scAAVengr, a transcriptome-based pipeline for quantitative ranking of engineered AAVs with single-cell resolution
- University of Pittsburgh, United States
- Carnegie Mellon University, United States
- University of California, Berkeley, United States
- University of Pennsylvania, United States
Abstract
Background:
Adeno-associated virus (AAV)-mediated gene therapies are rapidly advancing to the clinic, and AAV engineering has resulted in vectors with increased ability to deliver therapeutic genes. Although the choice of vector is critical, quantitative comparison of AAVs, especially in large animals, remains challenging.
Methods:
Here, we developed an efficient single-cell AAV engineering pipeline (scAAVengr) to simultaneously quantify and rank efficiency of competing AAV vectors across all cell types in the same animal.
Results:
To demonstrate proof-of-concept for the scAAVengr workflow, we quantified - with cell-type resolution - the abilities of naturally occurring and newly engineered AAVs to mediate gene expression in primate retina following intravitreal injection. A top performing variant identified using this pipeline, K912, was used to deliver SaCas9 and edit the rhodopsin gene in macaque retina, resulting in editing efficiency similar to infection rates detected by the scAAVengr workflow. scAAVengr was then used to identify top-performing AAV variants in mouse brain, heart and liver following systemic injection.
Conclusions:
These results validate scAAVengr as a powerful method for development of AAV vectors.
Funding:
This work was supported by funding from the Ford Foundation, NEI/NIH, Research to Prevent Blindness, Foundation Fighting Blindness, UPMC Immune Transplant and Therapy Center, and the Van Sloun fund for canine genetic research.
Data availability
Data, including count matrix files, raw fastq files as well as AAV/cell barcode tables generated from read quantification, have been uploaded to GEO under accession code GSE161645.
Article and author information
Author details
William R Stauffer
Funding
Ford Foundation
- Leah C Byrne
UPMC Immune Transplant and Therapy Center
- Leah C Byrne
Van Sloun Fund for Canine Genetic Research
- Gustavo D Aguirre
National Eye Institute (F32EY023891)
- Leah C Byrne
National Eye Institute (R24EY-022012)
- David V Schaffer
- John G Flannery
- William A Beltran
National Eye Institute (R01EY017549)
- Gustavo D Aguirre
- William A Beltran
National Eye Institute (P30EY001583)
- Gustavo D Aguirre
- William A Beltran
National Institute of Mental Health (UG3MH120094)
- Andreas R Pfenning
- William A Beltran
- Leah C Byrne
National Institute of Mental Health (DP2MH113095)
- William R Stauffer
Research to Prevent Blindness
- Leah C Byrne
Foundation Fighting Blindness
- Leah C Byrne
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures were performed in compliance with the ARVO statement for the Use of Animals in Ophthalmic and Vision Research, and for canine studies with approval by the University of Pennsylvania Institutional Animal Care and Use Committee (IACUC # 803813), and for the NHP and mouse studies with approval from the University of Pittsburgh Institutional Animal Care and Use Committee (IACUC #18042326).
Copyright
© 2021, Öztürk 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.
Metrics
-
- 8,217
- views
-
- 1,371
- downloads
-
- 44
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Citations by DOI
-
- 44
- citations for umbrella DOI https://doi.org/10.7554/eLife.64175
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
scAAVengr, a transcriptome-based pipeline for quantitative ranking of engineered AAVs with single-cell resolution
eLife 10:e64175.
https://doi.org/10.7554/eLife.64175
