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Benchmarking and optimization of a high-throughput sequencing based method for transgene sequence variant analysis in biotherapeutic cell line development
Authors:Joost Groot  Yizhou Zhou  Eric Marshall  Patrick Cullen  Thomas Carlile  Dongdong Lin  Chong-Feng Xu  Justin Crisafulli  Chao Sun  Fergal Casey  Baohong Zhang  Christina Alves
Affiliation:1. Genome Technologies and Computational Sciences, Biogen, Cambridge, Massachusetts, USA;2. Protein Development, Biogen, Cambridge, Massachusetts, USA;3. Analytical Development, Biogen, Cambridge, Massachusetts, USA
Abstract:In recent years, High-Throughput Sequencing (HTS) based methods to detect mutations in biotherapeutic transgene products have become a key quality step deployed during the development of manufacturing cell line clones. Previously we reported on a higher throughput, rapid mutation detection method based on amplicon sequencing (targeting transgene RNA) and detailed its implementation to facilitate cell line clone selection. By gaining experience with our assay in a diverse set of cell line development programs, we improved the computational analysis as well as experimental protocols. Here we report on these improvements as well as on a comprehensive benchmarking of our assay. We evaluated assay performance by mixing amplicon samples of a verified mutated antibody clone with a non-mutated antibody clone to generate spike-in mutations from ∼60% down to ∼0.3% frequencies. We subsequently tested the effect of 16 different sample and HTS library preparation protocols on the assay's ability to quantify mutations and on the occurrence of false-positive background error mutations (artifacts). Our evaluation confirmed assay robustness, established a high confidence limit of detection of ∼0.6%, and identified protocols that reduce error levels thereby significantly reducing a source of false positives that bottlenecked the identification of low-level true mutations.
Keywords:bioprocess engineering  cell line development  CHO cells  next-generation sequencing  sequence variant analysis
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