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RNA-aptamers-in-droplets (RAPID) high-throughput screening for secretory phenotypes

Author

Listed:
  • Joseph Abatemarco

    (The University of Texas at Austin)

  • Maen F. Sarhan

    (University of California San Francisco
    University of California San Francisco)

  • James M. Wagner

    (The University of Texas at Austin)

  • Jyun-Liang Lin

    (The University of Texas at Austin)

  • Leqian Liu

    (University of California San Francisco
    University of California San Francisco)

  • Wafa Hassouneh

    (University of California San Francisco
    University of California San Francisco
    Chan Zuckerberg Biohub)

  • Shuo-Fu Yuan

    (The University of Texas at Austin)

  • Hal S. Alper

    (The University of Texas at Austin
    The University of Texas at Austin)

  • Adam R. Abate

    (University of California San Francisco
    University of California San Francisco
    Chan Zuckerberg Biohub)

Abstract

Synthetic biology and metabolic engineering seek to re-engineer microbes into “living foundries” for the production of high value chemicals. Through a “design-build-test” cycle paradigm, massive libraries of genetically engineered microbes can be constructed and tested for metabolite overproduction and secretion. However, library generation capacity outpaces the rate of high-throughput testing and screening. Well plate assays are flexible but with limited throughput, whereas droplet microfluidic techniques are ultrahigh-throughput but require a custom assay for each target. Here we present RNA-aptamers-in-droplets (RAPID), a method that greatly expands the generality of ultrahigh-throughput microfluidic screening. Using aptamers, we transduce extracellular product titer into fluorescence, allowing ultrahigh-throughput screening of millions of variants. We demonstrate the RAPID approach by enhancing production of tyrosine and secretion of a recombinant protein in Saccharomyces cerevisiae by up to 28- and 3-fold, respectively. Aptamers-in-droplets affords a general approach for evolving microbes to synthesize and secrete value-added chemicals.

Suggested Citation

  • Joseph Abatemarco & Maen F. Sarhan & James M. Wagner & Jyun-Liang Lin & Leqian Liu & Wafa Hassouneh & Shuo-Fu Yuan & Hal S. Alper & Adam R. Abate, 2017. "RNA-aptamers-in-droplets (RAPID) high-throughput screening for secretory phenotypes," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00425-7
    DOI: 10.1038/s41467-017-00425-7
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    Cited by:

    1. Simon d’Oelsnitz & Daniel J. Diaz & Wantae Kim & Daniel J. Acosta & Tyler L. Dangerfield & Mason W. Schechter & Matthew B. Minus & James R. Howard & Hannah Do & James M. Loy & Hal S. Alper & Y. Jessie, 2024. "Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Charlotte Cautereels & Jolien Smets & Peter Bircham & Dries De Ruysscher & Anna Zimmermann & Peter De Rijk & Jan Steensels & Anton Gorkovskiy & Joleen Masschelein & Kevin J. Verstrepen, 2024. "Combinatorial optimization of gene expression through recombinase-mediated promoter and terminator shuffling in yeast," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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