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

Overview of attention for article published in Methods, January 2019
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A guide to nucleic acid detection by single-molecule kinetic fingerprinting
Published in
Methods, January 2019
DOI 10.1016/j.ymeth.2018.08.002
Pubmed ID

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


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.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 30%
Researcher 4 17%
Student > Doctoral Student 3 13%
Student > Master 3 13%
Student > Bachelor 1 4%
Other 0 0%
Unknown 5 22%
Readers by discipline Count As %
Engineering 5 22%
Biochemistry, Genetics and Molecular Biology 4 17%
Agricultural and Biological Sciences 3 13%
Computer Science 2 9%
Medicine and Dentistry 1 4%
Other 2 9%
Unknown 6 26%