Clustered regularly interspaced short palindromic repeats (CRISPR)-based nucleic acid-sensing systems have grown rapidly in the past few years. Nevertheless, an objective approach to benchmark the performances of different CRISPR sensing systems is lacking due to the heterogeneous experimental setup. Here, we developed a quantitative CRISPR sensing figure of merit (FOM) to compare different CRISPR methods and explore performance improvement strategies. The CRISPR sensing FOM is defined as the product of the limit of detection (LOD) and the associated CRISPR reaction time (T). A smaller FOM means that the method can detect smaller target quantities faster. We found that there is a tradeoff between the LOD of the assay and the required reaction time. With the proposed CRISPR sensing FOM, we evaluated five strategies to improve the CRISPR-based sensing: preamplification, enzymes of higher catalytic efficiency, multiple crRNAs, digitalization, and sensitive readout systems. We benchmarked the FOM performances of 57 existing studies and found that the effectiveness of these strategies on improving the FOM is consistent with the model prediction. In particular, we found that digitalization is the most promising amplification-free method for achieving comparable FOM performances (∼1 fM·min) as those using preamplification. The findings here would have broad implications for further optimization of the CRISPR-based sensing.
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 C t < 32. However, the STAMP-dCRISPR is limited in detecting low positive samples with C t > 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.
The development of new nucleic acid techniques to quantify 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 report the development of a self-digitalization through automated membrane-based partitioning (STAMP) technique to digitalize the CRISPR-Cas13 assay (dCRISPR) for amplification-free and absolute quantification of HIV-1 viral RNAs. The analytical performances of STAMP-dCRISPR were evaluated with synthetic HIV-1 RNA, and it was found samples spanning 4 orders of dynamic range between 100 aM to 1 pM can be quantified as fast as 30 min. We also examined the overall assay from RNA extraction to STAMP-dCRISPR quantification with spiked plasma samples. The overall assay showed a resolution of 42 aM at a 90% confidence level. Finally, a total of 20 clinical plasma samples from patients were evaluated with STAMP-dCRISPR. The obtained results agreed well with the RT-qPCR. Our result demonstrates a new type of easy-to-use, scalable, and highly specific digital platform that would offer a simple and accessible platform for amplification-free quantification of viral RNAs, which could be exploited for the quantitative determination of viral load for an array of infectious diseases.
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