During the development of cell lines for therapeutic protein production, a vector harboring a product transgene is integrated into the genome. To ensure production stability and consistent product quality, single-cell cloning is then performed. Since cells derived from the same parental clone have the same transgene integration locus, the identity of the integration site can also be used to verify the clonality of a production cell line. In this study, we present a high-throughput pipeline for clonality verification through integration site analysis. Sequence capture of genomic fragments that contain both vector and host cell genome sequences was used followed by next-generation sequencing to sequence the relevant vector-genome junctions. A Python algorithm was then developed for integration site identification and validated using a cell line with known integration sites. Using this system, we identified the integration sites of the host vector for 31 clonal cell lines from five independent vector integration events while using one set of probes against common features of the host vector for transgene integration. Cell lines from the same lineage had common integration sites, and they were distinct from unrelated cell lines. The integration sites obtained for each clone as part of the analysis may also be used for clone selection, as the sites can have a profound effect on the transgene's transcript level and the stability of the resulting cell line. This method thus provides a rapid system for integration site identification and clonality verification.
Bibliographical noteFunding Information:
S.A.O. was supported in part by the NIGMS Biotechnology Training Program (T32GM008347‐22). Computational resources were provided by the Minnesota Supercomputing Institute. This work was supported in part by Bayer Healthcare.
S.A.O. was supported in part by the NIGMS Biotechnology Training Program (T32GM008347-22). Computational resources were provided by the Minnesota Supercomputing Institute. This work was supported in part by Bayer Healthcare.
© 2020 American Institute of Chemical Engineers
- CHO cells
- cell culture
- next-generation sequencing
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, N.I.H., Extramural
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SAM Filtering Pipeline (SFP): Algorithm for the determination of integration sites from next generation sequencing data
O''Brien, S. A. & Hu, W., Data Repository for the University of Minnesota, 2019