数字聚合酶链反应
微流控
墨盒
多路复用
样品制备
纳米技术
色谱法
材料科学
计算机科学
化学
聚合酶链反应
生物
生物信息学
基因
生物化学
冶金
作者
Lidija Malic,Liviu Clime,Byeong‐Ui Moon,Christina Nassif,Dillon Da Fonte,D. Brassard,Ljuboje Lukic,Matthias Geißler,Keith Morton,Denis Charlebois,Teodor Veres
出处
期刊:Lab on a Chip
[Royal Society of Chemistry]
日期:2024-01-01
卷期号:24 (20): 4755-4765
被引量:16
摘要
Droplet digital polymerase chain reaction (ddPCR) stands out as a highly sensitive diagnostic technique that is gaining traction in infectious disease diagnostics due to its ability to quantitate very low numbers of viral gene copies. By partitioning the sample into thousands of droplets, ddPCR enables precise and absolute quantification without relying on a standard curve. However, current ddPCR systems often exhibit relatively low levels of integration, and the analytical process remains dependent on elaborate workflows for up-front sample preparation. Here, we introduce a fully-integrated system seamlessly combining viral lysis, RNA extraction, emulsification, reverse transcription (RT) ddPCR, and fluorescence readout in a sample-to-answer format. The system comprises a disposable microfluidic cartridge housing buffers and reagents required for the assay, and a centrifugal platform that allows for pneumatic actuation of liquids during rotation, enabling automation of the workflow. Highly monodisperse droplets (∼50 μm in diameter) are produced using centrifugal step emulsification and automatically transferred to an integrated heating module for target amplification. The platform is equipped with a miniature fluorescence imaging system enabling on-chip read-out of droplets after RT-ddPCR. We demonstrate sample-to-answer detection of SARS-CoV-2 N and E genes, along with RNase P endogenous reference, using hydrolysis probes and multiplexed amplification within single droplets for concentrations as low as 0.1 copy per μL. We also tested 14 nasopharyngeal swab specimens from patients and were able to distinguish positive and negative SARS-CoV-2 samples with 100% accuracy, surpassing results obtained by conventional real-time amplification.
科研通智能强力驱动
Strongly Powered by AbleSci AI