Evidence map›Paper›PMID 28835641›Full record

ArticleNature communications2017

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

Joseph Abatemarco, Maen F Sarhan, James M Wagner, Jyun-Liang Lin, Leqian Liu, Wafa Hassouneh, Shuo-Fu Yuan, Hal S Alper, Adam R Abate

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
8.0field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 139 citations in OpenAlex.

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  17. Advances in aptamer-based nuclear imaging.European journal of nuclear medicine and molecular imaging · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Joseph AbatemarcoDepartment of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St Stop C0400, Austin, Texas, 78712, USA.
Maen F SarhanBioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, 94158, California, USA.
James M WagnerDepartment of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St Stop C0400, Austin, Texas, 78712, USA.
Jyun-Liang LinDepartment of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St Stop C0400, Austin, Texas, 78712, USA.
Leqian LiuBioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, 94158, California, USA.
Wafa HassounehBioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, 94158, California, USA.
Shuo-Fu YuanInstitute for Cellular and Molecular Biology, The University of Texas at Austin, 2500 Speedway Avenue, Austin, Texas, 78712, USA.ORCID http://orcid.org/0000-0002-5530-8702
Hal S AlperDepartment of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St Stop C0400, Austin, Texas, 78712, USA. halper@che.utexas.edu.
Adam R AbateBioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, 94158, California, USA. adam@abatelab.org.
The University of Texas at Austin · USQB3 · US

Funding

Microfluidic immunoprofiling for biomarker discovery in rheumatoid arthritisDP2AR068129 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R. · 2014 to 2014
$2.4M
Accurate and unbiased single cell sequencing with digital droplet MDAR01HG008978 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ABATE, ADAM R. · 2016 to 2018
$1.9M
Environmental Health Perspectives, NIEHS27302C0031 · NIEHS · 2008 to 2008
$1.2M
2016 SETAC Asia/Pacific ConferenceR13ES027302 · NIEHS · SOCIETY/ENVIRONMENTAL TOXICOLOGY/CHEM · PI MENZIE, CHARLES · 2016 to 2016
$6k
NHGRI NIH HHS R01 HG008978NIAMS NIH HHS DP2 AR068129NIEHS NIH HHS 27302C0031
6 · The paper itself

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.Screening libraries of genetically engineered microbes for secreted products is limited by the available assay throughput. Here the authors combine aptamer-based fluorescent detection with droplet microfluidics to achieve high throughput screening of yeast strains engineered for enhanced tyrosine or streptavidin production.

Indexed as

Aptamers, NucleotideHigh-Throughput Screening AssaysMetabolic EngineeringMicrofluidic Analytical TechniquesMicroscopy, FluorescencePhenotypeRecombinant ProteinsReproducibility of ResultsSaccharomyces cerevisiaeStreptavidinTyrosineAptamers, NucleotideRecombinant ProteinsStreptavidinTyrosine

Identifiers

PMID28835641
PMCPMC5569033
OpenAlexW2747270418

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.