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Lagerborg KA, Normandin E, Bauer MR, et al. Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows. Nat Microbiol. 2021;7(1):108-119doi: 10.1038/s41564-021-01019-2.
Lagerborg, K. A., Normandin, E., Bauer, M. R., Adams, G., Figueroa, K., Loreth, C., Gladden-Young, A., Shaw, B. M., Pearlman, L. R., Berenzy, D., Dewey, H. B., Kales, S., Dobbins, S. T., Shenoy, E. S., Hooper, D., Pierce, V. M., Zachary, K. C., Park, D. J., MacInnis, B. L., Tewhey, R., Lemieux, J. E., Sabeti, P. C., Reilly, S. K., & Siddle, K. J. (2022). Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows. Nature microbiology, 7(1), 108-119. https://doi.org/10.1038/s41564-021-01019-2
Lagerborg, Kim A, et al. "Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows." Nature microbiology vol. 7,1 (2022): 108-119. doi: https://doi.org/10.1038/s41564-021-01019-2
Lagerborg KA, Normandin E, Bauer MR, Adams G, Figueroa K, Loreth C, Gladden-Young A, Shaw BM, Pearlman LR, Berenzy D, Dewey HB, Kales S, Dobbins ST, Shenoy ES, Hooper D, Pierce VM, Zachary KC, Park DJ, MacInnis BL, Tewhey R, Lemieux JE, Sabeti PC, Reilly SK, Siddle KJ. Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows. Nat Microbiol. 2022 Jan;7(1):108-119. doi: 10.1038/s41564-021-01019-2. Epub 2021 Dec 14. PMID: 34907347.
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Nat Microbiol. 2022 Jan;7(1):108-119. doi: 10.1038/s41564-021-01019-2. Epub 2021 Dec 14.

Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows.

Nature microbiology

Kim A Lagerborg, Erica Normandin, Matthew R Bauer, Gordon Adams, Katherine Figueroa, Christine Loreth, Adrianne Gladden-Young, Bennett M Shaw, Leah R Pearlman, Daniel Berenzy, Hannah B Dewey, Susan Kales, Sabrina T Dobbins, Erica S Shenoy, David Hooper, Virginia M Pierce, Kimon C Zachary, Daniel J Park, Bronwyn L MacInnis, Ryan Tewhey, Jacob E Lemieux, Pardis C Sabeti, Steven K Reilly, Katherine J Siddle

Affiliations

  1. Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  2. Harvard Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA.
  3. Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
  4. Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
  5. The Jackson Laboratory, Bar Harbor, ME, USA.
  6. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
  7. Pediatric Infectious Disease Unit, MassGeneral Hospital for Children, Boston, MA, USA.
  8. Department of Pathology, Harvard Medical School, Boston, MA, USA.
  9. Department of Medicine, Harvard Medical School, Boston, MA, USA.
  10. Infection Control Unit, Massachusetts General Hospital, Boston, MA, USA.
  11. Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA, USA.
  12. Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA.
  13. Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA.
  14. Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA.
  15. Howard Hughes Medical Institute, Chevy Chase, MD, USA.
  16. Broad Institute of Harvard and MIT, Cambridge, MA, USA. [email protected].
  17. Department of Genetics, Yale School of Medicine, New Haven, CT, USA. [email protected].

PMID: 34907347 DOI: 10.1038/s41564-021-01019-2

Abstract

The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will increase with the expanding global production of sequences across a variety of laboratories for epidemiological and clinical interpretation, as well as for genomic surveillance of emerging diseases in future outbreaks. We present SDSI + AmpSeq, an approach that uses 96 synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination throughout the sequencing workflow. We apply SDSIs to the ARTIC Consortium's amplicon design, demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and validate them across 6,676 diagnostic samples at multiple laboratories. We establish that SDSI + AmpSeq provides increased confidence in genomic data by detecting and correcting for relatively common, yet previously unobserved modes of error, including spillover and sample swaps, without impacting genome recovery.

© 2021. The Author(s), under exclusive licence to Springer Nature Limited.

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