Development of an accurate CRISPR/Cas13 diagnostic system for Wuchereria bancrofti
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
Published on: May 10, 2024
Skye R MacGregor1, Richard S Bradbury2,3, Malcolm K Jones1,4
1Infection and Inflammation Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Abstract:
Disease surveillance is critical for WHO's Global Programme to Eliminate Lymphatic Filariasis (LF), but current field-ready diagnostics exhibit insufficient sensitivity and/or specificity in low-endemic settings. We aim to develop an ultra-sensitive and field-friendly CRISPR/Cas13-based SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) tool for point-of-care detection of Wuchereria bancrofti infection. The SHERLOCK assay, combining recombinase polymerase amplification (RPA) with CRISPR/Cas13a detection, was performed in two-step and one-pot formats, with sensitivity compared to qPCR (the emerging gold standard). The W. bancrofti SHERLOCK assays achieved a detection limit of 0.1 fg/μL gBlock DNA and showed no cross-reaction with five other filarial species. Assays were validated using 149 pooled mosquito and 185 human blood samples from Samoa, which were previously tested for LF antigen using WHO-recommended Filariasis Test Strips (FTS) and microscopically examined for microfilariae (Mf). For both mosquito and human samples, the two-step SHERLOCK assay reached 100% sensitivity and specificity, while one-pot SHERLOCK showed 94.0-96.4% sensitivity and 100% specificity. To improve the field deployability of the assay, we also used lyophilised one-pot SHERLOCK master mix and assessed its thermostability after storage at 25°C, 4°C and -20°C. The lyophilised one-pot SHERLOCK mix maintained a detection limit of 10 fg/μL W. bancrofti gBlock after 10 days at 25°C, indicating reduced reliance on cold-chain for reagent storage and transport. However, when testing human blood samples with lyophilised SHERLOCK, reduced sensitivity (59.7%) was observed. This CRISPR-based platform offers a promising field-ready next-generation approach to LF diagnostics, potentially addressing current limitations, enhancing surveillance and elimination efforts.

