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A guide to nucleic acid detection by single-molecule kinetic fingerprinting

Overview of attention for article published in ImmunoMethods, August 2018
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Title
A guide to nucleic acid detection by single-molecule kinetic fingerprinting
Published in
ImmunoMethods, August 2018
DOI 10.1016/j.ymeth.2018.08.002
Pubmed ID
Authors

Alexander Johnson-Buck, Jieming Li, Muneesh Tewari, Nils G. Walter

Abstract

Conventional methods for detecting small quantities of nucleic acids require amplification by the polymerase chain reaction (PCR), which necessitates prior purification and introduces copying errors. While amplification-free methods do not have these shortcomings, they are generally orders of magnitude less sensitive and specific than PCR-based methods. In this review, we provide a practical guide to a novel amplification-free method, single-molecule recognition through equilibrium Poisson sampling (SiMREPS), that provides both single-molecule sensitivity and single-base selectivity by monitoring the repetitive interactions of fluorescent probes to immobilized targets. We demonstrate how this kinetic fingerprinting filters out background arising from the inevitable nonspecific binding of probes, yielding virtually zero background signal. As practical applications of this digital detection methodology, we present the quantification of microRNA miR-16 and the detection of the mutation EGFR L858R with an apparent single-base discrimination factor of over 3 million.

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Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 32%
Researcher 7 15%
Student > Master 6 13%
Student > Bachelor 3 6%
Student > Doctoral Student 3 6%
Other 3 6%
Unknown 10 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 17%
Engineering 7 15%
Chemistry 6 13%
Physics and Astronomy 4 9%
Agricultural and Biological Sciences 3 6%
Other 7 15%
Unknown 12 26%