unintended consequence of CRISPR-induced knockout can be the production of aberrant protein products of unknown function (Tuladhar et al., 2019). In order to mitigate the potential confounding effect of these factors, we strongly suggest using several guides across multiple exons targeting functional protein domains. Additionally, we recommend verifying protein ablation via western blot.
  4. The process of mutant mRNA NMD itself may also have unintended consequences. NMD is known to be a mechanism of mRNA regulation in many cell types, including liver and bone marrow cells (Lykke-Andersen and Jensen, 2015). A recent study suggested that CRISPR-induced NMD causes a compensatory upregulation of the homologs of a targeted gene, which could potentially obfuscate the true effects of the CRISPR knockout (El-Brolosy et al., 2019). However, experiments performed by our lab suggest that this compensatory upregulation may be rare or non-existent in cancer cells (Lin et al., 2019). If researchers are concerned about potential CRISPR induced-homolog upregulation, we suggest conducting gene expression assays in parallel with the competition assay to rule out this possible confounding factor. Notably, the limitations described thus far are consequences of the genetic lesions induced by Cas9. It follows that these limitations could potentially be minimized through use of alternative systems for perturbation that do not cleave DNA, such as CRISPR-interference.
  5. Beyond the limitations of the CRISPR system itself, it is important to note that the results of this GFP competition assay are by no means dispositive of a gene’s status as being necessary or dispensable for cellular fitness in other contexts. While the consistent lack of dropout for guides targeting a gene across multiple trials and cell lines indicates that a targeted gene is dispensable for cell-autonomous growth in vitro, it remains possible that the gene is a dependency in other cellular contexts, like anchorage-independent growth, low oxygen conditions, in the presence of chemotherapeutics, in vivo, etc. Thus, a gene that does not score as a genetic dependency through this assay can still provide a valuable drug target for anticancer therapies. On the other hand, if researchers do see consistent dropout for guides targeting a gene, this is strong evidence for the gene being a genetic dependency. Such results should be followed up with further investigation through orthogonal approaches.
  6. We believe this assay will provide researchers with a valuable approach to investigate putative cancer dependencies. Results from our lab show this assay to be robust in its findings, and its relative simplicity allows researchers to simultaneously conduct this assay in many cell types. This assay is best utilized as a first-pass approach, and complemented with thorough, in-depth investigation of any putative cancer dependencies.

Acknowledgments

Research in the Sheltzer Lab is supported by an NIH Early Independence Award (1DP5OD021385), a Damon Runyon-Rachleff Innovation Award, a Gates Foundation Innovative Technology Solutions grant, and a CSHL-Northwell Translational Cancer Research grant. Cell sorting was performed with assistance from the CSHL Flow Cytometry Shared Resource, which is supported by the CSHL Cancer Center Support grant 5P30CA045508. This protocol draws on work original published in Shi et al. (2015), Lin et al. (2017 and 2019) and Giuliano et al. (2019).

Competing interests

J.M.S. is a member of the advisory board at Tyra Biosciences and has received consulting fees from Ono Pharmaceutical Co. and Merck.

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简介

[摘要] CRISPR / Cas9系统是用于基因组编辑的强大工具,其中RNA引导的核酸酶Cas9可以直接在目标基因座处引入双链断裂(DSB)。在哺乳动物细胞中,这些DSB通常通过容易出错的过程进行修复,从而导致在目标基因座处插入或缺失(indel)。研究人员可以使用这些Cas9介导的病变来探究结果目标基因的功能丧失扰动。在这里,我们描述了一种优化的协议,可通过CRISPR介导的细胞竞争测定法来鉴定癌细胞适应性所需的特定基因。鉴定这些遗传依赖性至关重要,因为它们为抗癌药物的开发提供了潜在的靶标。该协议为研究人员提供了一种强大且可扩展的方法,以研究多种细胞系和癌症类型中的基因依赖性,并验证高通量或全基因组筛选的结果。

[背景] CRISPR / Cas9系统被认为已发展成为一种适应性的原核病毒防御系统(Mojica 等,2005; Makarova 等,2006)。它被发现后不久,就被研究人员选中,并进行了基因组编辑以供实验室使用(Doudna和Charpentier,2014年; Hsu 等人,2014年)。通过转基因表达Cas9核酸酶以及与靶序列互补的短链RNA(sgRNA),可以将双链断裂(DSB)引入各种细胞和生物体的目标位点(Cong 等,2013)。 )。哺乳动物细胞通常通过易出错的非同源末端连接(NHEJ)途径修复这些DSB,从而在目标基因座处导致插入或缺失(indels)(Bothmer et al。,2017)。这些插入缺失可破坏编码序列并导致无效等位基因的产生。

临床前癌症研究的主要目标是鉴定和表征癌症“遗传依赖性”,即癌细胞增殖或适应性所需的基因。这些基因有时被称为“癌症成瘾”,是抗癌药物开发的有吸引力的靶标。历史上,要鉴定的第一组癌症依赖性是反复突变的癌基因,并且已证明这些驱动基因(如BRAF和EGFR)的抑制剂是非常成功的临床药物(Paez 等人,2004; Luo 等人,2009;Lux 等人,2009)。 Chapman 等,2011)。具有RNA干扰(RNAi)的系统性筛选方法的出现为研究人员提供了一种强大的新方法,可用于鉴定癌细胞中其他潜在的遗传依赖性。通过使用短发夹和与目标转录物互补的小干扰RNA构建体,研究人员可以敲低目的基因的表达,以测试其丢失是否阻断了癌症的生长(Hannon和Rossi,2004)。尽管已经用这些方法确定了许多潜在的药物靶标,但已发现基于RNAi的扰动会引起明显的脱靶效应,并常常使许多非故意靶向的其他基因的表达沉默(Jackson 等,2003;伯明翰)。等人,2006)。特别是,我们实验室和其他几个实验室进行的实验表明,癌细胞可以耐受先前使用RNAi发现的许多推定依赖性的丧失,并且暗示脱靶基因敲低是这些差异结果的潜在原因之一(Lin 等, 2017和2019; Huang 等人,2017; Giuliano 等人,2018; Thomenius 等人,2018)。相比之下,面对面的比较表明,与基于RNAi的方法相比,CRISPR / Cas9构建体表现出的脱靶作用明显更少,强调了该技术在识别真正癌症依赖性方面的巨大潜力(Morgens 等,2016 ; Smith 等,2017)。

与RNAi一样,CRISPR的高度可编程性质有助于有针对性的筛选方法来发现癌细胞系中的基因成瘾(Shalem 等人,2014; Wang 等人,2014)。通过合理的指南设计,研究人员可以破坏蛋白质功能并审问潜在的药物靶标。在这个协议中,我们描述第一个通过施描述的优化的筛选方法ë 吨人。(2015),其中用共同表达sgRNA和GFP的载体转导组成型表达Cas9核酸酶的细胞。所得的群体由转导的(GFP +)细胞和未转导的细胞(GFP-)组成,并且在数次传代过程中通过流式细胞术跟踪了每个群体的相对丰度。靶向细胞健康所必需的基因的功能蛋白结构域的指南始终被未转导的癌细胞所竞争,因为随着时间的推移,GFP +细胞的丰度逐渐降低,因此很容易检测到。



D:\重新格式化\ 2020-5-6 \ 1902956--1438 JasonSheltzer 492105 \图jpg \图1.jpg

图1.竞争分析概述。A.使用sgRNA载体以低感染复数转导表达Cas9的细胞,并通过流式细胞术在五次传代过程中测量被转导群体的相对丰度。B.竞争分析的时间表。



在这里,我们描述了一个简单的协议,以确定特定癌细胞系的生存力或适应性是否需要感兴趣的基因。该协议可以轻松扩展,并在短短三周内提供可靠的结果。我们已经通过使用靶向已知必需基因和非必需基因座的对照gRNA广泛验证了这种方法。研究人员可以通过利用针对其感兴趣基因的指南与适当的阳性和阴性对照并行研究这种GFP竞争测定的潜在依赖性。我们已经发现这些GFP竞争测定的结果与其他测量细胞适应性的体外测定一致,包括2-D增殖测定和软琼脂测定以测量不依赖锚定的生长(Lin 等人,2017和201 9)。。该协议还代表了一种简单的方法来验证在高通量或全基因组CRISPR筛选中回收的单个命中。

关键字:癌症, CRISPR, 遗传依赖, 细胞适应性, 必需基因, 细胞竞争

材料和试剂


 


5 ml聚苯乙烯圆底管(Falcon,目录号:352054)
5 ml聚苯乙烯圆底管,带有细胞过滤器卡扣盖(猎鹰,目录号:352235)
BD 10毫升注射器鲁尔笔尖(BD,目录号:309604)
Millex -HV针筒过滤器单元,0.45 -µ m (Millipore,目录号:SLHVM33RS)
100 x 15毫米磅LB + 100微克/毫升氨苄青霉素培养板(VWR,目录号25384-342)
标准组织培养板(建议使用6和12孔板)
的One Shot Stbl3化学感受E. Ç OLI (Invitrogen公司,目录号:C737303)
HEK293T细胞(ATCC ,目录号:CRL-3216)
收件人癌细胞系(来自ATCC或其他来源)
LentiV-Cas9-Puro质粒(Addgene ,目录号:108100)
psPAX2.0质粒(Addgene ,目录号:12260)
VSVG质粒(Addgene ,目录号:98286)
Lenti -X TM qRT -PCR滴定试剂盒(Takara,目录号:631235)
gRNA质粒骨架(小号EE表1 中建议的质粒和它们各自的Addgene公司号码)
 


表1. 建议的CRISPR质粒


名称


描述


Addgene 目录号


LRG2.1


编码sgRNA和GFP


108098


LentiV_Cas9_puro


编码Cas9和Puro


108100


LRG2.1-m樱桃


编码sgRNA 和mCherry


108099


LRG2.1-BFP


编码sgRNA 和eBFP


120577


LRG2.1-标签BFP2


编码sgRNA和TagBFP2


124773


LRG2.1-莫兰


编码sgRNA 和mOrange


124772


LRG2.1-CyOFP1


编码sgRNA和CyOFP1


124771


 


LB + 100 µg / ml氨苄青霉素培养基(Sigma-Aldrich,目录号:A9518)
DMEM培养基中添加了10%FBS,1%Pen / Strep和谷氨酰胺(Life Technologies,目录号:11995-073)
适用于目标癌细胞系的合适细胞培养基
50%(v / v)甘油
抗FLAG抗体(Sigma-Aldrich,目录号:F1804)
2 M氯化钙2
2 x HEPES缓冲盐水
100 mM氯喹(Cayman Chemical,目录号:14194)
聚烯烃(Santa Cruz Biotechnology,目录号:sc-134220)
嘌呤霉素(Gibco,目录号:A11138-03)
带NEB缓冲液3.1的BsmBI 限制性核酸内切酶(NEB,目录号:R0580)
碱性磷酸酶,小腿肠(CIP)储备液(10,000 U / ml; NEB,目录号:M0290)
100 µM gRNA寡核苷酸(IDT或首选的寡核苷酸合成供应商)
T4多核苷酸激酶(NEB,目录号:M0201)
带T4 DNA连接酶缓冲液的T4 DNA连接酶(NEB,目录号:M0202)
 


设备


 


移液器
Pipetman P10(Gilson,目录号:F144802)
Pipetman P20(Gilson,目录号:F123600)
Pipetman P200(Gilson,目录号:F123601)
Pipetman P1000(Gilson,目录号:F123602)
哺乳动物细胞培养设备
CO 2 培养箱
层流柜
荧光显微镜
热循环仪
-20 °C冰柜
-80 °C冷冻室
Miltenyi Biotec MACSquant VYB流式细胞仪(Miltenyi Biotech,目录号:130-096-116)
替代品:Luminex Guava easyCyte 或同类流式细胞仪,能够测量GFP荧光


 


软件


 


基准测试(https://www.benchling.com/)
Microsoft Excel
棱镜8




程序


 


注意:在本节中,我们将Cas9转基因引入目标细胞系。如果研究人员已经有稳定的Cas9表达细胞系,则可以直接进行步骤B。             


表达Cas9的细胞系的产生
选择一个Cas9表达载体
我们建议使用LentiV-Cas9-Puro(Addgene )将Cas9引入目标细胞系,尽管任何具有哺乳动物选择标记的组成型SpCas9表达载体都足够。


产生Cas9病毒
我们建议使用慢病毒包装质粒psPAX2和VSVG(Addgene )。
我们在下面提供了慢病毒生产的简要概述。我们的实验室为此过程提供了更详细的协议,可以在Giuliano 等人的文章中找到。(2019)。
磷酸钙用LentiV-Cas9-Puro,psPAX2和VSVG转染HEK293T包装细胞。
转染后约8-14小时,用新鲜培养基替换包装细胞上的培养基。
转染后24小时收获病毒。我们建议使用0.45 µM过滤器过滤上清液以分离细胞碎片。在转染后72小时内,该病毒最多可收集3次。
该病毒可立即使用或在-80 °C下保存。
转导感兴趣的细胞系
平板细胞在约50%汇合处。
将病毒上清液与新鲜培养基以1:1的比例混合。
将聚乙烯加入稀释的病毒上清液中至终浓度为8 µg / ml。我们发现该浓度提高了我们实验室常用的几乎所有细胞系的转导效率。但是,可以根据需要调整该浓度。
用含聚乙烯的病毒上清液替换要转导的细胞上的培养基。
转导后24小时,用新鲜培养基更换转导细胞上的培养基。
选择表达Cas9的细胞
我们实验室中使用的Cas9表达载体(LentiV-Cas9-Puro)含有抗嘌呤霉素标记。嘌呤霉素的理想选择浓度取决于细胞系,应在实验开始前对其进行优化。
为了优化嘌呤霉素的选择浓度,我们建议在6或12孔板上以约50%汇合度铺板野生型细胞。
在细胞与含有嘌呤霉素以一定范围的媒体24个小时后更换介质的1 -4微克/毫升。我们通常以0.5的增量进行测试 μ克/米升,每孔含有嘌呤霉素的浓度不同。
选择药物后3-5天评估细胞存活率。嘌呤霉素的最佳浓度是没有存活细胞的最低浓度。对于贴壁细胞系,死亡/垂死的细胞通常会从板上脱落,这有助于鉴定存活的细胞。为此,悬浮细胞系可能需要活性染料。
虽然嘌呤霉素的浓度范围对于大多数细胞系已经足够,但有些细胞可能需要更高的工作浓度。在这种情况下,请测试最高10 µg / ml的其他浓度。
转导后48 h,用含嘌呤霉素的浓度为1-4 µg / ml或确定为对目标细胞系最佳的培养基替换转导细胞上的培养基。
保持选择,直到稳定表达Cas9。我们发现,对于大多数细胞系而言,只有一轮转导,然后进行3至5天的嘌呤霉素选择,足以产生稳定的Cas9表达。但是,某些细胞系可能需要一轮以上的转导和/或更长的选择时间。
验证Cas9表达
研究人员可以利用多种方法来验证Cas9的表达,包括:
定量PCR(qPCR)。通过使用靶向Cas9 mRNA转录物的PCR引物,可以确认Cas9核酸酶的表达。所述的qPCR引物我们实验室利用用于检测Cas9表达是正向:5 ' GGCCTACCACGAGAAGTACC 3 ' 和反向:5 ' CTGGCGTTGATGGGGTTTTC 3 ' 。
西方印迹。我们实验室中使用的Cas9表达载体(LentiV-Cas9-Puro)包含一个FLAG表位标签,该标签与Cas9的N端融合。这样,研究人员可以根据需要使用抗FLAG抗体代替Cas9特异性抗体。我们实验室使用的抗FLAG抗体可在“材料和试剂”部分(Sigma-Aldrich)中找到。
使用针对必需基因的指南进行转导。虽然这可能不适合您进行的第一次GFP竞争测定,但我们的实验室会常规执行带有几个阳性和阴性对照指南的对照竞争测定(如本协议后面所述),以验证目标细胞系中的Cas9功能。
值得注意的是,针对Cas9的qPCR和Western blotting可以确认Cas9的表达,但不能确认其功能。用具有已知生物活性的向导进行的转导可以用来确认Cas9的功能。由于这些原因,我们建议使用这些方法的组合来验证您生成的任何表达Cas9的细胞系中的Cas9表达和功能。
 


注意:在本节中,我们将设计针对目标基因的指南,并将这些指南以及适当的阳性和阴性对照指南克隆到用于sgRNA和荧光蛋白表达的指南质粒中。我们讨论了理想指导设计的注意事项,并提供了由我们的实验室和质粒克隆方法广泛验证的控制指南。             


sgRNA和指导质粒的设计
确定目标基因内的靶位点
使用在线工具(例如“ 基准测试” (https://www.benchling.com/))设计针对目标基因的合适指南。
对于此协议,我们使用与目标序列具有20个碱基对同源性的单向导,因为这最适合CRISPR敲除。
PAM序列应在指导序列的3 '侧为NGG ,因为我们使用的Cas9菌株是SpCas9。PAM序列是Cas9介导的DNA切割所必需的,并且在切割位点下游有3-4个碱基对。
靶向功能蛋白结构域的指南可产生最有效的靶基因敲除作用。因此,我们强烈建议交叉引用带有蛋白质结构域的外显子,以确定哪些指南最适合破坏蛋白质功能,因为仅针对第一个外显子的指南会导致假阴性(图2A)(Shi 等人,2015) )。
此外,我们建议为每个基因设计3个或更多的指导,理想地针对不同的外显子。
通过IDT或您首选的DNA合成商业来源订购寡核苷酸。
 


D:\重新格式化\ 2020-5-6 \ 1902956--1438 JasonSheltzer 492105 \图jpg \图2.jpg


图2.竞争测定的示例实验设计和数据分析。A.示例指南RNA设计。AURKB(Aurora B)编码一种在有丝分裂中起关键作用的激酶。显示了针对Aurora B激酶域内多个外显子的指南。B.示例辍学数据。被转导的群体的相对丰度由%GFP表示,并且在五次传代的过程中显示了计算的倍数缺失。C.示例辍学图。每个指南显示了五个通道的折痕。可以看出,针对Rosa26的阴性对照指南显示出最小的消耗,而针对RPA3的阳性对照指南在最终时间点显示出超过十倍的缺失。与现有知识一致,这些结果证实了AURKB是癌细胞依赖性的。


 


确定合适的正面和负面对照指南
阴性对照指南应针对非编码基因座或可用于细胞适应性的基因,例如Rosa26或AAVS1。这些基因座被认为是规范的安全港位点,因此在这些基因座上引入插入缺失将对癌细胞的适应性影响最小。
阳性对照指南应靶向必需基因,例如PCNA或RPA3。这些基因对于DNA复制至关重要,因此在这些基因座处引入插入缺失将对癌细胞的健康有害。
我们在表2中提供了我们的控制指南。
注意:提供的序列是20 bp的间隔子序列,不包括用于克隆到指导载体中的4-5 bp的衔接子序列。我们的指导载体的衔接子是CACC,如果间隔序列的第一个核苷酸不是鸟嘌呤,则必须添加G。例如,下面列出的带有添加的适配器的Rosa26 g1的完整指导序列是:5'- CACCG ACAGCAAGTTGTCTAACCCG (粗体适配器)。   


 


表2.建议的控制指南


指南


序列


罗莎26 g1


ACAGCAAGTTGTCTAACCCG


罗莎26 g2


CCGAAAGATTGGACACCCC


AAVS g1


ACTGTTGACGGCGGCGATGT


AAVS g2


GCTGATACCGTCGGCGTTGG


PCNA g1


CTACCGCTGCGACCGCAACC


PCNA g2


加加特AAAATTGCGGATAT


RPA3 g1


CCCAGGTCGCGCATCAACGC


RPA3 g2


GGTTGGAAGAGTATACGCGCCA


 


将sgRNA克隆到指导载体中
我们建议使用慢病毒GFP sgRNA表达载体(Addgene ),尽管任何具有荧光标记的可比性sgRNA表达载体均适用于该测定。此外,我们的实验室已经开发并验证了具有不同荧光蛋白标记的sgRNA表达载体的几种衍生物,可以在需要时使用。
我们在下面提供了指南克隆过程的简要概述。我们的实验室为此过程提供了更详细的协议,可以在Giuliano 等人的文章中找到。(2019)。
用BsmBI 摘要线性化引导载体主链。
用T4 PNK退火和磷酸化引导寡核苷酸。
将经过退火和磷酸化的向导连接到向导载体主链中。
将连接产物转化为感受态细胞。
使用U6引物(5序列-验证所述引导质粒' - GAGGGCCTATTTCCCATGATTCC)。
 


注意:在本节中,我们将把编码sgRNA和荧光蛋白的指导质粒引入表达Cas9的细胞系。我们讨论了慢病毒生产,病毒滴定的方法,以及竞争分析设置的一些注意事项。             


用指导质粒转导表达Cas9的细胞系
用指导质粒生产慢病毒
有关病毒产生过程的简要概述,请参阅步骤A2,或者请参阅Giuliano 等人的更全面的病毒产生方案。(2019)。


确定病毒滴度
由于此测定法中使用的指导质粒编码荧光蛋白,因此可以通过转导不同病毒量的细胞并在转导后三天测量显示荧光的细胞百分比来估算病毒滴度。
研究人员还可以利用基于定量PCR的方法来确定病毒滴度,如果他们愿意(例如,伦蒂-X TM 的qRT -PCR滴定套件[宝] ,小号EE制造商的详细信息协议)。虽然这种方法可以提供关于每微升上清液中病毒颗粒数量的更明确的答案,但我们发现通过测量显示荧光的细胞百分比提供的近似值足以进行此分析。
用滴定的病毒上清液转导表达Cas9的细胞系。
理想的转换效率为50%。然而,该测定法在其要求上是灵活的,使得10-80%范围内的转导效率适用于该测定法。
有关病毒转导过程的简要概述,请参见步骤A3。
我们建议在12或24孔板上进行此测定。如果需要,可以放大或缩小此测定。通常,在较大的平板上进行此测定可减少背景变化,但是如果有限制因素起作用(即病毒不足),则也可以在48孔或96孔板上进行该测定。
 


注意:在本节中,我们将采用竞争分析的第一个时间点。我们讨论了通过流式细胞仪测量表达GFP标记的细胞百分比的方法,以及进行竞争分析的注意事项。


开始竞争分析
收获细胞
通常,我们将第一个时间点设置为转导后三天,以便为转基因表达提供充足的时间。


种子细胞在适当的汇合度下一次传代
根据细胞系的增殖能力,进行适当的细胞分裂(即1:2至1:10)。
根据细胞系的选择及其倍增时间,研究人员可能选择每三或四天通过一次细胞。
例如,我们通常每三天分裂一次乳腺癌细胞系MDA-MB-231 1:4。
通过流式细胞仪测量GFP表达
铺板细胞用于下一次传代后,准备剩下的样品用于流式细胞仪。我们建议在进行流量分析之前,先将细胞通过单个细胞过滤器(Falcon)。不必使用任何流式细胞仪特异的重悬缓冲液,因为细胞培养基应足以进行该实验。
运行控制GFP细胞,为前向散射和侧向散射激光设置适当的电压。
运行对照GFP细胞和GFP +细胞,以调节流量参数并确定表达GFP的细胞的门。
运行实验样品。研究人员的目标应该是运行样本体积的至少10%(即,如果收获1毫升细胞从一个12 - 孔平板中,运行至少100微升)。如果可能,我们建议运行约25%的样品,因为较大的样品将产生更准确的GFP表达测量值。
记录所有样品的GFP%。
为了确保实验的一致性,我们建议使用您的激光电压和分析模板保存一个程序,以用于成功的时间点。


 


注意:在本节中,我们将继续完成竞争分析。


竞争分析的连续时间点
收获细胞
种子细胞在适当的汇合下进行下传代。
在第五个时间点,测定已完成,无需重新铺板细胞用于下一次传代。


通过流式细胞仪测量GFP表达。
 


数据分析


 


注意:在本节中,我们将从每个时间点将表达GFP的百分比细胞的测量值转换为倍数变化值。然后可以将来自各个指南的这些倍数变化值相互比较,并与阳性和阴性对照进行比较,以得出有关所测试细胞系中目标基因的必要性的结论。


 


确定倍数变化
在该测定中,倍数变化定义为(第1代GFP%)/(第X代GFP%)。
因此,通道1的倍数变化应为1。这些计算的示例可以在图2B中看到。
 


准备分组图
我们建议使用Prism,但是任何可以绘制数值数据图表的软件(即Excel)都足够(图2C)。


 


设置辍学阈值
该阈值应大于阴性对照的倍数变化,因为这些指南的目标是可用于癌细胞适应性的基因座。
我们的实验室使用2.5倍变化阈值来确定依赖性,因为我们发现该阈值始终高于用阴性对照指南观察到的耗竭水平。
可以根据需要根据细胞系进行调整,因为倍数变化的动态范围取决于多种因素,包括Cas9表达,细胞类型差异和传代1时的GFP%。
如果靶向该基因的多个向导在背景水平以上表现出最小的缺失,则该基因不太可能成为所测试细胞系的遗传依赖性。
如果靶向基因的多个向导在背景水平以上均表现出一致的缺失,则表明该基因对该细胞系具有依赖性。
 


记事簿


 


该协议为研究人员提供了一种简化的方法来探测癌细胞系中的个体遗传依赖性。它已被广泛用于识别白血病,小细胞肺癌,胰腺癌中的新依赖性,并在临床试验中询问其他几个目标(Shi 等人,2015; Lin 等人,2017和2019; Huang 等人。 ;,2018 ;Somerville 等,2018; Tarumoto 等,2018)。此外,我们的实验室会定期部署这种方法的变体,以探究其他情况下的遗传依赖性。例如,可以对该测定法进行修改以探测两个基因(即基因A和基因B)之间的合成致死关系。如果针对每个基因的指南分别表现出最小的缺失,但研究人员有证据表明这些基因具有一定程度的功能冗余,则可以进行双竞争分析。在此实验中,研究人员可以与两种表达不同荧光蛋白标记的向导共转导靶细胞(表1 ),其中向导1靶向基因A,向导2靶向基因B。通过测量单个荧光蛋白的表达以及在每次传代中同时表达两种荧光蛋白的细胞百分比,研究人员可以深入了解潜在的合成致死关系。如果双阳性人群表现出一致的耗竭,而单阳性人群则没有,这表明基因A和B之间可能存在潜在的合成致死关系。
用于转录调控的其他CRISPR工具的开发为研究人员提供了其他可能的探索遗传依赖性的方法。CRISPR干扰(CRISPRi )系统将无催化作用的Cas9(dCas9)与Krüppel 相关框(KRAB)域融合,使研究人员能够抑制靶基因表达而不会形成双链断裂(Gilbert 等。,2013)。由此产生的部分功能丧失表型为研究人员研究潜在的药物靶标提供了另一个背景。这种正交方法的使用补充了CRISPR / Cas9标准产生的总功能丧失表型。诸如CRISPRi之类的部分功能丧失方法提供的遗传扰动模型更类似于靶向疗法的效果,因为任何潜在遗传依赖性的药理抑制作用都不是绝对的。通过使用dCas9-KRAB表达载体代替Cas9表达载体(Addgene #85969),以及通过使用靶向启动子而非功能外显子的向导,可以轻松地将该竞争测定法调整为CRISPRi 方法。我们的实验室定期进行此类测定,我们发现这种改良方法的结果与传统的Cas9筛查基本一致(Lin 等人,2019)。
              尽管CRISPR / Cas9系统在研究遗传依赖性方面具有优于RNAi的众多优势,但仍需考虑一些限制。通常,Cas9核酸酶被用于在靶位点产生各种各样的插入缺失,并且认为所得的过早终止密码子导致突变体mRNA的无义介导的衰变(NMD)。但是,插入缺失的产生并不一定导致靶基因产物的消融。CRISPR诱导的遗传损伤可通过替代剪接和下游转录起始来绕过(Lalonde 等,2017; Sharpe and Cooper,2017)。另外,CRISPR诱导的敲除的意想不到的结果可能是功能未知的异常蛋白质产物的产生(Tuladhar 等,2019)。为了减轻这些因素的潜在混淆作用,我们强烈建议针对跨多个外显子的功能性蛋白结构域使用几种指南。此外,我们建议通过蛋白质印迹验证蛋白质消融。
              突变mRNA NMD本身的过程也可能产生意想不到的后果。已知NMD是许多细胞类型(包括肝和骨髓细胞)的mRNA调节机制(Lykke-Andersen和Jensen,2015)。最近的一项研究表明,CRISPR诱导的NMD导致靶基因同源物的补偿性上调,这可能会掩盖CRISPR敲除的真实作用(El-Brolosy 等,2019)。但是,我们实验室进行的实验表明,这种补偿性上调在癌细胞中可能很少发生或不存在(Lin 等人,2019)。如果研究人员担心潜在的CRISPR诱导的同源基因上调,我们建议与竞争分析同时进行基因表达分析,以排除这种可能的混淆因素。值得注意的是,迄今为止描述的局限性是Cas9诱导的遗传损伤的后果。因此,可以通过使用不切割DNA的替代系统来扰动这些限制,例如CRISPR干扰。
              除了CRISPR系统本身的局限性外,重要的是要注意,这种GFP竞争测定的结果决不能决定在其他情况下对于细胞适应性而言必不可少的基因状态。尽管在多个试验和细胞系中始终缺乏针对基因的指南的缺失,表明目标基因对于体外细胞自主生长是必不可少的,但该基因仍然有可能在其他细胞环境中具有依赖性,例如与锚定无关增长,低氧气条件下,在化学治疗剂的存在下,在体内,等 因此,通过该测定不得分为遗传依赖性的基因仍可为抗癌治疗提供有价值的药物靶标。另一方面,如果研究人员发现针对基因的指南的一致性下降,则有力证据表明该基因具有遗传依赖性。应通过正交方法进一步研究此类结果。
我们相信这种测定方法将为研究人员提供一种有价值的方法来研究推定的癌症依赖性。我们实验室的结果表明,该测定法的发现是可靠的,并且其相对简单性使得研究人员可以在多种细胞类型中同时进行该测定法。最好将该方法用作首过方法,并辅之以对任何假定的癌症依赖性的彻底,深入的研究。
 


致谢


 


Sheltzer 实验室的研究得到了NIH早期独立奖(1DP5OD021385),Damon Runyon- Rachleff 创新奖,盖茨基金会创新技术解决方案基金和CSHL-Northwell转化癌症研究基金的支持。在CSHL流式细胞术共享资源的协助下进行了细胞分选,该资源得到了CSHL癌症中心支持赠款5P30CA045508的支持。该协议借鉴了Shi 等人发表的原始著作。(2015 ),Lin 等。(2017 和2019)和Giuliano 等人。(2019 )。


 


利益争夺


 


JMS是Tyra Biosciences顾问委员会的成员,并已从Ono Pharmaceutical Co.收取咨询费。和默克


 


参考资料


 


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Copyright Girish and Sheltzer. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Girish, V. and Sheltzer, J. M. (2020). A CRISPR Competition Assay to Identify Cancer Genetic Dependencies. Bio-protocol 10(14): e3682. DOI: 10.21769/BioProtoc.3682.
  2. Lin, A., Giuliano, C. J., Sayles, N. M. and Sheltzer, J. M. (2017). CRISPR/Cas9 mutagenesis invalidates a putative cancer dependency targeted in on-going clinical trials. Elife 6: 24179.
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