The introduction of genome engineering technology has transformed biomedical research, making it possible to make precise changes to genetic information. However, creating an efficient gene-editing system requires a deep understanding of CRISPR technology, and the complex experimental systems under investigation. While Large Language Models (LLMs) have shown promise in various tasks, they often lack specific knowledge and struggle to accurately solve biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent augmented with domain knowledge and external tools to automate and enhance the design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages the reasoning ability of LLMs to facilitate the process of selecting CRISPR systems, designing guide RNAs, recommending cellular delivery methods, drafting protocols, and designing validation experiments to confirm editing outcomes. We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent's effectiveness in a real-world use case. Furthermore, we explore the ethical and regulatory considerations associated with automate
Gene-editing by CRISPR/Cas systems has revolutionized plant biology by serving as a functional genomics tool. It has tremendously advanced plant breeding and crop improvement by accelerating the development of improved cultivars, creating genetic variability, and aiding in domestication of wild and orphan crops. Gene-editing is a rapidly evolving field. Several advancements include development of different Cas effectors with increased target range, efficacy, and enhanced capacity for precise DNA modifications with base editing and prime editing. The existing toolbox of various CRISPR reagents facilitate gene knockouts, targeted gene insertions, precise base substitutions, and multiplexing. However, the major challenge in plant genome-editing remains the efficient delivery of these reagents into plant cells. Plants have larger and more complex genome structures compared to other living systems due to the common occurrence of polyploidy and other genome re-arrangements. Further, rigid cell walls surrounding plant cells deter the entry of any foreign biomolecules. Unfortunately, genetic transformation to deliver gene-editing reagents has been established only in a limited number of plant species. Recently, there has been significant progress in CRISPR reagents delivery in plants. This review focuses on exploring these delivery mechanisms categorized into Agrobacterium -mediated delivery and breakthroughs, particle bombardment-based delivery of biomolecules and recent improvements, and protoplasts, a versatile system for gene-editing and regeneration in plants. The ultimate goal in plant gene-editing is to establish highly efficient and genotype-independent reagent delivery mechanisms for editing multiple targets simultaneously and achieve DNA-free gene-edited plants at scale.
The CRISPR/Cas9 system has been demonstrated to efficiently induce targeted gene editing in a variety of organisms including plants. Recent work showed that CRISPR/Cas9-induced gene mutations in Arabidopsis were mostly somatic mutations in the early generation, although some mutations could be stably inherited in later generations. However, it remains unclear whether this system will work similarly in crops such as rice. In this study, we tested in two rice subspecies 11 target genes for their amenability to CRISPR/Cas9-induced editing and determined the patterns, specificity and heritability of the gene modifications. Analysis of the genotypes and frequency of edited genes in the first generation of transformed plants (T0) showed that the CRISPR/Cas9 system was highly efficient in rice, with target genes edited in nearly half of the transformed embryogenic cells before their first cell division. Homozygotes of edited target genes were readily found in T0 plants. The gene mutations were passed to the next generation (T1) following classic Mendelian law, without any detectable new mutation or reversion. Even with extensive searches including whole genome resequencing, we could not find any evidence of large-scale off-targeting in rice for any of the many targets tested in this study. By specifically sequencing the putative off-target sites of a large number of T0 plants, low-frequency mutations were found in only one off-target site where the sequence had 1-bp difference from the intended target. Overall, the data in this study point to the CRISPR/Cas9 system being a powerful tool in crop genome engineering.
Clustered regularly interspaced short palindromic repeats (CRISPR) system provides adaptive immunity against plasmids and phages in prokaryotes. This system inspires the development of a powerful genome engineering tool, the CRISPR/CRISPR-associated nuclease 9 (CRISPR/Cas9) genome editing system. Due to its high efficiency and precision, the CRISPR/Cas9 technique has been employed to explore the functions of cancer-related genes, establish tumor-bearing animal models and probe drug targets, vastly increasing our understanding of cancer genomics. Here, we review current status of CRISPR/Cas9 gene editing technology in oncological research. We first explain the basic principles of CRISPR/Cas9 gene editing and introduce several new CRISPR-based gene editing modes. We next detail the rapid progress of CRISPR screening in revealing tumorigenesis, metastasis, and drug resistance mechanisms. In addition, we introduce CRISPR/Cas9 system delivery vectors and finally demonstrate the potential of CRISPR/Cas9 engineering to enhance the effect of adoptive T cell therapy (ACT) and reduce adverse reactions.
Abstract Background Zebrafish have practical features that make them a useful model for higher-throughput tests of gene function using CRISPR/Cas9 editing to create ‘knockout’ models. In particular, the use of G 0 mosaic mutants has potential to increase throughput of functional studies significantly but may suffer from transient effects of introducing Cas9 via microinjection. Further, a large number of computational and empirical tools exist to design CRISPR assays but often produce varied predictions across methods leaving uncertainty in choosing an optimal approach for zebrafish studies. Methods To systematically assess accuracy of tool predictions of on- and off-target gene editing, we subjected zebrafish embryos to CRISPR/Cas9 with 50 different guide RNAs (gRNAs) targeting 14 genes. We also investigate potential confounders of G 0 -based CRISPR screens by assaying control embryos for spurious mutations and altered gene expression. Results We compared our experimental in vivo editing efficiencies in mosaic G 0 embryos with those predicted by eight commonly used gRNA design tools and found large discrepancies between methods. Assessing off-target mutations (predicted in silico and in vitro) found that the majority of tested loci had low in vivo frequencies (< 1%). To characterize if commonly used ‘mock’ CRISPR controls (larvae injected with Cas9 enzyme or mRNA with no gRNA) exhibited spurious molecular features that might exacerbate studies of G 0 mosaic CRISPR knockout fish, we generated an RNA-seq dataset of various control larvae at 5 days post fertilization. While we found no evidence of spontaneous somatic mutations of injected larvae, we did identify several hundred differentially-expressed genes with high variability between injection types. Network analyses of shared differentially-expressed genes in the ‘mock’ injected larvae implicated a number of key regulators of common metabolic pathways, and gene-ontology analysis revealed connections with response to wounding and cytoskeleton organization, highlighting a potentially lasting effect from the microinjection process that requires further investigation. Conclusion Overall, our results provide a valuable resource for the zebrafish community for the design and execution of CRISPR/Cas9 experiments.
Adeno-associated viral (AAV) vectors are a leading candidate for the delivery of CRISPR-Cas9 for therapeutic genome editing in vivo. However, AAV-based delivery involves persistent expression of the Cas9 nuclease, a bacterial protein. Recent studies indicate a high prevalence of neutralizing antibodies and T cells specific to the commonly used Cas9 orthologs from Streptococcus pyogenes (SpCas9) and Staphylococcus aureus (SaCas9) in humans. We tested in a mouse model whether pre-existing immunity to SaCas9 would pose a barrier to liver genome editing with AAV packaging CRISPR-Cas9. Although efficient genome editing occurred in mouse liver with pre-existing SaCas9 immunity, this was accompanied by an increased proportion of CD8+ T cells in the liver. This cytotoxic T cell response was characterized by hepatocyte apoptosis, loss of recombinant AAV genomes, and complete elimination of genome-edited cells, and was followed by compensatory liver regeneration. Our results raise important efficacy and safety concerns for CRISPR-Cas9-based in vivo genome editing in the liver. Adeno-associated viral (AAV) vectors are a leading candidate for the delivery of CRISPR-Cas9 for therapeutic genome editing in vivo. However, AAV-based delivery involves persistent expression of the Cas9 nuclease, a bacterial protein. Recent studies indicate a high prevalence of neutralizing antibodies and T cells specific to the commonly used Cas9 orthologs from Streptococcus pyogenes (SpCas9) and Staphylococcus aureus (SaCas9) in humans. We tested in a mouse model whether pre-existing immunity to SaCas9 would pose a barrier to liver genome editing with AAV packaging CRISPR-Cas9. Although efficient genome editing occurred in mouse liver with pre-existing SaCas9 immunity, this was accompanied by an increased proportion of CD8+ T cells in the liver. This cytotoxic T cell response was characterized by hepatocyte apoptosis, loss of recombinant AAV genomes, and complete elimination of genome-edited cells, and was followed by compensatory liver regeneration. Our results raise important efficacy and safety concerns for CRISPR-Cas9-based in vivo genome editing in the liver.
CRISPR-Cas13 is a system that utilizes single stranded RNAs for RNA editing. Prediction of on-target and off-target effects for the CRISPR-Cas13d dependency enables us to design specific single guide RNAs (sgRNAs) that help locate the desired RNA target positions. In this study, we compared the performance of multiple machine learning algorithms in predicting these effects using a reported dataset. Our results show that Catboost is the most accurate model with high sensitivity. This finding represents a significant advancement in our understanding of how to chose modeling methods to deal with RNA sequence feaatures effictivelys. Furthermore, our approach can potentially be applied to other CRISPR systems and genetic engineering techniques. Overall, this work has important implications for developing safer and more effective gene therapies and biotechnological applications.
Protein tagging with CRISPR-Cas9 enables the investigation of protein function in its native environment but is limited by low homology-directed repair (HDR) efficiency causing low knock-in rates. We present a detailed pipeline using HDR donor plasmids containing antibiotic resistance cassettes for rapid selection of gene-edited cells. Our protocol streamlines N- or C-terminal tagging in human cells, enabling HDR donor plasmid preparation in a single cloning step.
The CRISPR-Cas9-based multiplexed gene editing (MGE) provides a powerful method to modify multiple genomic regions simultaneously controlling different agronomic traits in crops. We applied the MGE construct built by combining the tandemly arrayed tRNA–gRNA units to generate heritable mutations in the TaGW2, TaLpx-1, and TaMLO genes of hexaploid wheat. The knockout mutations generated by this construct in all three homoeologous copies of one of the target genes, TaGW2, resulted in a substantial increase in seed size and thousand grain weight. We showed that the non-modified gRNA targets in the early generation plants can be edited by CRISPR-Cas9 in the following generations. Our results demonstrate that transgenerational gene editing activity can serve as the source of novel variation in the progeny of CRISPR-Cas9-expressing plants and suggest that the Cas9-inducible trait transfer for crop improvement can be achieved by crossing the plants expressing the gene editing constructs with the lines of interest.
Gene editing stands for the methods to precisely make changes to a specific nucleic acid sequence. With the recent development of the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system, gene editing has become efficient, convenient and programmable, leading to promising translational studies and clinical trials for both genetic and non-genetic diseases. A major concern in the applications of the CRISPR/Cas9 system is about its off-target effects, namely the deposition of unexpected, unwanted, or even adverse alterations to the genome. To date, many methods have been developed to nominate or detect the off-target sites of CRISPR/Cas9, which laid the basis for the successful upgrades of CRISPR/Cas9 derivatives with enhanced precision. In this review, we summarize these technological advancements and discuss about the current challenges in the management of off-target effects for future gene therapy.
Gene and RNA editing methods, technologies, and applications are emerging as innovative forms of therapy and medicine, offering more efficient implementation compared to traditional pharmaceutical treatments. Current trends emphasize the urgent need for advanced methods and technologies to detect public health threats, including diseases and viral agents. Gene and RNA editing techniques enhance the ability to identify, modify, and ameliorate the effects of genetic diseases, disorders, and disabilities. Viral detection and identification methods present numerous opportunities for enabling technologies, such as CRISPR, applicable to both RNA and gene editing through the use of specific Cas proteins. This article explores the distinctions and benefits of RNA and gene editing processes, emphasizing their contributions to the future of medical treatment. CRISPR technology, particularly its adaptation via the Cas13 protein for RNA editing, is a significant advancement in gene editing. The article will delve into RNA and gene editing methodologies, focusing on techniques that alter and modify genetic coding. A-to-I and C-to-U editing are currently the most predominant methods of RNA modif
Mathematical formulas serve as a language through which humans communicate with nature. Discovering mathematical laws from scientific data to describe natural phenomena has been a long-standing pursuit of humanity for centuries. In the field of artificial intelligence, this challenge is known as the symbolic regression problem. Among existing symbolic regression approaches, Genetic Programming (GP) based on evolutionary algorithms remains one of the most classical and widely adopted methods. GP simulates the evolutionary process across generations through genetic mutation and crossover. However, mutations and crossovers in GP are entirely random. While this randomness effectively mimics natural evolution, it inevitably produces both beneficial and detrimental variations. If there existed a metaphorical `God` capable of foreseeing which genetic mutations or crossovers would yield superior outcomes and performing targeted gene editing accordingly, the efficiency of evolution could be substantially improved. Motivated by this idea, we propose in this paper a symbolic regression approach based on gene editing, termed GESR. In GESR, we trained two "hands of God" (two BERT models). Among
Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas systems, however, non-self-targeting genome editing based on a single Cas3 of type I CRISPR-Cas systems has not been reported. Here, we present the subtype I-B-Svi CRISPR-Cas system (with three confirmed CRISPRs and a cas gene cluster) and genome editing based on this system found in Streptomyces virginiae IBL14. Importantly, like the animal-derived bacterial protein SpCas9 (1368 amino-acids), the single, compact, non-animal-derived bacterial protein SviCas3 (771 amino-acids) can also direct template-based microbial genome editing through the target cell's own homology-directed repair system, which breaks the view that the genome editing based on type I CRISPR-Cas systems requires a full Cascade. Notably, no off-target changes or indel-formation were detected in the analysis of potential off-target sites. This discovery broadens our understanding of the diversity of type I CRISPR-Cas systems and will facilitate new developments in genome editing tools.
Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as <person, action, object> triplets. Existing approaches split into two disjoint families: HOI generation synthesises scenes from structured triplets and layout, but fails to integrate mixed conditions like HOI and object-only entities; and HOI editing modifies interactions via text, yet struggles to decouple pose from physical contact and scale to multiple interactions. We introduce OneHOI, a unified diffusion transformer framework that consolidates HOI generation and editing into a single conditional denoising process driven by shared structured interaction representations. At its core, the Relational Diffusion Transformer (R-DiT) models verb-mediated relations through role- and instance-aware HOI tokens, layout-based spatial Action Grounding, a Structured HOI Attention to enforce interaction topology, and HOI RoPE to disentangle multi-HOI scenes. Trained jointly with modality dropout on our HOI-Edit-44K, along with HOI and object-centric datasets, OneHOI supports layout-guided, layout-free, arbitrary-mask, and mixed-condition control, achieving state-of-the-art
With the recent fast development of generative models, instruction-based image editing has shown great potential in generating high-quality images. However, the quality of editing highly depends on carefully designed instructions, placing the burden of task decomposition and sequencing entirely on the user. To achieve autonomous image editing, we present PhotoAgent, a system that advances image editing through explicit aesthetic planning. Specifically, PhotoAgent formulates autonomous image editing as a long-horizon decision-making problem. It reasons over user aesthetic intent, plans multi-step editing actions via tree search, and iteratively refines results through closed-loop execution with memory and visual feedback, without requiring step-by-step user prompts. To support reliable evaluation in real-world scenarios, we introduce UGC-Edit, an aesthetic evaluation benchmark consisting of 7,000 photos and a learned aesthetic reward model. We also construct a test set containing 1,017 photos to systematically assess autonomous photo editing performance. Extensive experiments demonstrate that PhotoAgent consistently improves both instruction adherence and visual quality compared wit
CRISPR-Cas systems are an adaptive immunity that protects prokaryotes against foreign genetic elements. Genetic templates acquired during past infection events enable DNA-interacting enzymes to recognize foreign DNA for destruction. Due to the programmability and specificity of these genetic templates, CRISPR-Cas systems are potential alternative antibiotics that can be engineered to self-target antimicrobial resistance genes on the chromosome or plasmid. However, several fundamental questions remain to repurpose these tools against drug-resistant bacteria. For endogenous CRISPR-Cas self-targeting, antimicrobial resistance genes and functional CRISPR-Cas systems have to co-occur in the target cell. Furthermore, these tools have to outplay DNA repair pathways that respond to the nuclease activities of Cas proteins, even for exogenous CRISPR-Cas delivery. Here, we conduct a comprehensive survey of CRISPR-Cas genomes. First, we address the co-occurrence of CRISPR-Cas systems and antimicrobial resistance genes in the CRISPR-Cas genomes. We show that the average number of these genes varies greatly by the CRISPR-Cas type, and some CRISPR-Cas types (IE and IIIA) have over 20 genes per ge
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field of Artificial Intelligence Generative Content (AIGC). Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models, which generate images according to text prompts. These models demonstrate remarkable generative capabilities and have become widely used tools for image editing. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs. In this survey, we provide a comprehensive review of multimodal-guided image editing techniques that leverage T2I diffusion models. First, we define the scope of image editing from a holistic perspective and detail various control signals and editing scenarios. We then propose a unified framework to formalize the editing process, categorizing it into two primary algorithm families. This framework offers a design space for users to achieve specific goals. Subsequently, we present an in-depth analysis of each component withi
RNA-guided gene editing based on the CRISPR-Cas system is currently the most effective genome editing technique. Here, we report that the SviCas3 from the subtype I-B-Svi Cas system in Streptomyces virginiae IBL14 is an RNA-guided and DNA-guided DNA endonuclease suitable for the HDR-directed gene and/or base editing of eukaryotic cell genomes. The genome editing efficiency of SviCas3 guided by DNA is no less than that of SviCas3 guided by RNA. In particular, t-DNA, as a template and a guide, does not require a proto-spacer-adjacent motif, demonstrating that CRISPR, as the basis for crRNA design, is not required for the SviCas3-mediated gene and base editing. This discovery will broaden our understanding of enzyme diversity in CRISPR-Cas systems, will provide important tools for the creation and modification of living things and the treatment of human genetic diseases, and will usher in a new era of DNA-guided gene editing and base editing.
Recent advancements in diffusion and flow models have greatly improved text-based image editing, yet methods that edit images independently often produce geometrically and photometrically inconsistent results across different views of the same scene. Such inconsistencies are particularly problematic for editing of 3D representations such as NeRFs or Gaussian splat models. We propose a training-free guidance framework that enforces multi-view consistency during the image editing process. The key idea is that corresponding points should look similar after editing. To achieve this, we introduce a consistency loss that guides the denoising process toward coherent edits. The framework is flexible and can be combined with widely varying image editing methods, supporting both dense and sparse multi-view editing setups. Experimental results show that our approach significantly improves 3D consistency compared to existing multi-view editing methods. We also show that this increased consistency enables high-quality Gaussian splat editing with sharp details and strong fidelity to user-specified text prompts. Please refer to our project page for video results: https://3d-consistent-editing.git
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a gene editing technology that has revolutionized the fields of biology and medicine. However, one of the challenges of using CRISPR is predicting the on-target efficacy and off-target sensitivity of single-guide RNAs (sgRNAs). This is because most existing methods are trained on separate datasets with different genes and cells, which limits their generalizability. In this paper, we propose a novel ensemble learning method for sgRNA design that is accurate and generalizable. Our method combines the predictions of multiple machine learning models to produce a single, more robust prediction. This approach allows us to learn from a wider range of data, which improves the generalizability of our model. We evaluated our method on a benchmark dataset of sgRNA designs and found that it outperformed existing methods in terms of both accuracy and generalizability. Our results suggest that our method can be used to design sgRNAs with high sensitivity and specificity, even for new genes or cells. This could have important implications for the clinical use of CRISPR, as it would allow researchers to design more effective and