Abstract
CRISPR-based screening methods using single-cell RNA sequencing (scRNA-seq) technology enable comprehensive profiling of gene perturbations from knock-out mutations. However, evaluating substitution mutations using scRNA-seq is currently limited. We combined CRISPR RNA-guided deaminase and scRNA-seq technology to develop a platform for introducing mutations in multiple genes and simultaneously assessing the mutation-associated perturbations and signatures in a high-throughput manner. Using this platform, we generated a library consisting of 420 sgRNAs, performed sgRNA-tracking analysis, and assessed the effect size of the response to vemurafenib in the melanoma A375 cell line, which has been well-studied via GeCKO but not transcriptome analysis. A convenient and efficient workflow not possible with abundance-based assays enabled the characterization of surviving cells. Our platform permits discrimination of several hit-mutations within a large-scale library by integrating sgRNA hits and gene expression readout. We anticipate that our platform will enable high-throughput analyses of the mechanisms related to a variety of biological events.
Innovative biological research approaches based on the CRISPR/Cas9 system have been developed to facilitate investigations of the functional effects of genetic mutations1, 2. Although the functions of thousands of variants have been determined using functional screening approaches based on gene knock-out and activation, screening other genetic features, such as single nucleotide variants (SNVs) and structural variations, to examine related phenotypes remains challenging. Among these genetic features, SNVs are the most frequently observed, and they are associated with many diseases in humans3. For example, mutations related to drug resistance and cancer susceptibility significantly impact both the prognosis of therapy and disease progression. In interrogating the function of SNVs, oligo-mediated saturation editing based on homology-directed repair enables base-resolution mutagenesis4, but this approach is limited to a single locus due to the difficulty of the preparation and size restriction of the oligos composed of various types of SNVs. An alternative approach based on a CRISPR RNA-guided deaminase5 permits the specific and efficient mutagenesis of C to T without the need for a DNA template. Using this technology, disease-related missense mutations can be generated and analyzed. However, to date, this approach has been only used for multigene knock-out6 and multiple point mutations within a single gene7, and introducing and screening multiple point mutations in multiple genes remains challenging.
As the methods described above are based on population analyses and thus dependent on the number of clones, data regarding only significantly dominant clones are obtained when using RIGER8 and other similar statistical algorithms9. However, recently developed techniques such as CROP-seq10, PERTURB-seq11 and CRISP-seq12, which leverage single-cell RNA-seq, enable analysis of the various phenotypes and perturbations with each cell by integrating both the gene expression readout and CRISPR-based perturbations. For the perturbation read out, CROP-seq utilizes a vector that adds a poly(A) tail to the sgRNA transcript, whereas PERTURB-seq and CRISP-seq generate a “perturbation barcode” linked to the sgRNA. The use of these methods has thus far been limited to investigations of perturbations associated with knock-out mutations and transcriptional regulations13 generated using a CRISPR library at the single-cell level, as it remains challenging to investigate perturbations for point mutations. However, a recent report demonstrated a spectrum of subclonal point mutations in the same tumor has implications for precision medicine and immune therapies14. This report emphasizes the need for an atlas of transcription profiles associated with SNVs.
To investigate the myriad of SNVs that affect biological function, a high-throughput, pooled screening method for SNVs which enables analysis of generated SNVs by tracking sgRNAs is required. Here, we demonstrate a novel method combining CRISPR RNA-guided deaminase and CROP-Seq technology that enables the introduction of SNVs in multiple genes and screening of the impact on function in addition to analyses of perturbations in single cells (Fig. 1a). To highlight the utility of our novel platform, we generated SNVs in each exon of three human genes (MAP2K1, KRAS, NRAS) associated with resistance to vemurafenib15, 16, which is a cancer drug targeting the BRAF V600E mutation in melanoma patients. We then screened for point mutations that conferred resistance to vemurafenib, analyzed the perturbations in individual resistant clones, and assessed gene expression signatures for individual clones. Using this platform, we could classify resistant clones into two sub-types according to drug response. Moreover, we were able to identify sgRNAs more likely to be hit by combined analysis of candidate sgRNAs based on trackable genomic integrated sequences with transcriptome data. We anticipate that our platform can be extended to characterize individual responses related to biological stimulation in heterogeneous cells harboring single substitution mutations.
RESULTS
Overview of the system
To facilitate the generation and tracking of point mutations, lentivirus- and piggyBac-based delivery systems are needed for integration of single-copy sgRNAs in each cell and stable expression of the CRISPR RNA-guided deaminase, respectively. Because complete genes are too long for delivery by lentivirus or transposase in our system, we designed the system to utilize two vectors (Fig. 1a): lentiviral and piggyBac vectors encoding the sgRNA and CRISPR RNA-guided deaminase, respectively. For sgRNAs, we adopted the CROPseq-Guide-Puro plasmid10 to enable simultaneous capture of the sgRNA with other mRNAs during the scRNA-seq preparation procedure and puromycin selection of sgRNA-bearing cells. For the CRISPR RNA-guided deaminase, we adopted the BE3 sequence–encoding Cas9 nickase fused with APOBEC and UGI5. We also constructed a cassette containing BE3 and a GFP reporter and blasticidine resistance marker, which was cloned into the piggyBac vector to provide high “cargo-carrying” capacity.
As BE3 can be used to introduce C to T mutations in the 4th to 8th positions from the PAM-distal end of the protospacer, only a limited variety of mutations can be introduced using the CRISPR RNA-guided deaminase (Fig. 1b). Thirteen of the 20 canonical amino acids can be converted to another amino acid by targeted deamination. The remaining seven amino acids can be converted only by either silent or nonsense mutations.
We utilized the above-described system to screen for mutations conferring resistance to vemurafenib to demonstrate that the system is suitable for large-scale screening of SNVs. Although mutations conferring resistance to vemurafenib have been thoroughly studied using approaches such as GeCKO1, to date, there are no reports of systematic analyses of transcriptional changes induced by various SNVs. We therefore explored how such mutations affect the drug-response mechanism using our novel CRISPR RNA-guided deaminase system.
Characterization of mutations by all possible sgRNAs in the human genome
To investigate the feasibility of our system, we designed all possible sgRNAs (approximately 3.8 million) from all gene isoforms within the human genome (Methods, Supplementary Table 1). Then, we calculated the breadth of coverage for each gene isoform. These calculations indicated that mutations could be introduced in 41% of residues per gene isoform by targeting the “GG” and “AG” PAMs17 (Supplementary Fig. 1a). One sgRNA could induce multiple types of mutations depending on the number and position of the cytosine residues in the activity window. In most cases, silent and missense mutations can be produced alone or in combination with sgRNA (Fig. 1c). Many loci were covered by more than one sgRNA; therefore, more than one sgRNA can be selected from the candidates for a more robust statistical analysis (Supplementary Fig. 1b).
To illuminate the potential relevance of our system to address human cancer mutations, we examined the coverage of the mutations listed in the Cancer Gene Census (CGC)3. Our system covered 36,211 missense mutations and 3,491 nonsense mutations. This indicates that a large proportion of known cancer-related mutations can be generated and examined using our system. When considering only the possible types of missense mutations that can result from conversion of the 13 canonical amino acids (Fig. 1b), our system covered 56% of the mutations listed in the CGC (Supplementary Fig. 1c).
Introduction and enrichment of resistant mutation MAP2K1 E203K
To assess the efficiency of our system, we investigated the rate of conversion of the C base targeted in a single locus. First, we introduced the MAP2K1 p.E203K mutation (c.G607A) into human A375 melanoma cells carrying a homozygous BRAF V600E mutation (c.T1799A). The E203K point mutation results in the substitution of glutamic acid with lysine at codon 203 in exon 6 of MAP2K1, resulting in resistance to vemurafenib15.
The piggyBac vector expressing BE3 was transfected into A375 cells to create a cell line that stably expresses BE3. Cells expressing the base editor were infected with a lentivirus expressing sgRNA, targeting codon 203 of MAP2K1 to induce the E203K mutation. The cells were cultured for 10 days to ensure selection of cells harboring the sgRNA and to promote base editing. Then, frequency of base editing in terms of base and amino acid resolution was investigated by deep sequencing after 25 days of treatment with either vemurafenib or vehicle (dimethyl sulfoxide [DMSO]). At 10 days after puromycin selection, the substitution efficiency ranged from 4.24–5.65% in the “activity window” with a small indel frequency of 1.4% (Fig. 1d). After treatment with vemurafenib for 25 days, the substitution efficiency increased significantly, to 41.11–77.17%, whereas DMSO vehicle treatment yielded an efficiency of only 9.67–12.08%. These data suggest that continuous engineering occurred during culture and that the mutation confers resistance to vemurafenib. Therefore, cells harboring the E203K mutation survived over wild-type cells in the presence of vemurafenib, whereas DMSO treatment did not promote enrichment of the mutant clones as much as vemurafenib. Consistent with the base resolution analysis, codon-based analysis indicated that 77.19% of the viable cells were E203K clones (Fig. 1e). The same analysis of DMSO-treated cells revealed that 12.06% of the viable cells were E203K clones. These results confirmed that significant enrichment of the mutation occurred with vemurafenib treatment. As shown in a previous study5, conversion of C residues can occur at a variety of positions, although conversion occurs most frequently within the activity window (Fig. 1f, Supplementary Fig. 2a). Furthermore, processive deamination tends to occur in same DNA strand (Supplementary Fig. 2b). Collectively, the results of our singleplex experiments suggest that the mutations artificially introduced via base editing functioned well and that mutants can be specifically enriched using drugs in a manner similar to naturally acquired mutations in patients. These data also demonstrate the possibility of using this system to measure the effect sizes of various mutations in terms of drug resistance.
Introduction and functional screening of multiple putative vemurafenib-resistance mutations using population analyses
To determine whether the use of our system can be extended to the analysis of multiple loci, we designed a sgRNA library for three genes (MAP2K1, KRAS, and NRAS) related to vemurafenib resistance in melanoma. We selected possible targets based on the criterion that spacers include cytosine residues in the activity window. A total of 420 sgRNAs were designed for all of the exons of MAP2K1, KRAS, NRAS (263, 80, and 77 sgRNAs, respectively, Supplementary Table 2, Supplementary Fig. 3a). We excluded the sgRNAs used in the previous singleplex experiments to avoid enrichment of the known E203K resistance clone in the pooled screen. The resulting sgRNA library covered 17.4% of the reported disease-related SNVs in the CGC (Fig. 2a). The designed library was then synthesized using a microarray and cloned into the CROP-guide-puro plasmid. A375 cells stably expressing BE3 were transduced in two independent replicates with the library and selected for 14 days using puromycin to ensure that most of the cells expressed sgRNA and were base edited. After selection, the cells were treated with either vehicle (DMSO) or vemurafenib for 28 days. Cells were sampled on the day treatment was started and 7, 14, 21, and 28 days after treatment (hereafter designated initial, D+7, D+14, D+21, and D+28). Before examining the transcriptome of single cells, the population of sgRNAs integrated into the genome was analyzed to determine which sgRNA was responsible for conferring resistance.
First, the distribution of sgRNAs for each condition was investigated. The sgRNA representation decreased over time, meaning that some sgRNAs were enriched over time (Fig. 2b, Supplementary Fig. 3b). An analysis using MAGeCK9 indicated that sgRNA #176, which targets close to codon E203 in the MAP2K1 gene, was enriched in both independent screens (Fig. 2c). It should be noted that sgRNAs targeting close to codon E203 (#176, #182) accounted for ∼45% of all sgRNAs, and E203K mutant cells constituted over 70% of the total cell population in replicate 1, suggesting that the abundance of sgRNAs close to E203 is reflective of the frequency of E203K mutant cells (Supplementary Fig. 3c, d). Although we excluded the highly active sgRNA generating E203K, the data suggested that other sgRNAs in which the protospacer included the E203 residue in the extra position of the activity window robustly generated the E203K mutation. In sgRNA #176, the E203 residue is in the 13th to 15th position from the PAM-distal end of the protospacer, such that the E203K mutation is predominant compared with the mutation in the editing window due to the TC motif preferred by BE5. The E203K mutation was also detected in replicate 2, with an allele frequency of ∼48% (Supplementary Fig. 3d, e). These results demonstrate that generating a library comprised of multiple mutants is possible with our novel system and that appropriate targets can be selected using our platform. However, further analyses using transcriptional data are warranted in order to identify other putative sgRNAs and elucidate the mechanism of enrichment.
Identifying cell subpopulations across different treatment periods using scRNA-seq
The scRNA-seq approach was employed to explore properties related to drug responses in individual cells. To assess the transcriptional changes occurring in each mutant, cells from each experimental condition (initial, DMSO[D+14], Vem[D+14], DMSO[D+28], Vem[D+28]) were individually harvested and subjected to Drop-seq18. The mRNAs and sgRNAs in each cell were captured and converted into a cDNA library for NGS. On average, we sequenced 82 million reads per sample (Supplementary Table 3). After filtering cells in replicates 1 and 2, 5707 and 7511 cells with more than 500 genes were identified, respectively, and 57.4 and 55.2% of the cells were assigned as sgRNAs, respectively. These results were comparable to data from a previous report10.
We first compared the abundance of each sgRNA to the abundance determined from genomic DNA obtained from bulk cells. High correlations were observed between the sgRNA and genomic DNA data (Fig. 3a). In particular, enrichment of sgRNAs introducing the E203K mutation was observed in drug-treated samples from both analyses, indicating that the transcriptome data were reliable for analyzing the perturbations associated with the assigned sgRNAs.
Next, we performed a principal component analysis (PCA) and trajectory analysis to determine whether there is a characteristic cluster pattern according to period and treatment. The transcriptome of resistant cells at D+28 and D+14 was distinct from that of naïve cells in each replicate (Supplementary Fig. 4a). However, no common pattern was observed between replicates (Supplementary Fig. 4b). Therefore, we focused on D+28 cells to assess the mutational effects of sgRNAs in more detail.
D+28 cells were visualized using t-SNE and grouped by unbiased clustering (Fig. 3b, c). We hypothesized that the transcriptome pattern of cells that acquired resistance due to the E203K mutation would differ significantly from that of natural survivors. Thus, we expected that the cluster including the #176 sgRNA introducing E203K would be separated from other clusters. In the t-SNE visualization, we observed two and three major clusters from replicates 1 and 2, respectively. One cluster in both replicate screens (rep1-1, rep2-1) consisted primarily of #176 sgRNA, which targets close to E203 in MAP2K1 (Fig. 3d, Supplementary Fig. 4c). In replicate 2, an additional cluster composed primarily of #56 sgRNA targeting close to Q61 of KRAS (rep2-2) was identified (Fig. 3d, Supplementary Fig. 4d). We determined that these clusters consisted of cells that had acquired resistance and therefore investigated them further to elucidate the resistance mechanism in more detail.
Investigation of cluster of resistance-acquired cells
We investigated whether transcriptional changes in the clusters composed primarily of #176 sgRNA targeting close to E203 in MAP2K1 (rep1-1 and rep2-1) were related to vemurafenib resistance by attempting to identify marker genes. Among candidate up-regulated marker genes identified using the Wilcoxon rank sum test, the top 10 with lowest p-values were selected as “signature genes” (Supplementary Table 4, Fig. 4a, Supplementary Figs. 5 and 6). Gene ontology and pathway enrichment analyses19, 20, 21, 22 of the signature genes showed that the clusters were enriched with gene sets related to immune responses such as antigen processing and presentation of peptides via MHC class II (Fig. 4b, Supplementary Figs. 7 and 8). This observation was similar to the results of a previous report23. CD74, HLA-DRA, SLC26A2, HLA-DRB1, FOS, and HLA-DPA1 were commonly identified as signature genes in the clusters from both replicates (Fig. 4c). When we extended the criterion for marker genes to include all listed genes with a p-value <0.05, a total of 66 up-regulated genes and 163 down-regulated genes were identified as common (Fig. 4c, Supplementary Figs. 9–11, Supplementary Tables 5 and 6). This result indicates that the members of these clusters are similar and that their perturbations are reproducible. We assume that most members of these clusters are perturbed by the E203K mutation and associated with immune responses.
The rep2-2 cluster was composed primarily of #56 sgRNA, which targets close to Q61 of KRAS, whereas representation of #176 sgRNA was low (Fig. 3c). As indicated above, the top 10 genes with the lowest p-values were selected as signature genes, and then ontology and pathway enrichment analyses were performed. The signature genes of this cluster (i.e., rep2-2) differed completely from those of the rep1-1 and rep2-1 clusters (Fig. 4c, Supplementary Fig. 11). Although their expression was not as significant compared with rep1-1 and rep2-1 (Supplementary Fig. 12), the signature genes were partially enriched in gene ontology (GO) terms and pathways associated with chemokine signaling (Fig 4b). It has been reported that activated CXC chemokine receptor (CXCR) signaling in melanoma cells contributes to vemurafenib resistance24, 25. Our results indicate that the transcriptional changes in the rep2-2 cluster are distinct from those of the rep1-1 and rep2-1 clusters. We hypothesize that the cells in the rep2-2 cluster composed primarily of #56 sgRNA survive via a mechanism different from that by which cells in the rep1-1 and rep2-1 clusters mainly composed of #176 sgRNA survive.
We next investigated whether other sgRNAs are common to rep1-1, rep2-1, and rep2-2. We identified nine, eight, and five sgRNAs with more than two cells in rep1-1, rep2-1, and rep2-2, respectively (Supplementary Table 8). Of these sgRNAs, two (#176, #182) that could introduce E203K in MAP2K1 were common in these clusters, and the #56, #126, and #217 sgRNAs account partially or almost completely for the rep2-1 and rep2-2 clusters (Supplementary Fig. 13). Other sgRNAs were minor components (1.4% on average). Mutations that can be introduced by these sgRNAs (#56, #126, and #217) are thus potential candidates for conferring resistance to vemurafenib. We next examined whether these sgRNAs introduce cognate mutations.
Validation of genomic loci targeted by candidate sgRNAs via deep sequencing
We performed deep sequencing of genomic loci targeted by the candidate sgRNAs to verify the introduced mutations. Genomic loci of the #56, #126, and #217 sgRNAs were sequenced. An indel mutation (7.7%) was introduced in the target region of #56 sgRNA (Supplementary Fig. 14a). Because we used BE3, which employs Cas9 nickase, the indel mutation could be introduced into a small proportion of cells. There were two main types of in-frame indel mutations introduced (c.171insCTGTTGGATATTCTCGAC and c.176delCAG), and no substitutions were observed (Supplementary Fig. 14b, c). Enrichment in the indel mutations was observed compared to control cells treated with DMSO (0.03%). These mutations were not reported in previous studies, but region in which the mutations were introduced is next to the sequence encoding the Q61 residue of K-Ras, which is involved in constitutive activation of intrinsic GTPase activity26. Activated Ras is known to positively regulate the expression of various chemokines and ultimately promote tumorigenesis27, 28, which is consistent with the present result demonstrating that chemokine signaling pathways and CXCR binding genes were enriched in clusters composed primarily of the #56 sgRNA. We concluded that cells with the #56 sgRNA that introduced indel mutations next to the KRAS Q61-encoding sequence conferred resistance to vemurafenib. In contrast, deep sequencing of the target regions of the #126 and #217 sgRNAs did not confirmintroductionofcognatemutations(SupplementaryFig.15), presumably because the cells with these sgRNAs were natural survivors.
In summary, we first observed enrichment of the #176 sgRNA in sgRNA abundance analyses and considered the #56, #126, and #217 sgRNAs as additional candidates as a result of transcriptome analyses. Of these selected sgRNAs, those targeting close to the sequences encoding E203 of the MAP2K1 gene and Q61 of the KRAS gene (#176, #56) generated cognate mutations and induced different transcriptional changes.
Discussion
In the present study, we established a platform combining CRISPR RNA-guided deaminase and scRNA-seq technologies that enables the introduction of SNVs into multiple genes and facilitates their functional screening and measurement of perturbations in single cells. We demonstrated the introduction of SNVs into each exon of three genes and screened for SNVs conferring resistance to vemurafenib using population analysis. The results of population analyses indicated enrichment of the E203K SNV in MAP2K1, consistent with a previous study15. In addition, by employing scRNA-seq technology, we identified the perturbation as well as the signature of SNVs at the transcriptome level.
A recent study using targeted AID reported the introduction of multiple SNVs and assessed the effect of the introduced SNVs on the mechanism of imatinib resistance7. The introduced SNVs were confirmed and validated by amplification of targeted exons using genomic DNA. However, this technology was not expanded to include multiple genes or multiple exons. Our platform was expanded to include all exons in three genes, which illustrates that the confirmation procedure is not labor intensive. Unlike previous approaches that require amplification of individual targeted regions, we utilized a lentivirus-expressing sgRNA that can be tracked by amplification of the integrated sgRNA. Using this sgRNA approach, we could narrow the scope and amplify only the sgRNA-targeted region and thus confirm which mutation was introduced.
Improved engineering efficiency could provide for more-accurate assessment of the perturbations associated with individual sgRNAs. A knock-out–based study reported that approximately 70% of sgRNA-bearing cells can be edited using sgRNA1. In contrast, experiments using BE3 indicated that 5–20% of sgRNA-bearing cells can be edited using sgRNAs5. These data suggest the possibility of discordant transcriptomic changes resulting from use of the same sgRNA, which could create confusion in analysis. Optimization of the engineering efficiency can be achieved using BE429 or BE4max30. By achieving a higher efficiency in base editing (i.e., increasing the likelihood that sgRNA-bearing cells will be engineered), perturbations in a larger number of cells can be characterized.
We envisage that our approach could be optimized further by employing an alternative to BE3 protein. Recently developed BE variants31 and other effector proteins32 provide a broader range of targets, thus increasing the analytical coverage of diseases related to SNVs. In addition, broadening of the editing range by using eA3A-BE3 would permit narrowing to control for off-target effects33. We believe that more comprehensive analyses of mutations present could be achieved by optimizing protein selection. We used oligo-dT sequence–linked beads to capture polyadenylated mRNAs in the scRNA-seq experiment. By synthesizing target sequence–linked beads, targeted sequences could be captured directly without laborious PCR amplification of each targeted sequence. Alternatively, ligating target sequences to conventional beads could also be used34. Another application involves coupling with methods targeting individual cells using barcoding35. A target cell’s cDNA can be amplified using this method in order to analyze the transcriptome, and the method is applicable to the study of cells with known mutations and validation of cells enriched with specific sgRNAs. Furthermore, although we demonstrated the effect of SNVs introduced into exons of three genes in the present study, further exploration of other exons in genes related to cancer3 is expected to accelerate the identification of cancer-driving and drug-resistance mutations. We expect our platform to be extended to the examination of substitution mutations related to a variety of biological responses.
Methods
piggyBac-BE3-GFP: Construction and establishment of BE3-expressing cells
The piggyBac-BE3-GFP plasmid was constructed by insertion of the BE3-blasticidin fragment into plasmid PB-CA (Addgene #20960). The BE3-blasticidin fragment was obtained by assembly of the BE3 fragment amplified from the pCMV-BE3 vector (Addgene #73021) and the blasticidin fragment amplified from lentiCas9-Blast (Addgene #52962). To establish a line of BE3-expressing cells, we co-transfected A375 cells with transposase (pPbase, Sanger Institute, UK) and the piggyBac-BE3-GFP plasmid at a 1:4 molar ratio using Lipofectamine 3000 (Invitrogen). At 48 h after transfection, the cells were cultured in medium containing 5 μg/ml of blasticidin for 14 days to enrich for BE3-expressing cells.
sgRNA design
All sgRNAs were designed using an in-house program. We searched for all “GGs” and “AGs” as PAM sequences and considered sgRNA sequences in the form 5’-N20-PAM-3’ as candidates in the CDS region of every gene isoform. The candidates were examined to determine whether at least one “C” was in the activity window (4th to 8th position from the PAM-distal end of the protospacer) and that a “C” was in the CDS region. All possible mutations in the activity window were calculated, and the associated amino acid changes were classified as either silent, nonsense, or missense by reference to the codon frame and strand of the gene. All information regarding region and frame were obtained from a GTF file of hg19 downloaded from the UCSC Table Browser.
Cell culture
A375 (ATCC) and HEK293T (ATCC) cells were maintained in RPMI medium (for A375 cells) or DMEM (for HEK293T cells) (Gibco) supplemented with 10% fetal bovine serum (Gibco) and penicillin/streptomycin (Gibco) in a humidified 5% CO2 incubator at 37°C. Solutions of vemurafenib (Selleckchem) were prepared bydissolvingthecolorlesspowderinDMSO(BioReagentgrade; Sigma-Aldrich). DMSO was used as a vehicle control. To analyze the response to vemurafenib, cells were treated with either DMSO alone or 2 μM vemurafenib in DMSO.
Generation of sgRNA library plasmid, virus, and infection
Designed sgRNA sequences were synthesized using programmable microarrays (CustomArray, USA). Sequences are listed in Supplementary Table 2. Oligos were cleaved from the microarray and PCR amplified using chip_fwd and chip_rev. PCR cycling was performed as follows: 95°C for 3 min, 25 cycles of 98°C for 20 s, 56°C for 15 s, 72°C for 30 s, and 72°C for 1 min. To ensure high-yield coverage of the PCR products, eight repeats of the PCR reaction were conducted. The second PCR was performed using the chip_2nd_fwd and chip_2nd_rev primers. The PCR cycling conditions were 95°C for 3 min, followed by 6 cycles of 98°C for 20 s, 58°C for 15 s, 72°C for 30 s, and 1 cycle of 72°C for 1 min. The primers used in these steps are listed in Supplementary Table 9. PCR products were purified using 2.0(by volume) AMPure XP beads (Beckman Coulter). The purified amplicons were cloned into BsmBI (NEB)-digested CROPseq-Guide-Puro plasmids (Addgene #86708) by Gibson assembly, as described previously1.
To ensure high-yield coverage, four repeats of the Gibson reactions were performed. The products of the Gibson reactions were combined and electroporated into Endura cells (Lucigen) following the manufacturer’s protocol. A 1000-fold dilution of the full transformation was spread on carbenicillin (50 μg/ml) LB agar plates to determine the library coverage, and the remainder of the culture was incubated overnight in 200 ml of LB medium. By counting the number of colonies on the plate, >300 library coverage was ensured. The plasmid library was then extracted using an EndoFree Plasmid Maxi kit (Qiagen). The lentivirus was then generated by co-transfecting the plasmid library, psPAX2 (Addgene #12260), and pMD2.G (Addgene #12259) into HEK293T cells using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s protocol.
A375 cells were transduced with the lentivirus at a multiplicity of infection of 0.1–0.3 in each of two independent biological replicates. The cells were selected by culturing in medium containing either 1 μg/ml of puromycin for 14 days or DMSO or 2 μM vemurafenib for 28 days.
Deep sequencing and analysis of sgRNA-integrated regions
To determine the frequency of each sgRNA, sgRNA sequences integrated into the genome were PCR amplified using the primers listed in Supplementary Table 9. The resulting amplicons were adaptor-ligated using a SPARK kit (Enzymatics) and deep sequenced using NextSeq 500. Raw reads were quality trimmed via trimmomatic v0.3336 using the following parameters: LEADING: 20, TRAILING: 20, SLIDINGWINDOW: 150:25, MINLEN: 36.
Reads containing each sgRNA spacer were counted, and these sgRNA read-counts were used as input for the MAGeCK software package to identify hits by comparing DMSO-treated and vemurafenib-treated cells. The p-value of every sgRNA was calculated based on the negative binomial model of read counts. Log2(fold-change) was calculated as log 2 ratio of the normalized sgRNA count of vemurafenib-treated cells to that of DMSO-treated cells. Normalized sgRNA counts were calculated as reported previously1.
Deep sequencing of genomic DNA samples and C to T substitution efficiency
To validate whether the genome was edited by specific sgRNAs, the targeted region was amplified from the genome and sequenced. The primers used in these amplifications are listed in Supplementary Table 9.
The PCR cycle conditions were as follows: 1 cycle at 95°C for 3 min, followed by 27 cycles of 98°C for 20 s, 56°C for 15 s, 72°C for 30 s, and 1 cycle of 72°C for 1 min. The resulting amplicons were adaptor-ligated and deep sequenced using NextSeq 500. If more than 50% of the bases that had a quality score lower than Q30, the sequenced reads were discarded, and base calls with a quality score below Q30 were converted to N. Ten-bp flanking sequences on both sides were used to identify the protospacer region for the targeted sgRNA. Protospacer reads that were not 20 bp in length were considered reads with indels. Base frequencies for each locus were calculated across the reads without indels. The plots are shown with the protein-coding strand, and if the protein-coding strand was the non-target strand of the sgRNA, G to A conversion efficiency was calculated (Supplementary Fig. 2a, 3c, 3e, 15).
Substitution efficiencies were calculated using the following equation:
scRNA-seq
A375 cells were harvested and divided into two pools on each sampling day. One pool was used for extraction of genomic DNA, and the other pool was methanol fixed for Drop-seq analysis. Methanol fixation was performed as described previously37. Briefly, cells were centrifuged at 300g for 5 min and resuspended in 80% methanol (BioReagent grade; Sigma-Aldrich) and 20% PBS (Gibco). The resuspended cells were incubated on ice for at least 15 min and then stored at 80°C. On the day of the Drop-seq experiment, cells were recovered by centrifugation at 2000g for 5 min, washed once with PBS-0.01% BSA, and resuspended in PBS-0.01% BSA to a concentration of 100 cells/μl. Finally, Drop-seq was performed as described previously18.
Droplets were collected into 50-ml conical tubes over a 15 min time period. After which, the droplets were broken, and the RNAs on beads were subjected to reverse transcription.
Aliquots of 2,000 beads were amplified in each tube using the following PCR steps: 95°C for 3 min, then four cycles of 98°C for 20 s, 65°C for 45 s, 72°C for 3 min, and 11 cycles of 98°C for 20 s, 67°C for 20 s, 72°C for 3 min, and 1 cycle of 72°C for 5 min. The amplified products were purified with 0.6 Ampure XP beads and fragmented, tagged, and amplified using a Nextera XT DNA Library Preparation kit (Illumina).
Preprocessing of single-cell transcriptome data
The pipeline was designed based on Drop-seq tools (ver.1.12) and CROP-seq software. Raw data were converted to bam files via FastqToSam in Picard. Cell barcodes and UMI for each mRNA were obtained from Read1. mRNA sequences obtained from Read2 were modified by FilterBAM, TrimStartingSequence, and PolyATrimmer in Drop-seq tools. The modified bam files were then converted to fastq files and aligned to the reference comprised of hg19 and BE genes and guide RNA sequences using STAR aligner. The aligned data were sorted via SortSam and merged with tags via MergeBamAlignment in Picard. Exon information was annotated using TagReadWithGeneExon in Drop-seq tools, and bead synthesis errors within a hamming distance of <4 were corrected via DetectBeadSynthesisErrors in Drop-seq tools. For assignment of sgRNAs per each cell, UMI counts of each sgRNA in the same cell were determined, and the most abundant sgRNA was selected. Finally, transcript data with more than 500 genes per cell were selected and converted to a digital gene expression matrix, and all matrices were merged and indexed by the cell barcode, condition, replicate, sgRNA, and gene. All program runs were managed using CROP-seq software10.
Transcriptome analysis
Analyses were carried out based on the modules in Crop-seq software, Seurat38, and Monocle 239.
Matrix modification
Merged matrices of digital gene expression were normalized per cell, pseudo count 1 was added, multiplied by 10,000, and the matrix was finally log2 transformed. After normalization, ribosomal and mitochondrial genes and pseudo references of sgRNAs and BE were filtered.
Dimensional reduction
First, PCA was performed using a PCA module in the sklearn package for global clustering. For characterization of D+28 samples, t-SNE was performed using Seurat software from the original matrix before normalization. Briefly, cells from the D+28 samples treated with vemurafenib or DMSO with the appropriate fraction of mitochondrial genes (<0.065) were selected, and gene expression counts were normalized by multiplying by 10,000. Highly variable genes were selected using the Seurat clustering algorithm, and the number of PCs was determined using PCElbowPlot and JackStaw. PCA was performed on selected variable genes using determined PCs.
Gene set enrichment analysis
The Wilcoxon rank-sum test of the FindMarkers function in Seurat was used to assess differences in gene expression. Marker genes with low p-values (Benjamini -Hochberg40 corrected p<0.05) for each cluster were obtained. GO and KEGG pathway enrichment analyses were carried out on the signature genes via clusterProfiler19 and Enrichr20. Multiple testing corrections using the Benjamini and Hochberg method with both p-value threshold and false discovery rate set to 0.05 were carried out.
Trajectory Analysis
Trajectory analysis was performed using the expression matrix obtained from Crop-seq software. Normalization and filtering were performed with default parameters, and ordered genes were obtained using the differentialGeneTest module by setting the fullModelFormulaStr option as a condition. Finally, DDRTree-based dimensional reduction and ordering were performed.
Data Availability
Raw sequencing data from this study are deposited in Short Read Archive (SRA) under project number PRJNA530348.
Acknowledgements
This work was supported by: (i) the Mid-career Researcher Program (NRF-2018R1A2A1A05079172), (ii) the Bio & Medical Technology Development Program (NRF-2016M3A9B6948494), (iii) the Bio & Medical Technology Development Program (NRF-2018M3A9H3024850), and (iv) Basic Science Research Program (NRF-2018R1A2B2001322) of the National Research Foundation of Korea, funded by the Ministry of Science, ICT & Planning.
Author contributions
S.J. and H.L. should be regarded as joint first authors.
D.B. and J.H.L. conceived, designed, managed, and supervised the study. S.J. and H.L. designed and performed the experiments, analyzed the data, and wrote the manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
↵4 These authors should be regarded as joint first authors.