Back

Attomolar Detection of HIV-1 with Label-Free RCA-rCRISPR on Smartphone

Mohammad, N.; Steksova, A.; Tang, Y.; Huang, L.; Velayati, A.; Zhang, S.; Dey Poonam, A.; Jamalzadegan, S.; Breen, M.; Jiang, G.; Wei, Q.

2025-06-17 bioengineering
10.1101/2025.06.15.659809 bioRxiv
Show abstract

Human Immunodeficiency Virus-1 (HIV) remains a major global public health challenge, having led to over 42.3 million deaths since its discovery in the early 1980s. Despite progress in prevention and treatment, around 60% of people with HIV (PWH) remain undiagnosed in resource-limited regions, disproportionately affecting vulnerable populations and underserved communities across the world. This illustrates the critical need for accessible, accurate, and equipment-free diagnostic tools to enhance detection and thus provide opportunities to curb its spread. Here, we developed a low-cost, robust, and label-free rolling circle amplification (RCA)-rCRISPR diagnostic platform for detecting HIV viral load with minimal instrumentation. Our strategy, combining the integration of RNA-detecting RCA reaction with plasmid reporter-based ratiometric CRISPR (rCRISPR), enables sensitive detection of unprocessed RNA targets without the need for intensive sample pre-treatment. This label-free RCA-rCRISPR diagnostic platform detected HIV RNA down to single-digit aM sensitivity (~3000 copies/mL) from PWH-derived HIV samples ex vivo. Unlike typical RCA, which requires sample fragmentations to break long RNA target sequences, our design harnesses the triple functions of the phi29 DNA polymerase (namely exonuclease activity, polymerization, and strand displacement), enabling the detection of the entire HIV genome without pre-fragmentation. For point-of-care (POC) applications, we constructed an all-in-one smartphone-based minigel electrophoresis device to facilitate equipment-free HIV viral load testing, making it accessible to resource-limited communities. Additionally, the assay has demonstrated the ability for point mutation detection (BRAF mutation in canine urothelial carcinoma), showcasing the robustness of our strategy for broad disease diagnostic applications.

Matching journals

The top 3 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.