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Assessment of genome packaging in AAVs using Orbitrap-based charge-detection mass spectrometry.

ABSTRACT: Adeno-associated viruses (AAVs) represent important gene therapy vectors with several approved clinical applications and numerous more in clinical trials. Genome packaging is an essential step in the bioprocessing of AAVs and needs to be tightly monitored to ensure the proper delivery of transgenes and the production of effective drugs. Current methods to monitor genome packaging have limited sensitivity, a high demand on labor, and struggle to distinguish between packaging of the intended genome or unwanted side-products. Here we show that Orbitrap-based charge-detection mass spectrometry allows the very sensitive quantification of all these different AAV bioprocessing products. A protocol is presented that allows the quantification of genome-packed AAV preparations in under half an hour, requiring only micro-liter quantities of typical AAV preparations with ∼10¹³ viral capsids per milliliter. The method quickly assesses the integrity and amount of genome packed AAV particles to support AAV bioprocessing and characterization of this rapidly emerging class of advanced drug therapies.

SUBMITTER: Worner TP

PROVIDER: S-EPMC8671526 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions' ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.

Dataset Information

Assessment of genome packaging in AAVs using Orbitrap-based charge-detection mass spectrometry.

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