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Updated: Aug 16, 2026

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在实验室中对CRISPR活动的高通量评估使得罕见变异的优化大规模多重丰富成为可能

Joo Hye Yeo¹, Seungho Lee^1,2, Seungmin Kim³

¹Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea.

Nature biomedical engineering

|October 31, 2025

概括

新的高吞吐量方法Cut-seq1和Cut-seq2准确地测量了Cas9裂解效率. 这使得DeepCut和CLOVE-seq的开发成为最佳的引导RNA设计和生物医学研究中罕见变异检测的基础.

科学领域:

分子生物学分子生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

之前的高通量CRISPR评估依赖于插入-删除频率,而不是直接切割效率.
这限制了在众多指导RNA和标序列中对Cas9核酶活性的准确评估.

研究的目的:

为精确的Cas9切割效率评估开发高通量体外方法.
创建深度学习模型以优化单导向RNA (sgRNA) 设计.
建立一种使用Cas9介导裂变来丰富罕见变体的方法.

主要方法:

开发了Cut-seq1和Cut-seq2用于高通量体外Cas9裂解效率的评估.
产生了大量数据集,将体外裂变效率与细胞插入-删除频率以及原体空间相邻动图的兼容性相关联.
开发了DeepCut,这是一个用于识别优化sgRNA的深度学习模型.
开发了CLOVE-seq (用于大规模优化变体丰富测序的分离) 用于复杂的罕见变体丰富.

主要成果:

在体外切割效率与细胞插入-删除频率之间的相关性很低.
在实验室和细胞数据之间的原体空间相邻动图兼容性中显示出高一致性.
DeepCut成功地确定了优化为选择性DNA裂变的sgRNAs.
CLOVE-seq通过Cas9特定的裂变实现了罕见变异的多重丰富.

结论:

切断-seq1/2提供准确的,高通量测量Cas9切割效率.
像DeepCut这样的深度学习模型可以优化sgRNA设计的特异性.
在多种生物医学应用中,CLOVE-seq为罕见变种检测提供了一种新的方法.
这些进展提高了对CRISPR核酶活性和变异检测能力的理解.

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