Functional Comparison between VP64-dCas9-VP64 and dCas9-VP192 CRISPR Activators in Human Embryonic Kidney Cells

Reversal in the transcriptional status of desired genes has been exploited for multiple research, therapeutic, and biotechnological purposes. CRISPR/dCas9-based activators can activate transcriptionally silenced genes after being guided by gene-specific gRNA(s). Here, we performed a functional comparison between two such activators, VP64-dCas9-VP64 and dCas9-VP192, in human embryonic kidney cells by the concomitant targeting of POU5F1 and SOX2. We found 22- and 6-fold upregulations in the mRNA level of POU5F1 by dCas9-VP192 and VP64-dCas9-VP64, respectively. Likewise, SOX2 was up-regulated 4- and 2-fold using dCas9-VP192 and VP64dCas9VP64, respectively. For the POU5F1 protein level, we observed 3.7- and 2.2-fold increases with dCas9-VP192 and VP64-dCas9-VP64, respectively. Similarly, the SOX2 expression was 2.4- and 2-fold higher with dCas9-VP192 and VP64-dCas9-VP64, respectively. We also confirmed that activation only happened upon co-transfecting an activator plasmid with multiplex gRNA plasmid with a high specificity to the reference genes. Our data revealed that dCas9-VP192 is more efficient than VP64-dCas9-VP64 for activating reference genes.


Introduction
The nuclear compartmentalization of DNA in eukaryotic cells is achieved by its association with histone proteins to form a structure called chromatin whose fundamental unit is the nucleosome. The folding pattern has categorized chromatin into euchromatin and heterochromatin, which are marked by multiple modifications such as acetylation, methylation, phosphorylation, ubiquitination, sumoylation, etc. [1][2][3]. The transcriptionally less active part of the DNA constitutes tightly folded chromatin, known as heterochromatin [4,5]. The degree of chromatin folding and associated gene expression is controlled in various biological processes such as transcription, recombination, and replication [6][7][8]. Hence, the transcriptional activation of epigenetically silenced genes is required for various commercial and therapeutic applications. For example, the reprogramming of specialized cells into induced pluripotent stem cells (iPSCs) by activating endogenous genes has been achieved using gene activators [9,10]. Various transcription factors bind to the DNA sequence of promoter and enhancer regions of target genes and use their effector domains to change the associated gene expression [11]. Artificial transcription factors (ATFs) have been developed based upon the same principle by binding the DNA binding domain with a functional domain to activate the expression of target genes [12,13]. The DNA specificity of transcription activator-like effectors (TALE) and zinc finger proteins has been explored to bind and activate the expression of target genes [14][15][16][17][18][19][20]. However, these ATFs are less favorable because of their lower specificity, time-consuming nature, and complex design. The degree of activated expression depends upon various factors, of which "type of CRISPR activator" is under consideration in our study. In this study, we used a single multiplex plasmid containing gRNAs targeting both POU5F1 and SOX2 genes simultaneously in order to perform a comparative functional analysis of two CRISPR activators, VP64-dCas9-VP64 and dCas9-VP192, at the mRNA and protein level. We used human embryonic kidney (HEK293T) cells because of their wide applications in molecular biology. Although these two activators have been reported previously, their mutual functional comparison with these reference genes in the same experimental conditions has not been completed yet. Concluding a better activator by comparative study will be an improvement in CRISPRa in terms of selecting a more efficient factor to activate the expression of the target gene of interest.

Results
2.1. dCas9-VP192 Activator Is More Efficient Than VP64-dCas9-VP64 to Induce mRNA Expression We used Zhang Lab's web tool (http://crispr.mit.edu/, Cambridge, MA, USA) to design five gRNAs for both reference genes, POU5F1 and SOX2, targeting almost 400 bp upstream to the transcription start site (TSS) [72]. The graphical depiction of the target region and expression cassette in an array plasmid is shown in Figure 1. For the simultaneous expression of these gRNAs from a single plasmid, we first cloned each gRNA into a separate array plasmid, followed by cloning all the array plasmids into a single multiplex plasmid, which was confirmed by colony PCR and sanger sequencing ( Figure S1). The graphical depiction of the cloning strategy used in our study is shown in Figure S1a. The degree of activated expression depends upon various factors, of which "type of CRISPR activator" is under consideration in our study. In this study, we used a single multiplex plasmid containing gRNAs targeting both POU5F1 and SOX2 genes simultaneously in order to perform a comparative functional analysis of two CRISPR activators, VP64-dCas9-VP64 and dCas9-VP192, at the mRNA and protein level. We used human embryonic kidney (HEK293T) cells because of their wide applications in molecular biology. Although these two activators have been reported previously, their mutual functional comparison with these reference genes in the same experimental conditions has not been completed yet. Concluding a better activator by comparative study will be an improvement in CRISPRa in terms of selecting a more efficient factor to activate the expression of the target gene of interest.

dCas9-VP192 Activator Is More Efficient Than VP64-dCas9-VP64 to Induce mRNA Expression
We used Zhang Lab's web tool (http://crispr.mit.edu/, Cambridge, MA, USA) to design five gRNAs for both reference genes, POU5F1 and SOX2, targeting almost 400 bp upstream to the transcription start site (TSS) [72]. The graphical depiction of the target region and expression cassette in an array plasmid is shown in Figure 1. For the simultaneous expression of these gRNAs from a single plasmid, we first cloned each gRNA into a separate array plasmid, followed by cloning all the array plasmids into a single multiplex plasmid, which was confirmed by colony PCR and sanger sequencing ( Figure S1). The graphical depiction of the cloning strategy used in our study is shown in Figure S1a. The multiplex expression plasmid (pFUS_B_gRNA10) was transfected alone as well as in combination with either VP64-dCas9-VP64 or dCas9-VP192 into human HEK293T cells in a 1:1 molar ratio. After 24 h of transfection, a GFP signal (a gene cloned in each activator plasmid) was detected with a fluorescence microscope. Both of the co-transfected The multiplex expression plasmid (pFUS_B_gRNA10) was transfected alone as well as in combination with either VP64-dCas9-VP64 or dCas9-VP192 into human HEK293T cells in a 1:1 molar ratio. After 24 h of transfection, a GFP signal (a gene cloned in each activator plasmid) was detected with a fluorescence microscope. Both of the co-transfected combinations markedly expressed a GFP signal, indicating successful transfection (Figure 2a). and SOX2 as compared to the control (only pFUS_B_gRNA10) (Figure 2b). In the case of POU5F1, we found a 22-fold and 6-fold upregulation by dCas9-VP192 and VP64-dCas9-VP64, respectively. Likewise, SOX2 was up-regulated 4-fold and 2-fold by dCas9-VP192 and VP64-dCas9-VP64, respectively. By relative comparison, the activation level of POU5F1 mRNA by the dCas9-VP192 activator was 3.7-fold (=22/6) greater than that of VP64-dCas9-VP64. Similarly, the SOX2 activation by the dCas9-VP192 activator was 2fold (=4/2) greater than that of VP64-dCas9-VP64 (Figure 2b).  Table S2. The represented values are the average of three independent experiments, where bars represent ± SEM (* p < 0.05, ** p < 0.01, *** p < 0.001) according to Tukey's method and one-way ANOVA. Scale bar, 50 μm.

dCas9-VP192 Activator Is Also Efficient at Protein Level Than VP64-dCas9-VP64
Based on the expressional changes in mRNA, we hypothesized the presence of a similar pattern at the protein level. To confirm this, we transfected HEK293T cells for 72 h with the plasmid combinations as explained above and analyzed the extracted proteins  Table S2. The represented values are the average of three independent experiments, where bars represent ± SEM (* p < 0.05, ** p < 0.01, *** p < 0.001) according to Tukey's method and one-way ANOVA. Scale bar, 50 µm.

dCas9-VP192 Activator Is Also Efficient at Protein Level Than VP64-dCas9-VP64
Based on the expressional changes in mRNA, we hypothesized the presence of a similar pattern at the protein level. To confirm this, we transfected HEK293T cells for 72 h with the plasmid combinations as explained above and analyzed the extracted proteins through Western blotting using the primary antibodies specific to POU5F1 and SOX2. We observed the activation of POU5F1 and SOX2 proteins with both activator plasmids as compared to the control (only pFUS_B_gRNA10 transfected cells) (Figure 3a). The densities of the protein bands on each blot were used to construct histograms of protein expression by using the ImageJ software ( Figure 3b). A 3.7-fold and 2.2-fold increase for POU5F1 was observed with dCas9-VP192 and VP64-dCas9-VP64, respectively. Similarly, the SOX2 expression was 2.4-fold and 2-fold higher with dCas9-VP192 and VP64-dCas9-VP64, respectively ( Figure 3b). However, we did not observe any change in the expression level of CAS9 as well as GFP protein by both activators-i.e., dCas9-VP192 and VP64-dCas9-VP64 ( Figure 3a). This indicates that the higher efficiency of dCas9-VP192 as compared to VP64-dCas9-VP64 is not because of the difference in the expression level of dCAS9 protein between them but due to their own attributes.

Discussion
The advanced sequencing technologies have categorized the transcribed loci based upon the instructions carried by them [73]. It is challenging to comprehend the biological functions of these transcripts and their effects on development and diseases. The function of many genes is still unknown despite advancements in molecular biology. The biological roles of various noncanonical transcripts such as lncRNAs, antisense RNAs, enhancer RNAs, or intergenic RNAs, are still not clear [74]. These secrets could be understood by Despite its specificity, the CRISPR/Cas9 system has been reported to have some offtarget effects (30)(31)(32)(33)(34). In order to address these, we performed immunoblotting by randomly picking some off-targets-for instance, nuclear factor-kappa B (NF-κB-p65 subunit) and mitogen-activated protein kinases (MAPKs such as JNK and ERK). As expected, we did not observe activation of any of these targets by both dCas9-VP192 and VP64-dCas9-VP64 activators ( Figure S2). We also confirmed that the effect seen in our results is the combined response by designed gRNAs and CRISPR activators, but not by only transfecting reagent, only plasmid, only gRNAs, or only activators, etc. This was achieved by performing experiments with various controls, including (1) control (only cells), (2) irrelevant plasmids (pEGFP-N1), (3) only lipofactamine3000 (lipo3000), (4) lipo3000 + dCas9-VP192, (5) lipo3000 + VP64-dCas9-VP64, (6) lipo3000 + pFUS_B_gRNA10, (7) lipo3000 + pFUS_B_gRNA10 + VP64-dCas9-VP64, (8) lipo3000 + pFUS_B_gRNA10 + dCas9-VP192, and (9) lipo3000 + pFUS_B_gRNA10 + pEGFP-N1. After 72 h of transfection, we extracted the whole protein for Western blotting with antibodies specific to POU5F1 and SOX2 and found activation with only combination 7 and 8 but not with others ( Figure S2). We also predicted the off-targets for our designed gRNAs using the Cas-OFFinder computational tool (Data File S1 (Supplementary Materials)) and evaluated the expression level of the genes with off-targets located between 0 and −400 bp from TSS. We did not find any significant change in their expression level by our designed gRNAs ( Figure S3).
Because of the marked difference in the expression of POU5F1 by two activators, we further confirmed this by the immunostaining of the 72 h post-transfected cells. Both activators with multiplex plasmid robustly activated the expression of POU5F1 protein ( Figure 3c). Nevertheless, there was a slight difference in the fluorescence intensity with each activator plasmid.

Discussion
The advanced sequencing technologies have categorized the transcribed loci based upon the instructions carried by them [73]. It is challenging to comprehend the biological functions of these transcripts and their effects on development and diseases. The function of many genes is still unknown despite advancements in molecular biology. The biological roles of various noncanonical transcripts such as lncRNAs, antisense RNAs, enhancer RNAs, or intergenic RNAs, are still not clear [74]. These secrets could be understood by adopting the molecular techniques able to control the expression of individual transcripts.
RNA interference (RNAi) has been used for a long time to find the function of target genes by disrupting their expression [75]; however, this technique also shows some off-targets [76][77][78]. As an alternative approach, sequence-specific endonucleases such as zinc finger nucleases, transcription activator-like effector nucleases (TALENs), and CRISPR/Cas9 are used [79,80]. Because of its simplicity, efficacy, and specificity, the CRISPR/Cas9 system has gained significant attention for various applications, such as loss-of-function screens, point-mutations, and transcription silencing/activation [50,[80][81][82][83][84][85]. The control of gene expression using synthetic transcription factors has multiple scientific and medical applications, such as tissue regeneration [86], the activation of tumor suppressors [87], stem cell differentiation [88,89], and performing genetic screening [90,91]. The deactivated version of Cas9 (dCas9) has been exploited for the transcriptional silencing or activation of target gene(s) [50,92,93]. The fusion of an effector domain to dCas9 has been reported to activate the transcription of reporter genes in human cells and Escherichia coli [50,92].
A single gRNA is able to guide the activator system for activating the transcription of the target gene; however, the effect was more prominent when multiple gRNAs were used in synergy [19,20,55,93]. So, we designed five gRNAs targeting up to 400 bp upstream of the transcription start site of each gene, as targeting this region could activate their transcription [72]. Instead of using individual gRNAs separately, we used them all together in a single transfection by developing a multiplex expression plasmid (Figure 1) to avoid a variable transfection efficiency among individual gRNAs and cell toxicity because of multiple transfections [94][95][96][97][98].
The first-generation CRISPR-based activators simply used dCas9-VP64 for the transcriptional activation of the target gene(s) [50]. The further development of CRISPR activators suggested that the dCas9-VP64 version is less effective than other advanced versions-for instance, VP64-dCas9-VP64 [64] and dCas-VP192 [9]. The former has an equal number of repeat units of the transactivation domain at both ends of dCas9, while the latter one has a greater number of repeat units at the single end of dCas9. Here, we compared their individual efficiency in human embryonic kidney cells (HEK293T) by targeting POU5F1 and SOX2 as reference genes. The purpose of developing both end-tagged CRISPR activators, VP64-dCas9-VP64, was to recruit the transcriptional machinery more effectively [64,99]. Nonetheless, according to our results both termini tagged dCas9 as less effective than the one tagged at one termini with a relatively greater number of repeats. We found that the dCas9-VP192-containing activator plasmid is more effective at both the RNA and protein level to activate the POU5F1 and SOX2 genes in HEK293T cells (Figures 2 and 3). This could be because of the presence of more VP16 units in dCas9-VP192-containing plasmids. Our results are supported by some previous reports which explain that the extent of the transcriptional activation of endogenous genes is affected by the number of VP16 repeated units [9,63]. This indicates that the number along with the orientation of activation domains should be emphasized when designing new activators in the future. However, adding too many VP16 repeat units could also affect the expression and stability of dCas9, so the selection of a suitable number is important [100]. The DNA binding and cleavage activity of Cas9 involve various conformational changes [101][102][103]; hence, it is possible that the stronger effect by dCas9-VP192 is because of its more favorable conformation rather than the number of VP16 units itself. There is a need to study the conformational changes occurring in CRISPR activator systems during their function. We also observed that reference targets were differently activated by VP64-dCas9-VP64 or dCas9-VP192 at both the mRNA and protein level, which has also been reported previously with other activator systems [59,104]. This differential activation of reference genes could be because of gRNA target sites, epigenetic status, or the basal expression level of the target genes [59,[104][105][106]. Besides the POU5F1 and SOX2 genes, examining the effect of these activators on additional genes (expressed at an average or higher level) will further support the current findings. Second-generation CRISPR activators are more efficient than original first-generation dCas-VP64 [59]. Based on our results, the activity of second-generation activators could be further improved by optimally increasing the repeat units of transactivation domain VP16.
Human iPSCs are promising for in vitro disease modelling and regenerative medicine because of their close resemblance to embryonic stem (ES) cells in their pluripotency, gene expression, and epigenetic states. The reprogramming of somatic cells into iPSCs can be carried out by injecting its nucleus into an oocyte [107][108][109][110] or by fusing it with an embryonic stem cell [111,112]. The Yamanaka team identified the key transcription factors whose overexpression enabled the reprogramming of somatic cells into iPSCs [113]. Afterwards, the generation of iPSCs became possible from mouse and human somatic cells with the ectopic expression of reprogramming factors such as POU5F1, SOX2, KLF4, c-MYC, NANOG, and/or LIN28 [114][115][116][117][118][119][120]. The utility of iPSCs is limited because of two issues: (i) their low efficiency and (ii) the integration of viral transgenes, especially oncogenes such as KLF4 and c-MYC. The reactivation of c-MYC is reported to cause tumors in iPSCS-driven chimera mice [114,121]. The iPSCs generated by POU5F1, SOX2, and KLF4 showed a low efficiency [121,122]. It is reported that only POU5F1 and SOX2 can reprogram primary human fibroblasts by combining histone deacetylase inhibitor, valporic acid [123]. The iPSCs generation has also been reported by using a single gene, POU5F1, in combination with some small molecules [124,125]. The reference genes targeted in our study, POU5F1 and SOX2, have the potential to reprogram specialized cells into iPSCs [9,10], which could be used in drug discovery, disease modelling, and regenerative medicine [126,127]. Therefore, the usage of a comparatively more efficient transcriptional activator will facilitate the efficient generation of iPSCs for research and therapeutic purposes.

Designing gRNAs
To validate the CRISPR activators, we selected POU5F1 and SOX2 as reference genes of human cells and designed gRNAs targeting their promoter regions. It has been reported that three to five gRNAs within 300-400 bp upstream region of transcription start site (TSS) are most effective to activate the transcription of POU5F1, SOX2 and IL1RN [72]. So, we selected 400 bp region upstream to TSS for designing gRNAs by using Zhang Lab's web tool (http://crispr.mit.edu/, Cambridge, MA, USA). For each gene, five promoter targeting gRNA sequences were selected based upon their score and relatively even distribution along the promoter region (Table S1). The strand orientation was not taken under consideration while selecting the gRNAs.

Construction and Confirmation of Array and Multiplexed CRISPR Plasmids
The Golden Gate cloning [128] was applied to construct multiplex plasmid by using the vectors pMA_spCas9_T# and pFUS_B10 (kindly gifted by Dr. Yonglun Luo, Department of Biomedicine, Aarhus University, Denmark). The 10 µM final concentration of oligonucleotides corresponding to each gRNA was annealed in ATP (1× final concentration; NEB Inc., Ipswich, MA, USA), T4 PNK buffer (1× final concentration; NEB) and T4 PNK enzyme (10 units; NEB). The annealing conditions for the thermocycler were adjusted to 95 • C for 5 min followed by a −5 • C/3 min ramp down to 65 • C; 63 • C for 3 min, followed by −3 • C/3 min ramp down to 27 • C; and a final incubation at 25 • C for 10 min. The PNK annealed oligonucleotides were diluted to a final concentration of 1 µM for cloning. In the first round of cloning, annealed oligonucleotides corresponding to each gRNA were cloned one by one into respective pMA_SpCas9_T# plasmids in a 20 µL reaction volume (30 ng annealed oligo, 50 ng of respective pMA_SpCas9_T#, 1 µL BbsI (NEB), 1 µL T4 DNA ligase (NEB), 2 µL 10 × T4 ligase buffer (NEB), nuclease-free water), followed by processing through thermal reaction (1 cycle: 37 • C for 5 min, 22 • C for 5 min; 15 cycles: 37 • C for 30 min; 75 • C for 15 min; and storage at 4 • C). The ligation mixture was transformed into the bacterial strain DH10B (Thermo Fisher Scientific, Inc., Waltham, MA, USA) which was further grown on 2XYT agar plate under ampicillin (Thermo Fisher Scientific, Inc., Waltham, MA, USA) selection (100 µg/mL). The grown colonies showing the right-sized amplicon after colony-PCR were confirmed by Sanger sequencing. PCR primers included the forward primer U6-F and the cloned oligo sequence as a reverse primer. The positive constructs were re-named as pMA_SpCas9_gRNA1 through pMA_SpCas9_gRNA10 depending upon the type of gRNA cloned into them. Next, a second round of cloning was conducted by digesting and ligating all eleven plasmids in the same 20 µL reaction mixture (50 ng of pFUS_B10 plasmid, 30 ng of all 10 pMA_SpCas9_gRNA1 to pMA_SpCas9_gRNA10 plasmids, 1 µL BsaI (Thermo Fisher Scientific, Inc., Waltham, MA, USA), 1 µL T4 DNA ligase (NEB), 2 µL 10 × T4 ligase buffer (NEB), nuclease-free water), followed by processing through thermal reaction (1 cycle: 5 min, 37 • C, 5 min 22 • C, 15 cycles: 30 min, 37 • C; 15 min, 75 • C; end: 4 • C). The blue-white screening under spectinomycin (Sigma-Aldrich, St. Louis, MO, USA) selection helped to pick the colonies for colony-PCR analysis. Primers for PCR include forward primer U6-F and reverse primer Scr-R. The positive construct was named pFUS_B_gRNA10 after confirmation through Sanger sequencing. The PCR mixtures was run at 2% agarose gel at 60 V for 1 h and visualized under a UV-illuminator. The CRISPR/dCas9 activator plasmids pCXLE-dCas9VP192-T2A-EGFP (cat #69536) and pLV hUbC-VP64 dCas9 VP64-T2A-GFP (cat #59791) were purchased from Addgene (Addgene, Watertown, MA, USA). The sequences of primers are mentioned in Table S2.

Transfection
HEK293T cells were seeded in a 6-well plate (BD Biosciences, San Jose, CA, USA) to reach 70-90% confluency before the transfection. All the transfection steps were performed with Lipofectamine 3000 including p3000 (Thermo Fisher Scientific, Inc., Waltham, MA, USA) by following the manufacturer's protocol. The cells were co-transfected with a total of 2.5 µg of plasmids, including pFUS_B_gRNA10 and activator plasmid VP64-dCas9-VP64 or dCas9-VP192 at the same molar ratio. The cells without transfection served as a negative control in all the experiments. All the cells were incubated in humidified environment at 37 • C and 5% of CO 2 for 24 h and the GFP signal was visualized by an inverted microscope (OLYMPUS IX53; Olympus Corporation, Tokyo, Japan).  Table S2. All of the PCR reactions were run in triplicate.

Western Blotting
The treated cells were centrifuged and washed with 1% PBS. Total protein was extracted using the M-PER mammalian protein extraction reagent (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The bicinchoninic acid (BCA) assay kit (Sigma-Aldrich, St. Louis, MO, USA) was used to measure the protein. Mini-PROTEAN Tetra Cell and a mini transblot electrophoretic transfer cell system (Bio-Rad Laboratories, Hercules, CA, USA) were used for the Western blot analysis, including gel electrophoresis and transfer. A total of 30 µg of each protein sample was separated by SDS-PAGE and then transferred to a nitrocellulose membrane (Amersham Pharmacia Biotech, Buckinghamshire, UK). The membranes were blocked with 5% skim milk (BioRad Lab, Irvine, CA, USA) for 1 h, then were incubated overnight with 2000-fold-diluted primary antibodies specific to OCT-4A (POU5F1), CAS9, ERK, JNK (Cell Signaling Technology Inc., Danvers, MA, USA), SOX2 (Abcam, Cambridge, MA, USA), GFP, P65, or β-actin (Santa Cruz Biotechnology Inc., Dallas, TX, USA). Membranes were next incubated with anti-mouse or anti-rabbit horseradish peroxidase-conjugated secondary antibodies (Thermo Fisher Scientific Inc., Waltham, MA, USA) for 2 h at room temperature. The protein signals were developed by means of the SuperSignal West Pico ECL solution (Thermo Fisher Scientific Inc., Waltham, MA, USA), visualized on a ChemiDoc™ Touch Imaging System (Bio-Rad Laboratories), and processed through the EvolutionCapt software (Vilbert Lourmat, Collégien, France). The signal quantification for each relevant protein band was determined with the EvolutionCapt software (Vilbert Lourmat, Collégien, France). The bands were quantified by using the ImageJ software (National Institute of Health, Maryland, USA).

Immunocytochemical Staining
Cells were fixed with 4.0% formaldehyde (Sigma-Aldrich, St. Louis, MO, USA) for 5 min, followed by cell permeabilization with 0.2% Triton X-100 for 5 min. The blocking step involved incubation with 5% FBS (Thermo Scientific, Inc., Waltham, MA, USA) in PBS for 30 min and then 1 h with the 1000-fold-diluted anti-OCT-4A antibody (Cell Signaling Technology Inc., Danvers, MA, USA). The cells were then rinsed well with 1% PBS and incubated with 1000-fold-diluted secondary antibodies conjugated with Alexa Fluor 546 (Invitrogen, Carlsbad, CA, USA). Hoechst 33,258 solution (5 µM; Sigma-Aldrich, St. Louis, MO, USA) was added to stain the nuclei for 10 min. Each step required thorough washing with 1% PBS. The stained cells were visualised under the inverted microscope (Olympus IX53; Olympus Corporation, Tokyo, Japan) and processed through IMT iSolution Auto Plus software (IMT i-solution Inc., Vancouver, BC, Canada).

Off-Target Prediction
The Cas-OFFinder tool (http://www.rgenome.net/cas-offinder/, Cambridge, MA, USA) was used to predict the off-targets for all 10 gRNAs used in our study. The off-targets were searched for Streptococcus pyogenes Cas9 enzyme (protospacer adjacent motif sequence: NGG) with 3 mismatches in GRCh38/hg38 target genome of Homo sapiens. The distance of off-targets from the transcription start site (TSS) of nearby genes and the reference sequence accession number (RefSeq IDs) of that gene were obtained using the Homer software (http://homer.ucsd.edu/homer/ngs/annotation.html, San Diego, CA, USA). The genes with off-targets located between 0 and −400 bp of TSS were selected to design primers using the primer designing tool of the National Center for Biotechnology Information. The complete list of off-targets is mentioned in Data File S1 (Supplementary Materials), while the primers used for the qRT-PCR are mentioned in Table S2.

Statistical Analysis
All the in vitro data analyses were performed using Tukey's method one-way ANOVA. All the data are presented as means of three independent experiments where bars represent ± SEMs. p value < 0.05 was considered statistically significant.

Conclusions
Taken together, these data support the usage of CRISPR/dCas9 activator systems for the specific transcriptional upregulation of target genes. They also highlight that increasing the number of transcription activator units could increase the extent of transcriptional activation. For instance, the selection of an appropriate activator system for POU5F1 and SOX2 can support the efficient generation of iPSCs. Moreover, the optimally efficient activation of endogenous genes could be helpful in medicine, agriculture, and biotechnology.