Defining a sample preparation workflow for advanced virus detection and understanding sensitivity by next-generation sequencing.

Abstract

The application of next-generation sequencing (also known as deep sequencing or massively parallel sequencing) for adventitious agent detection is an evolving field that is steadily gaining acceptance in the biopharmaceutical industry. In order for this technology to be successfully applied, a robust method that can isolate viral nucleic acids from a variety of biological samples (such as host cell substrates, cell-free culture fluids, viral vaccine harvests, and animal-derived raw materials) must be established by demonstrating recovery of model virus spikes. In this report, we implement the sample preparation workflow developed by Feng et. al. and assess the sensitivity of virus detection in a next-generation sequencing readout using the Illumina MiSeq platform. We describe a theoretical model to estimate the detection of a target virus in a cell lysate or viral vaccine harvest sample. We show that nuclease treatment can be used for samples that contain a high background of non-relevant nucleic acids (e.g., host cell DNA) in order to effectively increase the sensitivity of sequencing target viruses and reduce the complexity of data analysis. Finally, we demonstrate that at defined spike levels, nucleic acids from a panel of model viruses spiked into representative cell lysate and viral vaccine harvest samples can be confidently recovered by next-generation sequencing.