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Optimizing high-throughput viral vector characterization with density gradient equilibrium analytical ultracentrifugation

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DOI: 10.1007/s00249-023-01654-z

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AUC; Analytical ultracentrifugation; Density gradient; Adenovirus; Gene therapy

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Accurate characterization of capsid species is crucial for viral vector-based gene therapies and vaccines. The current standard method, sedimentation velocity analytical ultracentrifugation (SV-AUC), is often size-limited and requires specialized software for analysis. The density gradient equilibrium AUC (DGE-AUC) provides a simplified and high-resolution method for separating biologics of different densities. It offers improvements in sensitivity and can be used with larger viral particles. This serotype-agnostic method enables intuitive interpretation and analysis without the need for specialized software.
Viral vector-based gene therapies and vaccines require accurate characterization of capsid species. The current gold standard for assessing capsid loading of adeno-associated virus (AAV) is sedimentation velocity analytical ultracentrifugation (SV-AUC). However, routine SV-AUC analysis is often size-limited, especially without the use of advanced techniques (e.g., gravitational-sweep) or when acquiring the multiwavelength data needed for assessing the loading fraction of viral vectors, and requires analysis by specialized software packages. Density gradient equilibrium AUC (DGE-AUC) is a highly simplified analytical method that provides high-resolution separation of biologics of different densities (e.g., empty and full viral capsids). The analysis required is significantly simpler than SV-AUC, and larger viral particles such as adenovirus (AdV) are amenable to characterization by DGE-AUC using cesium chloride gradients. This method provides high-resolution data with significantly less sample (estimated 56-fold improvement in sensitivity compared to SV-AUC). Multiwavelength analysis can also be used without compromising data quality. Finally, DGE-AUC is serotype-agnostic and amenable to intuitive interpretation and analysis (not requiring specialized AUC software). Here, we present suggestions for optimizing DGE-AUC methods and demonstrate a high-throughput AdV packaging analysis with the AUC, running as many as 21 samples in 80 min.

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