1. Genetics and Genomics
  2. Stem Cells and Regenerative Medicine

Highly efficient generation of isogenic pluripotent stem cell models using prime editing

  1. Hanqin Li
  2. Oriol Busquets
  3. Yogendra Verma
  4. Khaja Mohieddin Syed
  5. Nitzan Kutnowski
  6. Gabriella R Pangilinan
  7. Luke A Gilbert
  8. Helen S Bateup
  9. Donald C Rio  Is a corresponding author
  10. Dirk Hockemeyer
  11. Frank Soldner  Is a corresponding author
  1. University of California, Berkeley, United States
  2. Albert Einstein College of Medicine, United States
  3. University of California, San Francisco, United States
https://doi.org/10.7554/eLife.79208
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  1. Hanqin Li
  2. Oriol Busquets
  3. Yogendra Verma
  4. Khaja Mohieddin Syed
  5. Nitzan Kutnowski
  6. Gabriella R Pangilinan
  7. Luke A Gilbert
  8. Helen S Bateup
  9. Donald C Rio
  10. Dirk Hockemeyer
  11. Frank Soldner
(2022)
Highly efficient generation of isogenic pluripotent stem cell models using prime editing
eLife 11:e79208.
https://doi.org/10.7554/eLife.79208
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Abstract

The recent development of prime editing (PE) genome engineering technologies has the potential to significantly simplify the generation of human pluripotent stem cell (hPSC)-based disease models. PE is a multi-component editing system that uses a Cas9-nickase fused to a reverse transcriptase (nCas9-RT) and an extended PE guide RNA (pegRNA). Once reverse transcribed, the pegRNA extension functions as a repair template to introduce precise designer mutations at the target site. Here, we systematically compared the editing efficiencies of PE to conventional gene editing methods in hPSCs. This analysis revealed that PE is overall more efficient and precise than homology-directed repair (HDR) of site-specific nuclease-induced double-strand breaks (DSBs). Specifically, PE is more effective in generating heterozygous editing events to create autosomal dominant disease-associated mutations. By stably integrating the nCas9-RT into hPSCs we achieved editing efficiencies equal to those reported for cancer cells, suggesting that the expression of the PE components, rather than cell-intrinsic features, limit PE in hPSCs. To improve the efficiency of PE in hPSCs, we optimized the delivery modalities for the PE components. Delivery of the nCas9-RT as mRNA combined with synthetically generated, chemically-modified pegRNAs and nicking guide RNAs (ngRNAs) improved editing efficiencies up to 13-fold compared to transfecting the prime editing components as plasmids or ribonucleoprotein particles (RNPs). Finally, we demonstrated that this mRNA-based delivery approach can be used repeatedly to yield editing efficiencies exceeding 60% and to correct or introduce familial mutations causing Parkinson's disease in hPSCs.

Data availability

Sequencing data can be accessed through the repository platform Zenodo (10.5281/zenodo.6941502). The datasets for AAVS1 knock-in genotyping, aCGH karyotyping, and the source data files used to generate the featured graphs and tables can be found on Zenodo (10.5281/zenodo.6941599). Plasmids referred to in this paper have been deposited to Addgene's Michael J. Fox Foundation Plasmid Resource and their associated RRID can be found in Supplemental table 2.

The following data sets were generated

Article and author information

Author details

  1. Hanqin Li

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7995-1084

  2. Oriol Busquets

    Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1372-9699

  3. Yogendra Verma

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Khaja Mohieddin Syed

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Nitzan Kutnowski

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3012-4616

  6. Gabriella R Pangilinan

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Luke A Gilbert

    Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Helen S Bateup

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0135-0972

  9. Donald C Rio

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    For correspondence
    don_rio@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4775-3515

  10. Dirk Hockemeyer

    Department of Molecular and Cellular Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5598-5092

  11. Frank Soldner

    Dominick P Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, United States
    For correspondence
    frank.soldner@einsteinmed.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7102-8655

Funding

Aligning Science Across Parkinson's (ASAP-000486)

  • Luke A Gilbert
  • Helen S Bateup
  • Donald C Rio
  • Dirk Hockemeyer
  • Frank Soldner

Albert Einstein College of Medicine, Yeshiva University (Internal research support from the Department of Neuroscience)

  • Frank Soldner

Siebel Stem Cell Institute (Fellow)

  • Helen S Bateup

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Li et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Hanqin Li
  2. Oriol Busquets
  3. Yogendra Verma
  4. Khaja Mohieddin Syed
  5. Nitzan Kutnowski
  6. Gabriella R Pangilinan
  7. Luke A Gilbert
  8. Helen S Bateup
  9. Donald C Rio
  10. Dirk Hockemeyer
  11. Frank Soldner
(2022)
Highly efficient generation of isogenic pluripotent stem cell models using prime editing
eLife 11:e79208.
https://doi.org/10.7554/eLife.79208

Share this article

https://doi.org/10.7554/eLife.79208

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