image: A library with single guide RNA (sgRNA) is packed into lentiviral particles and transduced into editor-expressing cells. The sgRNA-transduced cells are selected to generate mutant cells. Mutant cells are treated and untreated. DNA is extracted, and sgRNA is amplified via polymerase chain reaction (PCR). Screening is conducted via deep sequencing before bioinformatics analysis (By Figdraw). ABE: adenine base editor; CBE: cytosine base editor; NGS: next-generation sequencing; SNV: single nucleotide variant; VUS: variants of uncertain significance.
Credit: ©Science China Press
In recent years, gene editing technology has spearheaded transformative advancements in the life sciences. Represented prominently by CRISPR/Cas-based base editing technology has emerged as a potent force in genome editing. Which enables effect precise single-base substitutions at specific genomic sites without the necessity for double-strand breaks. This breakthrough circumvents some of the limitations inherent in traditional gene editing methodologies, fostering greater precision and minimizing off-target effects. Its versatility and efficacy also render it a promising candidate for in vivo applications, holding immense potential for therapeutic interventions and biomedical research alike.
The emergence of base editing screening platforms has revolutionized functional genomic studies by enabling investigations at the single nucleotide level, offering researchers unparalleled insights into molecular mechanisms. Interestingly, these systems have expanded to encompass high-throughput screening methodologies and find applications in functional genome research. Presently, high-throughput screening leveraging base editing technology has found diverse applications, including simulated single nucleotide variant mutation screening, stop codon mutation screening, functional post-translational modification screening, and saturation mutagenesis screening. The screening process entails the delivery of sgRNA libraries targeting specific genes at the nucleotide level via lentivirus to target cells that stably express the base editor. Following editing, the mutagenized cell population undergoes screening to identify specific phenotypes of interest. Deep sequencing technology plays a pivotal role in this process, facilitating the mapping of loss-of-function (LOF) or gain-of-function (GOF) mutations. This is achieved by identifying sgRNAs that exhibit reduced abundance or are lost in cells during negative screening, or conversely, are enriched in the final surviving cell population during positive screening. Taken together, high-throughput base editing screens have significantly enhanced our capacity to probe key nucleotide functions in their native context, offering a powerful tool for dissecting intricate molecular pathways and accelerating scientific discovery.
The rapid advancement of artificial intelligence (AI) technology is profoundly shaping genome editing and the data analysis of base editing systems. AI now serves as a pivotal tool in various aspects of these fields, offering invaluable assistance in both the design and execution of genome editing experiments, as well as streamlining the data analysis process to expedite scientific research. In summary, the integration of AI technology into genome editing and base editing data analysis holds immense promise for advancing scientific understanding and accelerating the pace of discovery in these critical fields.
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Medicine Plus