Accurate quantification of SARS-CoV-2 RNA by isotope dilution mass spectrometry and providing a correction of reverse transcription efficiency in droplet digital PCR
The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 505 million confirmed cases, including over 6 million deaths. Reference materials (RMs) of SARS-CoV-2 RNA played a crucial role in performance evaluation and qu...
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Published in | Analytical and bioanalytical chemistry Vol. 414; no. 23; pp. 6771 - 6777 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2022
Springer Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 505 million confirmed cases, including over 6 million deaths. Reference materials (RMs) of SARS-CoV-2 RNA played a crucial role in performance evaluation and quality control of testing laboratories. As the potential primary characterization method of RMs, reverse transcription digital PCR (RT-dPCR) measures the copy number of RNA, but the accuracy of reverse transcription (RT) efficiency has yet to be confirmed. This study established a method of enzymatic digestion followed by isotope dilution mass spectrometry (IDMS), which does not require an RT reaction, to quantify in vitro–transcribed SARS-CoV-2 RNA. RNA was digested to nucleotide monophosphate (NMP) within 15 min and analyzed by IDMS within 5 min. The consistency among the results of four different NMPs demonstrated the reliability of the proposed method. Compared to IDMS, the quantitative result of RT-dPCR turned out to be about 10% lower, possibly attributed to the incompleteness of the reverse transcription process. Therefore, the proposed approach could be valuable and reliable for quantifying RNA molecules and evaluating the RT efficiency of RT-based methods.
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1618-2642 1618-2650 |
DOI: | 10.1007/s00216-022-04238-6 |