Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants
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Abstract
The coronavirus disease 2019 (COVID-19) pandemic has demonstrated a clear need for high-throughput, multiplexed and sensitive assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other respiratory viruses and their emerging variants. Here, we present a cost-effective virus and variant detection platform, called microfluidic Combinatorial Arrayed Reactions for Multiplexed Evaluation of Nucleic acids (mCARMEN), which combines CRISPR-based diagnostics and microfluidics with a streamlined workflow for clinical use. We developed the mCARMEN respiratory virus panel to test for up to 21 viruses, including SARS-CoV-2, other coronaviruses and both influenza strains, and demonstrated its diagnostic-grade performance on 525 patient specimens in an academic setting and 166 specimens in a clinical setting. We further developed an mCARMEN panel to enable the identification of 6 SARS-CoV-2 variant lineages, including Delta and Omicron, and evaluated it on 2,088 patient specimens with near-perfect concordance to sequencing-based variant classification. Lastly, we implemented a combined Cas13 and Cas12 approach that enables quantitative measurement of SARS-CoV-2 and influenza A viral copies in samples. The mCARMEN platform enables high-throughput surveillance of multiple viruses and variants simultaneously, enabling rapid detection of SARS-CoV-2 variants.
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SciScore for 10.1101/2021.12.14.21267689: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Human specimens from patients with SARS-CoV-2, HCoV-HKU1, HCoV-NL63, FLUAV, FLUBV, HRSV, and HMPV were obtained under a waiver of consent from the Mass General Brigham IRB Protocol #2019P003305. Sex as a biological variable not detected. Randomization The final droplet pool was pipetted up and down gently to fully randomize the arrangement of the droplets in the pool. Blinding not detected. Power Analysis not detected. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, FLUAV, FLUBV, HMPV, HRSV, HPIV-1,2,3,4, AdV, HEV-A,B,C,D, SARS-CoV, MERS-CoV, and HRV. HCoV-NL63suggested: RRID:CVCL_RW88)Prepara… SciScore for 10.1101/2021.12.14.21267689: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Human specimens from patients with SARS-CoV-2, HCoV-HKU1, HCoV-NL63, FLUAV, FLUBV, HRSV, and HMPV were obtained under a waiver of consent from the Mass General Brigham IRB Protocol #2019P003305. Sex as a biological variable not detected. Randomization The final droplet pool was pipetted up and down gently to fully randomize the arrangement of the droplets in the pool. Blinding not detected. Power Analysis not detected. Cell Line Authentication not detected. Table 2: Resources
Experimental Models: Cell Lines Sentences Resources HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, FLUAV, FLUBV, HMPV, HRSV, HPIV-1,2,3,4, AdV, HEV-A,B,C,D, SARS-CoV, MERS-CoV, and HRV. HCoV-NL63suggested: RRID:CVCL_RW88)Preparation of contrived samples prior to extraction: Contrived patient samples of viruses HCoV-HKU1, HCoV-OC43, HCoV-NL63, FLUAV-g4, HPIV-3, and HMPV were prepared by diluting either viral seed stock (HCoV-OC43 and HPIV-3) or template RNA (HCoV-HKU1 and HCoV-NL63). HCoV-OC43suggested: NoneSoftware and Algorithms Sentences Resources These aligned sequences were then fed into ADAPT for crRNA design with high coverage using the ‘minimize guides’ objective (>90% of sequences detected). ADAPTsuggested: (ADAPT, RRID:SCR_006769)Target control - PIC1 and PIC2: The consensus sequences generated directly above after multiple genome alignment with MAFFT were used to order a 500 bp dsDNA fragment encompassing the primer and crRNA binding sites. MAFFTsuggested: (MAFFT, RRID:SCR_011811)In brief, pre-merge imaging data was processed using custom Python scripts to detect fluorescently-encoded droplets in microwells and identify their inputs based on their fluorescence intensity in three encoding channels, 647 nm, 594 nm, and 555 nm. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
Results from scite Reference Check: We found no unreliable references.
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