Dataset Information

Amplification-Free Detection of SARS-CoV-2 and Respiratory Syncytial Virus Using CRISPR Cas13a and Graphene Field-Effect Transistors.

ABSTRACT: The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems have recently received notable attention for their applications in nucleic acid detection. Despite many attempts, the majority of current CRISPR-based biosensors in infectious respiratory disease diagnostic applications still require target preamplifications. This study reports a new biosensor for amplification-free nucleic acid detection via harnessing the trans-cleavage mechanism of Cas13a and ultrasensitive graphene field-effect transistors (gFETs). CRISPR Cas13a-gFET achieves the detection of SARS-CoV-2 and respiratory syncytial virus (RSV) genome down to 1 attomolar without target preamplifications. Additionally, we validate the detection performance using clinical SARS-CoV-2 samples, including those with low viral loads (Ct value >30). Overall, these findings establish our CRISPR Cas13a-gFET among the most sensitive amplification-free nucleic acid diagnostic platforms to date.

SUBMITTER: Li H

PROVIDER: S-EPMC9347639 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Amplification-Free Detection of SARS-CoV-2 and Respiratory Syncytial Virus Using CRISPR Cas13a and Graphene Field-Effect Transistors.

Li Huijie H Yang Jie J Wu Guangfu G Weng Zhengyan Z Song Yang Y Zhang Yuxuan Y Vanegas Jeffrey A JA Avery Lori L Gao Zan Z Sun He H Chen Yupeng Y Dieckhaus Kevin D KD Gao Xue X Zhang Yi Y

Angewandte Chemie (International ed. in English) 20220531 32

The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) systems have recently received notable attention for their applications in nucleic acid detection. Despite many attempts, the majority of current CRISPR-based biosensors in infectious respiratory disease diagnostic applications still require target preamplifications. This study reports a new biosensor for amplification-free nucleic acid detection via harnessing the trans-cleavage mechanism of Cas13a an ...[more]

PMID: 35559592

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Project description: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 Ct < 32. However, the STAMP-dCRISPR is limited in detecting low positive samples with Ct > 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.

Dataset Information

Amplification-Free Detection of SARS-CoV-2 and Respiratory Syncytial Virus Using CRISPR Cas13a and Graphene Field-Effect Transistors.

Publications

Amplification-Free Detection of SARS-CoV-2 and Respiratory Syncytial Virus Using CRISPR Cas13a and Graphene Field-Effect Transistors.

Similar Datasets

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