Automated high-throughput genome editing platform with an AI learning in situ prediction model

Siwei Li, Jingjing An, Yaqiu Li, Xiagu Zhu, Dongdong Zhao, Lixian Wang, Yonghui Sun, Yuanzhao Yang, Changhao Bi (), Xueli Zhang () and Meng Wang ()
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Siwei Li: Chinese Academy of Sciences
Jingjing An: Chinese Academy of Sciences
Yaqiu Li: Chinese Academy of Sciences
Xiagu Zhu: Chinese Academy of Sciences
Dongdong Zhao: Chinese Academy of Sciences
Lixian Wang: Chinese Academy of Sciences
Yonghui Sun: Chinese Academy of Sciences
Yuanzhao Yang: Chinese Academy of Sciences
Changhao Bi: Chinese Academy of Sciences
Xueli Zhang: Chinese Academy of Sciences
Meng Wang: Chinese Academy of Sciences

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-35056-0

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