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scAAVengr, a transcriptome-based pipeline for quantitative ranking of engineered AAVs with single-cell resolution

Authors :
Bilge E Öztürk
Molly E Johnson
Michael Kleyman
Serhan Turunç
Jing He
Sara Jabalameli
Zhouhuan Xi
Meike Visel
Valérie L Dufour
Simone Iwabe
Luis Felipe L Pompeo Marinho
Gustavo D Aguirre
José-Alain Sahel
David V Schaffer
Andreas R Pfenning
John G Flannery
William A Beltran
William R Stauffer
Leah C Byrne
Source :
eLife, Vol 10 (2021)
Publication Year :
2021
Publisher :
eLife Sciences Publications Ltd, 2021.

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.

Details

Language :
English
ISSN :
2050084X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
eLife
Publication Type :
Academic Journal
Accession number :
edsdoj.2f839da7c7b643a2acc12975971ae2e2
Document Type :
article
Full Text :
https://doi.org/10.7554/eLife.64175