病毒载量
核糖核酸
检出限
RNA提取
生物
化学
分子生物学
色谱法
病毒学
人类免疫缺陷病毒(HIV)
基因
生物化学
作者
Reza Nouri,Yuqian Jiang,Anthony J. Politza,Tianyi Liu,Wallace Greene,Yusheng Zhu,Jonathan Nunez,Xiaojun Lian,Weihua Guan
出处
期刊:ACS Nano
[American Chemical Society]
日期:2023-05-30
卷期号:17 (11): 10701-10712
被引量:16
标识
DOI:10.1021/acsnano.3c01917
摘要
Quantification of HIV RNA in plasma is critical for identifying the disease progression and monitoring the effectiveness of antiretroviral therapy. While RT-qPCR has been the gold standard for HIV viral load quantification, digital assays could provide an alternative calibration-free absolute quantification method. Here, we reported a Self-digitization Through Automated Membrane-based Partitioning (STAMP) method to digitalize the CRISPR-Cas13 assay (dCRISPR) for amplification-free and absolute quantification of HIV-1 viral RNAs. The HIV-1 Cas13 assay was designed, validated, and optimized. We evaluated the analytical performances with synthetic RNAs. With a membrane that partitions ∼100 nL of reaction mixture (effectively containing 10 nL of input RNA sample), we showed that RNA samples spanning 4 orders of dynamic range between 1 fM (∼6 RNAs) to 10 pM (∼60k RNAs) could be quantified as fast as 30 min. We also examined the end-to-end performance from RNA extraction to STAMP-dCRISPR quantification using 140 μL of both spiked and clinical plasma samples. We demonstrated that the device has a detection limit of approximately 2000 copies/mL and can resolve a viral load change of 3571 copies/mL (equivalent to 3 RNAs in a single membrane) with 90% confidence. Finally, we evaluated the device using 140 μL of 20 patient plasma samples (10 positives and 10 negatives) and benchmarked the performance with RT-PCR. The STAMP-dCRISPR results agree very well with RT-PCR for all negative and high positive samples with Ct < 32. However, the STAMP-dCRISPR is limited in detecting low positive samples with Ct > 32 due to the subsampling errors. Our results demonstrated a digital Cas13 platform that could offer an accessible amplification-free quantification of viral RNAs. By further addressing the subsampling issue with approaches such as preconcentration, this platform could be further exploited for quantitatively determining viral load for an array of infectious diseases.
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