Rapid and accurate nucleobase detection using FnCas9 and its application in COVID-19 diagnosis

  1. Mohd. Azhar
  2. Rhythm Phutela
  3. Manoj Kumar
  4. Asgar Hussain Ansari
  5. Riya Rauthan
  6. Sneha Gulati
  7. Namrata Sharma
  8. Dipanjali Sinha
  9. Saumya Sharma
  10. Sunaina Singh
  11. Sundaram Acharya
  12. Sajal Sarkar
  13. Deepanjan Paul
  14. Poorti Kathpalia
  15. Meghali Aich
  16. Paras Sehgal
  17. Gyan Ranjan
  18. Rahul C. Bhoyar
  19. Khushboo Singhal
  20. Harsha Lad
  21. Pradeep Kumar Patra
  22. Govind Makharia
  23. Giriraj Ratan Chandak
  24. Bala Pesala
  25. Debojyoti Chakraborty
  26. Souvik Maiti

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Abstract

No abstract available

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  1. Version published to 10.1016/j.bios.2021.113207
  2. ScreenIT

    SciScore for 10.1101/2020.09.13.20193581: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: For the SNV detection using FELUDA, SCD samples were collected after informed consent from volunteers in CSIR-Sickle Cell Anemia Mission Laboratory, Chhattisgarh Institute of Medical Sciences,
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data was analysed using NanoTemper analysis software and plotted using OriginPro 8.5 software.
    OriginPro
    suggested: None
    Base calling was carried out using sequencing analysis software (v5.3.1) (ABI, US) and sequence was analyzed using Chromas v2.6.5 (Technelysium, Australia).
    Chromas
    suggested: (Chromas, RRID:SCR_000598)
    Disease selection from ClinVar Database: ClinVar dataset (version: 20180930) was used to find out disease variation spectrum that can be targeted by FELUDA46.
    ClinVar
    suggested: (ClinVar, RRID:SCR_006169)
    Finally, variations with higher frequency in Indian Population were selected for the validation using customized python script.
    python
    suggested: (IPython, RRID:SCR_001658)
    And works with a customized python-based Flask framework along with genome analysis tools like BWA (Burrows-Wheeler aligner) and bedtools47-48.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    Further, to remove non-specific crRNA, off-target analysis was done by mapping them to human host viruses from Influenza Virus Database37 and human transcriptome (GENCODE GRCh38).
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are certain limitations of FELUDA which the COVID-19 pandemic gave an opportunity to address. The LFA gives a semi-quantitative readout of viral load in a given sample. However, for accurate and sensitive diagnosis it is imperative that a large number of primer and sgRNA pairs are tested to account for robust readout, prevention of off-target amplification and minimizing chances of targeting mutated viral regions44. Of the 21 regions we targeted, only two (N and S gene) satisfied these criteria (Supplementary Table 1). Importantly, optimized conditions and high-quality PCR reagents are necessary to ensure robust and consistent FELUDA results (Supplementary Figure 6B). We uncovered inconsistencies in qRT PCR Ct values between samples that were measured before and after freezing (Supplementary Figure 7A-B). This is relevant when comparing validation of new tests with results of qPCR-validated samples from the freezer, particularly those with high Ct values (Supplementary Figure 8). Indeed, our single gene FELUDA done on frozen samples and compared with qRT-PCR done at an earlier time point was less sensitive than double gene FELUDA on samples where simultaneous qPCR was done (Figure 3C-D). The on-body RT-RPA FELUDA prototype combined with extraction-free RNA isolation can potentially bring the benefits of testing to home. Although RT-RPA is rapid and sensitive, it is also prone to aerosol-based contamination necessitating an efficient cold-chain transportation of pre-pipe...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. ScreenIT

    SciScore for 10.1101/2020.09.13.20193581: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementFor the SNV detection using FELUDA, SCD samples were collected after informed consent from volunteers in CSIR-Sickle Cell Anemia Mission Laboratory, Chhattisgarh Institute of Medical Sciences,Randomizationnot detected.BlindingTo address the robustness of detecting the 3 genotype categories, we performed a blinded experiment using DNA obtained from 49 subjects with all three SCA genotypes from a CSIR-Sickle Cell Anaemia Mission Laboratory in Chhattisgarh state of India.Power Analysisnot detected.Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data was analysed using NanoTemper analysis software and plotted using OriginPro 8.5 software.
    OriginPro
    suggested: None
    Base calling was carried out using sequencing analysis software (v5.3.1) (ABI, US) and sequence was analyzed using Chromas v2.6.5 (Technelysium, Australia).
    Chromas
    suggested: (Chromas, RRID:SCR_000598)
    Disease selection from ClinVar Database: ClinVar dataset (version: 20180930) was used to find out disease variation spectrum that can be targeted by FELUDA46.
    ClinVar
    suggested: (ClinVar, RRID:SCR_006169)
    Finally, variations with higher frequency in Indian Population were selected for the validation using customized python script.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

    There are certain limitations of FELUDA which the COVID-19 pandemic gave an opportunity to address. The LFA gives a semi-quantitative readout of viral load in a given sample. However, for accurate and sensitive diagnosis it is imperative that a large number of primer and sgRNA pairs are tested to account for robust readout, prevention of off-target amplification and minimizing chances of targeting mutated viral regions44. Of the 21 regions we targeted, only two (N and S gene) satisfied these criteria (Supplementary Table 1). Importantly, optimized conditions and highquality PCR reagents are necessary to ensure robust and consistent FELUDA results (Supplementary Figure 6B). We uncovered inconsistencies in qRT PCR Ct values between samples that were measured before and after freezing (Supplementary Figure 7A-B). This is relevant when comparing validation of new tests with results of qPCR-validated samples from the freezer, particularly those with high Ct values (Supplementary Figure 8). Indeed, our single gene FELUDA done on frozen samples and compared with qRT-PCR done at an earlier time point was less sensitive than double gene FELUDA on samples where simultaneous qPCR was done (Figure 3C-D). The on-body RT-RPA FELUDA prototype combined with extraction-free RNA isolation can potentially bring the benefits of testing to home. Although RT-RPA is rapid and sensitive, it is also prone to aerosol-based contamination necessitating an efficient cold-chain transportation of pre-pipet...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.09.13.20193581v1 on medRxiv
  5. Version published to 10.1101/2020.04.07.028167v2 on bioRxiv
  6. Version published to 10.1101/2020.04.07.028167v1 on bioRxiv