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Massively parallel quantification of CRISPR editing in cells by TRAP-seq enables better design of Cas9, ABE, CBE gRNAs of high efficiency and accuracy

Xi Xiang, Kunli Qu, Xue Liang, Xiaoguang Pan, Jun Wang, Peng Han, Zhanying Dong, Lijun Liu, Jiayan Zhong, Tao Ma, Yiqing Wang, Jiaying Yu, Xiaoying Zhao, Siyuan Li, Zhe Xu, Jinbao Wang, Xiuqing Zhang, Hui Jiang, Fengping Xu, Lijin Zou, Huajing Teng, Xin Liu, Xun Xu, Jian Wang, Huanming Yang, Lars Bolund, George M. Church, Lin Lin, Yonglun Luo
doi: https://doi.org/10.1101/2020.05.20.103614
Xi Xiang
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
3BGI-Shenzhen, Shenzhen 518083, China
4Department of Biomedicine, Aarhus University, Aarhus 8000, Denmark
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Kunli Qu
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
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Xue Liang
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
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Xiaoguang Pan
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
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Jun Wang
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
3BGI-Shenzhen, Shenzhen 518083, China
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Peng Han
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
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Zhanying Dong
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
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Lijun Liu
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
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Jiayan Zhong
6MGI, BGI-Shenzhen, Shenzhen 518083, China
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Tao Ma
6MGI, BGI-Shenzhen, Shenzhen 518083, China
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Yiqing Wang
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
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Jiaying Yu
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
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Xiaoying Zhao
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
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Siyuan Li
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
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Zhe Xu
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
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Jinbao Wang
6MGI, BGI-Shenzhen, Shenzhen 518083, China
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Xiuqing Zhang
2BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
3BGI-Shenzhen, Shenzhen 518083, China
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Hui Jiang
6MGI, BGI-Shenzhen, Shenzhen 518083, China
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Fengping Xu
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
3BGI-Shenzhen, Shenzhen 518083, China
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Lijin Zou
7The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China
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Huajing Teng
8Key Laboratory of Carcinogenesis and Translational Research, Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
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Xin Liu
3BGI-Shenzhen, Shenzhen 518083, China
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Xun Xu
3BGI-Shenzhen, Shenzhen 518083, China
9Guangdong Provincial Key Laboratory of Genome Read and Write,BGI-Shenzhen, Shenzhen, 518120,China
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Jian Wang
3BGI-Shenzhen, Shenzhen 518083, China
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Huanming Yang
3BGI-Shenzhen, Shenzhen 518083, China
10Guangdong Provincial Academician Workstation of BGI Synthetic Genomics,BGI-Shenzhen, Shenzhen, 518120,China
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Lars Bolund
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
3BGI-Shenzhen, Shenzhen 518083, China
4Department of Biomedicine, Aarhus University, Aarhus 8000, Denmark
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
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George M. Church
11Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
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Lin Lin
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
4Department of Biomedicine, Aarhus University, Aarhus 8000, Denmark
12Steno Diabetes Center Aarhus, Aarhus University, Aarhus 8200, Denmark
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  • For correspondence: lin.lin@biomed.au.dk alun@biomed.au.dk
Yonglun Luo
1Lars Bolund Institute of Regenerative Medicine, BGI-Qingdao, Qingdao 266555, China
3BGI-Shenzhen, Shenzhen 518083, China
4Department of Biomedicine, Aarhus University, Aarhus 8000, Denmark
5Qingdao-Europe Advanced Institute for Life Sciences, BGI-Shenzhen, Qingdao 266555, China
12Steno Diabetes Center Aarhus, Aarhus University, Aarhus 8200, Denmark
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  • For correspondence: lin.lin@biomed.au.dk alun@biomed.au.dk
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Abstract

The CRISPR RNA-guided endonucleases Cas9, and Cas9-derived adenine/cytosine base editors (ABE/CBE), have been used in both research and therapeutic applications. However, broader use of this gene editing toolbox is hampered by the great variability of efficiency among different target sites. Here we present TRAP-seq, a versatile and scalable approach in which the CRISPR gRNA expression cassette and the corresponding surrogate site are captured by Targeted Reporter Anchored Positional Sequencing in cells. TRAP-seq can faithfully recapitulate the CRISPR gene editing outcomes introduced to the corresponding endogenous genome site and most importantly enables massively parallel quantification of CRISPR gene editing in cells. We demonstrate the utility of this technology for high-throughput quantification of SpCas9 editing efficiency and indel outcomes for 12,000 gRNAs in human embryonic kidney cells. Using this approach, we also showed that TRAP-seq enables high throughput quantification of both ABE and CBE efficiency at 12,000 sites in cells. This rich amount of ABE/CBE outcome data enable us to reveal several novel nucleotide features (e.g. preference of flanking bases, nucleotide motifs, STOP recoding types) affecting base editing efficiency, as well as designing improved machine learning-based prediction tools for designing SpCas9, ABE and CBE gRNAs of high efficiency and accuracy (>70%). We have integrated all the 12,000 CRISPR gene editing outcomes for SpCas9, ABE and CBE into a CRISPR-centered portal: The Human CRISPR Atlas. This study extends our knowledge on CRISPR gene and base editing, and will facilitate the application and development of CRISPR in both research and therapy.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵# lead contact

  • http://www.crispratlas.com/crispr

  • https://github.com/TerminatorJ/CRISPR-TRAP-seq

  • https://github.com/TerminatorJ/Trap-seq-Data-Processing

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 21, 2020.
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Massively parallel quantification of CRISPR editing in cells by TRAP-seq enables better design of Cas9, ABE, CBE gRNAs of high efficiency and accuracy
Xi Xiang, Kunli Qu, Xue Liang, Xiaoguang Pan, Jun Wang, Peng Han, Zhanying Dong, Lijun Liu, Jiayan Zhong, Tao Ma, Yiqing Wang, Jiaying Yu, Xiaoying Zhao, Siyuan Li, Zhe Xu, Jinbao Wang, Xiuqing Zhang, Hui Jiang, Fengping Xu, Lijin Zou, Huajing Teng, Xin Liu, Xun Xu, Jian Wang, Huanming Yang, Lars Bolund, George M. Church, Lin Lin, Yonglun Luo
bioRxiv 2020.05.20.103614; doi: https://doi.org/10.1101/2020.05.20.103614
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Massively parallel quantification of CRISPR editing in cells by TRAP-seq enables better design of Cas9, ABE, CBE gRNAs of high efficiency and accuracy
Xi Xiang, Kunli Qu, Xue Liang, Xiaoguang Pan, Jun Wang, Peng Han, Zhanying Dong, Lijun Liu, Jiayan Zhong, Tao Ma, Yiqing Wang, Jiaying Yu, Xiaoying Zhao, Siyuan Li, Zhe Xu, Jinbao Wang, Xiuqing Zhang, Hui Jiang, Fengping Xu, Lijin Zou, Huajing Teng, Xin Liu, Xun Xu, Jian Wang, Huanming Yang, Lars Bolund, George M. Church, Lin Lin, Yonglun Luo
bioRxiv 2020.05.20.103614; doi: https://doi.org/10.1101/2020.05.20.103614

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