Combinatorial PCR Method for Efficient, Selective Oligo Retrieval from Complex Oligo Pools.


Journal

ACS synthetic biology
ISSN: 2161-5063
Titre abrégé: ACS Synth Biol
Pays: United States
ID NLM: 101575075

Informations de publication

Date de publication:
20 05 2022
Historique:
pubmed: 23 2 2022
medline: 24 5 2022
entrez: 22 2 2022
Statut: ppublish

Résumé

With the rapidly decreasing cost of array-based oligo synthesis, large-scale oligo pools offer significant benefits for advanced applications including gene synthesis, CRISPR-based gene editing, and DNA data storage. The selective retrieval of specific oligos from these complex pools traditionally uses polymerase chain reaction (PCR). Designing a large number of primers to use in PCR presents a serious challenge, particularly for DNA data storage, where the size of an oligo pool is orders of magnitude larger than other applications. Although a nested primer address system was recently developed to increase the number of accessible files for DNA storage, it requires more complicated lab protocols and more expensive reagents to achieve high specificity, as well as more DNA address space. Here, we present a new combinatorial PCR method that has none of those drawbacks and outperforms in retrieval specificity. In experiments, we accessed three files that each comprised 1% of a DNA prototype database that contained 81 different files and enriched them to over 99.9% using our combinatorial primer method. Our method provides a viable path for scaling up DNA data storage systems and has broader utility whenever one must access a specific target oligo and can design their own primer regions.

Identifiants

pubmed: 35191684
doi: 10.1021/acssynbio.1c00482
doi:

Substances chimiques

DNA Primers 0
DNA 9007-49-2

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1727-1734

Auteurs

Claris Winston (C)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.

Lee Organick (L)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.

David Ward (D)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.

Luis Ceze (L)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.

Karin Strauss (K)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.
Microsoft Research, Redmond, Washington 98052, United States.

Yuan-Jyue Chen (YJ)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States.
Microsoft Research, Redmond, Washington 98052, United States.

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Classifications MeSH