Base editing and gene modification techniques have seen significant advancements, particularly in the context of human hematopoietic cells and mitochondrial DNA. A study by Martin-Rufino et al. introduced massively parallel base-editing screens in human hematopoietic stem and progenitor cells, enabling the systematic evaluation of genetic variants' impacts on human physiology and disease (ref: Martin-Rufino doi.org/10.1016/j.cell.2023.03.035/). Complementing this, Davis et al. reported on the efficient delivery of prime editing in vivo using dual AAVs, achieving unprecedented levels of prime editing in mouse brain, liver, and heart, which could facilitate the treatment of genetic disorders (ref: Davis doi.org/10.1038/s41587-023-01758-z/). Furthermore, Yi et al. developed mitochondrial DNA base editors (mitoBEs) that achieved up to 77% efficiency in editing mitochondrial DNA, addressing the challenge of delivering CRISPR components into mitochondria (ref: Yi doi.org/10.1038/s41587-023-01791-y/). These studies collectively highlight the innovative methodologies being developed to enhance the precision and efficiency of gene editing techniques across various biological contexts. In addition to these advancements, Kim et al. introduced deep learning models to predict editing efficiencies of diverse base editors, which could streamline the selection process for optimal base editors and sgRNA pairs (ref: Kim doi.org/10.1038/s41587-023-01792-x/). Seo et al. further expanded on this by evaluating the activities of 17 small Cas9s, developing computational models to predict their effectiveness at specific target sequences (ref: Seo doi.org/10.1038/s41592-023-01875-2/). These predictive models are crucial for enhancing the efficiency of gene editing by reducing the need for extensive experimental validation. Overall, the integration of computational tools with experimental techniques is paving the way for more effective and targeted gene editing strategies.

The application of CRISPR technology in disease models has opened new avenues for understanding and potentially treating genetic disorders. Lee et al. demonstrated a novel approach to reactivate the FMR1 gene in fragile X syndrome by inducing R-loop formation, leading to significant repeat contraction and gene reactivation in cellular models (ref: Lee doi.org/10.1016/j.cell.2023.04.035/). This study highlights the potential of CRISPR to harness endogenous repair mechanisms for therapeutic purposes. Similarly, Ely et al. developed a prime editor mouse model that allows for the modeling of a broad spectrum of somatic mutations in vivo, addressing the limitations of traditional genetically engineered models that often fail to capture the complexity of human cancers (ref: Ely doi.org/10.1038/s41587-023-01783-y/). Moreover, Ruland et al. utilized CRISPR to engineer human hepatocyte organoids, successfully recreating fibrolamellar carcinoma mutations, which could provide insights into the tumorigenesis of this rare liver cancer (ref: Ruland doi.org/10.1038/s41467-023-37951-6/). These studies collectively underscore the versatility of CRISPR technology in modeling diseases, enabling researchers to dissect the underlying genetic mechanisms and explore potential therapeutic interventions. The integration of CRISPR with organoid technology further enhances the relevance of these models to human disease, paving the way for personalized medicine approaches.

Gene delivery methods are critical for the successful application of CRISPR technologies in therapeutic contexts. Gencay et al. engineered phages with CRISPR-Cas systems to selectively target and reduce E. coli burdens in mice, demonstrating a promising alternative to traditional antibiotic treatments that often disrupt the microbiome (ref: Gencay doi.org/10.1038/s41587-023-01759-y/). This study emphasizes the potential of phage therapy as a targeted approach to combat bacterial infections while minimizing collateral damage to beneficial microbiota. In parallel, Foss et al. explored peptide-mediated delivery of CRISPR enzymes, significantly enhancing the editing efficiency in primary human lymphocytes compared to conventional electroporation methods (ref: Foss doi.org/10.1038/s41551-023-01032-2/). This innovative delivery strategy could facilitate the development of more effective cell-based therapies. Additionally, the study by Kim et al. on deep learning models for predicting editing efficiencies also plays a crucial role in optimizing gene delivery strategies by enabling the selection of the most effective base editors and sgRNAs (ref: Kim doi.org/10.1038/s41587-023-01792-x/). The combination of advanced delivery methods with predictive modeling represents a significant step forward in the field of gene therapy, potentially leading to more effective treatments for a variety of genetic disorders.

The efficiency of genome editing is a critical factor influencing the success of CRISPR applications, and recent studies have focused on enhancing this efficiency through predictive modeling. Kim et al. developed deep learning models to predict the editing efficiencies of various base editors, which can significantly streamline the selection process for optimal editing tools (ref: Kim doi.org/10.1038/s41587-023-01792-x/). This approach allows researchers to bypass extensive experimental validation, thereby accelerating the development of gene editing applications. Furthermore, Seo et al. evaluated the activities of 17 small Cas9s, creating computational models to predict their effectiveness at specific target sequences, which is essential for optimizing CRISPR-based interventions (ref: Seo doi.org/10.1038/s41592-023-01875-2/). In addition, Erokhin et al. investigated the role of Crol in Polycomb group protein recruitment in Drosophila, shedding light on the epigenetic mechanisms that may influence genome editing outcomes (ref: Erokhin doi.org/10.1093/nar/). The integration of these predictive models with experimental findings is crucial for enhancing the precision and efficiency of genome editing technologies. Overall, the advancements in predictive modeling are set to revolutionize the field of genome editing by providing researchers with powerful tools to optimize their approaches.

Mitochondrial gene editing is a burgeoning area of research, particularly in addressing mitochondrial diseases caused by point mutations. Yi et al. introduced mitochondrial DNA base editors (mitoBEs), achieving up to 77% efficiency in A-to-G and C-to-T base editing, which represents a significant advancement in the ability to correct mitochondrial mutations (ref: Yi doi.org/10.1038/s41587-023-01791-y/). This study highlights the innovative combination of TALE-fused nickases and deaminases for precise editing within the challenging context of mitochondrial DNA. Additionally, a study on CRISPR-free, strand-selective mitochondrial DNA base editing using a nickase further emphasizes the potential for developing non-CRISPR approaches to mitochondrial gene editing, which could mitigate some of the challenges associated with CRISPR delivery (ref: Unknown doi.org/10.1038/s41587-023-01820-w/). The advancements in mitochondrial gene editing are crucial for developing therapies for a range of mitochondrial diseases, which often lack effective treatments. The ability to achieve high specificity and efficiency in editing mitochondrial DNA could pave the way for novel therapeutic strategies aimed at correcting genetic defects at the source. These studies collectively underscore the importance of continued innovation in mitochondrial gene editing technologies.

The ethical and societal implications of gene editing technologies are becoming increasingly significant as these tools advance. Honigberg et al. conducted a large-scale study on the polygenic prediction of preeclampsia and gestational hypertension, highlighting the potential for genetic insights to inform clinical practices and improve maternal and child health outcomes (ref: Honigberg doi.org/10.1038/s41591-023-02374-9/). This research raises important ethical questions regarding the use of genetic information in clinical settings and the potential for discrimination based on genetic predispositions. Furthermore, the findings from the ECMM Candida III study on guideline adherence and patient outcomes in candidaemia cases underscore the need for ethical considerations in clinical decision-making processes, particularly in the context of emerging therapies (ref: Hoenigl doi.org/10.1016/S1473-3099(22)00872-6/). As gene editing technologies continue to evolve, it is essential to engage in discussions about their societal implications, including access to these technologies, potential misuse, and the long-term effects on human genetics. The intersection of genetic research and ethical considerations will play a crucial role in shaping the future landscape of gene editing and its applications in medicine.

CRISPR technology is making significant strides in cancer research, particularly in enhancing therapeutic strategies for solid tumors. Chen et al. explored non-invasive activation of intratumoral gene editing to improve adoptive T-cell therapy, addressing the challenges posed by the immunosuppressive tumor microenvironment (ref: Chen doi.org/10.1038/s41565-023-01378-3/). This approach enhances T-cell infiltration and therapeutic efficacy, showcasing the potential of CRISPR to revolutionize cancer immunotherapy. Additionally, Zhao et al. investigated the therapeutic targeting of metabolic vulnerabilities in cancers with MLL3/4-COMPASS mutations, identifying cellular dependencies that could inform targeted treatment strategies (ref: Zhao doi.org/10.1172/JCI169993/). Moreover, the study by Powell et al. on the immune epigenome across cattle breeds provides insights into the genetic factors influencing immune responses, which could have implications for understanding cancer susceptibility and treatment responses in humans (ref: Powell doi.org/10.1186/s13059-023-02964-3/). These studies collectively highlight the transformative potential of CRISPR technology in cancer research, enabling more effective and personalized therapeutic strategies.

Innovative gene editing technologies are at the forefront of advancing genetic research and therapeutic applications. Seo et al. conducted a comprehensive evaluation of 17 small Cas9s, developing computational models to predict their activities and specificities, which is crucial for optimizing CRISPR-based interventions (ref: Seo doi.org/10.1038/s41592-023-01875-2/). This systematic approach not only enhances the understanding of small Cas9 functionality but also aids in the selection of the most effective editing tools for specific applications. Additionally, de Matos Simoes et al. identified lineage-preferential molecular dependencies in multiple myeloma through genome-scale CRISPR studies, revealing potential therapeutic targets that could lead to more effective treatments for this challenging cancer (ref: de Matos Simoes doi.org/10.1038/s43018-023-00550-x/). These advancements in gene editing technologies underscore the importance of integrating computational and experimental approaches to enhance the precision and efficacy of genetic modifications. The ongoing development of innovative tools and methodologies is essential for addressing the complexities of genetic diseases and improving therapeutic outcomes.