Microfluidic long DNA sample preparation from cells

Paridhi Agrawal, Kevin Dorfman

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

A number of outstanding problems in genomics, such as identifying structural variations and sequencing through centromeres and telomeres, stand poised to benefit tremendously from emerging long-read genomics technologies such as nanopore sequencing and genome mapping in nanochannels. However, optimal application of these new genomics technologies requires facile methods for extracting long DNA from cells. These sample preparation tools should be amenable to automation and minimize fragmentation of the long DNA molecules by shear. We present one such approach in a poly(dimethylsiloxane) device, where gel-based high molecular weight DNA extraction and continuous flow purification in a 3D cell culture-inspired geometry is followed by electrophoretic extraction of the long DNA from the miniaturized gel. Molecular combing reveals that the device produces molecules that are typically in excess of 100 kilobase pairs in size, with the longest molecule extending up to 4 megabase pairs. The microfluidic format reduces the standard day-long and labor-intensive DNA extraction process to 4 hours, making it a promising prototype platform for routine long DNA sample preparation.

Original languageEnglish (US)
Pages (from-to)281-290
Number of pages10
JournalLab on a chip
Volume19
Issue number2
DOIs
StatePublished - Jan 21 2019

Bibliographical note

Funding Information:
We thank Yuval Ebenstein (Tel Aviv University), Jeffrey G. Reifenberger (BioNano Genomics, Inc.), Michael T. Bowser and Pranav Agrawal for useful discussions that informed the content of this manuscript. We also thank Samira Azarin for providing the MCF7 cell line and cell culture facilities. This work was supported by NIH (R21-HG009208). Portions of this work were conducted in the Minnesota Nano Center, which is supported by the National Science Foundation through the National Nano Coordinated Infrastructure Network (NNCI) under Award Number ECCS-1542202. COMSOL modeling was performed using the resources at Minnesota Supercomputing Institute.

Publisher Copyright:
© The Royal Society of Chemistry.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, N.I.H., Extramural

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