Droplet digital recombinase polymerase amplification for multiplexed detection of human coronavirus.

Since the outbreak of coronavirus 2019 (COVID-19), detection technologies have been attracting a great deal of attention in molecular diagnosis applications. In particular, the droplet digital PCR (ddPCR) has become a promising tool as it offers absolute quantification of target nucleic acids with high specificity and sensitivity. In recent years, the combination of the isothermal amplification strategies has made ddPCR a popular method for on-site testing by enabling amplification at a constant temperature. However, the current isothermal ddPCR assays are still challenging due to inherent non-specific amplification. In this paper, we present a multiplexed droplet digital recombinase polymerase amplification (MddRPA) with precise initiation of the reaction. First, the reaction temperature and dynamic range of reverse transcription (RT) and RPA were characterized by real-time monitoring of fluorescence intensities. Using a droplet-based microfluidic chip, the master mix and the initiator were fractionated and rapidly mixed within well-confined droplets. Due to the high heat transfer and mass transfer of the droplets, the precise initiation of the amplification was enabled and the entire assay could be conducted within 30 min. The concentrations of target RNA in the range from 5 copies per µL to 2500 copies per µL could be detected with high linearity (R2 > 0.999). Furthermore, the multiplexed detection of three types of human coronaviruses was successfully demonstrated with high specificity (>96%). Finally, we compared the performance of the assay with a commercial RT-qPCR system using COVID-19 clinical samples. The MddRPA assay showed a 100% concordance with the RT-qPCR results, indicating its reliability and accuracy in detecting SARS-CoV-2 nucleic acids in clinical samples. Therefore, our MddRPA assay with rapid detection, precise quantification, and multiplexing capability would be an interesting method for molecular diagnosis of viral infections.

Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus.

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.