Accounting for small variations in the tracrRNA sequence improves sgRNA activity predictions for CRISPR screening

Application of CRISPR-Based Epigenome Editing Tools for Engineering Programmable Embryo Models

Stem cell-based embryo models (SEMs) have the potential to transform our understanding of early human embryogenesis. A critical step in engineering SEMs is the generation of the major cell types that compose preimplantation embryos including two primary extraembryonic lineages: (i) trophoblast cells, which are crucial for implantation and the establishment of maternal-fetal exchange, and (ii) hypoblast cells, which contribute to yolk sac formation. In addition, both cell types provide key signaling cues necessary for embryonic development. CRISPR-based epigenome editors are programmable devices that allow for efficient and precise activation (CRISPRa) or repression (CRISPRi) of cell fate-determining factors by modulating endogenous regulatory elements. Here, we present a step-by-step method to implement CRISPRa for controlling cell fate in embryonic stem cells based on our work in generation of CRISPR-programmed mouse embryo models.

Robust prediction of synthetic gRNA activity and cryptic DNA repair by disentangling cellular CRISPR cleavage outcomes

The ability to robustly predict guide RNA (gRNA) activity is a long-standing goal for CRISPR applications, as it would reduce the need to pre-screen gRNAs. Quantification of formation of short insertions and deletions (indels) after DNA cleavage by transcribed gRNAs has been typically used to measure and predict gRNA activity. We evaluate the effect of chemically synthesized Cas9 gRNAs on different cellular DNA cleavage outcomes and find that the activity of different gRNAs is largely similar and often underestimated when only indels are scored. We provide a simple linear model that reliably predicts synthetic gRNA activity across cell lines, robustly identifies inefficient gRNAs across different published datasets, and is easily accessible via online genome browser tracks. In addition, we develop a homology-directed repair efficiency prediction tool and show that unintended large-scale repair events are common for Cas9 but not for Cas12a, which may be relevant for safety in gene therapy applications.

CRISPR-BEasy: a free web-based service for designing sgRNA tiling libraries for CRISPR-dependent base editing screens

CRISPR-dependent base editing (BE) enables the modeling and correction of genetic mutations at single-base resolution. Base editing screens, where point mutations are queried en masse, are powerful tools to systematically draw genotype-phenotype associations and characterise the function of genes and other genomic elements. However, the lack of user-friendly web-based tools for designing base editing screens can hinder broad technology adoption. Here, we introduce CRISPR-BEasy (https://crispr-beasy.cerc-genomic-medicine.ca), a free, automated web-based server that streamlines the creation of single guide (sg)RNA tiling libraries for base editing screens. Researchers can provide their genes or genomic features of interest, their base editors of choice, and target sequences to act as positive and negative controls. The server designs and annotates sgRNA libraries by integrating custom code with publicly available tools such as crisprVerse and Ensembl's Variant Effect Predictor. CRISPR-BEasy provides downloadable results, including sgRNA on/off-target scores, predicted mutational outcomes per base editor, and intuitive interactive visualizations for data quality assessment. CRISPR-BEasy also provides a separate tool that assembles sgRNA libraries into oligonucleotides for cloning following the detailed protocol documented in the searchable web server manual. Together, CRISPR-BEasy ensures the seamless design of cloning-ready sgRNA libraries, seeking to democratise access to base editing screening technologies.

CRISPR-dependent base editing as a therapeutic strategy for rare monogenic disorders

Rare monogenic disorders are caused by mutations in single genes and have an incidence rate of less than 0.5%. Due to their low prevalence, these diseases often attract limited research and commercial interest, leading to significant unmet medical needs. In a therapeutic landscape where treatments are targeted to manage symptoms, gene editing therapy emerges as a promising approach to craft curative and lasting treatments for these patients, often referred to as "one-and-done" therapeutics. CRISPR-dependent base editing enables the precise correction of genetic mutations by direct modification of DNA bases without creating potentially deleterious DNA double-strand breaks. Base editors combine a nickase version of Cas9 with cytosine or adenine deaminases to convert C·G to T·A and A·T to G·C, respectively. Together, cytosine (CBE) and adenine (ABE) base editors can theoretically correct ∼95% of pathogenic transition mutations cataloged in ClinVar. This mini-review explores the application of base editing as a therapeutic approach for rare monogenic disorders. It provides an overview of the state of gene therapies and a comprehensive compilation of preclinical studies using base editing to treat rare monogenic disorders. Key considerations for designing base editing-driven therapeutics are summarized in a user-friendly guide for researchers interested in applying this technology to a specific rare monogenic disorder. Finally, we discuss the prospects and challenges for bench-to-bedside translation of base editing therapies for rare monogenic disorders.

An Overview and Comparative Analysis of CRISPR-SpCas9 gRNA Activity Prediction Tools

Design of guide RNA (gRNA) with high efficiency and specificity is vital for successful application of the CRISPR gene editing technology. Although many machine learning (ML) and deep learning (DL)-based tools have been developed to predict gRNA activities, a systematic and unbiased evaluation of their predictive performance is still needed. Here, we provide a brief overview of in silico tools for CRISPR design and assess the CRISPR datasets and statistical metrics used for evaluating model performance. We benchmark seven ML and DL-based CRISPR-Cas9 editing efficiency prediction tools across nine CRISPR datasets covering six cell types and three species. The DL models CRISPRon and DeepHF outperform the other models exhibiting greater accuracy and higher Spearman correlation coefficient across multiple datasets. We compile all CRISPR datasets and in silico prediction tools into a GuideNet resource web portal, aiming to facilitate and streamline the sharing of CRISPR datasets. Furthermore, we summarize features affecting CRISPR gene editing activity, providing important insights into model performance and the further development of more accurate CRISPR prediction models.

Universal Prime Editing Therapeutic Strategy for RyR1-Related Myopathies: A Protective Mutation Rescues Leaky RyR1 Channel

RyR1-related myopathies (RyR1-RMs) include a wide range of genetic disorders that result from mutations in the RYR1 gene. Pathogenic variants lead to defective intracellular calcium homeostasis and muscle dysfunction. Fixing intracellular calcium leaks by stabilizing the RyR1 calcium channel has been identified as a promising therapeutic target. Gene therapy via prime editing also holds great promise as it can cure diseases by correcting genetic mutations. However, as more than 700 variants have been identified in the RYR1 gene, a universal treatment would be a more suitable solution for patients. Our investigation into the RyR1-S2843A mutation has yielded promising results. Using a calcium leak assay, we determined that the S2843A mutation was protective when combined with pathogenic mutations and significantly reduced the Ca2+ leak of the RyR1 channel. Our study demonstrated that prime editing can efficiently introduce the protective S2843A mutation. In vitro experiments using the RNA electroporation of the prime editing components in human myoblasts achieved a 31% introduction of this mutation. This article lays the foundation for a new therapeutic approach for RyR1-RM, where a unique once-in-a-lifetime prime editing treatment could potentially be universally applied to all patients with a leaky RyR1 channel.

A benchmark comparison of CRISPRn guide-RNA design algorithms and generation of small single and dual-targeting libraries to boost screening efficiency

Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale.

Optimal SpCas9- and SaCas9-mediated gene editing by enhancing gRNA transcript levels through scaffold poly-T tract reduction

Ensuring sufficient gRNA transcript levels is critical for obtaining optimal CRISPR-Cas9 gene editing efficiency. The standard gRNA scaffold contains a sequence of four thymine nucleotides (4T), which is known to inhibit transcription from Pol III promoters such as the U6 promoter. Our study showed that using standard plasmid transfection protocols, the presence of these 4Ts did not significantly affect editing efficiency, as most of the gRNAs tested (55 gRNAs) achieved near-perfect editing outcomes. We observed that gRNAs with lower activity were T-rich and had reduced gRNA transcript levels. However, this issue can be effectively resolved by increasing transcript levels, which can be readily achieved by shortening the 4T sequences. In this study, we demonstrated this by modifying the sequences to 3TC. Although the 3TC scaffold modification did not improve editing efficiency for already efficient gRNAs when high vector quantities were available, it proved highly beneficial under conditions of limited vector availability, where the 3TC scaffold yielded higher editing efficiency. Additionally, we demonstrated that the 3TC scaffold is compatible with SpCas9 high-fidelity variants and ABEmax base editing, enhancing their editing efficiency. Another commonly used natural Cas9 variant, SaCas9, also benefited from the 3TC scaffold sequence modification, which increased gRNA transcription and subsequently improved editing activity. This modification was applied to the EDIT-101 therapeutic strategy, where it demonstrated marked improvements in performance. This study highlights the importance of shortening the 4T sequences in the gRNA scaffold to optimize gRNA transcript expression for enhanced CRISPR-Cas9 gene editing efficiency. This optimization is particularly important for therapeutic applications, where the quantity of vector is often limited, ensuring more effective and optimal outcomes.

A STAG2-PAXIP1/PAGR1 axis suppresses lung tumorigenesis

The cohesin complex is a critical regulator of gene expression. STAG2 is the most frequently mutated cohesin subunit across several cancer types and is a key tumor suppressor in lung cancer. Here, we coupled somatic CRISPR-Cas9 genome editing and tumor barcoding with an autochthonous oncogenic KRAS-driven lung cancer model and showed that STAG2 is uniquely tumor-suppressive among all core and auxiliary cohesin components. The heterodimeric complex components PAXIP1 and PAGR1 have highly correlated effects with STAG2 in human lung cancer cell lines, are tumor suppressors in vivo, and are epistatic to STAG2 in oncogenic KRAS-driven lung tumorigenesis in vivo. STAG2 inactivation elicits changes in gene expression, chromatin accessibility, and 3D genome conformation that impact the cancer cell state. Gene expression and chromatin accessibility similarities between STAG2- and PAXIP1-deficient neoplastic cells further relate STAG2-cohesin to PAXIP1/PAGR1. These findings reveal a STAG2-PAXIP1/PAGR1 tumor-suppressive axis and uncover novel PAXIP1-dependent and PAXIP1-independent STAG2-cohesin-mediated mechanisms of lung tumor suppression.

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Higher resolution pooled genome-wide CRISPR knockout screening in Drosophila cells using Integration and Anti-CRISPR (IntAC)

CRISPR screens enable systematic, scalable genotype-to-phenotype mapping. We previously developed a pooled CRISPR screening method for Drosophila melanogaster and mosquito cell lines using plasmid transfection and site-specific integration to introduce single guide (sgRNA) libraries, followed by PCR and sequencing of integrated sgRNAs. While effective, the method relies on early constitutive Cas9 activity that potentially can lead to discrepancies between genome edits and sgRNAs detected by PCR, reducing screen accuracy. To address this issue, we introduce a new method to co-transfect a plasmid expressing the anti-CRISPR protein AcrIIa4 to suppress Cas9 activity during early sgRNA expression, which we term "IntAC" (integrase with anti-CRISPR). IntAC allowed us to construct a new CRISPR screening approach driven by the high strength dU6:3 promoter. This new library dramatically improved precision-recall of fitness genes across the genome, retrieving 90-95% of essential gene groups within 5% error, allowing us to generate the most comprehensive list of cell fitness genes yet assembled for Drosophila. Our analysis determined that elevated sgRNA levels, made permissible by the IntAC approach, drove much of the improvement. The Drosophila fitness genes show strong correlation with human fitness genes and underscore the effects of paralogs on gene essentiality. We further demonstrate that IntAC combined with a targeted sgRNA sub-library enabled precise positive selection of a transporter under solute overload. IntAC represents a straightforward enhancement to existing Drosophila CRISPR screening methods, dramatically increasing accuracy, and might also be broadly applicable to virus-free CRISPR screens in other cell types, including mosquito, lepidopteran, tick, and mammalian cells.

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

CRISPR activation screens: navigating technologies and applications

Clustered regularly interspaced short palindromic repeats (CRISPR) activation (CRISPRa) has become an integral part of the molecular biology toolkit. CRISPRa genetic screens are an exciting high-throughput means of identifying genes the upregulation of which is sufficient to elicit a given phenotype. Activation machinery is continually under development to achieve greater, more robust, and more consistent activation. In this review, we offer a succinct technological overview of available CRISPRa architectures and a comprehensive summary of pooled CRISPRa screens. Furthermore, we discuss contemporary applications of CRISPRa across broad fields of research, with the aim of presenting a view of exciting emerging applications for CRISPRa screening.

Integrating machine learning and genome editing for crop improvement

Genome editing is a promising technique that has been broadly utilized for basic gene function studies and trait improvements. Simultaneously, the exponential growth of computational power and big data now promote the application of machine learning for biological research. In this regard, machine learning shows great potential in the refinement of genome editing systems and crop improvement. Here, we review the advances of machine learning to genome editing optimization, with emphasis placed on editing efficiency and specificity enhancement. Additionally, we demonstrate how machine learning bridges genome editing and crop breeding, by accurate key site detection and guide RNA design. Finally, we discuss the current challenges and prospects of these two techniques in crop improvement. By integrating advanced genome editing techniques with machine learning, progress in crop breeding will be further accelerated in the future.

Modular vector assembly enables rapid assessment of emerging CRISPR technologies

The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.

Epigenetic profiles guide improved CRISPR/Cas9-mediated gene knockout in human T cells

Genetic modification of specific genes is emerging as a useful tool to enhance the functions of antitumor T cells in adoptive immunotherapy. Current advances in CRISPR/Cas9 technology enable gene knockout during in vitro preparation of infused T-cell products through transient transfection of a Cas9-guide RNA (gRNA) ribonucleoprotein complex. However, selecting optimal gRNAs remains a major challenge for efficient gene ablation. Although multiple in silico tools to predict the targeting efficiency have been developed, their performance has not been validated in cultured human T cells. Here, we explored a strategy to select optimal gRNAs using our pooled data on CRISPR/Cas9-mediated gene knockout in human T cells. The currently available prediction tools alone were insufficient to accurately predict the indel percentage in T cells. We used data on the epigenetic profiles of cultured T cells obtained from transposase-accessible chromatin with high-throughput sequencing (ATAC-seq). Combining the epigenetic information with sequence-based prediction tools significantly improved the gene-editing efficiency. We further demonstrate that epigenetically closed regions can be targeted by designing two gRNAs in adjacent regions. Finally, we demonstrate that the gene-editing efficiency of unstimulated T cells can be enhanced through pretreatment with IL-7. These findings enable more efficient gene editing in human T cells.

Modular vector assembly enables rapid assessment of emerging CRISPR technologies

The diversity of CRISPR systems, coupled with scientific ingenuity, has led to an explosion of applications; however, to test newly-described innovations in their model systems, researchers typically embark on cumbersome, one-off cloning projects to generate custom reagents that are optimized for their biological questions. Here, we leverage Golden Gate cloning to create the Fragmid toolkit, a modular set of CRISPR cassettes and delivery technologies, along with a web portal, resulting in a combinatorial platform that enables scalable vector assembly within days. We further demonstrate that multiple CRISPR technologies can be assessed in parallel in a pooled screening format using this resource, enabling the rapid optimization of both novel technologies and cellular models. These results establish Fragmid as a robust system for the rapid design of CRISPR vectors, and we anticipate that this assembly approach will be broadly useful for systematic development, comparison, and dissemination of CRISPR technologies.

Guide-specific loss of efficiency and off-target reduction with Cas9 variants

High-fidelity clustered regularly interspaced palindromic repeats (CRISPR)-associated protein 9 (Cas9) variants have been developed to reduce the off-target effects of CRISPR systems at a cost of efficiency loss. To systematically evaluate the efficiency and off-target tolerance of Cas9 variants in complex with different single guide RNAs (sgRNAs), we applied high-throughput viability screens and a synthetic paired sgRNA-target system to assess thousands of sgRNAs in combination with two high-fidelity Cas9 variants HiFi and LZ3. Comparing these variants against wild-type SpCas9, we found that ∼20% of sgRNAs are associated with a significant loss of efficiency when complexed with either HiFi or LZ3. The loss of efficiency is dependent on the sequence context in the seed region of sgRNAs, as well as at positions 15-18 in the non-seed region that interacts with the REC3 domain of Cas9, suggesting that the variant-specific mutations in the REC3 domain account for the loss of efficiency. We also observed various degrees of sequence-dependent off-target reduction when different sgRNAs are used in combination with the variants. Given these observations, we developed GuideVar, a transfer learning-based computational framework for the prediction of on-target efficiency and off-target effects with high-fidelity variants. GuideVar facilitates the prioritization of sgRNAs in the applications with HiFi and LZ3, as demonstrated by the improvement of signal-to-noise ratios in high-throughput viability screens using these high-fidelity variants.

Guide-specific loss of efficiency and off-target reduction with Cas9 variants

High-fidelity Cas9 variants have been developed to reduce the off-target effects of CRISPR systems at a cost of efficiency loss. To systematically evaluate the efficiency and off-target tolerance of Cas9 variants in complex with different single guide RNAs (sgRNAs), we applied high-throughput viability screens and a synthetic paired sgRNA-target system to assess thousands of sgRNAs in combination with two high-fidelity Cas9 variants HiFi and LZ3. Comparing these variants against WT SpCas9, we found that ~20% of sgRNAs are associated with a significant loss of efficiency when complexed with either HiFi or LZ3. The loss of efficiency is dependent on the sequence context in the seed region of sgRNAs, as well as at positions 15-18 in the non-seed region that interacts with the REC3 domain of Cas9, suggesting that the variant-specific mutations in REC3 domain account for the loss of efficiency. We also observed various degrees of sequencedependent off-target reduction when different sgRNAs are used in combination with the variants. Given these observations, we developed GuideVar, a transfer-learning-based computational framework for the prediction of on-target efficiency and off-target effect with high-fidelity variants. GuideVar facilitates the prioritization of sgRNAs in the applications with HiFi and LZ3, as demonstrated by the improvement of signal-to-noise ratios in high-throughput viability screens using these high-fidelity variants.

A comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs across nucleases and technologies

The success of CRISPR-mediated gene perturbation studies is highly dependent on the quality of gRNAs, and several tools have been developed to enable optimal gRNA design. However, these tools are not all adaptable to the latest CRISPR modalities or nucleases, nor do they offer comprehensive annotation methods for advanced CRISPR applications. Here, we present a new ecosystem of R packages, called crisprVerse, that enables efficient gRNA design and annotation for a multitude of CRISPR technologies. This includes CRISPR knockout (CRISPRko), CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR base editing (CRISPRbe) and CRISPR knockdown (CRISPRkd). The core package, crisprDesign, offers a user-friendly and unified interface to add off-target annotations, rich gene and SNP annotations, and on- and off-target activity scores. These functionalities are enabled for any RNA- or DNA-targeting nucleases, including Cas9, Cas12, and Cas13. The crisprVerse ecosystem is open-source and deployed through the Bioconductor project ( https://github.com/crisprVerse ).