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Amplification‐Free, Sequencing‐Free, Detection of Viral RNAs with Variant Specification by Discrete Nanocounting
This study describes an amplification‐free, sequencing‐free platform (NanoPick‐array) for fast analysis of viral RNAs. The platform combines selective short‐cut of viral RNAs, cherry‐picking isolation of target genes, and micro‐arrayed discrete nanoimaging to enable the detection of severe acute res...
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Published in: | Advanced functional materials 2024-02 |
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Main Authors: | , , , , , , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | This study describes an amplification‐free, sequencing‐free platform (NanoPick‐array) for fast analysis of viral RNAs. The platform combines selective short‐cut of viral RNAs, cherry‐picking isolation of target genes, and micro‐arrayed discrete nanoimaging to enable the detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) at a concentration of 60 copies µL
−1
, a detection limit that is hardly achieved by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)‐based methods without amplification, regardless of electrochemical or colorimetric approaches. Notably, the NanoPick‐array provides specific virus variant information with differentiation to single‐nucleotide genetic mutation. The avoidance of the amplification procedure gives direct quantification of viral copy number and reduces false positive results caused by amplicon contamination; the sequencing‐free viral variant specification significantly reduces the turn‐over time for the acquisition of a complete diagnostic viral picture within just 2 h. In a demonstration using clinical samples with a wide range of viral loads of cycle threshold (Ct) value ranging from 18 to 36, the technique achieves an overall accuracy of 89.7% viral detection and 100% accuracy for identifying all Delta variants. The viral detection accuracy is further tested to be 100% for the clinical samples with Ct values around or less than 28. |
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ISSN: | 1616-301X 1616-3028 |
DOI: | 10.1002/adfm.202310157 |