Department of Electrical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States.
Department of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States.
ACS Nano. 2023 Jun 13;17(11):10701-10712. doi: 10.1021/acsnano.3c01917. Epub 2023 May 30.
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 elf-digitization hrough utomated embrane-based artitioning (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 < 32. However, the STAMP-dCRISPR is limited in detecting low positive samples with > 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.
血浆中 HIV RNA 的定量对于识别疾病进展和监测抗逆转录病毒治疗的效果至关重要。虽然 RT-qPCR 一直是 HIV 病毒载量定量的金标准,但数字检测方法可以提供一种无校准的替代绝对定量方法。在这里,我们报告了一种通过自动膜基分区(STAMP)方法对 CRISPR-Cas13 检测(dCRISPR)进行数字化的方法,用于无扩增和绝对定量 HIV-1 病毒 RNA。设计、验证和优化了 HIV-1 Cas13 检测方法。我们用合成 RNA 评估了分析性能。使用一种将约 100nL 反应混合物(有效包含 10nL 输入 RNA 样品)分区的膜,我们表明可以在 30 分钟内定量 4 个数量级动态范围内的 RNA 样品,范围从 1fM(约 6 个 RNA)到 10pM(约 60k 个 RNA)。我们还使用 140μL 加标和临床血浆样本,从 RNA 提取到 STAMP-dCRISPR 定量,检查了端到端性能。我们证明该设备的检测限约为 2000 拷贝/mL,能够以 90%的置信度分辨出 3571 拷贝/mL(相当于单个膜中的 3 个 RNA)的病毒载量变化。最后,我们使用 140μL 的 20 个患者血浆样本(10 个阳性和 10 个阴性)评估了该设备,并与 RT-PCR 进行了性能比较。对于所有阴性和高阳性样本,STAMP-dCRISPR 的结果与 RT-PCR 非常吻合,<32。然而,由于亚采样误差,STAMP-dCRISPR 对 >32 的低阳性样本的检测受到限制。我们的结果表明,数字 Cas13 平台可以提供一种易于访问的无扩增病毒 RNA 定量方法。通过进一步解决亚采样问题,例如采用预浓缩等方法,该平台可以进一步用于定量确定多种传染病的病毒载量。