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  1. Article ; Online: Purely electrical SARS-CoV-2 sensing based on single-molecule counting.

    van Kooten, Xander F / Rozevsky, Yana / Marom, Yulia / Ben Sadeh, Efrat / Meller, Amit

    Nanoscale

    2022  Volume 14, Issue 13, Page(s) 4977–4986

    Abstract: The majority of RNA based COVID-19 diagnostics employ enzymatic amplification to achieve high sensitivity, but this relies on arbitrary thresholding, which complicates the comparison of test results and may lead to false outcomes. Here we introduce solid- ...

    Abstract The majority of RNA based COVID-19 diagnostics employ enzymatic amplification to achieve high sensitivity, but this relies on arbitrary thresholding, which complicates the comparison of test results and may lead to false outcomes. Here we introduce solid-state nanopore sensing for label-free quantification of SARS-CoV-2 RNA in clinical nasal swab samples. This PCR-free method involves reverse transcribing a target gene on the viral RNA before enzymatically digesting all but the resulting dsDNA. Ratiometric quantification of RNA abundance is achieved by single-molecule counting and length-based nanopore identification of dsDNA from a SARS-CoV-2 gene and a human reference gene. We graded nasal swab samples from >15 subjects and find that the SARS-CoV-2 ratiometric nanopore index correlates well with the reported RT-qPCR threshold cycle for positive classified samples. Remarkably, nanopore analysis also reports quantitative positive outcomes for clinical samples classified as negative by RT-qPCR, suggesting that the method may be used to diagnose COVID-19 in samples that may evade detection. We show that the sample preparation workflow can be implemented using a compact microfluidic device with integrated thermal control for semi-automated processing of extremely small sample volumes, offering a viable route towards automated, fast and affordable RNA quantification in a small and portable device.
    MeSH term(s) COVID-19/diagnosis ; Humans ; Nucleic Acid Amplification Techniques/methods ; RNA, Viral/genetics ; SARS-CoV-2/genetics ; Sensitivity and Specificity
    Chemical Substances RNA, Viral
    Language English
    Publishing date 2022-03-31
    Publishing country England
    Document type Journal Article
    ZDB-ID 2515664-0
    ISSN 2040-3372 ; 2040-3364
    ISSN (online) 2040-3372
    ISSN 2040-3364
    DOI 10.1039/d1nr07787b
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Quantification of mRNA Expression Using Single-Molecule Nanopore Sensing.

    Rozevsky, Yana / Gilboa, Tal / van Kooten, Xander F / Kobelt, Dennis / Huttner, Diana / Stein, Ulrike / Meller, Amit

    ACS nano

    2020  Volume 14, Issue 10, Page(s) 13964–13974

    Abstract: RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases ... ...

    Abstract RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases of rare transcripts or inhibiting contaminants in the sample, an extensive amplification could bias the copy number estimation, leading to quantification errors and false diagnosis. Single-molecule techniques may bypass amplification but commonly rely on fluorescence detection and probe hybridization, which introduces noise and limits multiplexing. Here, we introduce reverse transcription quantitative nanopore sensing (RT-qNP), an RNA quantification method that involves synthesis and single-molecule detection of gene-specific cDNAs without the need for purification or amplification. RT-qNP allows us to accurately quantify the relative expression of metastasis-associated genes MACC1 and S100A4 in nonmetastasizing and metastasizing human cell lines, even at levels for which RT-qPCR quantification produces uncertain results. We further demonstrate the versatility of the method by adapting it to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA against a human reference gene. This internal reference circumvents the need for producing a calibration curve for each measurement, an imminent requirement in RT-qPCR experiments. In summary, we describe a general method to process complicated biological samples with minimal losses, adequate for direct nanopore sensing. Thus, harnessing the sensitivity of label-free single-molecule counting, RT-qNP can potentially detect minute expression levels of RNA biomarkers or viral infection in the early stages of disease and provide accurate amplification-free quantification.
    MeSH term(s) Betacoronavirus/genetics ; Biosensing Techniques/methods ; Biosensing Techniques/standards ; HCT116 Cells ; Humans ; Nanopores ; RNA, Messenger/analysis ; RNA, Messenger/genetics ; RNA, Messenger/metabolism ; RNA, Viral/genetics ; RNA, Viral/metabolism ; S100 Calcium-Binding Protein A4/genetics ; S100 Calcium-Binding Protein A4/metabolism ; SARS-CoV-2 ; Single Molecule Imaging/methods ; Single Molecule Imaging/standards ; Trans-Activators/genetics ; Trans-Activators/metabolism
    Chemical Substances MACC1 protein, human ; RNA, Messenger ; RNA, Viral ; S100 Calcium-Binding Protein A4 ; Trans-Activators ; S100A4 protein, human (142662-27-9)
    Keywords covid19
    Language English
    Publishing date 2020-09-23
    Publishing country United States
    Document type Journal Article
    ISSN 1936-086X
    ISSN (online) 1936-086X
    DOI 10.1021/acsnano.0c06375
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Quantification of mRNA Expression Using Single-Molecule Nanopore Sensing

    Rozevsky, Yana / Gilboa, Tal / van Kooten, Xander F / Kobelt, Dennis / Huttner, Diana / Stein, Ulrike / Meller, Amit

    ACS Nano

    Abstract: RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases ... ...

    Abstract RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases of rare transcripts or inhibiting contaminants in the sample, an extensive amplification could bias the copy number estimation, leading to quantification errors and false diagnosis. Single-molecule techniques may bypass amplification but commonly rely on fluorescence detection and probe hybridization, which introduces noise and limits multiplexing. Here, we introduce reverse transcription quantitative nanopore sensing (RT-qNP), an RNA quantification method that involves synthesis and single-molecule detection of gene-specific cDNAs without the need for purification or amplification. RT-qNP allows us to accurately quantify the relative expression of metastasis-associated genes MACC1 and S100A4 in nonmetastasizing and metastasizing human cell lines, even at levels for which RT-qPCR quantification produces uncertain results. We further demonstrate the versatility of the method by adapting it to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA against a human reference gene. This internal reference circumvents the need for producing a calibration curve for each measurement, an imminent requirement in RT-qPCR experiments. In summary, we describe a general method to process complicated biological samples with minimal losses, adequate for direct nanopore sensing. Thus, harnessing the sensitivity of label-free single-molecule counting, RT-qNP can potentially detect minute expression levels of RNA biomarkers or viral infection in the early stages of disease and provide accurate amplification-free quantification.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #766008
    Database COVID19

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