Prediction of on-target and off-target activity of CRISPR-Cas13d guide RNAs using deep learning

Accurate Prediction of CRISPR/Cas13a Guide Activity Using Feature Selection and Deep Learning

CRISPR/Cas13a serves as a key tool for nucleic acid tests; therefore, accurate prediction of its activity is essential for creating robust and sensitive diagnosis. In this study, we create a dual-branch neural network model that achieves high prediction accuracy and classification performance across two independent CRISPR/Cas13a data sets, outperforming previously published models relying solely on sequence features. The model integrates direct sequence encoding with descriptive features and yields 99 key descriptive features out of 1553, extracted through statistical analysis, which critically influence guide-target interactions and Cas13a guide activity. By employing Shapley Additive Explanations and Integrated Gradients for feature importance analysis, we show that sequence composition, mismatch type and frequency, and the protospacer flanking site region are primary features. These findings underscore the importance of using descriptive features as complementary inputs to deep learning-based encoding and provide valuable insights into the mechanisms underlying guide-target interaction. All in all, this study not only introduces a reliable and efficient model for Cas13a guide activity prediction but also offers a foundation for future rational design efforts.

Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations

CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translating this technology into clinical applications. Traditional protein engineering methods, such as rational design, mutagenesis screens, and directed evolution, have been used to address issues like low efficacy, specificity, and high immunogenicity. These methods are labor-intensive, time-consuming, and resource-intensive and often require detailed structural knowledge. Recently, computational strategies have emerged as powerful solutions to these limitations. Using artificial intelligence (AI) and machine learning (ML), the discovery and design of novel gene-editing enzymes can be streamlined. AI/ML models predict activity, specificity, and immunogenicity while also enhancing mutagenesis screens and directed evolution. These approaches not only accelerate rational design but also create new opportunities for developing safer and more efficient genome-editing tools, which could eventually be translated into the clinic.

Enhanced RNA-targeting CRISPR-Cas technology in zebrafish

CRISPR-Cas13 RNA-targeting systems are widely used in basic and applied sciences. However, its application has recently generated controversy due to collateral activity in mammalian cells and mouse models. Moreover, its competence could be improved in vivo. Here, we optimized transient formulations as ribonucleoprotein complexes or mRNA-gRNA combinations to enhance the CRISPR-RfxCas13d system in zebrafish. We i) use chemically modified gRNAs to allow more penetrant loss-of-function phenotypes, ii) improve nuclear RNA targeting, and iii) compare different computational models and determine the most accurate to predict gRNA activity in vivo. Furthermore, we demonstrate that transient CRISPR-RfxCas13d can effectively deplete endogenous mRNAs in zebrafish embryos without inducing collateral effects, except when targeting extremely abundant and ectopic RNAs. Finally, we implement alternative RNA-targeting CRISPR-Cas systems such as CRISPR-Cas7-11 and CRISPR-DjCas13d. Altogether, these findings contribute to CRISPR-Cas technology optimization for RNA targeting in zebrafish through transient approaches and assist in the progression of in vivo applications.

Recent advances in the application of CRISPR/Cas-based gene editing technology in Filamentous Fungi

Filamentous fungi are essential industrial microorganisms that can serve as sources of enzymes, organic acids, terpenoids, and other bioactive compounds with significant applications in food, medicine, and agriculture. However, the underdevelopment of gene editing tools limits the full exploitation of filamentous fungi, which still present numerous untapped potential applications. In recent years, the CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system, a versatile genome-editing tool, has advanced significantly and been widely applied in filamentous fungi, showcasing considerable research potential. This review examines the development and mechanisms of genome-editing tools in filamentous fungi, and contrasts the CRISPR/Cas9 and CRISPR/Cpf1 systems. The transformation and delivery strategies of the CRISPR/Cas system in filamentous fungi are also examined. Additionally, recent applications of CRISPR/Cas systems in filamentous fungi are summarized, such as gene disruption, base editing, and gene regulation. Strategies to enhance editing efficiency and reduce off-target effects are also highlighted, with the aim of providing insights for the future construction and optimization of CRISPR/Cas systems in filamentous fungi.

Decoding heterogeneous single-cell perturbation responses

Understanding how cells respond differently to perturbation is crucial in cell biology, but existing methods often fail to accurately quantify and interpret heterogeneous single-cell responses. Here we introduce the perturbation-response score (PS), a method to quantify diverse perturbation responses at a single-cell level. Applied to single-cell perturbation datasets such as Perturb-seq, PS outperforms existing methods in quantifying partial gene perturbations. PS further enables single-cell dosage analysis without needing to titrate perturbations, and identifies 'buffered' and 'sensitive' response patterns of essential genes, depending on whether their moderate perturbations lead to strong downstream effects. PS reveals differential cellular responses on perturbing key genes in contexts such as T cell stimulation, latent HIV-1 expression and pancreatic differentiation. Notably, we identified a previously unknown role for the coiled-coil domain containing 6 (CCDC6) in regulating liver and pancreatic cell fate decisions. PS provides a powerful method for dose-to-function analysis, offering deeper insights from single-cell perturbation data.

Comprehensive dissection of cis-regulatory elements in a 2.8 Mb topologically associated domain in six human cancers

Cis-regulatory elements (CREs), such as enhancers and promoters, are fundamental regulators of gene expression and, across different cell types, the MYC locus utilizes a diverse regulatory architecture driven by multiple CREs. To better understand differences in CRE function, we perform pooled CRISPR inhibition (CRISPRi) screens to comprehensively probe the 2.8 Mb topologically-associated domain containing MYC in 6 human cancer cell lines with nucleotide resolution. We map 32 CREs where inhibition leads to changes in cell growth, including 8 that overlap previously identified enhancers. Targeting specific CREs decreases MYC expression by as much as 60%, and cell growth by as much as 50%. Using 3-D enhancer contact mapping, we find that these CREs almost always contact MYC but less than 10% of total MYC contacts impact growth when silenced, highlighting the utility of our approach to identify phenotypically-relevant CREs. We also detect an enrichment of lineage-specific transcription factors (TFs) at MYC CREs and, for some of these TFs, find a strong, tumor-specific correlation between TF and MYC expression not found in normal tissue. Taken together, these CREs represent systematically identified, functional regulatory regions and demonstrate how the same region of the human genome can give rise to complex, tissue-specific gene regulation.

Precise RNA targeting with CRISPR-Cas13d

The possibility of collateral RNA degradation poses a concern for transcriptome perturbations and therapeutic applications using CRISPR-Cas13. We show that collateral activity only occurs with high RfxCas13d expression. Using low-copy RfxCas13d in transcriptome-scale and combinatorial pooled screens, we achieve high on-target knockdown without extensive collateral activity. Furthermore, analysis of a high-fidelity Cas13 variant suggests that its reduced collateral activity may be due to overall diminished nuclease capability.

Topologically constrained DNA-mediated one-pot CRISPR assay for rapid detection of viral RNA with single nucleotide resolution

BACKGROUND

The widespread and evolution of RNA viruses, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlights the importance of fast identification of virus subtypes, particularly in non-laboratory settings. Rapid and inexpensive at-home testing of viral nucleic acids with single-base resolution remains a challenge.

METHODS

Topologically constrained DNA ring is engineered as substrates for the trans-cleavage of Cas13a to yield an accelerated post isothermal amplification. The capacity of CRISPR/Cas13a for discriminating single nucleotide variant (SNV) in viral genome is leveraged by designing synthetic mismatches and hairpin structure in CRISPR RNA (crRNA), enabling robust discrimination of different SARS-CoV-2 variants. Via optimisation of CasTDR3pot to be one-pot assay, CasTDR1pot can detect Omicron and its subvariants, with only a few copies in clinical samples in less than 30 min without pre-amplification.

FINDINGS

The detection system boasts high sensitivity (0.1 aM), single-base specificity, and the advantage of a rapid "sample-to-answer" process, which takes only 30 min. In the detection of SARS-CoV-2 clinical samples and their variant strains, CasTDR1pot has achieved 100% accuracy. Furthermore, the design of a portable signal-reading device facilitates user-friendly result interpretation. For the detection needs of different RNA viruses, the system can be adapted simply by designing the corresponding crRNA.

INTERPRETATION

Our study provides a rapid and accurate molecular diagnostic tool for point-of-care testing, epidemiological screening, and the detection of diseases associated with other RNA biomarkers with excellent single nucleotide differentiation, high sensitivity, and simplicity.

FUNDING

National Key Research and Development Program of China (No. 2023YFB3208302), National Natural Science Foundation of China (No. 22377110, 22034004, 82402749, 82073787, 22122409), National Key Research and Development Program of China (No. 2021YFA1200104), Henan Province Fund for Cultivating Advantageous Disciplines (No. 222301420019).

Multiplexed inhibition of immunosuppressive genes with Cas13d for combinatorial cancer immunotherapy

The complex nature of the immunosuppressive tumor microenvironment (TME) requires multi-agent combinations for optimal immunotherapy. Here we describe multiplex universal combinatorial immunotherapy via gene silencing (MUCIG), which uses CRISPR-Cas13d to silence multiple endogenous immunosuppressive genes in the TME, promoting TME remodeling and enhancing antitumor immunity. MUCIG vectors targeting four genes delivered by adeno-associated virus (AAV) (Cd274/Pdl1, Lgals9/Galectin9, Lgals3/Galectin3 and Cd47; AAV-Cas13d-PGGC) demonstrate significant antitumor efficacy across multiple syngeneic tumor models, remodeling the TME by increasing CD8+ T-cell infiltration while reducing neutrophils. Whole transcriptome profiling validates the on-target knockdown of the four target genes and shows limited potential off-target or downstream gene alterations. AAV-Cas13d-PGGC outperforms corresponding shRNA treatments and individual gene knockdown. We further optimize MUCIG by employing high-fidelity Cas13d (hfCas13d), which similarly showed potent gene silencing and in vivo antitumor efficacy, without weight loss or liver toxicity. MUCIG represents a universal method to silence multiple immune genes in vivo in a programmable manner, offering broad efficacy across multiple tumor types.

Discovery platforms for RNA therapeutics

RNA therapeutics are emerging as a unique opportunity to drug currently "undruggable" molecules and diseases. While their advantages over conventional, small molecule drugs, their therapeutic implications and the tools for their effective in vivo delivery have been extensively reviewed, little attention has been so far paid to the technological platforms exploited for the discovery of RNA therapeutics. Here, we provide an overview of the existing platforms and ex vivo assays for RNA discovery, their advantages and disadvantages, as well as their main fields of application, with specific focus on RNA therapies that have reached either phase 3 or market approval. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Transcriptome-scale RNA-targeting CRISPR screens reveal essential lncRNAs in human cells

Mammalian genomes host a diverse array of RNA that includes protein-coding and noncoding transcripts. However, the functional roles of most long noncoding RNAs (lncRNAs) remain elusive. Using RNA-targeting CRISPR-Cas13 screens, we probed how the loss of ∼6,200 lncRNAs impacts cell fitness across five human cell lines and identified 778 lncRNAs with context-specific or broad essentiality. We confirm their essentiality with individual perturbations and find that the majority of essential lncRNAs operate independently of their nearest protein-coding genes. Using transcriptome profiling in single cells, we discover that the loss of essential lncRNAs impairs cell-cycle progression and drives apoptosis. Many essential lncRNAs demonstrate dynamic expression across tissues during development. Using ∼9,000 primary tumors, we pinpoint those lncRNAs whose expression in tumors correlates with survival, yielding new biomarkers and potential therapeutic targets. This transcriptome-wide survey of functional lncRNAs advances our understanding of noncoding transcripts and demonstrates the potential of transcriptome-scale noncoding screens with Cas13.

From bench to bedside: cutting-edge applications of base editing and prime editing in precision medicine

CRISPR-based gene editing technology theoretically allows for precise manipulation of any genetic target within living cells, achieving the desired sequence modifications. This revolutionary advancement has fundamentally transformed the field of biomedicine, offering immense clinical potential for treating and correcting genetic disorders. In the treatment of most genetic diseases, precise genome editing that avoids the generation of mixed editing byproducts is considered the ideal approach. This article reviews the current progress of base editors and prime editors, elaborating on specific examples of their applications in the therapeutic field, and highlights opportunities for improvement. Furthermore, we discuss the specific performance of these technologies in terms of safety and efficacy in clinical applications, and analyze the latest advancements and potential directions that could influence the future development of genome editing technologies. Our goal is to outline the clinical relevance of this rapidly evolving scientific field and preview a roadmap for successful DNA base editing therapies for the treatment of hereditary or idiopathic diseases.

Principles of CRISPR-Cas13 mismatch intolerance enable selective silencing of point-mutated oncogenic RNA with single-base precision

Single-nucleotide variants (SNVs) are extremely prevalent in human cancers, although most of these remain clinically unactionable. The programmable RNA nuclease CRISPR-Cas13 has been deployed to specifically target oncogenic RNAs. However, silencing oncogenic SNVs with single-base precision remains extremely challenging due to the intrinsic mismatch tolerance of Cas13. Here, we show that introducing synthetic mismatches at precise positions of the spacer sequence enables de novo design of guide RNAs [CRISPR RNAs (crRNAs)] with strong preferential silencing of point-mutated transcripts. We applied these design principles to effectively silence the oncogenic KRAS G12 hotspot, NRAS G12D and BRAF V600E transcripts with minimal off-target silencing of the wild-type transcripts, underscoring the adaptability of this platform to silence various SNVs. Unexpectedly, the SNV-selective crRNAs harboring mismatched nucleotides reduce the promiscuous collateral activity of the RfxCas13d ortholog. These findings demonstrate that the CRISPR-Cas13 system can be reprogrammed to target mutant transcripts with single-base precision, showcasing the tremendous potential of this tool in personalized transcriptome editing.

Iterative crRNA design and a PAM-free strategy enabled an ultra-specific RPA-CRISPR/Cas12a detection platform

CRISPR/Cas12a is a highly promising detection tool. However, detecting single nucleotide variations (SNVs) remains challenging. Here, we elucidate Cas12a specificity through crRNA engineering and profiling of single- and double-base mismatch tolerance across three targets. Our findings indicate that Cas12a specificity depends on the number, type, location, and distance of mismatches within the R-loop. We also find that introducing a wobble base pair at position 14 of the R-loop does not affect the free energy change when the spacer length is truncated to 17 bp. Therefore, we develop a new universal specificity enhancement strategy via iterative crRNA design, involving truncated spacers and a wobble base pair at position 14 of the R-loop, which tremendously increases specificity without sacrificing sensitivity. Additionally, we construct a PAM-free one-pot detection platform for SARS-CoV-2 variants, which effectively distinguishes SNV targets across various GC contents. In summary, our work reveals new insights into the specificity mechanism of Cas12a and demonstrates significant potential for in vitro diagnostics.

Optimizing a CRISPR-Cas13d Gene Circuit for Tunable Target RNA Downregulation with Minimal Collateral RNA Cutting

The invention of RNA-guided DNA cutting systems has revolutionized biotechnology. More recently, RNA-guided RNA cutting by Cas13d entered the scene as a highly promising alternative to RNA interference to engineer cellular transcriptomes for biotechnological and therapeutic purposes. Unfortunately, "collateral damage" by indiscriminate off-target cutting tampered enthusiasm for these systems. Yet, how collateral activity, or even RNA target reduction depends on Cas13d and guide RNA abundance has remained unclear due to the lack of expression-tuning studies to address this question. Here we use precise expression-tuning gene circuits to show that both nonspecific and specific, on-target RNA reduction depend on Cas13d and guide RNA levels, and that nonspecific RNA cutting from trans cleavage might contribute to on-target RNA reduction. Using RNA-level control techniques, we develop new Multi-Level Optimized Negative-Autoregulated Cas13d and crRNA Hybrid (MONARCH) gene circuits that achieve a high dynamic range with low basal on-target RNA reduction while minimizing collateral activity in human kidney cells and green monkey cells most frequently used in human virology. MONARCH should bring RNA-guided RNA cutting systems to the forefront, as easily applicable, programmable tools for transcriptome engineering in biotechnological and medical applications.

Enhanced RNA-targeting CRISPR-Cas technology in zebrafish

CRISPR-Cas13 systems are widely used in basic and applied sciences. However, its application has recently generated controversy due to collateral activity in mammalian cells and mouse models. Moreover, its efficiency could be improved in vivo. Here, we optimized transient formulations as ribonucleoprotein complexes or mRNA-gRNA combinations to enhance the CRISPR-RfxCas13d system in zebrafish. We i) used chemically modified gRNAs to allow more penetrant loss-of-function phenotypes, ii) improved nuclear RNA-targeting, and iii) compared different computational models and determined the most accurate to predict gRNA activity in vivo. Furthermore, we demonstrated that transient CRISPR-RfxCas13d can effectively deplete endogenous mRNAs in zebrafish embryos without inducing collateral effects, except when targeting extremely abundant and ectopic RNAs. Finally, we implemented alternative RNA-targeting CRISPR-Cas systems with reduced or absent collateral activity. Altogether, these findings contribute to CRISPR-Cas technology optimization for RNA targeting in zebrafish through transient approaches and assist in the progression of in vivo applications.

Systems-Level Modeling for CRISPR-Based Metabolic Engineering

The CRISPR-Cas system has enabled the development of sophisticated, multigene metabolic engineering programs through the use of guide RNA-directed activation or repression of target genes. To optimize biosynthetic pathways in microbial systems, we need improved models to inform design and implementation of transcriptional programs. Recent progress has resulted in new modeling approaches for identifying gene targets and predicting the efficacy of guide RNA targeting. Genome-scale and flux balance models have successfully been applied to identify targets for improving biosynthetic production yields using combinatorial CRISPR-interference (CRISPRi) programs. The advent of new approaches for tunable and dynamic CRISPR activation (CRISPRa) promises to further advance these engineering capabilities. Once appropriate targets are identified, guide RNA prediction models can lead to increased efficacy in gene targeting. Developing improved models and incorporating approaches from machine learning may be able to overcome current limitations and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic engineering.

The rise and future of CRISPR-based approaches for high-throughput genomics

Clustered regularly interspaced short palindromic repeats (CRISPR) has revolutionized the field of genome editing. To circumvent the permanent modifications made by traditional CRISPR techniques and facilitate the study of both essential and nonessential genes, CRISPR interference (CRISPRi) was developed. This gene-silencing technique employs a deactivated Cas effector protein and a guide RNA to block transcription initiation or elongation. Continuous improvements and a better understanding of the mechanism of CRISPRi have expanded its scope, facilitating genome-wide high-throughput screens to investigate the genetic basis of phenotypes. Additionally, emerging CRISPR-based alternatives have further expanded the possibilities for genetic screening. This review delves into the mechanism of CRISPRi, compares it with other high-throughput gene-perturbation techniques, and highlights its superior capacities for studying complex microbial traits. We also explore the evolution of CRISPRi, emphasizing enhancements that have increased its capabilities, including multiplexing, inducibility, titratability, predictable knockdown efficacy, and adaptability to nonmodel microorganisms. Beyond CRISPRi, we discuss CRISPR activation, RNA-targeting CRISPR systems, and single-nucleotide resolution perturbation techniques for their potential in genome-wide high-throughput screens in microorganisms. Collectively, this review gives a comprehensive overview of the general workflow of a genome-wide CRISPRi screen, with an extensive discussion of strengths and weaknesses, future directions, and potential alternatives.

Evaluation of the effect of RNA secondary structure on Cas13d-mediated target RNA cleavage

The clustered regularly interspaced short palindromic repeats (CRISPR)-Cas13d system was adapted as a powerful tool for targeting viral RNA sequences, making it a promising approach for antiviral strategies. Understanding the influence of template RNA structure on Cas13d binding and cleavage efficiency is crucial for optimizing its therapeutic potential. In this study, we investigated the effect of local RNA secondary structure on Cas13d activity. To do so, we varied the stability of a hairpin structure containing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) target sequence, allowing us to determine the threshold RNA stability at which Cas13d activity is affected. Our results demonstrate that Cas13d possesses the ability to effectively bind and cleave highly stable RNA structures. Notably, we only observed a decrease in Cas13d activity in the case of exceptionally stable RNA hairpins with completely base-paired stems, which are rarely encountered in natural RNA molecules. A comparison of Cas13d and RNA interference (RNAi)-mediated cleavage of the same RNA targets demonstrated that RNAi is more sensitive for local target RNA structures than Cas13d. These results underscore the suitability of the CRISPR-Cas13d system for targeting viruses with highly structured RNA genomes.

CRISPR-Cas13: Pioneering RNA Editing for Nucleic Acid Therapeutics

The CRISPR-Cas13 system has emerged as a revolutionary tool for RNA editing, offering new opportunities for the development of nucleic acid therapeutics. Unlike DNA-targeting CRISPR-Cas9, Cas13 targets and cleaves RNA, enabling gene silencing and preventing genomic instability. Its applications include suppressing disease-causing genes, correcting splicing errors, and modulating immune responses. Despite these advances, challenges persist, such as the need to refine specificity, mitigate off-target impacts, and ensure effective delivery. This review provides an overview of the CRISPR-Cas13 mechanism, elucidating its role in RNA-targeted therapies and its transformative potential for disease treatment. Furthermore, it addresses the ongoing challenges that the scientific community is striving to overcome.

A systematic method for solving data imbalance in CRISPR off-target prediction tasks

Accurately identifying potential off-target sites in the CRISPR/Cas9 system is crucial for improving the efficiency and safety of editing. However, the imbalance of available off-target datasets has posed a major obstacle in enhancing prediction performance. Despite several prediction models have been developed to address this issue, there remains a lack of systematic research on handling data imbalance in off-target prediction. This article systematically investigates the data imbalance issue in off-target datasets and explores numerous methods to process data imbalance from a novel perspective. First, we highlight the impact of the imbalance problem on off-target prediction tasks by determining the imbalance ratios present in these datasets. Then, we provide a comprehensive review of various sampling techniques and cost-sensitive methods to mitigate class imbalance in off-target datasets. Finally, systematic experiments are conducted on several state-of-the-art prediction models to illustrate the impact of applying data imbalance solutions. The results show that class imbalance processing methods significantly improve the off-target prediction capabilities of the models across multiple testing datasets. The code and datasets used in this study are available at https://github.com/gzrgzx/CRISPR_Data_Imbalance.

Efficient, specific, and combinatorial control of endogenous exon splicing with dCasRx-RBM25

Efficient targeted control of splicing is a major goal of functional genomics and therapeutic applications. Guide (g)RNA-directed, deactivated (d)Cas CRISPR enzymes fused to splicing effectors represent a promising strategy due to the flexibility of these systems. However, efficient, specific, and generalizable activation of endogenous exons using this approach has not been previously reported. By screening over 300 dCasRx-splicing factor fusion proteins tethered to splicing reporters, we identify dCasRx-RBM25 as a potent activator of exons. Moreover, dCasRx-RBM25 efficiently activates the splicing of ∼90% of targeted endogenous alternative exons and displays high on-target specificity. Using gRNA arrays for combinatorial targeting, we demonstrate that dCasRx-RBM25 enables multiplexed activation and repression of exons. Using this feature, the targeting of neural-regulated exons in Ptpb1 and Puf60 in embryonic stem cells reveals combinatorial effects on downstream alternative splicing events controlled by these factors. Collectively, our results enable versatile, combinatorial exon-resolution functional assays and splicing-directed therapeutic applications.

Precise Gene Knock-In Tools with Minimized Risk of DSBs: A Trend for Gene Manipulation

Gene knock-in refers to the insertion of exogenous functional genes into a target genome to achieve continuous expression. Currently, most knock-in tools are based on site-directed nucleases, which can induce double-strand breaks (DSBs) at the target, following which the designed donors carrying functional genes can be inserted via the endogenous gene repair pathway. The size of donor genes is limited by the characteristics of gene repair, and the DSBs induce risks like genotoxicity. New generation tools, such as prime editing, transposase, and integrase, can insert larger gene fragments while minimizing or eliminating the risk of DSBs, opening new avenues in the development of animal models and gene therapy. However, the elimination of off-target events and the production of delivery carriers with precise requirements remain challenging, restricting the application of the current knock-in treatments to mainly in vitro settings. Here, a comprehensive review of the knock-in tools that do not/minimally rely on DSBs and use other mechanisms is provided. Moreover, the challenges and recent advances of in vivo knock-in treatments in terms of the therapeutic process is discussed. Collectively, the new generation of DSBs-minimizing and large-fragment knock-in tools has revolutionized the field of gene editing, from basic research to clinical treatment.

Tunable translation-level CRISPR interference by dCas13 and engineered gRNA in bacteria

Although CRISPR-dCas13, the RNA-guided RNA-binding protein, was recently exploited as a translation-level gene expression modulator, it has still been difficult to precisely control the level due to the lack of detailed characterization. Here, we develop a synthetic tunable translation-level CRISPR interference (Tl-CRISPRi) system based on the engineered guide RNAs that enable precise and predictable down-regulation of mRNA translation. First, we optimize the Tl-CRISPRi system for specific and multiplexed repression of genes at the translation level. We also show that the Tl-CRISPRi system is more suitable for independently regulating each gene in a polycistronic operon than the transcription-level CRISPRi (Tx-CRISPRi) system. We further engineer the handle structure of guide RNA for tunable and predictable repression of various genes in Escherichia coli and Vibrio natriegens. This tunable Tl-CRISPRi system is applied to increase the production of 3-hydroxypropionic acid (3-HP) by 14.2-fold via redirecting the metabolic flux, indicating the usefulness of this system for the flux optimization in the microbial cell factories based on the RNA-targeting machinery.

Big data and deep learning for RNA biology

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.

Repair of CRISPR-guided RNA breaks enables site-specific RNA excision in human cells

Genome editing with CRISPR RNA-guided endonucleases generates DNA breaks that are resolved by cellular DNA repair machinery. However, analogous methods to manipulate RNA remain unavailable. We show that site-specific RNA breaks generated with type-III CRISPR complexes are repaired in human cells and that this repair can be used for programmable deletions in human transcripts to restore gene function. Collectively, this work establishes a technology for precise RNA manipulation with potential therapeutic applications.

Low copy CRISPR-Cas13d mitigates collateral RNA cleavage

While CRISPR-Cas13 systems excel in accurately targeting RNA, the potential for collateral RNA degradation poses a concern for therapeutic applications and limits broader adoption for transcriptome perturbations. We evaluate the extent to which collateral RNA cleavage occurs when Rfx Cas13d is delivered via plasmid transfection or lentiviral transduction and find that collateral activity only occurs with high levels of Rfx Cas13d expression. Using transcriptome-scale and combinatorial CRISPR pooled screens in cell lines with low-copy Rfx Cas13d, we find high on-target knockdown, without extensive collateral activity regardless of the expression level of the target gene. In contrast, transfection of Rfx Cas13d, which yields higher nuclease expression, results in collateral RNA degradation. Further, our analysis of a high-fidelity Cas13 variant uncovers a marked decrease in on-target efficiency, suggesting that its reduced collateral activity may be due to an overall diminished nuclease capability.

Repurposing CRISPR-Cas13 systems for robust mRNA trans-splicing

Type VI CRISPR enzymes have been developed as programmable RNA-guided Cas proteins for eukaryotic RNA editing. Notably, Cas13 has been utilized for site-targeted single base edits, demethylation, RNA cleavage or knockdown and alternative splicing. However, the ability to edit large stretches of mRNA transcripts remains a significant challenge. Here, we demonstrate that CRISPR-Cas13 systems can be repurposed to assist trans-splicing of exogenous RNA fragments into an endogenous pre-mRNA transcript, a method termed CRISPR Assisted mRNA Fragment Trans-splicing (CRAFT). Using split reporter-based assays, we evaluate orthogonal Cas13 systems, optimize guide RNA length and screen for optimal trans-splicing site(s) across a range of intronic targets. We achieve markedly improved editing of large 5' and 3' segments in different endogenous mRNAs across various mammalian cell types compared to other spliceosome-mediated trans-splicing methods. CRAFT can serve as a versatile platform for attachment of protein tags, studying the impact of multiple mutations/single nucleotide polymorphisms, modification of untranslated regions (UTRs) or replacing large segments of mRNA transcripts.

A versatile CRISPR-Cas13d platform for multiplexed transcriptomic regulation and metabolic engineering in primary human T cells

CRISPR technologies have begun to revolutionize T cell therapies; however, conventional CRISPR-Cas9 genome-editing tools are limited in their safety, efficacy, and scope. To address these challenges, we developed multiplexed effector guide arrays (MEGA), a platform for programmable and scalable regulation of the T cell transcriptome using the RNA-guided, RNA-targeting activity of CRISPR-Cas13d. MEGA enables quantitative, reversible, and massively multiplexed gene knockdown in primary human T cells without targeting or cutting genomic DNA. Applying MEGA to a model of CAR T cell exhaustion, we robustly suppressed inhibitory receptor upregulation and uncovered paired regulators of T cell function through combinatorial CRISPR screening. We additionally implemented druggable regulation of MEGA to control CAR activation in a receptor-independent manner. Lastly, MEGA enabled multiplexed disruption of immunoregulatory metabolic pathways to enhance CAR T cell fitness and anti-tumor activity in vitro and in vivo. MEGA offers a versatile synthetic toolkit for applications in cancer immunotherapy and beyond.

Past, present, and future of CRISPR genome editing technologies

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.

Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis

Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping.

Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head

Artificial intelligence (AI) is progressively spreading through the world of health, particularly in the field of oncology. AI offers new, exciting perspectives in drug development as toxicity and efficacy can be predicted from computer-designed active molecular structures. AI-based in silico clinical trials are still at their inception in oncology but their wider use is eagerly awaited as they should markedly reduce durations and costs. Health authorities cannot neglect this new paradigm in drug development and should take the requisite measures to include AI as a new pillar in conducting clinical research in oncology.

New design strategies for ultra-specific CRISPR-Cas13a-based RNA detection with single-nucleotide mismatch sensitivity

An increasingly pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and Cas13a variants to use in future easier-to-implement Cas13-based RNA detection applications.

Massively parallel profiling of RNA-targeting CRISPR-Cas13d

CRISPR-Cas13d cleaves RNA and is used in vivo and for diagnostics. However, a systematic understanding of its RNA binding and cleavage specificity is lacking. Here, we describe an RNA Chip-Hybridized Association-Mapping Platform (RNA-CHAMP) for measuring the binding affinity for > 10,000 RNAs containing structural perturbations and other alterations relative to the CRISPR RNA (crRNA). Deep profiling of Cas13d reveals that it does not require a protospacer flanking sequence but is exquisitely sensitive to secondary structure within the target RNA. Cas13d binding is penalized by mismatches in the distal crRNA-target RNA region, while alterations in the proximal region inhibit nuclease activity. A biophysical model built from these data reveals that target recognition initiates in the distal end of the target RNA. Using this model, we design crRNAs that can differentiate between SARS-CoV-2 variants by modulating nuclease activation. This work describes the key determinants of RNA targeting by a type VI CRISPR enzyme.

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

Programmable RNA targeting with CRISPR-Cas13

The RNA-targeting CRISPR-Cas13 system has enabled precise engineering of endogenous RNAs, significantly advancing our understanding of RNA regulation and the development of RNA-based diagnostic and therapeutic applications. This review aims to provide a summary of Cas13-based RNA targeting tools and applications, discuss limitations and challenges of existing tools and suggest potential directions for further development of the RNA targeting system.

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting

Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons. Our Cas13d guide efficiency model was successfully generalized to DjCas13d, illustrating the power of combining machine learning with ortholog discovery to advance RNA targeting in human cells.

Decoding Heterogenous Single-cell Perturbation Responses

Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets.

RNA structure modulates Cas13 activity and enables mismatch detection

The RNA-targeting CRISPR nuclease Cas13 has emerged as a powerful tool for applications ranging from nucleic acid detection to transcriptome engineering and RNA imaging1-6. Cas13 is activated by the hybridization of a CRISPR RNA (crRNA) to a complementary single-stranded RNA (ssRNA) protospacer in a target RNA1,7. Though Cas13 is not activated by double-stranded RNA (dsRNA) in vitro, it paradoxically demonstrates robust RNA targeting in environments where the vast majority of RNAs are highly structured2,8. Understanding Cas13's mechanism of binding and activation will be key to improving its ability to detect and perturb RNA; however, the mechanism by which Cas13 binds structured RNAs remains unknown9. Here, we systematically probe the mechanism of LwaCas13a activation in response to RNA structure perturbations using a massively multiplexed screen. We find that there are two distinct sequence-independent modes by which secondary structure affects Cas13 activity: structure in the protospacer region competes with the crRNA and can be disrupted via a strand-displacement mechanism, while structure in the region 3' to the protospacer has an allosteric inhibitory effect. We leverage the kinetic nature of the strand displacement process to improve Cas13-based RNA detection, enhancing mismatch discrimination by up to 50-fold and enabling sequence-agnostic mutation identification at low (<1%) allele frequencies. Our work sets a new standard for CRISPR-based nucleic acid detection and will enable intelligent and secondary-structure-guided target selection while also expanding the range of RNAs available for targeting with Cas13.

Predicting RNA structures and functions by artificial intelligence

RNA functions by interacting with its intended targets structurally. However, due to the dynamic nature of RNA molecules, RNA structures are difficult to determine experimentally or predict computationally. Artificial intelligence (AI) has revolutionized many biomedical fields and has been progressively utilized to deduce RNA structures, target binding, and associated functionality. Integrating structural and target binding information could also help improve the robustness of AI-based RNA function prediction and RNA design. Given the rapid development of deep learning (DL) algorithms, AI will provide an unprecedented opportunity to elucidate the sequence-structure-function relation of RNAs.

Cas13d-mediated isoform-specific RNA knockdown with a unified computational and experimental toolbox

Alternative splicing is an essential mechanism for diversifying proteins, in which mature RNA isoforms produce proteins with potentially distinct functions. Two major challenges in characterizing the cellular function of isoforms are the lack of experimental methods to specifically and efficiently modulate isoform expression and computational tools for complex experimental design. To address these gaps, we developed and methodically tested a strategy which pairs the RNA-targeting CRISPR/Cas13d system with guide RNAs that span exon-exon junctions in the mature RNA. We performed a high-throughput essentiality screen, quantitative RT-PCR assays, and PacBio long read sequencing to affirm our ability to specifically target and robustly knockdown individual RNA isoforms. In parallel, we provide computational tools for experimental design and screen analysis. Considering all possible splice junctions annotated in GENCODE for multi-isoform genes and our gRNA efficacy predictions, we estimate that our junction-centric strategy can uniquely target up to 89% of human RNA isoforms, including 50,066 protein-coding and 11,415 lncRNA isoforms. Importantly, this specificity spans all splicing and transcriptional events, including exon skipping and inclusion, alternative 5' and 3' splice sites, and alternative starts and ends.

Repair of CRISPR-guided RNA breaks enables site-specific RNA editing in human cells

Genome editing with CRISPR RNA-guided endonucleases generates DNA breaks that are resolved by cellular DNA repair machinery. However, analogous methods to manipulate RNA remain unavailable. Here, we show that site-specific RNA breaks generated with RNA-targeting CRISPR complexes are repaired in human cells, and this repair can be used for programmable deletions in human transcripts that restore gene function. Collectively, this work establishes a technology for precise RNA manipulation with potential therapeutic applications.

New design strategies for ultra-specific CRISPR-Cas13a-based RNA-diagnostic tools with single-nucleotide mismatch sensitivity

The pressing need for clinical diagnostics has required the development of novel nucleic acid-based detection technologies that are sensitive, fast, and inexpensive, and that can be deployed at point-of-care. Recently, the RNA-guided ribonuclease CRISPR-Cas13 has been successfully harnessed for such purposes. However, developing assays for detection of genetic variability, for example single-nucleotide polymorphisms, is still challenging and previously described design strategies are not always generalizable. Here, we expanded our characterization of LbuCas13a RNA-detection specificity by performing a combination of experimental RNA mismatch tolerance profiling, molecular dynamics simulations, protein, and crRNA engineering. We found certain positions in the crRNA-target-RNA duplex that are particularly sensitive to mismatches and establish the effect of RNA concentration in mismatch tolerance. Additionally, we determined that shortening the crRNA spacer or modifying the direct repeat of the crRNA leads to stricter specificities. Furthermore, we harnessed our understanding of LbuCas13a allosteric activation pathways through molecular dynamics and structure-guided engineering to develop novel Cas13a variants that display increased sensitivities to single-nucleotide mismatches. We deployed these Cas13a variants and crRNA design strategies to achieve superior discrimination of SARS-CoV-2 strains compared to wild-type LbuCas13a. Together, our work provides new design criteria and new Cas13a variants for easier-to-implement Cas13-based diagnostics.

KEY POINTS

Certain positions in the Cas13a crRNA-target-RNA duplex are particularly sensitive to mismatches.Understanding Cas13a's allosteric activation pathway allowed us to develop novel high-fidelity Cas13a variants.These Cas13a variants and crRNA design strategies achieve superior discrimination of SARS-CoV-2 strains.

GRAPHICAL ABSTRACT