A great number of cell disease models with pathogenic SNVs are needed for the development of genome editing based therapeutics or broadly basic scientific research. However, the generation of traditional cell disease models is heavily dependent on large-scale manual operations, which is not only time-consuming, but also costly and error-prone. In this study, we devise an automated high-throughput platform, through which thousands of samples are automatically edited within a week, providing edited cells with high efficiency. Based on the large in situ genome editing data obtained by the automatic high-throughput platform, we develop a Chromatin Accessibility Enabled Learning Model (CAELM) to predict the performance of cytosine base editors (CBEs), both chromatin accessibility and the context-sequence are utilized to build the model, which accurately predicts the result of in situ base editing. This work is expected to accelerate the development of BE-based genetic therapies.

开发基于基因组编辑的疗法或广泛的基础科学研究需要大量具有致病性 SNV 的细胞疾病模型。然而，传统细胞疾病模型的生成严重依赖于大规模的人工操作，不仅耗时长，而且成本高且容易出错。在这项研究中，我们设计了一个自动化的高通量平台，通过该平台可以在一周内自动编辑数千个样本，从而提供高效率的编辑细胞。基于自动高通量平台获得的大量原位基因组编辑数据，我们开发了染色质可及性支持学习模型（CAELM）来预测胞嘧啶碱基编辑器（CBE）的性能，染色质可及性和上下文序列都是用于构建模型，准确预测了原位碱基编辑的结果。这项工作有望加速基于 BE 的基因疗法的发展。