Benchmarking and optimization of a high-throughput sequencing based method for transgene sequence variant analysis in biotherapeutic cell line development

Deciphering integration loci of CHO manufacturing cell lines using long read nanopore sequencing

Despite advances in genetic characterization of Chinese hamster ovary (CHO) cell lines regarding identification of integration sites using next generation sequencing, e.g. targeted locus amplification sequencing (TLA-seq), the concatemer structure of the integrated vectors remains elusive. Here, the entire integration locus of two CHO manufacturing cell lines was reconstructed combining CRISPR/Cas9 target enrichment, nanopore sequencing and the Canu de novo assembly pipeline. An IgG producing CHO cell line integrated 3 vector copies, which were near full-length and contained all relevant vector elements such as transgenes and their promoters on each of the vector copies. In contrast, a second CHO cell line producing a bivalent bispecific antibody integrated 7 highly fragmented vector copies in different orientations leading to head-to-head and tail-to-tail fusions. The size of the vector fragments ranged from 3.0 to 11.4 kbp each carrying 1-3 transgenes. The breakpoints of the genome-vector and vector-vector junctions were validated using Sanger sequencing and Southern blotting. A comparison to TLA-seq data confirmed the genomic breakpoints, but most of the breakpoints of the vector-vector fusions were missed by TLA-seq. For the first time, the complete transgene locus of CHO manufacturing cell lines could be deciphered. Strikingly, the application of the nanopore long-read sequencing technology led to novel insights into the complexity of genomic transgene integrations of CHO manufacturing cell lines generated via random integration.

Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

High-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.