AI-Driven CRISPR-Cas9 sgRNA Design for PDCD1, TRAC, and B2M Knockout to Improve CAR T Cell Therapy

AI-Driven CRISPR-Cas9 sgRNA Design for PDCD1, TRAC, and B2M Knockout to Improve CAR T Cell Therapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Driven CRISPR-Cas9 sgRNA Design for PDCD1, TRAC, and B2M Knockout to Improve CAR T Cell Therapy kirti sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6596407/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: CRISPR-Cas9 technology is a powerful tool for precise genome editing and is increasingly applied to correct genetic mutations associated with various diseases, including cancer. This system utilizes a single-guide RNA (sgRNA), typically 20 base pairs long and complementary to the target DNA sequence, to direct the Cas9 nuclease for targeted gene activation (knock-in) or repression (knockout). In recent advancements in cancer immunotherapy, CRISPR-Cas9 has been extensively used to enhance the efficacy of Chimeric Antigen Receptor (CAR) T-cell therapy. The development of universal CAR T cells involves the knockout of key genes such as TRAC (T-cell receptor alpha chain), B2M (Beta-2 microglobulin), and PDCD1 (Programmed cell death protein 1), which improves T-cell persistence, immune evasion, and anti-tumor function. Method: In this study, sgRNAs targeting PDCD1, B2M, and TRAC were designed using nine widely recognized AI-driven bioinformatics tools: CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, CRISPRscan, and ATUM gRNA Tool. These platforms use various algorithms and genomic datasets to predict sgRNA candidates with high on-target activity and minimal off-target effects. The selected sgRNAs were assessed based on criteria including GC content, self-complementarity, and exon targeting. Results: The sgRNA design tools consistently identified high-confidence target sites within exon 1 of the PDCD1, TRAC, and B2M genes. For PDCD1 (PD-1), the sgRNA sequence (5′-CACGAAGCTCTCCGATGTGT-3′) was selected as the most optimal candidate, showing strong consensus across all platforms. Similarly, for TRAC, the sgRNA (5′-TCTCTCAGCTGGTACACGGC-3′) targeting exon 1 was chosen based on its high predicted efficiency and specificity. In the case of B2M, the sgRNA (5′-GAGTAGCGCGAGCACAGCTA-3′) was identified as an ideal target site within exon 1, a region critical for MHC class I expression and immune evasion. These sgRNAs demonstrated favorable characteristics including appropriate GC content, minimal self-complementarity, and low predicted off-target activity. To ensure their functional reliability, all selected sgRNAs were validated through an extensive review of scientific literature and previously published patent data, confirming their utility in gene knockout studies related to CAR T-cell enhancement. Conclusion: Among the tools evaluated, CHOPCHOP, Benchling, and CRISPOR emerged as the most comprehensive, offering robust information on GC content, self-complementarity, exon identification, and detailed off-target predictions. Additionally, this study compiled a list of relevant clinical trials involving gene knockouts of PDCD1, TRAC, and B2M to further support the therapeutic relevance of these targets in CAR T-cell development. Molecular Biology Computational Biology Immunology Oncology CRISPR-Cas9 B2M TRAC PDCD1 Genome Editing AI Tools sgRNA Design CAR T cells Figures Figure 1 Introduction Genome editing technologies have revolutionized molecular biology and therapeutic research by enabling precise and targeted alterations to the genetic material of living cells ( 1 – 3 ). Among these technologies, the CRISPR-Cas9 system has emerged as a transformative tool due to its simplicity, programmability, and efficiency. Initially described as part of a bacterial adaptive immune mechanism, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) was first identified by Yoshizumi Ishino and colleagues in Escherichia coli ( 4 ). Although the biological role of these repeat sequences was not understood at the time, subsequent research by Philippe Horvath and Rodolphe Barrangou in 2007 demonstrated that CRISPR-Cas systems function as an adaptive immune defense in prokaryotes by integrating viral DNA fragments into host genomes to recognize and neutralize future infections ( 5 ). The breakthrough in genome engineering came in 2012, when Emmanuelle Charpentier and Jennifer Doudna re-engineered the CRISPR-Cas9 system from Streptococcus pyogenes into a two-component platform capable of introducing site-specific double-stranded DNA breaks. This achievement laid the foundation for a highly versatile genome editing tool and earned them the Nobel Prize in Chemistry in 2020 ( 6 ). Unlike earlier genome-editing technologies such as zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs), CRISPR-Cas9 requires only a single guide RNA (sgRNA) to direct the Cas9 endonuclease to the target site, thereby eliminating the need for complex protein engineering ( 7 ). The CRISPR-Cas9 system functions in three key stages: adaptation, crRNA biogenesis, and interference. During adaptation, short fragments of invading viral DNA are incorporated into the CRISPR array in the host genome. These fragments are transcribed and processed into CRISPR RNA (crRNA), which, in complex with trans-activating CRISPR RNA (tracrRNA) and Cas9, guide the nuclease to complementary target sequences, where Cas9 induces a double-strand break. This break is then repaired by endogenous cellular mechanisms, resulting in targeted insertions, deletions, or sequence replacements ( 8 ). A critical component of CRISPR-Cas9 genome editing is the design of single guide RNAs (sgRNA), which determine the specificity and efficiency of target site recognition. Traditional approaches to sgRNA design rely on sequence homology and empirical testing, which can be time-consuming and prone to off-target effects. To address these limitations, artificial intelligence (AI)-based tools have been developed to enhance sgRNA design. These tools leverage large-scale genomic datasets, machine learning algorithms, and predictive modeling to optimize sgRNA selection, minimize off-target activity, and improve editing efficiency. The integration of AI with CRISPR technology has significantly accelerated the development of precise genome-editing strategies, particularly for therapeutic applications. One of the most promising areas of CRISPR-Cas9 application is cancer immunotherapy, particularly chimeric antigen receptor (CAR) T-cell therapy, genome editing is increasingly used to enhance the function, persistence, and safety of engineered T cells. Knockout of key genes such as PDCD1 (which encodes PD-1, a negative immune regulator), TRAC (T-cell receptor alpha constant, to eliminate native TCRs), and B2M (beta-2 microglobulin, to prevent MHC-I expression and reduce immunogenicity) has shown promise in generating universal, allogeneic CAR T cells. Designing effective sgRNAs targeting these genes is a critical step in achieving functional gene disruption and improving therapeutic outcomes especially in the engineering of chimeric antigen receptor (CAR) T cells. To recognize tumor antigens efficiently. CRISPR-Cas9 enables precise genetic modifications in T cells, such as knocking out immune checkpoint genes like PDCD1 (which encodes PD-1), disrupting endogenous T-cell receptor (TCR) genes (e.g., TRAC), eliminating B2M (Beta-2 microglobulin) to reduce MHC class1 expression and evade host immune rejection, and enhancing T cell persistence and tumor-killing capacity. Furthermore, AI-driven optimization of sgRNAs facilitates the efficient design of CAR constructs, reducing immunogenicity and potential adverse effects while improving therapeutic outcomes. CRISPR-Cas9 relies on a synthetic single-guide RNA (sgRNA) to direct the Cas9 nuclease to a complementary DNA target site, resulting in site-specific double-stranded breaks. The outcome of CRISPR-mediated editing is strongly influenced by the design of the sgRNA, which determines both the efficiency of editing and the risk of off-target effects. While manual sgRNA design is possible, the emergence of artificial intelligence (AI)-driven bioinformatics tools has greatly enhanced the accuracy and efficiency of this process. Tools such as CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, CRISPRscan, and ATUM sgRNA Tool use diverse algorithms and genomic datasets to predict optimal sgRNA candidates with minimal off-target activity and high on-target efficacy. In this study, guide RNAs (sgRNA) with high predicted efficiency were carefully designed using web-based bioinformatics tools. In this study, we designed sgRNAs targeting PDCD1, B2M, and TRAC using nine widely utilized sgRNA design tools: CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, CRISPRscan, and ATUM sgRNA Tool. The selection of highly effective sgRNA is a critical step in CRISPR-Cas9-mediated gene knockout, as it directly influences the precision, on-target activity, and overall success of gene disruption. By leveraging these computational platforms, which analyze target sequences based on specificity, GC content, and potential off-target effects, we ensured the identification of sgRNA that maximize editing efficiency while minimizing unintended modifications. Further validation was conducted through a detailed literature review and analysis of published data demonstrating successful gene knockouts and functional restoration of T cell activity. Additionally, we compiled a comprehensive table of clinical trials in which the knockout of TRAC, PDCD1, and B2M has been employed to enhance CAR T-cell functionality. This approach provides a thorough evaluation of AI-driven sgRNA design tools and supports the development of optimized CRISPR-Cas9 strategies for gene editing in CAR T-cell therapy. Mechanism of CRISPR - Cas9 and role of sgRNA CRISPR-Cas9 works as an efficient genome editing tool which allows the targeted modifications in DNA. The CRISPR Cas6 systems consists of two main components. Cas9 nuclease enzyme that introduces double strand brake at a specific DNA sequence While second component is Guide RNA (sgRNA), synthetic RNA molecule that direct Cas9 to the target DNA. The mechanism of CRISPR Cas9 occurs in three main steps: Targeting Recognition by sgRNA, DNA Cleavage by Cas9 and DNA repair. Step1:Targeting Recognition by sgRNA - The guide RNA (sgRNA) is a chimeric RNA composed of two parts: CRISPR RNA (crRNA) which is 20bp nucleotide sequence complementary to target DNA, ensures high specificity and minimizes off target effects., it’s also requires a PAM protospacer adjacent motif (PAM) sequence (NGG for SpCas9) in the target DNA to initiate binding. Another component Trans-activating CRISPR RNA (tracrRNA) that helps in binding of Cas9 induces a conformational change in Cas9, switching it to an active state, this activation allows the Cas9 to scan the genome and bind to target gene and stabilizing the Cas9-sgRNA complex. This complex directs the Cas9 to specific DNA by base pairing with the target strand. Once the target sequence is recognized, Cas9 unwinds the DNA and allows the sgRNA to form a stable R loop structure with the complementary strand. Cas9 has two nuclease domains, one is RuvC which cuts one DNA strand and other is HNH domain (Histidine-Asparagine-Histidine) that cuts the complementary strand. This results in a precise double strand break (DSA) in target DNA. After Cas9 creates a DSB, the cell repair it using Non-Homologous End joining (NHEJ) joins DNA ends, introduces small insertions or deletions (indels), disrupting gene function(useful for gene knockouts). Other repair mechanism is Homology-Directed Repair (HDR) uses a donor template to introduce modifications, allowing for gene correction, insertion and activation to enhance the particular gene function. sgRNA can be modified for targeting multiple genes (Fig. 1). AI Tools Use in sgRNA Design The advent of CRISPR-Cas9 technology has significantly transformed genome editing due to its simplicity, efficiency, and versatility. However, challenges such as off-target effects and precision limitations remain. Integrating Artificial Intelligence (AI) addresses these issues by improving target identification, optimizing guide RNA (sgRNA) design, and predicting potential off-target interactions. The combination of AI and CRISPR-Cas9 has the potential to enhance the accuracy, safety, and effectiveness of CAR T-cell therapy, paving the way for more advanced and accessible treatments. To knockout the gene using CRISPR /Cas9, critical region of the gene should be targeted to disrupt its function. To ensure effective knockout, should target an early functionally significant exon of a gene. This will prevent the production of a functional protein. Target the exon 1 or exon 2 (these are typically essential for proper translation).If gene has multiple isoforms, choose an exon common to all isoforms. The 5’ region of exon1 is ideal because any deletion or insertion created by CRISPR can lead a frameshift mutation, resulting in a non-functional protein of the mRNA. Emerging CRISPR designing tool identify the best suitable transcript to design the sgRNA adjacent to the PAM sequence with high on target efficacy and low target effects. The choice of a sgRNA (guide RNA) designing tool for CRISPR-Cas9 genome editing depends on several factors, including the specific organism, target gene, and the purpose of your experiment. Different tools have their strengths and may be better suited for certain applications. Here are some popular sgRNA designing tools for CRISPR: CHOPCHOP: CHOPCHOP is a versatile sgRNA design tool that allows users to target multiple organisms. It provides information on off-target sites and is frequently updated. Benchling: Benchling offers a suite of CRISPR design tools that include sgRNA design. It provides flexibility in choosing target sites, checking off-target potential, and optimizing sgRNA sequences for specific applications. CRISPRscan: CRISPRscan is a web-based tool that provides an easy-to-use interface for designing sgRNA. It considers off-target potential and provides a score to rank potential sgRNA. GenScript: provides comprehensive function with just Simply enter desired gene symbol or sequence for guided support from design to ordering and supports knock-ins, knockouts, and sequence replacement, with knock-ins up to 100 nt. Cas-Designer: Cas-Designer is a tool provided by the Zhang lab (Broad Institute) that helps design sgRNA for CRISPR-Cas9 experiments. It includes features to minimize off-target effects. E-CRISP: E-CRISP is a tool for designing sgRNA with a focus on minimizing off-target effects. It provides detailed information on potential off-target sites. CRISPR-ERA: CRISPR-ERA is designed for sgRNA design in the context of pooled CRISPR screens. It can optimize sgRNAs for specific experimental conditions. CRISPOR: CRISPOR is a user-friendly web tool that offers sgRNA design for a wide range of organisms. It provides information on potential off-targets and allows users to filter sgRNA based on their preferences. ATUM sgRNA Design Tool: ATUM offers a sgRNA design tool that allows users to design sgRNA for their CRISPR experiments. It also provides information on potential off-targets. Comparison of AI-Driven sgRNA Design Tools To ensure the accuracy, efficiency, and reliability of sgRNA selection, we evaluated several widely used AI-powered CRISPR sgRNA design platforms. Based on algorithmic approaches, prediction accuracy, and overlap in results, we identified CHOPCHOP, Benchling, CRISPRscan, Cas-Designer, and GenScript as the most effective tools. These tools frequently yielded overlapping sgRNA sequences due to their utilization of similar scoring systems (e.g., Doench 2014, Doench 2016, Moray-Mateos models). Overlapping results were prioritized to improve sgRNA selection confidence and minimize experimental failure (Table 1). This comparative analysis highlights the strengths of each tool, emphasizing the comprehensive capabilities of CHOPCHOP, Benchling, and Cas-Designer in particular. CRISPOR stands out for its detailed off-target prediction, while GenScript, and CRISPRscan offer ease of use for rapid preliminary design. Integration of results from these tools enables the identification of sgRNA with high specificity, robust on-target activity, and minimal off-target effects. PDCD1 Gene and Its Role in T cell Exhaustion Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is an inhibitory immune checkpoint receptor expressed on activated T cells. It plays a key role in maintaining immune homeostasis and preventing autoimmunity ( 9 ). However, in the tumor microenvironment, PD-1 interacts with its ligands PD-L1/PD-L2 to inhibit T cell activity, resulting in T cell exhaustion and allowing cancer cells to escape immune surveillance ( 10 ). Therefore, genetic disruption of PDCD1 via CRISPR/Cas9 is a promising strategy to enhance the efficacy of CAR T cells by preventing their exhaustion and improving tumor clearance. PDCD1 Knockout in CAR T cell Therapy Blocking PD-1 signaling has become a standard strategy in cancer immunotherapy using monoclonal antibodies. However, transient antibody blockade has limitations. A more durable and cost-effective alternative is the CRISPR-mediated PDCD1 knockout in CAR T cells, which renders the T cells resistant to PD-L1-mediated immunosuppression. This approach leads to enhanced persistence, proliferation, and cytotoxic activity of engineered T cells in the tumor microenvironment ( 11 , 12 ). sgRNA Design and Off-Target Analysis for PDCD1 Gene Knockout The PDCD1 gene located on chromosome no. 2 and contains 5 exons, and according to NCBI, two transcript variants are available: NM_005018.3, which consists of 5 exons with a total spliced length of 2097 bp, and XM_006712573.3 (transcript variant X1), which contains 4 exons with a spliced length of 736 bp. For designing sgRNAs, it is essential to select the longer transcript—in this case, NM_005018.3—as it is more likely to represent the full-length, functionally relevant mRNA. Targeting this transcript ensures that the designed sgRNAs effectively disrupt the functional gene product. Hundreds of candidate sequences were obtained from the web-based tools, and a specific sgRNA sequence for PDCD1 were pre-selected from the list according to the criteria: 1) Sequences which were obtained from multiple tools, 2) Sequences which exist in an exon, and 3) Sequences which have high rank in each tool. To design an efficient sgRNA for knockout of the PDCD1 (PD-1/PD-L1) gene, we utilized eight web-based CRISPR sgRNA design tools: CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, ATUM sgRNA tool and CRISPRscan. GenScript was initially used to obtain three validated top sgRNAs hits, which were subsequently compared with results from other web-based tools to identify the most optimal sgRNA. CHOP tool was initially used to and obtained three validated top hits, compared these hits with other web-based tools to identify the best sgRNA or find common hits across all tools. All tools, except for ATUM sgRNA tool, E-CRISP, GeneScript, CRISPRscan, identified a highly conserved and overlapping sgRNAs sequence targeting exon 1 of the PDCD1 gene. This strong consensus suggests a robust and specific target site for CRISPR-Cas9-mediated gene knockout. Off target analysis checked with CRISPOR showed lower predicted on-target activity (Doench 2016 score: 50), but it exhibited superior specificity metrics, including a higher CFD score of 96 and an MIT specificity score of 95, with significantly fewer predicted off-target sites (n = 36) and minimal risk to coding exons (maximum exonic CFD: 0.3) (Table 2). Validation of PDCD1 targeting sgRNA based on existing data sources The selected sgRNA sequence has been validated in multiple studies and patents, confirming its use in PDCD1 gene knockout for immune checkpoint modulation in CAR T cells. This approach has been shown to improve tumor killing capacity, T cell persistence, and resistance to exhaustion. Philips and colleagues employed CRISPR–Cas9 to disrupt the PDCD1 (PD-1) gene in Jurkat T cells by co-electroporating cells with a PX458 (pSpCas9 (BB)-2A-GFP) plasmid harboring two guide RNAs, including (5′-CACGAAGCTCTCCGATGTGT-3′). Post-electroporation, GFP⁺ cells were single-cell sorted, and clones lacking PD-1 surface expression were selected for functional analyses ( 13 ). Similarly, Hanamura et al. describes the successful generation of PD-1 knockout in human B lymphoma (VAL) cells using CRISPR-Cas9 technology. The researchers designed a sgRNA with the sequence (5′-CACGAAGCTCTCCGATGTGT-3′), which specifically targets exon 2 of the PDCD1 gene encoding PD-1. This sgRNA was cloned into the BbsI site of the PX458 plasmid, a Cas9–GFP expression vector. VAL cells were transfected with the recombinant PX458 plasmid, and three days post-transfection, GFP⁺ cells were isolated by fluorescence-activated cell sorting (FACS). These sorted cells were expanded into clones, and subsequent analysis confirmed a complete lack of PD-1 expression, verifying successful knockout ( 14 ). This guide RNA sequence (5′-CACGAAGCTCTCCGATGTGT-3′) has been consistently documented across several patent filings. In US20190247432A1, filed by Zhao and Liu and assigned to the University of Pennsylvania, it is described as SEQ ID NO: 81 (“PD1.21-3”), used to edit PDCD1 in NY-ESO-1–specific T cells via Cas9 RNP delivery to augment anti-tumor immunity ( 15 ). Likewise, US20210388389A1 from Yale University (Chen and Dai) employs this sgRNA, labeled “hPDCD1 sg-2” (SEQ ID NO:23), for multiplex gene editing of primary human T cells—frequently in combination with TRAC gene disruption—to generate optimized CAR-T cells ( 16 ). Furthermore, the Chinese patent CN105671083B by Sun and assigned to Anhui Kedgene Biotechnology details the use of this guide in a lentiviral system (Lenti-PD-1-Puro) to knock out PDCD1 in T cells derived from tumor patients. The sgRNA, corresponding to positions 2859–2878 of the PD-1 gene, was annealed with its reverse complement, cloned into a Lenti-CRISPR/Cas9 vector, and used to generate modified T cells with successful gene disruption ( 17 ). TRAC Gene and Its Role in GvHD Prevention The T cell receptor alpha constant (TRAC) gene located on chromosome 14, encodes the constant region of alpha subunit of T cell receptor ( 18 ). It forms the T cell receptor complex, which recognizes the antigenic peptides presented by MHC molecules ( 19 ). It forms the heterodimer with TCR beta chain which interacts with CD3 complex, transmitting activation signals into the T cell ( 20 ). The MHC molecule presenting an antigenic peptide is recognized by the T-cell receptor (TCR), leading to the activation of T cells and the subsequent killing of tumor cells ( 21 ). Antigenic peptides, especially those restricted to MHC class I, are generated through the degradation of proteins within tumor cells or pathogen-infected somatic cells ( 22 ). T cells, via their T-cell receptors (TCRs), play a critical role in identifying these altered cells by monitoring their protein-derived peptide profiles ( 23 ). Autologous T cell therapy has several limitation in terms of manufacturing time and expenses, as well as the poor quality and quantity of obtainable T cell especially in case of infants or heavily treated patients. To overcome these limitation allogenic (donor derived) T cell therapy are currently being explored ( 24 ). In the context of allogeneic CAR T therapy, residual TCR expression can trigger graft-versus-host disease (GvHD). Disruption of TRAC via CRISPR/Cas9 enables the generation of TCR-deficient T cells, which are incapable of recognizing and attacking host tissues, thereby facilitating the development of universal CAR T cells with minimal risk of GvHD ( 25 , 26 ). TRAC Knockout in Universal CAR T cell Therapy By knocking out the TRAC gene, the endogenous TCR complex is inactivated, preventing unwanted alloreactive responses. This strategy is fundamental in allogeneic or off-the-shelf CAR T products, making them s afe for administration across HLA mismatches. sgRNA Design and Off-Target Analysis for TRAC Gene Knockout To design the sgRNA targeting the TRAC gene, the gene was first located using the GeneCards database. The corresponding Ensembl ID (ENSG00000277734), was used to access the Ensembl Asia genome browser, which listed a single transcript of 974 bp. Upon selecting the transcript ID, the exon and intron structures were displayed by clicking on the "Exons" tab located on the right-hand side of the page. This section provided the sequence and genomic coordinates for each exon and intron. The sequence of Exon 1, spanning from chromosomal position 22547506 to 22547778, was extracted and used as input for sgRNA design. The sequence was analyzed using the CHOPCHOP tool, and the results were compared with predictions generated by other platforms, including Benchling, Cas-Designer, E-CRISP, CRISPOR and CRISPR-ERA, to identify optimal sgRNA candidates with high specificity and minimal off-target effects. In selecting the optimal single guide RNA (sgRNA) for targeting the TRAC gene, utilized the CHOPCHOP tool and applied several filtering criteria, including a mismatch2 score of 0, no self-complementarity, an efficiency greater than 45%, and a GC content between 40% and 60%. From these parameters, two sgRNAs were identified with efficiencies of 48.14% and 47.27%. These candidates were then cross-validated using multiple CRISPR design tools, including Benchling, Cas-Designer, CRISPOR, CRISPR ERA and E-CRISP,. All tool except E CRISPR identified a highly conserved and overlapping sgRNA sequence targeting the exon1of TRAC gene. Off target analysis of sgRNA checked with CRISPOR showed lower predicted on-target activity (Doench 2016 score: 48), but it exhibited superior specificity metrics, including a higher CFD score of 85 and an MIT specificity score of 92, with significantly fewer predicted off-target sites (n = 113) and minimal risk to coding exons (maximum exonic CFD: 0.122) (Table 4). Validation of TRAC targeting sgRNA based on existing data sources The sgRNA has been widely validated in universal CAR T research and commercial patents, showing efficient TRAC disruption and prevention of TCR expression. Several studies have utilized the sgRNA sequence (5′-TCTCTCAGCTGGTACACGGC-3′) targeting exon 1 of the TRAC gene to engineer TCR-negative CAR-T cells with high efficiency. Eyquem et al. achieved targeted CAR knock-in at the TRAC locus by electroporating human T cells with Cas9 mRNA and TRAC-specific gRNA, followed by AAV6-mediated delivery of the CAR cassette, resulting in > 95% CAR⁺TCR⁻ cells and enhanced antitumor function in vitro and in vivo ( 27 ). Similarly, Li et al. used a CRISPR-Cas9 RNP complex with the same TRAC crRNA in primary CD8⁺ T cells and observed ~ 51% TCRα surface loss and 40–47% allele disruption ( 28 ). Preece et al. developed a lentiviral vector incorporating the TRAC sgRNA into the 3′ LTR, linking TRAC disruption and CAR delivery in a single vector, generating > 96% CAR⁺TCR⁻ populations ( 29 ). Albers et al. used an RNP format with this sgRNA and showed 51% TCRα loss and 40–47% disruption by ddPCR, enabling efficient CAR knock-in at the TRAC locus ( 30 ). Ye et al. also demonstrated robust TRAC editing with the same sgRNA, yielding CAR⁺CD3⁻ T cells upon electroporation of Cas9 RNP and AAV6 donor ( 31 ). Zhou et al. disrupted both TRAC and TRBC in Jurkat cells using plasmid-based CRISPR, isolating TCRαβ-negative clones ( 32 ). Kamali et al. reported ~ 89% TRAC indels in HEK293T cells and 12–14% allele disruption in primary T cells, using plasmid delivery ( 33 ). Patent documents also support the therapeutic utility of this sgRNA. The EP3686275A1 patent lists the same guide as SEQ ID NO: 5, used with Cas9 mRNA to generate universal CD3⁻/TCR⁻ T cells ( 34 ). Similarly, CN112512557A discloses its use (as “TRAC 3”, SEQ ID NO: 2) with Cas9 RNP to produce anti-BCMA CAR-T cells via electroporation ( 35 ). B2M Gene and Its Role in Immune Evasion Beta-2 microglobulin (B2M) is the light chain of the major histocompatibility complex (MHC) class I molecule, responsible for presenting intracellular antigenic peptides to CD8⁺ cytotoxic T cells. The B2M gene, located on chromosome 15, comprises four exons. Numerous studies have highlighted the role of B2M loss in enabling immune evasion in various cancers, primarily through genetic deficiencies, mutations, or epigenetic suppression mechanisms. ( 36 , 37 ) B2M Knockout in Allogeneic CAR T cell Therapy Autologous CAR T cell therapy, though effective, faces several limitations including patient-specific manufacturing, long production times (typically 2–3 weeks), limited T cell availability, and inconsistent product quality. To overcome these issues, allogeneic CAR T cell therapy also termed "universal" or "off-the-shelf" CAR T therapy has emerged. This method utilizes T cells derived from healthy donors or alternative sources like umbilical cord blood or pluripotent stem cells, which are genetically engineered, expanded ex vivo, and infused into patients ( 38 , 39 ). However, two major immunological challenges hinder the efficacy of allogeneic CAR T cells: graft-versus-host disease (GvHD) and host-versus-graft alloreactivity (HvGA). Advancements in genome editing tools, particularly CRISPR/Cas9, have enabled targeted disruption of the T cell receptor (TCR) and MHC class I molecules through B2M gene knockout, thereby significantly reducing the risks of alloreactivity and immune rejection ( 40 , 41 ). sgRNA Design and Off-Target Analysis for B2M Gene Knockout To target the B2M gene, we initially designed sgRNAs using the GenScript CRISPR sgRNA design tool, which provided six top-ranked, validated candidates. These sgRNAs were then cross-validated using multiple web-based tools, including CHOPCHOP, CRISPOR, E-CRISP, CRISPR-ERA, and Cas-Designer, to ensure consistency and robustness in target site selection. For off-target analysis, we selected the most promising sgRNA and evaluated its genome-wide specificity using the CRISPOR tool. The analysis revealed a total of 27 potential off-target sites, all located within introns or intergenic regions across multiple chromosomes, including chromosomes 1, 2, 3, 4, 5, 7, 8, 9, 11, 16, 17, 19, and 20. These results support the high specificity of the selected sgRNA, with minimal risk of disrupting coding sequences (Table 5). Validation of B2M targeting sgRNA based on existing data sources To validate the real-world application of our selected sgRNA, we explored previously published literature and patent filings where the same sequence was used for B2M gene knockout. These studies demonstrate its successful application in a variety of contexts—from human embryonic stem cells and induced pluripotent stem cells to NK and T cells in immunotherapy settings. Several recent studies have utilized the same guide RNA sequence (5′GAGTAGCGCGAGCACAGCTA-3′), targeting exon 1 of the B2M gene, to achieve efficient gene knockout across diverse human cell types and contexts (42). Hamilton et al. developed antibody-targeted virus-like vesicles termed Cas9-EDVs, which deliver Cas9–sgRNA ribonucleoproteins (RNPs) to specific immune cells via scFv targeting domains. Using an anti-CD19 scFv, they directed B2M-targeting Cas9-EDVs to CD19⁺ T cells, achieving selective and efficient B2M knockout in vivo without affecting bystander cells (43). Lamarthée et al. used synthetic crRNAs targeting B2M, including the same sequence, to generate HLA class I/II–null human glomerular endothelial cells. This enabled the creation of immunologically silent cells for transplant immunogenicity assays ( 44 ). Similarly, Hoerster et al. engineered allogeneic human NK cells using lentiviral CRISPR/Cas9 vectors co-expressing sgRNAs against B2M and NKG2A, eliminating HLA-I expression to reduce immunogenicity while enhancing therapeutic utility ( 45 ). Hiatt et al. employed Cas9 RNP nucleofection in primary human monocytes, using the same B2M-targeting sgRNA to generate HLA-I-deficient macrophages and dendritic cells. This allowed evaluation of immune function post-editing ( 46 ). In iPSC-based approaches, Song et al. disrupted B2M in human pluripotent stem cells, producing HLA-I-null endothelial derivatives for "HLA exchange" strategies in regenerative medicine, ( 47 ) while Bogomiakova et al. used the same sequence to evaluate NK cell responses to B2M -knockout iPSC-derived fibroblasts ( 48 ). Additionally, McAlexander et al. used the same sgRNA as a positive control to assess CRISPR editing efficiency in activated primary human CD4⁺ T cells during genome-wide enhancer profiling. As a benchmark for editing efficiency, McAlexander et al. used the same B2M-targeting sgRNA to knock out B2M in CD4⁺ T cells during enhancer mapping studies, confirming successful CRISPR activity ( 49 ). This widely adopted sgRNA is also included in the patent US20220017926A1, which describes methods and compositions for CRISPR/Cas-mediated B2M disruption to eliminate HLA-I expression for immunomodulation ( 50 ). These independent studies consistently validated the sgRNA sequence (5′GAGTAGCGCGAGCACAGCTA-3′) as an efficient and specific tool for ablating B2M expression and eliminating surface HLA-I across a range of primary human immune and non-immune cells, stem cells, and in vivo delivery platforms (Table 5). Results To enhance the effectiveness and safety of CAR T-cell therapy, sgRNAs targeting the PDCD1, TRAC, and B2M genes were designed using multiple AI-powered CRISPR sgRNA design tools. The selection process involved a comprehensive cross-validation among various tools, focusing on the overlap of sgRNA sequences across platforms and evaluating their on-target efficiency and off-target specificity. Three tools—CHOPCHOP, Benchling, and CRISPOR—were prioritized due to their unique and complementary analytical strengths. For PDCD1 (PD-1), a highly conserved sgRNA (5′-CACGAAGCTCTCCGATGTGT-3′) targeting exon 2 was identified as the most optimal candidate. This sgRNA was chosen based on several factors, including its high specificity and minimal predicted off-target effects. Analysis by CHOPCHOP revealed no self-complementarity, a GC content of 55%, and low mismatch values (MM0–1), indicating structural stability and a reduced risk of secondary structure formation. Benchling allowed for precise mapping of the sgRNA within the full genomic sequence, confirming its location in exon 2. CRISPOR further supported the design, providing an excellent MIT specificity score of 95 and a CFD score of 96, with only 36 potential off-target sites, none of which posed significant risks due to low CFD scores in coding regions. The same sgRNA sequence was validated in patent CN105671083B for an efficient knockout in T cells ( 13 ), and previous studies, such as Philips et al. (2024), have demonstrated its role in modulating PD-1 dimerization and restoring T cell activity in vitro ( 18 ). For TRAC, the sgRNA sequence (5′-TCTCTCTCTCAGCTGGTACACGGC-3′), targeting exon 1, was selected based on strong cross-platform consensus. CHOPCHOP reported a GC content of 60%, absence of self-complementarity, and an on-target efficiency score of 48.14%, with no predicted high-risk off-targets. Benchling provided visual confirmation of the guide’s precise location within the exon 1 region, ensuring disruption of the constant region of the TCR α chain. CRISPOR validated the guide with a MIT specificity score of 92 and a CFD score of 85, supporting high editing precision. This sgRNA has been widely validated in literature and patent disclosures. In a foundational study by Eyquem et al. (2017), CRISPR-mediated integration of a CAR construct into the TRAC locus using Cas9 mRNA and AAV6 in primary human T cells led to > 95% CAR⁺TCR⁻ cells and enhanced antitumor activity, demonstrating efficient disruption and knock-in ( 28 ). Similarly, Li et al. (2022) employed an RNP approach (Cas9 protein with crRNA:tracrRNA duplex) in CD8⁺ T cells, achieving ~ 51% TCRα loss and ~ 40–47% indels, validating this guide in a high-throughput setting ( 29 ). Preece (2020) used a lentiviral terminal-CRISPR vector and Cas9 mRNA electroporation, resulting in > 96% CAR⁺TCR⁻ cells, showcasing a robust self-inactivating delivery system ( 30 ). Further supporting data comes from Albers et al. (2019), who used electroporated RNP complexes in CD8⁺ T cells, reporting ~ 51% TCRα disruption by flow cytometry ( 31 ). Ye et al. (2022) combined RNP and AAV6 donor delivery to produce a high fraction of CAR⁺CD3⁻ cells, suggesting functional knockout ( 32 ). Zhou et al. (2022) applied plasmid-based CRISPR to Jurkat cells for dual TRAC and TRBC knockout, confirming the guide’s efficacy in T cell lines ( 33 ). Kamali et al. (2021) used a plasmid system in HEK293T and primary T cells, achieving ~ 89% indels in HEK293T but only ~ 12–14% in T cells, indicating cell-type-dependent efficiency ( 34 ). Finally, European Patent EP3686275A1 demonstrated successful use of this sgRNA with chemically modified sgRNA and Cas9 mRNA via electroporation in human T cells to generate universal CAR-T cells with a TCR⁻/CD3⁻ phenotype, supporting its application in clinical manufacturing workflows ( 35 ). The sgRNA sequence 5′-GAGTAGCGCGAGCACAGCTA-3′, targeting exon 1 of B2M, was consistently validated across multiple independent studies using various delivery platforms and cell types. Tools like CHOPCHOP and CRISPOR predicted high on-target activity (48.12%), GC content of 60%, low off-target distribution (27 sites in non-coding regions), and high specificity scores (MIT: 93; CFD: 97), supporting its suitability for gene editing applications. This sgRNA was used successfully in primary T cells (McAlexander et al., 2024; US20220017926A1), NK cells (Hoerster et al., 2021), iPSCs (Song et al., 2022; Bogomiakova et al., 2023), endothelial cells (Lamarthée et al., 2021), and monocytes/macrophages (Hiatt et al., 2021), showing efficient B2M knockout and consequent loss of surface MHC class I (HLA-I) expression. In therapeutic contexts, this loss enabled immune evasion and facilitated the generation of universal CAR-T and NK cell products. Notably, US20220017926A1 demonstrated up to 90% knockout efficiency in primary T cells and CAR-T cells using optimized RNP electroporation protocols. In conclusion, the sgRNA for PDCD1, TRAC, and B2M were carefully designed and validated through a combination of AI tools, cross-platform analysis, and literature support, ensuring their high specificity, minimal off-target effects, and potential to enhance CAR T-cell therapy’s efficacy and safety. Clinical Trial Support Multiple trials are using CRISPR to knock out PDCD1, TRAC, and B2M. These support the therapeutic potential of AI-designed sgRNAs in CAR T-cell therapy. Initial findings from clinical trials suggest that using CRISPR/Cas9 to edit genes in CAR-T cells appears to be safe, with no significant off target effects ( 51 ) however, instances of cytokine release syndrome have been reported ( 52 ). A clinical study investigating renal cell carcinoma (NCT04438083) demonstrated that CAR-T cells engineered with disruptions in CD70, TRAC, and B2M genes achieved long-lasting remission in one participant (7.7%) and disease stabilization in nine others (69.2%) ( 52 ). Further enhancements, such as knocking out Regnase-1 and TGFβR2, may boost the tumor-fighting ability of these modified CAR-T cells ( 53 ). One of the main challenges of CAR-T cell therapy is its dependence on recognizing antigens present only on the cell surface. In contrast, T cell receptors (TCRs) are capable of detecting both surface and intracellular proteins, as they respond to peptide fragments presented by MHC molecules. These intracellular targets include tumor-associated antigens, cancer-testis antigens, and tumor-specific neoantigens typically located within the cytoplasm or nucleus of cancer cells ( 54 ). To improve recognition of such self-antigens linked to tumors, T cells can be genetically modified to express TCRs with enhanced affinity for specific tumor epitopes. TCRs, being naturally evolved, can detect target antigens at much lower levels compared to the antigen thresholds required for CAR-T cell activation. Consequently, TCR-based T cell therapy holds greater potential for treating solid tumors, despite the limitation of MHC dependency. To counteract the immunosuppressive tumor microenvironment, these engineered T cells can also be modified to resist checkpoint inhibition. Notably, four ongoing clinical trials (NCT03081715, NCT02793856, and NCT03044743) are assessing TCR-T therapies combined with CRISPR/Cas9-mediated knockout of PD-1 in the context of solid tumors. In one such study involving advanced non-small cell lung cancer (NCT02793856), autologous T cells were electroporated with plasmids encoding Cas9 and a PD-1-targeting sgRNA to eliminate PD-1 expression. These gene-edited cells, achieving a median editing efficiency of 16%, were expanded outside the body and then reintroduced into patients. Among 12 individuals who had previously undergone multiple unsuccessful treatments, the median progression-free survival was 7.7 weeks and overall survival was 42.6 weeks. Minimal off-target effects were observed, supporting the clinical promise of CRISPR-modified TCR-T cells ( 55 ) (Table 7). Conclusion The convergence of AI-driven sgRNA design, prior experimental evidence, and ongoing clinical trial data strengthens the translational potential of targeting PDCD1, TRAC, and B2M for next-generation CAR T cell therapies. The strategic knockout of these genes not only enhances antitumor efficacy but also improves immune evasion and safety profiles, addressing current limitations in adoptive T cell therapy. While all tools provided reliable sgRNA candidates, CHOPCHOP, Benchling, and CRISPOR emerged as the most informative: CHOPCHOP is optimal for checking self-complementarity and GC content, both critical for sgRNA structural stability. Benchling enables a comprehensive genomic view, precisely mapping sgRNA to specific exons—especially useful when transcript annotation is ambiguous. 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Stem Cells 36:36–44 Tables Table1: The table below compares key features and algorithmic capabilities across nine popular CRISPR sgRNA design platforms. Feature CHOPCHOP Benchling CRISPRscan GenScript Cas-Designer E-CRISP CRISP-ERA CRISPOR ATUM sgRNA Design Tool Algorithm Type Rule-based and AI-assisted AI-integrated Machine Learning AI and Experimental Data AI-based scoring Rule-based AI-enhanced Machine Learning AI-driven & proprietary On-Target Prediction Yes (High Accuracy) Yes Yes Yes Yes Yes Yes Yes Yes Off-Target Prediction Yes (Genome-wide) Yes (Detailed) Limited Yes (Detailed) Yes (Genome-wide) Yes Yes Yes (Detailed) Yes Low Mismatch Analysis Yes Yes Limited Yes Yes Yes Yes Yes Yes High Mismatch Analysis Yes Yes No Yes Yes Yes Yes Yes Yes Adjust ssgRNA Length Yes Yes No Yes Yes No No Yes Yes Feature Aware Yes Yes No Yes Yes No Yes Yes Yes SNP Aware Yes Yes No Yes Yes No Yes Yes Yes Secondary Structure Aware Yes Yes No Yes Yes No Yes Yes Yes Microhomology Aware Yes Yes No Yes Yes No Yes Yes Yes Specificity Scoring High High Medium High High Medium High High High Efficiency Scoring Yes Yes Yes Yes Yes Yes Yes Yes Yes Genome Compatibility Multiple species Multiple species Human, mouse Human-focused Human & other species Human & Mouse Human & other species Multiple species Multiple species Organism Support Wide range Wide range Limited Human-focused Multiple species Human & Mouse Multiple species Multiple species Multiple species Sequence Input Format FASTA, GenBank FASTA, Plain FASTA FASTA, Plain FASTA Plain FASTA FASTA FASTA, Plain Identifier Support Gene Name, Accession Gene Name Gene Name Accession Gene Name, Accession Gene Name Gene Name Gene Name, Accession Gene Name, Accession Load Options Online & Offline Online Online Online Online Offline Online Online Online & Offline User Interface Web-based, simple Web-based, interactive Web-based Web-based, requires login Web-based, advanced Web-based, simple Web-based Web-based Web-based, advanced Offline Availability No No No No No Yes No No Yes CLI Support No No No No Yes No No Yes Yes GUI Support Yes Yes Yes Yes Yes Yes Yes Yes Yes Multiplex Design Yes Yes No Yes Yes No Yes Yes Yes Multi-Method Design Yes Yes No Yes Yes No Yes Yes Yes Single-Method Design Yes Yes Yes Yes Yes Yes Yes Yes Yes Scoring Model Doench (2014), Doench (2016), and Morean mateos Doench (2016) Morean mateos (2015) Proprietary ML model based on Doench-like features Doench (2014) and Doench (2016) Heuristic + Doench (2014) Custom rule-based scoring Doench (2014), Doench (2016) , Proprietary (ML-based, Doench-like) Table 2: Comparison table of the web based tool with overlapping result: Tool Name sgRNA Sequence Target Exon On-Target Score Off-Target Summary PAM Notes CHOPCHOP (5′- CACGAAGCTCTCCGATGTGT -3′) Exon 2 50.21 MM0-1, MM1-0, MM2-0, MM3-2 No high-risk off-targets NGG Overlapping CRISPOR (5′- CACGAAGCTCTCCGATGTGT -3′) Exon 2 50 (Doench 2016) MIT(95),CFD(96) NGG Overlapping Benchling (5′- CACGAAGCTCTCCGATGTGT -3′) Exon 2 56.2 (Fusi et al., 2016) 48.0 (Hsu et al., 2013) NGG Overlapping Cas-Designer (5′- CACGAAGCTCTCCGATGTGT -3′) Exon 2 61 MM0-1, MM1-0, MM2-0 NGG Overlapping CRISPR-ERA (5′- CACGAAGCTCTCCGATGTGT -3′) Exon 2 10(E score) No off target found NGG Overlapping Table 3: Validation of sgRNA Designed to Knockout PDCD1 Study (Author, Year) Delivery Method sgRNA Target Sequence Cas9 Format Cell Type(s) Editing Efficiency / Outcome Notes Philips et al., 2024 Electroporation of PX458 plasmid (5′- CACGAAGCTCTCCGATGTGT -3′) Plasmid (PX458: SpCas9-2A-GFP) Jurkat T cells PD-1 KO confirmed via surface expression loss GFP⁺ cells single-cell sorted; functional assays performed Hanamura et al., 2021 Transfection of PX458 plasmid (5′- CACGAAGCTCTCCGATGTGT -3′) Plasmid (PX458: SpCas9-2A-GFP) VAL B lymphoma cells PD-1 KO confirmed in clones Targets exon 2; same sgRNA as Philips et al. US20190247432A1 (Zhao et al., 2019) Cas9 RNP complex (5′- CACGAAGCTCTCCGATGTGT -3′)(SEQ ID: 81) RNP NY-ESO-1–specific T cells Enhanced anti-tumor activity post PD-1 KO Used in multiplex editing of T cells US20210388389A1 (Chen et al., 2021) Cas9 or Cas12a RNP or plasmid; delivery via electroporation or viral vectors (5′- CACGAAGCTCTCCGATGTGT -3′)(SEQ ID: 23) Cas9 or Cas12a Primary human T cells Used in CAR-T engineering with TRAC editing Supports modular CAR knock-ins and PD-1 KO CN105671083B (Sun et al., 2016) Lentiviral transduction (Lenti-Puro) (5′- CACGAAGCTCTCCGATGTGT -3′) Lentiviral CRISPR/Cas9 Patient-derived T cells PD-1 successfully knocked out in T cells Lentiviral delivery of guide; used for cancer immunotherapy Table 4: Comparison table of the web based tool with overlapping result Tool Name sgRNA Sequence Target Exon On-Target Score Off-Target Summary PAM Notes CHOPCHOP (5′- TCTCTCAGCTGGTACACGGC -3′) Exon 1 48.14 MM0-1, MM1-0, MM2-0,MM3-9 No high-risk off-targets NGG Overlapping CRISPOR (5′- TCTCTCAGCTGGTACACGGC -3′) Exon 1 48 (Doench 2016) MIT(92),CFD(85) NGG Overlapping Benchling (5′- TCTCTCAGCTGGTACACGGC -3′) Exon 1 46.10 (Fusi et al., 2016) 63.2 (Hsu et al., 2013) NGG Overlapping Cas-Designer (5′- TCTCTCAGCTGGTACACGGC -3′) Exon 1 -- MM0-1, MM1-0, MM2-0 NGG Overlapping CRISPR-ERA (5′- TCTCTCAGCTGGTACACGGC -3′) Exon 1 10(E score) No off target found NGG Overlapping Table 5: Validation of sgRNA Designed to Knockout TRAC Study (Author, Year) Delivery Method sgRNA Target Sequence Cas9 Format Cell Type(s) Editing Efficiency / Outcome Notes Eyquem et al., 2017 Cas9 mRNA + AAV6 (5′- TCTCTCAGCTGGTACACGGC -3′) Cas9 mRNA Primary human PB T cells ~70% indels (T7E1/ddPCR), >95% CAR⁺TCR⁻ Knock-in under TRAC promoter, enhanced antitumor activity Li et al., 2022 RNP: Cas9 protein + crRNA:tracrRNA (5′- TCTCTCAGCTGGTACACGGC -3′) RNP Primary CD8⁺ T cells ~51% TCRα loss (flow), ~40–47% indels (ddPCR) High-throughput screen context Preece et al., 2020 Lentiviral “terminal-CRISPR” vector + Cas9 mRNA electrop. (5′- TCTCTCAGCTGGTACACGGC -3′) Cas9 mRNA Primary T cells >96% CAR⁺TCRαβ⁻ cells sgRNA embedded in lentiviral vector, self-inactivating Albers et al., 2019 RNP: Cas9 + crRNA:tracrRNA (Neon: 1600V, 3×10ms) (5′- TCTCTCAGCTGGTACACGGC -3′) RNP Primary CD8⁺ T cells ~51% TCRα⁻ (flow), ~40–47% TRAC indels (ddPCR) Used for HDR TCR knock-in Ye et al., 2022 RNP + AAV6 donor (5′- TCTCTCAGCTGGTACACGGC -3′) RNP Primary CD8⁺ T cells Large CAR⁺CD3⁻ population (flow) High functional knockout, CAR⁺ cells purified Zhou et al., 2022 Plasmid CRISPR (5′- TCTCTCAGCTGGTACACGGC -3′) Plasmid Jurkat cells TCRαβ⁻ clones by flow cytometry Dual knockout of TRAC and TRBC Kamali et al., 2021 Plasmid CRISPR (Cas9 + sgRNA plasmid) (5′- TCTCTCAGCTGGTACACGGC -3′) Plasmid Primary T cells, HEK293T ~89% indels in 293T (TIDE/IDAA); ~12–14% indels in T cells, ~7–8% CD3⁻ Low–moderate editing efficiency in primary T cells EP3686275A1 (Patent) Cas9 mRNA + chem.-mod sgRNA electroporation (5′- TCTCTCAGCTGGTACACGGC -3′) Cas9 mRNA + mod. sgRNA Human T cells CD3⁻/TCR⁻ phenotype (selected) “Universal” CAR-T cell production, patent disclosure Table 6: Comparison table of the web based tool with overlapping result Tool Name sgRNA Sequence Target Exon On-Target Score Off-Target Summary PAM Notes CHOPCHOP (5′- GAGTAGCGCGAGCACAGCTA -3′) Exon 1 48.12 MM0-1, MM1-0, MM2-0, MM3-2 No high-risk off-targets NGG Overlapping CRISPOR (5′- GAGTAGCGCGAGCACAGCTA -3′) Exon 1 48 (Doench 2016) MIT(93),CFD(97) NGG Overlapping GeneScript (5′- GAGTAGCGCGAGCACAGCTA -3′) Exon 1 0.79 0.10 NGG Overlapping Benchling (5′- GAGTAGCGCGAGCACAGCTA -3′) Exon 1 46.78 (Fusi et al., 2016) 48.1 (Hsu et al., 2013) NGG Overlapping Cas-Designer (5′- GAGTAGCGCGAGCACAGCTA -3′) Exon 1 72.7 MM0-1, MM1-0, MM2-0 NGG Overlapping CRISPR-ERA (5′- GAGTAGCGCGAGCACAGCTA -3′) Exon 1 20(E score) No off target found NGG Overlapping Table 7: Validation of sgRNA Designed to Knockout B2M Study / Patent (Author, Year) Delivery Method sgRNA Target Sequence Cas9 Format Cell Type(s) Editing Efficiency / Outcome Notes Hamilton et al., 2024 Cas9-EDV (antibody-targeted vesicles) (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 RNP CD19⁺ T cells Efficient B2M KO in target cells only Receptor-mediated delivery; selective in vivo targeting Lamarthée et al., 2021 Cas9 RNP (synthetic crRNA) (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 RNP Endothelial cells Complete HLA-I loss For transplant compatibility assays Hoerster et al., 2021 Lentiviral CRISPR/Cas9 vector (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 + dual sgRNAs NK cells Surface HLA-I loss; NK cells resistant to T cells Used in allogeneic NK cell therapy Hiatt et al., 2021 Nucleofection of Cas9 RNP (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 RNP Monocytes, macrophages HLA-I loss confirmed by flow cytometry Maintained differentiation capacity post-KO Song et al., 2022 Cas9 RNP in iPSCs (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 RNP iPSCs Biallelic B2M KO For creating HLA-A monoallelic endothelial cells Bogomiakova et al., 2023 PX458 Cas9 vector (5′- GAGTAGCGCGAGCACAGCTA -3′) Plasmid (PX458) iPSCs → Fibroblasts B2M KO clones Studied immune response of iPSC-derivatives McAlexander et al., 2024 Electroporation of Cas9 RNP (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 RNP CD4⁺ T cells Functional HLA-I loss Used as editing control in chromatin study US20220017926A1 (2022) MaxCyte Electroporation (5′- GAGTAGCGCGAGCACAGCTA -3′) Cas9 RNP Primary human T cells, CAR-T cells Up to 90% B2M knockout Optimized RNP delivery with Cas9:gRNA ratio of 1:4; Cas9 concentration ≥1 μM; efficient generation of universal CAR-T cells Table 8: A summary of key clinical trials utilizing CRISPR-Cas9 mediated knockout of PDCD1, TRAC, and B2M is provided below Clinical Trial ID Target Genes Cell Type Cancer Type Phase Status NCT03399448 PDCD1, TRAC, TRBC T cells Multiple Myeloma, Liposarcoma I Completed NCT04637763 PDCD1, TRAC CD19 CAR-T Relapsed/Refractory B-cell Non-Hodgkin Lymphoma I Active, not recruiting NCT04035434 TRAC, B2M CD19 CAR-T Relapsed/Refractory B-cell Malignancies I/II Recruiting NCT04244656 B2M, TRAC BCMA CAR-T Relapsed/Refractory Multiple Myeloma I Active, not recruiting NCT05643742 TRAC, B2M, Regnase-1, TGFBR2 CD19 CAR-T Relapsed/Refractory B-cell Malignancies I/II Recruiting NCT04502446 TRAC, B2M, CD70 CD70 CAR-T Relapsed/Refractory T or B-cell Malignancies I Active, not recruiting NCT04438083 TRAC, B2M, CD70 CD70 CAR-T Advanced Relapsed/Refractory Renal Cell Carcinoma I Active, not recruiting NCT03166878 TRAC, B2M CD19 CAR-T Relapsed/Refractory CD19+ Leukemia and Lymphoma I/II Completed NCT03545815 PDCD1, TCR Mesothelin CAR-T Multiple Solid Tumors I Completed NCT03747965 PDCD1 Mesothelin CAR-T Mesothelin-positive Multiple Solid Tumors I Completed NCT03081715 PDCD1 T cells Esophageal Cancer I Completed NCT02793856 PDCD1 T cells Non-Small Cell Lung Cancer I Completed NCT05812326 PDCD1 MUC1 CAR-T MUC1-positive Advanced Breast Cancer I/II Completed NCT03044743 PDCD1 T cells EBV-associated Malignancies I/II Recruiting Additional Declarations The authors declare no competing interests. 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Among these technologies, the CRISPR-Cas9 system has emerged as a transformative tool due to its simplicity, programmability, and efficiency. Initially described as part of a bacterial adaptive immune mechanism, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) was first identified by Yoshizumi Ishino and colleagues in \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Although the biological role of these repeat sequences was not understood at the time, subsequent research by Philippe Horvath and Rodolphe Barrangou in 2007 demonstrated that CRISPR-Cas systems function as an adaptive immune defense in prokaryotes by integrating viral DNA fragments into host genomes to recognize and neutralize future infections (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe breakthrough in genome engineering came in 2012, when Emmanuelle Charpentier and Jennifer Doudna re-engineered the CRISPR-Cas9 system from \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e into a two-component platform capable of introducing site-specific double-stranded DNA breaks. This achievement laid the foundation for a highly versatile genome editing tool and earned them the Nobel Prize in Chemistry in 2020 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Unlike earlier genome-editing technologies such as zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs), CRISPR-Cas9 requires only a single guide RNA (sgRNA) to direct the Cas9 endonuclease to the target site, thereby eliminating the need for complex protein engineering (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe CRISPR-Cas9 system functions in three key stages: adaptation, crRNA biogenesis, and interference. During adaptation, short fragments of invading viral DNA are incorporated into the CRISPR array in the host genome. These fragments are transcribed and processed into CRISPR RNA (crRNA), which, in complex with trans-activating CRISPR RNA (tracrRNA) and Cas9, guide the nuclease to complementary target sequences, where Cas9 induces a double-strand break. This break is then repaired by endogenous cellular mechanisms, resulting in targeted insertions, deletions, or sequence replacements (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA critical component of CRISPR-Cas9 genome editing is the design of single guide RNAs (sgRNA), which determine the specificity and efficiency of target site recognition. Traditional approaches to sgRNA design rely on sequence homology and empirical testing, which can be time-consuming and prone to off-target effects. To address these limitations, artificial intelligence (AI)-based tools have been developed to enhance sgRNA design. These tools leverage large-scale genomic datasets, machine learning algorithms, and predictive modeling to optimize sgRNA selection, minimize off-target activity, and improve editing efficiency. The integration of AI with CRISPR technology has significantly accelerated the development of precise genome-editing strategies, particularly for therapeutic applications.\u003c/p\u003e \u003cp\u003eOne of the most promising areas of CRISPR-Cas9 application is cancer immunotherapy, particularly chimeric antigen receptor (CAR) T-cell therapy, genome editing is increasingly used to enhance the function, persistence, and safety of engineered T cells. Knockout of key genes such as PDCD1 (which encodes PD-1, a negative immune regulator), TRAC (T-cell receptor alpha constant, to eliminate native TCRs), and B2M (beta-2 microglobulin, to prevent MHC-I expression and reduce immunogenicity) has shown promise in generating universal, allogeneic CAR T cells. Designing effective sgRNAs targeting these genes is a critical step in achieving functional gene disruption and improving therapeutic outcomes especially in the engineering of chimeric antigen receptor (CAR) T cells. To recognize tumor antigens efficiently. CRISPR-Cas9 enables precise genetic modifications in T cells, such as knocking out immune checkpoint genes like PDCD1 (which encodes PD-1), disrupting endogenous T-cell receptor (TCR) genes (e.g., TRAC), eliminating B2M (Beta-2 microglobulin) to reduce MHC class1 expression and evade host immune rejection, and enhancing T cell persistence and tumor-killing capacity. Furthermore, AI-driven optimization of sgRNAs facilitates the efficient design of CAR constructs, reducing immunogenicity and potential adverse effects while improving therapeutic outcomes.\u003c/p\u003e \u003cp\u003eCRISPR-Cas9 relies on a synthetic single-guide RNA (sgRNA) to direct the Cas9 nuclease to a complementary DNA target site, resulting in site-specific double-stranded breaks. The outcome of CRISPR-mediated editing is strongly influenced by the design of the sgRNA, which determines both the efficiency of editing and the risk of off-target effects. While manual sgRNA design is possible, the emergence of artificial intelligence (AI)-driven bioinformatics tools has greatly enhanced the accuracy and efficiency of this process. Tools such as CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, CRISPRscan, and ATUM sgRNA Tool use diverse algorithms and genomic datasets to predict optimal sgRNA candidates with minimal off-target activity and high on-target efficacy. In this study, guide RNAs (sgRNA) with high predicted efficiency were carefully designed using web-based bioinformatics tools.\u003c/p\u003e \u003cp\u003eIn this study, we designed sgRNAs targeting PDCD1, B2M, and TRAC using nine widely utilized sgRNA design tools: CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, CRISPRscan, and ATUM sgRNA Tool. The selection of highly effective sgRNA is a critical step in CRISPR-Cas9-mediated gene knockout, as it directly influences the precision, on-target activity, and overall success of gene disruption. By leveraging these computational platforms, which analyze target sequences based on specificity, GC content, and potential off-target effects, we ensured the identification of sgRNA that maximize editing efficiency while minimizing unintended modifications. Further validation was conducted through a detailed literature review and analysis of published data demonstrating successful gene knockouts and functional restoration of T cell activity. Additionally, we compiled a comprehensive table of clinical trials in which the knockout of TRAC, PDCD1, and B2M has been employed to enhance CAR T-cell functionality. This approach provides a thorough evaluation of AI-driven sgRNA design tools and supports the development of optimized CRISPR-Cas9 strategies for gene editing in CAR T-cell therapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMechanism of CRISPR\u003c/b\u003e-\u003cb\u003eCas9 and role of sgRNA\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCRISPR-Cas9 works as an efficient genome editing tool which allows the targeted modifications in DNA. The CRISPR Cas6 systems consists of two main components. Cas9 nuclease enzyme that introduces double strand brake at a specific DNA sequence While second component is Guide RNA (sgRNA), synthetic RNA molecule that direct Cas9 to the target DNA. The mechanism of CRISPR Cas9 occurs in three main steps: Targeting Recognition by sgRNA, DNA Cleavage by Cas9 and DNA repair. Step1:Targeting Recognition by sgRNA - The guide RNA (sgRNA) is a chimeric RNA composed of two parts: CRISPR RNA (crRNA) which is 20bp nucleotide sequence complementary to target DNA, ensures high specificity and minimizes off target effects., it\u0026rsquo;s also requires a PAM protospacer adjacent motif (PAM) sequence (NGG for SpCas9) in the target DNA to initiate binding. Another component Trans-activating CRISPR RNA (tracrRNA) that helps in binding of Cas9 induces a conformational change in Cas9, switching it to an active state, this activation allows the Cas9 to scan the genome and bind to target gene and stabilizing the Cas9-sgRNA complex. This complex directs the Cas9 to specific DNA by base pairing with the target strand. Once the target sequence is recognized, Cas9 unwinds the DNA and allows the sgRNA to form a stable R loop structure with the complementary strand. Cas9 has two nuclease domains, one is RuvC which cuts one DNA strand and other is HNH domain (Histidine-Asparagine-Histidine) that cuts the complementary strand. This results in a precise double strand break (DSA) in target DNA. After Cas9 creates a DSB, the cell repair it using Non-Homologous End joining (NHEJ) joins DNA ends, introduces small insertions or deletions (indels), disrupting gene function(useful for gene knockouts). Other repair mechanism is Homology-Directed Repair (HDR) uses a donor template to introduce modifications, allowing for gene correction, insertion and activation to enhance the particular gene function. sgRNA can be modified for targeting multiple genes (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eAI Tools Use in sgRNA Design\u003c/h3\u003e\n\u003cp\u003eThe advent of CRISPR-Cas9 technology has significantly transformed genome editing due to its simplicity, efficiency, and versatility. However, challenges such as off-target effects and precision limitations remain. Integrating Artificial Intelligence (AI) addresses these issues by improving target identification, optimizing guide RNA (sgRNA) design, and predicting potential off-target interactions. The combination of AI and CRISPR-Cas9 has the potential to enhance the accuracy, safety, and effectiveness of CAR T-cell therapy, paving the way for more advanced and accessible treatments. To knockout the gene using CRISPR /Cas9, critical region of the gene should be targeted to disrupt its function. To ensure effective knockout, should target an early functionally significant exon of a gene. This will prevent the production of a functional protein. Target the exon 1 or exon 2 (these are typically essential for proper translation).If gene has multiple isoforms, choose an exon common to all isoforms. The 5\u0026rsquo; region of exon1 is ideal because any deletion or insertion created by CRISPR can lead a frameshift mutation, resulting in a non-functional protein of the mRNA. Emerging CRISPR designing tool identify the best suitable transcript to design the sgRNA adjacent to the PAM sequence with high on target efficacy and low target effects.\u003c/p\u003e \u003cp\u003eThe choice of a sgRNA (guide RNA) designing tool for CRISPR-Cas9 genome editing depends on several factors, including the specific organism, target gene, and the purpose of your experiment. Different tools have their strengths and may be better suited for certain applications. Here are some popular sgRNA designing tools for CRISPR:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCHOPCHOP: CHOPCHOP is a versatile sgRNA design tool that allows users to target multiple organisms. It provides information on off-target sites and is frequently updated.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBenchling: Benchling offers a suite of CRISPR design tools that include sgRNA design. It provides flexibility in choosing target sites, checking off-target potential, and optimizing sgRNA sequences for specific applications.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCRISPRscan: CRISPRscan is a web-based tool that provides an easy-to-use interface for designing sgRNA. It considers off-target potential and provides a score to rank potential sgRNA.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGenScript: provides comprehensive function with just Simply enter desired gene symbol or sequence for guided support from design to ordering and supports knock-ins, knockouts, and sequence replacement, with knock-ins up to 100 nt.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCas-Designer: Cas-Designer is a tool provided by the Zhang lab (Broad Institute) that helps design sgRNA for CRISPR-Cas9 experiments. It includes features to minimize off-target effects.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eE-CRISP: E-CRISP is a tool for designing sgRNA with a focus on minimizing off-target effects. It provides detailed information on potential off-target sites.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCRISPR-ERA: CRISPR-ERA is designed for sgRNA design in the context of pooled CRISPR screens. It can optimize sgRNAs for specific experimental conditions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCRISPOR: CRISPOR is a user-friendly web tool that offers sgRNA design for a wide range of organisms. It provides information on potential off-targets and allows users to filter sgRNA based on their preferences.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eATUM sgRNA Design Tool: ATUM offers a sgRNA design tool that allows users to design sgRNA for their CRISPR experiments. It also provides information on potential off-targets.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eComparison of AI-Driven sgRNA Design Tools\u003c/h2\u003e \u003cp\u003eTo ensure the accuracy, efficiency, and reliability of sgRNA selection, we evaluated several widely used AI-powered CRISPR sgRNA design platforms. Based on algorithmic approaches, prediction accuracy, and overlap in results, we identified CHOPCHOP, Benchling, CRISPRscan, Cas-Designer, and GenScript as the most effective tools. These tools frequently yielded overlapping sgRNA sequences due to their utilization of similar scoring systems (e.g., Doench 2014, Doench 2016, Moray-Mateos models). Overlapping results were prioritized to improve sgRNA selection confidence and minimize experimental failure (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThis comparative analysis highlights the strengths of each tool, emphasizing the comprehensive capabilities of CHOPCHOP, Benchling, and Cas-Designer in particular. CRISPOR stands out for its detailed off-target prediction, while GenScript, and CRISPRscan offer ease of use for rapid preliminary design. Integration of results from these tools enables the identification of sgRNA with high specificity, robust on-target activity, and minimal off-target effects.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePDCD1 Gene and Its Role in T cell Exhaustion\u003c/h3\u003e\n\u003cp\u003eProgrammed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is an inhibitory immune checkpoint receptor expressed on activated T cells. It plays a key role in maintaining immune homeostasis and preventing autoimmunity (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, in the tumor microenvironment, PD-1 interacts with its ligands PD-L1/PD-L2 to inhibit T cell activity, resulting in T cell exhaustion and allowing cancer cells to escape immune surveillance (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, genetic disruption of PDCD1 via CRISPR/Cas9 is a promising strategy to enhance the efficacy of CAR T cells by preventing their exhaustion and improving tumor clearance.\u003c/p\u003e\n\u003ch3\u003ePDCD1 Knockout in CAR T cell Therapy\u003c/h3\u003e\n\u003cp\u003eBlocking PD-1 signaling has become a standard strategy in cancer immunotherapy using monoclonal antibodies. However, transient antibody blockade has limitations. A more durable and cost-effective alternative is the CRISPR-mediated PDCD1 knockout in CAR T cells, which renders the T cells resistant to PD-L1-mediated immunosuppression. This approach leads to enhanced persistence, proliferation, and cytotoxic activity of engineered T cells in the tumor microenvironment (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003esgRNA Design and Off-Target Analysis for PDCD1 Gene Knockout\u003c/h3\u003e\n\u003cp\u003eThe PDCD1 gene located on chromosome no. 2 and contains 5 exons, and according to NCBI, two transcript variants are available: NM_005018.3, which consists of 5 exons with a total spliced length of 2097 bp, and XM_006712573.3 (transcript variant X1), which contains 4 exons with a spliced length of 736 bp. For designing sgRNAs, it is essential to select the longer transcript\u0026mdash;in this case, NM_005018.3\u0026mdash;as it is more likely to represent the full-length, functionally relevant mRNA. Targeting this transcript ensures that the designed sgRNAs effectively disrupt the functional gene product. Hundreds of candidate sequences were obtained from the web-based tools, and a specific sgRNA sequence for PDCD1 were pre-selected from the list according to the criteria: 1) Sequences which were obtained from multiple tools, 2) Sequences which exist in an exon, and 3) Sequences which have high rank in each tool.\u003c/p\u003e \u003cp\u003eTo design an efficient sgRNA for knockout of the PDCD1 (PD-1/PD-L1) gene, we utilized eight web-based CRISPR sgRNA design tools: CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, ATUM sgRNA tool and CRISPRscan. GenScript was initially used to obtain three validated top sgRNAs hits, which were subsequently compared with results from other web-based tools to identify the most optimal sgRNA. CHOP tool was initially used to and obtained three validated top hits, compared these hits with other web-based tools to identify the best sgRNA or find common hits across all tools. All tools, except for ATUM sgRNA tool, E-CRISP, GeneScript, CRISPRscan, identified a highly conserved and overlapping sgRNAs sequence targeting exon 1 of the PDCD1 gene. This strong consensus suggests a robust and specific target site for CRISPR-Cas9-mediated gene knockout. Off target analysis checked with CRISPOR showed lower predicted on-target activity (Doench 2016 score: 50), but it exhibited superior specificity metrics, including a higher CFD score of 96 and an MIT specificity score of 95, with significantly fewer predicted off-target sites (n\u0026thinsp;=\u0026thinsp;36) and minimal risk to coding exons (maximum exonic CFD: 0.3) (Table\u0026nbsp;2).\u003c/p\u003e\n\u003ch3\u003eValidation of PDCD1 targeting sgRNA based on existing data sources\u003c/h3\u003e\n\u003cp\u003eThe selected sgRNA sequence has been validated in multiple studies and patents, confirming its use in PDCD1 gene knockout for immune checkpoint modulation in CAR T cells. This approach has been shown to improve tumor killing capacity, T cell persistence, and resistance to exhaustion. Philips and colleagues employed CRISPR\u0026ndash;Cas9 to disrupt the PDCD1 (PD-1) gene in Jurkat T cells by co-electroporating cells with a PX458 (pSpCas9 (BB)-2A-GFP) plasmid harboring two guide RNAs, including (5\u0026prime;-CACGAAGCTCTCCGATGTGT-3\u0026prime;). Post-electroporation, GFP⁺ cells were single-cell sorted, and clones lacking PD-1 surface expression were selected for functional analyses (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Similarly, Hanamura \u003cem\u003eet al.\u003c/em\u003e describes the successful generation of PD-1 knockout in human B lymphoma (VAL) cells using CRISPR-Cas9 technology. The researchers designed a sgRNA with the sequence (5\u0026prime;-CACGAAGCTCTCCGATGTGT-3\u0026prime;), which specifically targets exon 2 of the PDCD1 gene encoding PD-1. This sgRNA was cloned into the BbsI site of the PX458 plasmid, a Cas9\u0026ndash;GFP expression vector. VAL cells were transfected with the recombinant PX458 plasmid, and three days post-transfection, GFP⁺ cells were isolated by fluorescence-activated cell sorting (FACS). These sorted cells were expanded into clones, and subsequent analysis confirmed a complete lack of PD-1 expression, verifying successful knockout (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis guide RNA sequence (5\u0026prime;-CACGAAGCTCTCCGATGTGT-3\u0026prime;) has been consistently documented across several patent filings. In US20190247432A1, filed by Zhao and Liu and assigned to the University of Pennsylvania, it is described as SEQ ID NO: 81 (\u0026ldquo;PD1.21-3\u0026rdquo;), used to edit \u003cem\u003ePDCD1\u003c/em\u003e in NY-ESO-1\u0026ndash;specific T cells via Cas9 RNP delivery to augment anti-tumor immunity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Likewise, US20210388389A1 from Yale University (Chen and Dai) employs this sgRNA, labeled \u0026ldquo;hPDCD1 sg-2\u0026rdquo; (SEQ ID NO:23), for multiplex gene editing of primary human T cells\u0026mdash;frequently in combination with TRAC gene disruption\u0026mdash;to generate optimized CAR-T cells (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the Chinese patent CN105671083B by Sun and assigned to Anhui Kedgene Biotechnology details the use of this guide in a lentiviral system (Lenti-PD-1-Puro) to knock out \u003cem\u003ePDCD1\u003c/em\u003e in T cells derived from tumor patients. The sgRNA, corresponding to positions 2859\u0026ndash;2878 of the PD-1 gene, was annealed with its reverse complement, cloned into a Lenti-CRISPR/Cas9 vector, and used to generate modified T cells with successful gene disruption (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTRAC Gene and Its Role in GvHD Prevention\u003c/h2\u003e \u003cp\u003eThe T cell receptor alpha constant (TRAC) gene located on chromosome 14, encodes the constant region of alpha subunit of T cell receptor (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). It forms the T cell receptor complex, which recognizes the antigenic peptides presented by MHC molecules (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). It forms the heterodimer with TCR beta chain which interacts with CD3 complex, transmitting activation signals into the T cell (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The MHC molecule presenting an antigenic peptide is recognized by the T-cell receptor (TCR), leading to the activation of T cells and the subsequent killing of tumor cells (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Antigenic peptides, especially those restricted to MHC class I, are generated through the degradation of proteins within tumor cells or pathogen-infected somatic cells (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). T cells, via their T-cell receptors (TCRs), play a critical role in identifying these altered cells by monitoring their protein-derived peptide profiles (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Autologous T cell therapy has several limitation in terms of manufacturing time and expenses, as well as the poor quality and quantity of obtainable T cell especially in case of infants or heavily treated patients. To overcome these limitation allogenic (donor derived) T cell therapy are currently being explored (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of allogeneic CAR T therapy, residual TCR expression can trigger graft-versus-host disease (GvHD). Disruption of TRAC via CRISPR/Cas9 enables the generation of TCR-deficient T cells, which are incapable of recognizing and attacking host tissues, thereby facilitating the development of universal CAR T cells with minimal risk of GvHD (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTRAC Knockout in Universal CAR T cell Therapy\u003c/h3\u003e\n\u003cp\u003eBy knocking out the TRAC gene, the endogenous TCR complex is inactivated, preventing unwanted alloreactive responses. This strategy is fundamental in allogeneic or off-the-shelf CAR T products, making them \u003cb\u003es\u003c/b\u003eafe for administration across HLA mismatches.\u003c/p\u003e\n\u003ch3\u003esgRNA Design and Off-Target Analysis for TRAC Gene Knockout\u003c/h3\u003e\n\u003cp\u003eTo design the sgRNA targeting the TRAC gene, the gene was first located using the GeneCards database. The corresponding Ensembl ID (ENSG00000277734), was used to access the Ensembl Asia genome browser, which listed a single transcript of 974 bp. Upon selecting the transcript ID, the exon and intron structures were displayed by clicking on the \"Exons\" tab located on the right-hand side of the page. This section provided the sequence and genomic coordinates for each exon and intron.\u003c/p\u003e \u003cp\u003eThe sequence of Exon 1, spanning from chromosomal position 22547506 to 22547778, was extracted and used as input for sgRNA design. The sequence was analyzed using the CHOPCHOP tool, and the results were compared with predictions generated by other platforms, including Benchling, Cas-Designer, E-CRISP, CRISPOR and CRISPR-ERA, to identify optimal sgRNA candidates with high specificity and minimal off-target effects.\u003c/p\u003e \u003cp\u003eIn selecting the optimal single guide RNA (sgRNA) for targeting the TRAC gene, utilized the CHOPCHOP tool and applied several filtering criteria, including a mismatch2 score of 0, no self-complementarity, an efficiency greater than 45%, and a GC content between 40% and 60%. From these parameters, two sgRNAs were identified with efficiencies of 48.14% and 47.27%. These candidates were then cross-validated using multiple CRISPR design tools, including Benchling, Cas-Designer, CRISPOR, CRISPR ERA and E-CRISP,. All tool except E CRISPR identified a highly conserved and overlapping sgRNA sequence targeting the exon1of TRAC gene. Off target analysis of sgRNA checked with CRISPOR showed lower predicted on-target activity (Doench 2016 score: 48), but it exhibited superior specificity metrics, including a higher CFD score of 85 and an MIT specificity score of 92, with significantly fewer predicted off-target sites (n\u0026thinsp;=\u0026thinsp;113) and minimal risk to coding exons (maximum exonic CFD: 0.122) (Table\u0026nbsp;4).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eValidation of TRAC targeting sgRNA based on existing data sources\u003c/h2\u003e \u003cp\u003eThe sgRNA has been widely validated in universal CAR T research and commercial patents, showing efficient TRAC disruption and prevention of TCR expression. Several studies have utilized the sgRNA sequence (5\u0026prime;-TCTCTCAGCTGGTACACGGC-3\u0026prime;) targeting exon 1 of the TRAC gene to engineer TCR-negative CAR-T cells with high efficiency. Eyquem et al. achieved targeted CAR knock-in at the TRAC locus by electroporating human T cells with Cas9 mRNA and TRAC-specific gRNA, followed by AAV6-mediated delivery of the CAR cassette, resulting in \u0026gt;\u0026thinsp;95% CAR⁺TCR⁻ cells and enhanced antitumor function in vitro and in vivo (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Similarly, Li et al. used a CRISPR-Cas9 RNP complex with the same TRAC crRNA in primary CD8⁺ T cells and observed\u0026thinsp;~\u0026thinsp;51% TCRα surface loss and 40\u0026ndash;47% allele disruption (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreece et al. developed a lentiviral vector incorporating the TRAC sgRNA into the 3\u0026prime; LTR, linking TRAC disruption and CAR delivery in a single vector, generating\u0026thinsp;\u0026gt;\u0026thinsp;96% CAR⁺TCR⁻ populations (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Albers et al. used an RNP format with this sgRNA and showed 51% TCRα loss and 40\u0026ndash;47% disruption by ddPCR, enabling efficient CAR knock-in at the TRAC locus (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Ye et al. also demonstrated robust TRAC editing with the same sgRNA, yielding CAR⁺CD3⁻ T cells upon electroporation of Cas9 RNP and AAV6 donor (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eZhou et al. disrupted both TRAC and TRBC in Jurkat cells using plasmid-based CRISPR, isolating TCRαβ-negative clones (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Kamali et al. reported\u0026thinsp;~\u0026thinsp;89% TRAC indels in HEK293T cells and 12\u0026ndash;14% allele disruption in primary T cells, using plasmid delivery (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatent documents also support the therapeutic utility of this sgRNA. The EP3686275A1 patent lists the same guide as SEQ ID NO: 5, used with Cas9 mRNA to generate universal CD3⁻/TCR⁻ T cells (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Similarly, CN112512557A discloses its use (as \u0026ldquo;TRAC 3\u0026rdquo;, SEQ ID NO: 2) with Cas9 RNP to produce anti-BCMA CAR-T cells via electroporation (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eB2M Gene and Its Role in Immune Evasion\u003c/h2\u003e \u003cp\u003eBeta-2 microglobulin (B2M) is the light chain of the major histocompatibility complex (MHC) class I molecule, responsible for presenting intracellular antigenic peptides to CD8⁺ cytotoxic T cells. The B2M gene, located on chromosome 15, comprises four exons. Numerous studies have highlighted the role of B2M loss in enabling immune evasion in various cancers, primarily through genetic deficiencies, mutations, or epigenetic suppression mechanisms. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eB2M Knockout in Allogeneic CAR T cell Therapy\u003c/h2\u003e \u003cp\u003eAutologous CAR T cell therapy, though effective, faces several limitations including patient-specific manufacturing, long production times (typically 2\u0026ndash;3 weeks), limited T cell availability, and inconsistent product quality. To overcome these issues, allogeneic CAR T cell therapy also termed \"universal\" or \"off-the-shelf\" CAR T therapy has emerged. This method utilizes T cells derived from healthy donors or alternative sources like umbilical cord blood or pluripotent stem cells, which are genetically engineered, expanded ex vivo, and infused into patients (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, two major immunological challenges hinder the efficacy of allogeneic CAR T cells: graft-versus-host disease (GvHD) and host-versus-graft alloreactivity (HvGA). Advancements in genome editing tools, particularly CRISPR/Cas9, have enabled targeted disruption of the T cell receptor (TCR) and MHC class I molecules through B2M gene knockout, thereby significantly reducing the risks of alloreactivity and immune rejection (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003esgRNA Design and Off-Target Analysis for B2M Gene Knockout\u003c/h2\u003e \u003cp\u003eTo target the B2M gene, we initially designed sgRNAs using the GenScript CRISPR sgRNA design tool, which provided six top-ranked, validated candidates. These sgRNAs were then cross-validated using multiple web-based tools, including CHOPCHOP, CRISPOR, E-CRISP, CRISPR-ERA, and Cas-Designer, to ensure consistency and robustness in target site selection.\u003c/p\u003e \u003cp\u003eFor off-target analysis, we selected the most promising sgRNA and evaluated its genome-wide specificity using the CRISPOR tool. The analysis revealed a total of 27 potential off-target sites, all located within introns or intergenic regions across multiple chromosomes, including chromosomes 1, 2, 3, 4, 5, 7, 8, 9, 11, 16, 17, 19, and 20. These results support the high specificity of the selected sgRNA, with minimal risk of disrupting coding sequences (Table\u0026nbsp;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eValidation of B2M targeting sgRNA based on existing data sources\u003c/h2\u003e \u003cp\u003eTo validate the real-world application of our selected sgRNA, we explored previously published literature and patent filings where the same sequence was used for B2M gene knockout. These studies demonstrate its successful application in a variety of contexts\u0026mdash;from human embryonic stem cells and induced pluripotent stem cells to NK and T cells in immunotherapy settings. Several recent studies have utilized the same guide RNA sequence (5\u0026prime;GAGTAGCGCGAGCACAGCTA-3\u0026prime;), targeting exon 1 of the B2M gene, to achieve efficient gene knockout across diverse human cell types and contexts (42). Hamilton \u003cem\u003eet al.\u003c/em\u003e developed antibody-targeted virus-like vesicles termed Cas9-EDVs, which deliver Cas9\u0026ndash;sgRNA ribonucleoproteins (RNPs) to specific immune cells via scFv targeting domains. Using an anti-CD19 scFv, they directed B2M-targeting Cas9-EDVs to CD19⁺ T cells, achieving selective and efficient B2M knockout in vivo without affecting bystander cells (43).\u003c/p\u003e \u003cp\u003eLamarth\u0026eacute;e \u003cem\u003eet al.\u003c/em\u003e used synthetic crRNAs targeting B2M, including the same sequence, to generate HLA class I/II\u0026ndash;null human glomerular endothelial cells. This enabled the creation of immunologically silent cells for transplant immunogenicity assays (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Similarly, Hoerster \u003cem\u003eet al.\u003c/em\u003e engineered allogeneic human NK cells using lentiviral CRISPR/Cas9 vectors co-expressing sgRNAs against B2M and NKG2A, eliminating HLA-I expression to reduce immunogenicity while enhancing therapeutic utility (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHiatt \u003cem\u003eet al.\u003c/em\u003e employed Cas9 RNP nucleofection in primary human monocytes, using the same B2M-targeting sgRNA to generate HLA-I-deficient macrophages and dendritic cells. This allowed evaluation of immune function post-editing (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In iPSC-based approaches, Song \u003cem\u003eet al.\u003c/em\u003e disrupted B2M in human pluripotent stem cells, producing HLA-I-null endothelial derivatives for \"HLA exchange\" strategies in regenerative medicine, (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) while Bogomiakova \u003cem\u003eet al.\u003c/em\u003e used the same sequence to evaluate NK cell responses to \u003cem\u003eB2M\u003c/em\u003e-knockout iPSC-derived fibroblasts (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, McAlexander \u003cem\u003eet al.\u003c/em\u003e used the same sgRNA as a positive control to assess CRISPR editing efficiency in activated primary human CD4⁺ T cells during genome-wide enhancer profiling. As a benchmark for editing efficiency, McAlexander et al. used the same B2M-targeting sgRNA to knock out B2M in CD4⁺ T cells during enhancer mapping studies, confirming successful CRISPR activity (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). This widely adopted sgRNA is also included in the patent US20220017926A1, which describes methods and compositions for CRISPR/Cas-mediated B2M disruption to eliminate HLA-I expression for immunomodulation (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese independent studies consistently validated the sgRNA sequence (5\u0026prime;GAGTAGCGCGAGCACAGCTA-3\u0026prime;) as an efficient and specific tool for ablating B2M expression and eliminating surface HLA-I across a range of primary human immune and non-immune cells, stem cells, and in vivo delivery platforms (Table\u0026nbsp;5).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eTo enhance the effectiveness and safety of CAR T-cell therapy, sgRNAs targeting the PDCD1, TRAC, and B2M genes were designed using multiple AI-powered CRISPR sgRNA design tools. The selection process involved a comprehensive cross-validation among various tools, focusing on the overlap of sgRNA sequences across platforms and evaluating their on-target efficiency and off-target specificity. Three tools\u0026mdash;CHOPCHOP, Benchling, and CRISPOR\u0026mdash;were prioritized due to their unique and complementary analytical strengths. For PDCD1 (PD-1), a highly conserved sgRNA (5\u0026prime;-CACGAAGCTCTCCGATGTGT-3\u0026prime;) targeting exon 2 was identified as the most optimal candidate. This sgRNA was chosen based on several factors, including its high specificity and minimal predicted off-target effects. Analysis by CHOPCHOP revealed no self-complementarity, a GC content of 55%, and low mismatch values (MM0\u0026ndash;1), indicating structural stability and a reduced risk of secondary structure formation. Benchling allowed for precise mapping of the sgRNA within the full genomic sequence, confirming its location in exon 2. CRISPOR further supported the design, providing an excellent MIT specificity score of 95 and a CFD score of 96, with only 36 potential off-target sites, none of which posed significant risks due to low CFD scores in coding regions. The same sgRNA sequence was validated in patent CN105671083B for an efficient knockout in T cells (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), and previous studies, such as Philips et al. (2024), have demonstrated its role in modulating PD-1 dimerization and restoring T cell activity in vitro (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor TRAC, the sgRNA sequence (5\u0026prime;-TCTCTCTCTCAGCTGGTACACGGC-3\u0026prime;), targeting exon 1, was selected based on strong cross-platform consensus. CHOPCHOP reported a GC content of 60%, absence of self-complementarity, and an on-target efficiency score of 48.14%, with no predicted high-risk off-targets. Benchling provided visual confirmation of the guide\u0026rsquo;s precise location within the exon 1 region, ensuring disruption of the constant region of the TCR α chain. CRISPOR validated the guide with a MIT specificity score of 92 and a CFD score of 85, supporting high editing precision.\u003c/p\u003e \u003cp\u003eThis sgRNA has been widely validated in literature and patent disclosures. In a foundational study by Eyquem et al. (2017), CRISPR-mediated integration of a CAR construct into the TRAC locus using Cas9 mRNA and AAV6 in primary human T cells led to \u0026gt;\u0026thinsp;95% CAR⁺TCR⁻ cells and enhanced antitumor activity, demonstrating efficient disruption and knock-in (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Similarly, Li et al. (2022) employed an RNP approach (Cas9 protein with crRNA:tracrRNA duplex) in CD8⁺ T cells, achieving\u0026thinsp;~\u0026thinsp;51% TCRα loss and ~\u0026thinsp;40\u0026ndash;47% indels, validating this guide in a high-throughput setting (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Preece (2020) used a lentiviral terminal-CRISPR vector and Cas9 mRNA electroporation, resulting in \u0026gt;\u0026thinsp;96% CAR⁺TCR⁻ cells, showcasing a robust self-inactivating delivery system (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther supporting data comes from Albers et al. (2019), who used electroporated RNP complexes in CD8⁺ T cells, reporting\u0026thinsp;~\u0026thinsp;51% TCRα disruption by flow cytometry (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Ye et al. (2022) combined RNP and AAV6 donor delivery to produce a high fraction of CAR⁺CD3⁻ cells, suggesting functional knockout (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Zhou et al. (2022) applied plasmid-based CRISPR to Jurkat cells for dual TRAC and TRBC knockout, confirming the guide\u0026rsquo;s efficacy in T cell lines (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Kamali et al. (2021) used a plasmid system in HEK293T and primary T cells, achieving\u0026thinsp;~\u0026thinsp;89% indels in HEK293T but only\u0026thinsp;~\u0026thinsp;12\u0026ndash;14% in T cells, indicating cell-type-dependent efficiency (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Finally, European Patent EP3686275A1 demonstrated successful use of this sgRNA with chemically modified sgRNA and Cas9 mRNA via electroporation in human T cells to generate universal CAR-T cells with a TCR⁻/CD3⁻ phenotype, supporting its application in clinical manufacturing workflows (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sgRNA sequence 5\u0026prime;-GAGTAGCGCGAGCACAGCTA-3\u0026prime;, targeting exon 1 of B2M, was consistently validated across multiple independent studies using various delivery platforms and cell types. Tools like CHOPCHOP and CRISPOR predicted high on-target activity (48.12%), GC content of 60%, low off-target distribution (27 sites in non-coding regions), and high specificity scores (MIT: 93; CFD: 97), supporting its suitability for gene editing applications.\u003c/p\u003e \u003cp\u003eThis sgRNA was used successfully in primary T cells (McAlexander et al., 2024; US20220017926A1), NK cells (Hoerster et al., 2021), iPSCs (Song et al., 2022; Bogomiakova et al., 2023), endothelial cells (Lamarth\u0026eacute;e et al., 2021), and monocytes/macrophages (Hiatt et al., 2021), showing efficient B2M knockout and consequent loss of surface MHC class I (HLA-I) expression. In therapeutic contexts, this loss enabled immune evasion and facilitated the generation of universal CAR-T and NK cell products. Notably, US20220017926A1 demonstrated up to 90% knockout efficiency in primary T cells and CAR-T cells using optimized RNP electroporation protocols.\u003c/p\u003e \u003cp\u003eIn conclusion, the sgRNA for PDCD1, TRAC, and B2M were carefully designed and validated through a combination of AI tools, cross-platform analysis, and literature support, ensuring their high specificity, minimal off-target effects, and potential to enhance CAR T-cell therapy\u0026rsquo;s efficacy and safety.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical Trial Support\u003c/h2\u003e \u003cp\u003eMultiple trials are using CRISPR to knock out PDCD1, TRAC, and B2M. These support the therapeutic potential of AI-designed sgRNAs in CAR T-cell therapy. Initial findings from clinical trials suggest that using CRISPR/Cas9 to edit genes in CAR-T cells appears to be safe, with no significant off target effects (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) however, instances of cytokine release syndrome have been reported (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). A clinical study investigating renal cell carcinoma (NCT04438083) demonstrated that CAR-T cells engineered with disruptions in CD70, TRAC, and B2M genes achieved long-lasting remission in one participant (7.7%) and disease stabilization in nine others (69.2%) (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Further enhancements, such as knocking out Regnase-1 and TGFβR2, may boost the tumor-fighting ability of these modified CAR-T cells (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). One of the main challenges of CAR-T cell therapy is its dependence on recognizing antigens present only on the cell surface. In contrast, T cell receptors (TCRs) are capable of detecting both surface and intracellular proteins, as they respond to peptide fragments presented by MHC molecules. These intracellular targets include tumor-associated antigens, cancer-testis antigens, and tumor-specific neoantigens typically located within the cytoplasm or nucleus of cancer cells (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). To improve recognition of such self-antigens linked to tumors, T cells can be genetically modified to express TCRs with enhanced affinity for specific tumor epitopes. TCRs, being naturally evolved, can detect target antigens at much lower levels compared to the antigen thresholds required for CAR-T cell activation. Consequently, TCR-based T cell therapy holds greater potential for treating solid tumors, despite the limitation of MHC dependency. To counteract the immunosuppressive tumor microenvironment, these engineered T cells can also be modified to resist checkpoint inhibition. Notably, four ongoing clinical trials (NCT03081715, NCT02793856, and NCT03044743) are assessing TCR-T therapies combined with CRISPR/Cas9-mediated knockout of PD-1 in the context of solid tumors. In one such study involving advanced non-small cell lung cancer (NCT02793856), autologous T cells were electroporated with plasmids encoding Cas9 and a PD-1-targeting sgRNA to eliminate PD-1 expression. These gene-edited cells, achieving a median editing efficiency of 16%, were expanded outside the body and then reintroduced into patients. Among 12 individuals who had previously undergone multiple unsuccessful treatments, the median progression-free survival was 7.7 weeks and overall survival was 42.6 weeks. Minimal off-target effects were observed, supporting the clinical promise of CRISPR-modified TCR-T cells (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) (Table\u0026nbsp;7).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe convergence of AI-driven sgRNA design, prior experimental evidence, and ongoing clinical trial data strengthens the translational potential of targeting PDCD1, TRAC, and B2M for next-generation CAR T cell therapies. The strategic knockout of these genes not only enhances antitumor efficacy but also improves immune evasion and safety profiles, addressing current limitations in adoptive T cell therapy.\u003c/p\u003e \u003cp\u003eWhile all tools provided reliable sgRNA candidates, CHOPCHOP, Benchling, and CRISPOR emerged as the most informative:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCHOPCHOP is optimal for checking self-complementarity and GC content, both critical for sgRNA structural stability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBenchling enables a comprehensive genomic view, precisely mapping sgRNA to specific exons\u0026mdash;especially useful when transcript annotation is ambiguous.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCRISPOR offers the most detailed off-target analysis, incorporating both quantitative scores (MIT, CFD) and qualitative metrics (genomic target site context).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThus, overlapping sgRNAs identified by these three platforms were prioritized to ensure high design precision, effective gene knockout, and minimal off-target risk, thereby reducing the chances of experimental failure in CAR T cell engineering.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCharpentier E, Marraffini LA, Harnessing (2014) CRISPR-Cas9 immunity for genetic engineering. 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CN Patent CN112512557A\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernal M et al (2012) Implication of the β2-microglobulin gene in the generation of tumor escape phenotypes. Cancer Immunol Immunother 61:1359\u0026ndash;1371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrow P et al (2019) Confirmation that somatic mutations of beta-2 microglobulin correlate with a lack of recurrence in a subset of stage II mismatch repair-deficient colorectal cancers from the QUASAR trial. Histopathology 75:236\u0026ndash;246\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JH et al (2020) Risk of relapse in patients with B-ALL undergoing CAR T-cell therapy. 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Cell Metab 27:977\u0026ndash;987e4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhen CY, Qian CR (2022) Development of a LentiCRISPRv2-PD1-Puro vector system for efficient PD-1 knockout in human T cells. Mol Biol Rep 49:10799\u0026ndash;10808\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao L et al (2021) PD-1 silencing using CRISPR/Cas9 enhances the efficacy of CAR-T cell therapy in non-small cell lung cancer models. Cell Death Dis 12:957\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng X et al (2022) CRISPR-Cas9\u0026ndash;mediated PD-1 disruption in CD133-targeted CAR T cells enhances efficacy against colorectal cancer. Cancer Lett 530:1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheih A et al (2020) Clonal kinetics and single-cell transcriptional profiling of CAR-T cells in patients undergoing CD19 CAR-T therapy. Nat Commun 11:219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRupp LJ et al (2017) CRISPR/Cas9-mediated PD-1 disruption enhances anti-tumor efficacy of human chimeric antigen receptor T cells. Sci Rep 7:737\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang ZL et al (2018) Rewiring T-cell responses to soluble factors with chimeric receptors. Nat Chem Biol 14:317\u0026ndash;324\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu S et al (2016) CRISPR\u0026ndash;Cas9 mediated efficient PD-1 disruption on human primary T cells from cancer patients. Sci Rep 6:20070\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Z et al (2015) Structural design of engineered costimulation determines tumor rejection kinetics and persistence of CAR T cells. Cancer Cell 28:415\u0026ndash;428\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrentjens RJ et al (2011) Safety and persistence of adoptively transferred autologous CD19-targeted T cells in patients with relapsed or chemotherapy refractory B-cell leukemias. Blood 118:4817\u0026ndash;4828\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKochenderfer JN et al (2015) Chemotherapy-refractory diffuse large B-cell lymphoma and indolent B-cell malignancies can be effectively treated with autologous T cells expressing an anti-CD19 chimeric antigen receptor. J Clin Oncol 33:540\u0026ndash;549\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNofia AM, Maude SL (2020) Chimeric antigen receptor T-cell therapy clinical results in pediatric and young adult B-ALL. Hematol Oncol Clin North Am 34:483\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJune CH, Sadelain M (2018) Chimeric antigen receptor therapy. 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Stem Cells 36:36\u0026ndash;44\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable1: The table below compares key features and algorithmic capabilities across nine popular CRISPR sgRNA design platforms.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eCHOPCHOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eBenchling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eCRISPRscan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eGenScript\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eCas-Designer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eE-CRISP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eCRISP-ERA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eCRISPOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eATUM sgRNA Design Tool\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eAlgorithm Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eRule-based and\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAI-assisted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eAI-integrated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eMachine Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eAI\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand Experimental Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eAI-based scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eRule-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eAI-enhanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMachine Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eAI-driven \u0026amp; proprietary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOn-Target Prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes (High Accuracy)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOff-Target Prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes (Genome-wide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes (Detailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eLimited\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes (Detailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes (Genome-wide)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes (Detailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eLow Mismatch Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eLimited\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHigh Mismatch Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eAdjust ssgRNA Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eFeature Aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eSNP Aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eSecondary Structure Aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMicrohomology Aware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eSpecificity Scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eEfficiency Scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGenome Compatibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eHuman, mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eHuman-focused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHuman \u0026amp; other species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHuman \u0026amp; Mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHuman \u0026amp; other species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOrganism Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eWide range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eWide range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eLimited\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eHuman-focused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eHuman \u0026amp; Mouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiple species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eSequence Input Format\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eFASTA, GenBank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eFASTA, Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eFASTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eFASTA, Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eFASTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003ePlain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eFASTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eFASTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eFASTA, Plain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eIdentifier Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eGene Name, Accession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eAccession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGene Name, Accession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGene Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGene Name, Accession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGene Name, Accession\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eLoad Options\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eOnline \u0026amp; Offline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOffline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOnline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOnline \u0026amp; Offline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eUser Interface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eWeb-based, simple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eWeb-based, interactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eWeb-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eWeb-based, requires login\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eWeb-based, advanced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eWeb-based, simple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eWeb-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eWeb-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eWeb-based, advanced\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eOffline Availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eCLI Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eGUI Support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMultiplex Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eMulti-Method Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eSingle-Method Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eScoring Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3968%;\"\u003e\n \u003cp\u003eDoench\u003c/p\u003e\n \u003cp\u003e(2014), Doench (2016), and Morean mateos\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.6553%;\"\u003e\n \u003cp\u003e\u0026nbsp;Doench\u003c/p\u003e\n \u003cp\u003e(2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.3264%;\"\u003e\n \u003cp\u003eMorean mateos (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.79285%;\"\u003e\n \u003cp\u003eProprietary ML model based on Doench-like features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eDoench (2014) and Doench\u0026nbsp;\u003cbr\u003e\u0026nbsp;(2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003eHeuristic + Doench (2014)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eCustom rule-based scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoench (2014),\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDoench (2016)\u003c/strong\u003e,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.9851%;\"\u003e\n \u003cp\u003eProprietary (ML-based, Doench-like)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Comparison table of the web based tool with overlapping result:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esgRNA Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Exon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOn-Target Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOff-Target Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCHOPCHOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExon 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMM0-1, MM1-0, MM2-0, MM3-2\u003cbr\u003e\u0026nbsp;No high-risk off-targets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRISPOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExon 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Doench 2016)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMIT(95),CFD(96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBenchling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExon 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56.2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Fusi et al., 2016)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.0 (Hsu et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas-Designer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExon 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMM0-1, MM1-0, MM2-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRISPR-ERA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExon 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10(E score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo off target found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Validation of sgRNA Designed to Knockout PDCD1\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy (Author, Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelivery Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esgRNA Target Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCas9 Format\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell Type(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEditing Efficiency / Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhilips et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectroporation of PX458 plasmid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasmid (PX458: SpCas9-2A-GFP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJurkat T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD-1 KO confirmed via surface expression loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGFP⁺ cells single-cell sorted; functional assays performed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHanamura et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTransfection of PX458 plasmid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasmid (PX458: SpCas9-2A-GFP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVAL B lymphoma cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD-1 KO confirmed in clones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTargets exon 2; same sgRNA as Philips et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUS20190247432A1 (Zhao et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;)(SEQ ID: 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNY-ESO-1\u0026ndash;specific T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEnhanced anti-tumor activity post PD-1 KO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsed in multiplex editing of T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUS20210388389A1 (Chen et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 or Cas12a RNP or plasmid; delivery via electroporation or viral vectors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;)(SEQ ID: 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 or Cas12a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary human T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsed in CAR-T engineering with TRAC editing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSupports modular CAR knock-ins and PD-1 KO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCN105671083B (Sun et al., 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLentiviral transduction (Lenti-Puro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eCACGAAGCTCTCCGATGTGT\u003c/strong\u003e-3\u0026prime;) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLentiviral CRISPR/Cas9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePatient-derived T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD-1 successfully knocked out in T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLentiviral delivery of guide; used for cancer immunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 4: Comparison table of the web based tool with overlapping result\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esgRNA Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Exon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOn-Target Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOff-Target Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCHOPCHOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e48.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMM0-1, MM1-0, MM2-0,MM3-9\u0026nbsp;\u003cbr\u003e\u0026nbsp;No high-risk off-targets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCRISPOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e48\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Doench 2016)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMIT(92),CFD(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eBenchling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e46.10\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Fusi et al., 2016)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e63.2 (Hsu et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCas-Designer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMM0-1, MM1-0, MM2-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCRISPR-ERA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e10(E score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNo off target found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: Validation of sgRNA Designed to Knockout TRAC\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy (Author, Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelivery Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esgRNA Target Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCas9 Format\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell Type(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEditing Efficiency / Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eEyquem et al., 2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCas9 mRNA + AAV6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCas9 mRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePrimary human PB T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e~70% indels (T7E1/ddPCR), \u0026gt;95% CAR⁺TCR⁻\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eKnock-in under TRAC promoter, enhanced antitumor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eLi et al., 2022\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRNP: Cas9 protein + crRNA:tracrRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eRNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePrimary CD8⁺ T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e~51% TCR\u0026alpha; loss (flow), ~40\u0026ndash;47% indels (ddPCR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eHigh-throughput screen context\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003ePreece et al., 2020\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eLentiviral \u0026ldquo;terminal-CRISPR\u0026rdquo; vector + Cas9 mRNA electrop.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCas9 mRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePrimary T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026gt;96% CAR⁺TCR\u0026alpha;\u0026beta;⁻ cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003esgRNA embedded in lentiviral vector, self-inactivating\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eAlbers et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRNP: Cas9 + crRNA:tracrRNA (Neon: 1600V, 3\u0026times;10ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eRNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePrimary CD8⁺ T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e~51% TCR\u0026alpha;⁻ (flow), ~40\u0026ndash;47% TRAC indels (ddPCR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eUsed for HDR TCR knock-in\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eYe et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRNP + AAV6 donor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eRNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePrimary CD8⁺ T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eLarge CAR⁺CD3⁻ population (flow)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eHigh functional knockout, CAR⁺ cells purified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eZhou et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003ePlasmid CRISPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003ePlasmid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eJurkat cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eTCR\u0026alpha;\u0026beta;⁻ clones by flow cytometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eDual knockout of TRAC and TRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eKamali et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003ePlasmid CRISPR (Cas9 + sgRNA plasmid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003ePlasmid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003ePrimary T cells, HEK293T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e~89% indels in 293T (TIDE/IDAA); ~12\u0026ndash;14% indels in T cells, ~7\u0026ndash;8% CD3⁻\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eLow\u0026ndash;moderate editing efficiency in primary T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eEP3686275A1 (Patent)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCas9 mRNA + chem.-mod sgRNA electroporation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eTCTCTCAGCTGGTACACGGC\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eCas9 mRNA + mod. sgRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eHuman T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eCD3⁻/TCR⁻ phenotype (selected)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026ldquo;Universal\u0026rdquo; CAR-T cell production, patent disclosure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6: Comparison table of the web based tool with overlapping result\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"649\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTool Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esgRNA Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Exon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOn-Target Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOff-Target Summary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCHOPCHOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e48.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMM0-1, MM1-0, MM2-0, MM3-2\u003cbr\u003e\u0026nbsp;No high-risk off-targets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCRISPOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e48 (Doench 2016)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMIT(93),CFD(97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eGeneScript\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBenchling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e46.78 (Fusi et al., 2016)\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48.1 (Hsu et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCas-Designer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMM0-1, MM1-0, MM2-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCRISPR-ERA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eExon 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20(E score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo off target found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eNGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eOverlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7: Validation of sgRNA Designed to Knockout B2M\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy / Patent (Author, Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelivery Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003esgRNA Target Sequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCas9 Format\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell Type(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEditing Efficiency / Outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHamilton et al., 2024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9-EDV (antibody-targeted vesicles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD19⁺ T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEfficient B2M KO in target cells only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceptor-mediated delivery; selective in vivo targeting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLamarth\u0026eacute;e et al., 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP (synthetic crRNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEndothelial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eComplete HLA-I loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFor transplant compatibility assays\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHoerster et al., 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLentiviral CRISPR/Cas9 vector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 + dual sgRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNK cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurface HLA-I loss; NK cells resistant to T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsed in allogeneic NK cell therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHiatt et al., 2021\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNucleofection of Cas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonocytes, macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHLA-I loss confirmed by flow cytometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaintained differentiation capacity post-KO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSong et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP in iPSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eiPSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBiallelic B2M KO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFor creating HLA-A monoallelic endothelial cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBogomiakova et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePX458 Cas9 vector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlasmid (PX458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eiPSCs \u0026rarr; Fibroblasts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB2M KO clones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudied immune response of iPSC-derivatives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMcAlexander et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eElectroporation of Cas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD4⁺ T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFunctional HLA-I loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUsed as editing control in chromatin study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUS20220017926A1 (2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMaxCyte Electroporation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(5\u0026prime;-\u003cstrong\u003eGAGTAGCGCGAGCACAGCTA\u003c/strong\u003e-3\u0026prime;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCas9 RNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrimary human T cells, CAR-T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUp to 90% B2M knockout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOptimized RNP delivery with Cas9:gRNA ratio of 1:4; Cas9 concentration \u0026ge;1 \u0026mu;M; efficient generation of universal CAR-T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eTable 8: A summary of key clinical trials utilizing CRISPR-Cas9 mediated knockout of PDCD1, TRAC, and B2M is provided below\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Trial ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT03399448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1, TRAC, TRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMultiple Myeloma, Liposarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT04637763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1, TRAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCD19 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRelapsed/Refractory B-cell Non-Hodgkin Lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eActive, not recruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT04035434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTRAC, B2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCD19 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRelapsed/Refractory B-cell Malignancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRecruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT04244656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eB2M, TRAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eBCMA CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRelapsed/Refractory Multiple Myeloma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eActive, not recruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT05643742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTRAC, B2M, Regnase-1, TGFBR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCD19 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRelapsed/Refractory B-cell Malignancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRecruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT04502446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTRAC, B2M, CD70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCD70 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRelapsed/Refractory T or B-cell Malignancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eActive, not recruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT04438083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTRAC, B2M, CD70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCD70 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAdvanced Relapsed/Refractory Renal Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eActive, not recruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT03166878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003eTRAC, B2M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCD19 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eRelapsed/Refractory CD19+ Leukemia and Lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT03545815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1, TCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eMesothelin CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMultiple Solid Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT03747965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eMesothelin CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMesothelin-positive Multiple Solid Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT03081715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eEsophageal Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT02793856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eNon-Small Cell Lung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT05812326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eMUC1 CAR-T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMUC1-positive Advanced Breast Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eCompleted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNCT03044743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003ePDCD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eT cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eEBV-associated Malignancies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eI/II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRecruiting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CRISPR-Cas9, B2M, TRAC, PDCD1, Genome Editing, AI Tools, sgRNA Design, CAR T cells","lastPublishedDoi":"10.21203/rs.3.rs-6596407/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6596407/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCRISPR-Cas9 technology is a powerful tool for precise genome editing and is increasingly applied to correct genetic mutations associated with various diseases, including cancer. This system utilizes a single-guide RNA (sgRNA), typically 20 base pairs long and complementary to the target DNA sequence, to direct the Cas9 nuclease for targeted gene activation (knock-in) or repression (knockout). In recent advancements in cancer immunotherapy, CRISPR-Cas9 has been extensively used to enhance the efficacy of Chimeric Antigen Receptor (CAR) T-cell therapy. The development of universal CAR T cells involves the knockout of key genes such as TRAC (T-cell receptor alpha chain), B2M (Beta-2 microglobulin), and PDCD1 (Programmed cell death protein 1), which improves T-cell persistence, immune evasion, and anti-tumor function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, sgRNAs targeting PDCD1, B2M, and TRAC were designed using nine widely recognized AI-driven bioinformatics tools: CHOPCHOP, CRISPOR, GenScript, Benchling, Cas-Designer, E-CRISP, CRISPR-ERA, CRISPRscan, and ATUM gRNA Tool. These platforms use various algorithms and genomic datasets to predict sgRNA candidates with high on-target activity and minimal off-target effects. The selected sgRNAs were assessed based on criteria including GC content, self-complementarity, and exon targeting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sgRNA design tools consistently identified high-confidence target sites within exon 1 of the PDCD1, TRAC, and B2M genes. For PDCD1 (PD-1), the sgRNA sequence (5′-CACGAAGCTCTCCGATGTGT-3′) was selected as the most optimal candidate, showing strong consensus across all platforms. Similarly, for TRAC, the sgRNA (5′-TCTCTCAGCTGGTACACGGC-3′) targeting exon 1 was chosen based on its high predicted efficiency and specificity. In the case of B2M, the sgRNA (5′-GAGTAGCGCGAGCACAGCTA-3′) was identified as an ideal target site within exon 1, a region critical for MHC class I expression and immune evasion. These sgRNAs demonstrated favorable characteristics including appropriate GC content, minimal self-complementarity, and low predicted off-target activity. To ensure their functional reliability, all selected sgRNAs were validated through an extensive review of scientific literature and previously published patent data, confirming their utility in gene knockout studies related to CAR T-cell enhancement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the tools evaluated, CHOPCHOP, Benchling, and CRISPOR emerged as the most comprehensive, offering robust information on GC content, self-complementarity, exon identification, and detailed off-target predictions. Additionally, this study compiled a list of relevant clinical trials involving gene knockouts of PDCD1, TRAC, and B2M to further support the therapeutic relevance of these targets in CAR T-cell development.\u003c/p\u003e","manuscriptTitle":"AI-Driven CRISPR-Cas9 sgRNA Design for PDCD1, TRAC, and B2M Knockout to Improve CAR T Cell Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 09:20:24","doi":"10.21203/rs.3.rs-6596407/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1319872-d6e6-4d05-b2c8-2acb713c9c20","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48097646,"name":"Molecular Biology"},{"id":48097647,"name":"Computational Biology"},{"id":48097648,"name":"Immunology"},{"id":48097649,"name":"Oncology"}],"tags":[],"updatedAt":"2025-05-07T09:20:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 09:20:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6596407","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6596407","identity":"rs-6596407","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}