Multiplexed genome regulation in vivo with hyper-efficient Cas12a

Guo, Lucie Y; Bian, Jing; Davis, Alexander E.; Liu, Pingting; Kempton, Hannah R; Zhang, Xiaowei; Chemparathy, Augustine; Gu, Baokun; Lin, Xueqiu; Rane, Draven A.; Jamiolkowski, Ryan M.; Hu, Yang; Wang, Sui; Qi, Lei S

doi:10.1038/s41556-022-00870-7

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Multiplexed genome regulation in vivo with hyper-efficient Cas12a

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Author(s): Lucie Y. Guo ¹ ^, ² ^, ^† , Jing Bian ¹ , Alexander E. Davis ² , Pingting Liu ² , Hannah R. Kempton ¹ , Xiaowei Zhang ¹ , Augustine Chemparathy ¹ , Baokun Gu ¹ , Xueqiu Lin ¹ , Draven A. Rane ¹ , Ryan M. Jamiolkowski ¹ , Yang Hu ² , Sui Wang ² ^, ^† , Lei S. Qi ¹ ^, ³ ^, ⁴ ^, ^†

Publication date (Electronic): 12 April 2022

Journal: Nature cell biology

Keywords: CRISPR, Cas12a, hyperCas12a, CRISPRa, CRISPRi, gene editing, off-target, in vivo, multiplexed, gene regulation, retina differentiation

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Abstract

Multiplexed modulation of endogenous genes is crucial for sophisticated gene therapy and cell engineering. CRISPR-Cas12a systems enable versatile multiple genomic loci targeting by processing numerous crRNAs from a single transcript, however, their low efficiency has hindered applications in vivo. Through structure-guided protein engineering, we develop a hyper-efficient LbCas12a variant, termed hyperCas12a, with its catalytically dead version hyperdCas12a showing significantly enhanced efficacy for gene activation, particularly at low crRNA conditions. We demonstrate that hyperdCas12a has minimal off-target effects compared to the wildtype system and exhibits enhanced activity for gene editing and repression. Delivery of the hyperdCas12a-activator and a single crRNA array simultaneously activating endogenous Oct4, Sox2, and Klf4 genes in the retina of postnatal mice alters the differentiation of retinal progenitor cells. The hyperCas12a system offers a versatile in vivo tool for a broad range of gene modulation and gene therapy applications.

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HTSeq—a Python framework to work with high-throughput sequencing data

Simon Anders, Paul Pyl, Wolfgang Huber (2014)

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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Author and article information

Journal

Journal ID (nlm-journal-id): 100890575

Journal ID (pubmed-jr-id): 21417

Journal ID (nlm-ta): Nat Cell Biol

Journal ID (iso-abbrev): Nat Cell Biol

Title: Nature cell biology

ISSN (Print): 1465-7392

ISSN (Electronic): 1476-4679

Publication date Nihms-submitted: 23 February 2022

Publication date (Print): April 2022

Publication date (Electronic): 12 April 2022

Publication date PMC-release: 12 October 2022

Volume: 24

Issue: 4

Pages: 590-600

Affiliations

[1 ]Department of Bioengineering, Stanford University, Stanford, CA 94305, United States

[2 ]Department of Ophthalmology, Stanford University, Stanford, CA 94305, United States

[3 ]Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, United States

[4 ]Stanford ChEM-H Institute, Stanford University, Stanford, CA 94305, United States

Author notes

[† ]Correspondence to: L.Y.G. ( lucieguo@ 123456stanford.edu ), S.W. ( suiwang@ 123456stanford.edu ), L.S.Q. ( stanley.qi@ 123456stanford.edu )

[*]

Co-first authors

Author Contributions

L.Y.G. and L.S.Q. conceived of the idea. L.Y.G., J.B., S.W., and L.S.Q. designed the experiments. L.Y.G., J.B., X.Z., H.R.K., B.G., D.R., and R.M.J. performed ex vivo experiments. L.Y.G., J.B., A.E.D., and P.L. performed in vivo experiments with guidance from S.W. and Y.H. L.Y.G., J.B., S.W., and L.S.Q. analyzed the experimental data. A.C. and X.L. performed computational analysis of sequencing data, and X.Z. and R.M.J. analyzed imaging data. L.Y.G. and L.S.Q. wrote the manuscript with input from all authors.

Article

Manuscript ID: NIHMS1780715

DOI: 10.1038/s41556-022-00870-7

PMC ID: 9035114

PubMed ID: 35414015

SO-VID: fdec3801-d238-469d-a76b-a0edfc91aca9

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