Back

A Novel Primer Probe Set for Detection of SARS-CoV-2 by Sensitive Droplet Digital PCR

Wang, F.; Pervaiz, U.; Tian, H.; Gahdary, M. O. A. O. A.; Hamid, M. A. M.; Wang, D.

2020-11-04 infectious diseases
10.1101/2020.11.03.20224972 medRxiv
Show abstract

BackgroundThe current increase in the spread of (SARS-CoV-2) critically needs a multitarget diagnostic assays to promote analytical sensitivity to facilitate the public health actions. ObjectiveThe aim of this study was to develop a new primer-probe set targeting N gene of SARS-CoV-2 to improve the sensitivity for detection of COVID-19(Corona Virus Disease 2019)in multiplex rRT-PCR (Reversetranscript Realtime PCR) and ddPCR (Droplet Digital PCR). ResultsWe designed primers/probes set N(LZU3) targeting the N gene of 2019-nCov and proved its sensitivity in both rRT-PCR and ddPCR. When the quantity of template was 105 copies/reaction, the mean Ct value of N(LZU3) was 32.563, the detection rate was 91.7%. If the quantity of template was 52.5 copies/reaction, the mean Ct value of N(LZU3) was 33.835, and the detection rate was 83.3%, which were similar with that of N(CDC) and N(USA). The calculated lower limit of detection (LOD) of the new primer-probe set N(LZU3) used in rRT-PCR was 118 copies/reaction. We also did one-step ddPCR for detection the same serial dilution of RNA template. It shows good linearity for primer/probe sets N(LZU3). The calculated lower limit of detection (LOD) of N(LZU3) was 22.4 copies/reaction, which was 1.12 copies/ul. ConclusionThe novel primer-probe set(LZU3) targeting N gene of SARS-CoV-2 could be both used in rRT-PCR and ddPCR with better sensitivity, furthermore, ddPCR method had higer sensitivity than rRT-PCR, hence it could significantly improve SARS-CoV-2 detection efficiency in low virus load and asymptomatic infection.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.