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Multiplex digital PCR with digital melting curve analysis on a self-partitioning SlipChip.

Authors :
Yu, Yan
Yu, Ziqing
Pan, Xufeng
Xu, Lei
Guo, Rui
Qian, Xiaohua
Shen, Feng
Source :
Analyst. 2/21/2022, Vol. 147 Issue 4, p625-633. 9p.
Publication Year :
2022

Abstract

Digital polymerase chain reaction (digital PCR) can provide absolute quantification of target nucleic acids with high sensitivity, excellent precision, and superior resolution. Digital PCR has broad applications in both life science research and clinical molecular diagnostics. However, limited by current fluorescence imaging methods, parallel quantification of multiple target molecules in a single digital PCR remains challenging. Here, we present a multiplex digital PCR method using digital melting curve analysis (digital MCA) with a SlipChip microfluidic system. The self-partitioning SlipChip (sp-SlipChip) can generate an array of nanoliter microdroplets with trackable physical positions using a simple loading-and-slipping operation. A fluorescence imaging adaptor and an in situ thermal cycler can be used to perform digital PCR and digital MCA on the sp-SlipChip. The unique signature melting temperature (Tm) designed for amplification products can be used as a fingerprint to further classify the positive amplification partitions into different subgroups. Amplicons with Tm differences as low as 1.5 degrees celsius were clearly separated, and multiple amplicons in the same partition could also be distinguished by digital MCA. We further demonstrated this digital MCA method with simultaneous digital quantification of five common respiratory pathogens, including Staphylococcus aureus, Acinetobacter baumannii, Streptococcus pneumoniae, Hemophilus influenzae, and Klebsiella pneumoniae. Since digital MCA only requires an intercalation dye instead of sequence-specific hydrolysis probes to perform multiplex digital PCR analysis, it can be less expensive and not limited to the number of fluorescence channels. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00032654
Volume :
147
Issue :
4
Database :
Academic Search Index
Journal :
Analyst
Publication Type :
Academic Journal
Accession number :
155236127
Full Text :
https://doi.org/10.1039/d1an01916c