Back

STAMP-Based Digital CRISPR-Cas13a (STAMP-dCRISPR) for Amplification-Free Quantification of HIV-1 Plasma Viral Load

Nouri, R.; Jiang, Y.; Politza, A. J.; Liu, T.; Greene, W.; Nunez, J.; Lian, X. L.; Guan, W.

2022-10-14 bioengineering
10.1101/2022.10.13.512138 bioRxiv
Show abstract

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.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.