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Detection of SARS-CoV-2 with SHERLOCK One-Pot Testing



To the editor:

CRISPR (grouped regularly interspaced short palindromic recurrences) -based diagnostic tests1.2 collectively provide an emerging platform for the detection of viral and bacterial pathogens. Methods such as SHERLOCK (specific high-sensitivity enzymatic reporter locking), which typically use a two-step process (target amplification followed by CRISPR-mediated nucleic acid detection),1.2 has been used to detect SARS-CoV-2.3 However, these approaches are more complex than those used in healthcare testing because they involve one RNA extraction step and several liquid handling steps that increase the risk of cross-contamination of samples.

Here we describe a simple test for detection of SARS-CoV-2. The sensitivity of this test is similar to that of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays. STOP (SHERLOCK test in a pot) is a streamlined assay that combines simplified extraction of viral RNA with isothermal amplification and CRISPR-mediated detection. This test can be performed at a single temperature in less than an hour and with minimal equipment.

The integration of isothermal amplification with CRISPR-mediated detection required the development of a common reaction buffer that could accommodate both steps. To amplify viral RNA, we chose reverse transcription followed by loop-mediated isothermal amplification (LAMP)4 because LAMP reagents are widely available and use defined buffers that are susceptible to Cas enzymes. LAMP operates at 55 to 70 ° C and requires a thermostable Cas enzyme such as Cas1

2b from Alicyclobacillus acidiphilus (AapCas12b).5 We systematically evaluated several LAMP primer sets and AapCas12b guide RNA (a guide RNA helps AapCas12b recognize and cut target DNA) to identify the best combination to target the gene N, encoding the SARS-CoV-2 nucleocapsid protein, in a reaction mixture with a pot (see Figs. S1 to S3 in the Supplementary Appendix, available in full text in this letter at NEJM.org). We called this analysis STOPCovid, version 1 (STOPCovid.v1). As expected, STOPCovid.v1 detection produced a signal only when the target was present, while LAMP alone can produce a non-specific signal (Fig. S3E). STOPCovid.v1 is compatible with page flow and fluorescence readings and can detect an internal control using a fluorescence reading (Figs. S4 to S6).

STOPCovid, version 2 (STOPCovid.v2) Test and performance evaluation.

Panel A shows a nasopharyngeal or anterior nasal swab dipped in 400 μl of extraction solution containing lysis buffer and magnetic beads (step 1). After 10 minutes at room temperature, the sample was placed on a magnet (step 2) and extraction buffer was aspirated (step 3). A total of 50 | 1l of the STOPCovid.v2 reaction mixture was added to the beads (step 4) and the sample was heated to 60 ° C (step 5). For a lateral reading, detection strips were flowed after 80 minutes in the reaction mixture (steps 6 and 7, top). After 45 minutes, a fluorescence reader was used to measure the fluorescence of the reaction mixture (steps 6 and 7, bottom). Panel B shows STOPCovid.v2 results for 202 SARS-CoV-2 positive samples from nasopharyngeal swabs obtained from patients and detected using a fluorescence reading and measured in relative fluorescence units (RFU). A stick with 50 | The viral transport medium was dipped in the extraction buffer. Cycle thresholds (Ct) were determined using standard reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assays. Each point indicates a sample and the red dashed line indicates the threshold above which the samples were classified as positive. Endpoint fluorescence at 45 minutes is displayed. Panel C shows STOPCovid.v2 results for 200 SARS-CoV-2 – negative nasopharyngeal swabs obtained from patients. The samples were sorted by endpoint fluorescence and measured in RFU. Each dot indicates a sample, and the red dashed line indicates the sample classification threshold.

To simplify RNA extraction and increase the sensitivity, we adapted a magnetic bead purification method (Fig. S9). The magnetic beads concentrated SARS-CoV-2 RNA genome from an entire nasopharyngeal or anterior nasal swab to a STOPCovid reaction mixture. We streamlined the test by combining lysis and steps with magnetic bead bonding and eliminating the ethanol washing and elution steps to reduce the duration of the sample extraction to 15 minutes with minimal practical time. We refer to this streamlined test as STOPCovid, version 2 (STOPCovid.v2) (Figure 1A).

We compared STOPCovid.v2 with the Centers for Disease Control and Prevention (CDC) standard two-step test (ie RNA extraction followed by RT-qPCR) (Fig. S10C). The concentration of substrate of magnetic beads in STOPCovid.v2 enabled the detection of viral RNA from the whole sample, giving an insert (expressed in quantity of viral RNA) which was 600 times that given by the CDC test. As a result, STOPCovid.v2 reliably detected a virus load that was one-thirtieth detected by the CDC RT-qPCR test (100 copies per sample or 33 copies per milliliter, compared to 1000 copies per milliliter). Analysis of two independent dilution series from nasopharyngeal swabs revealed that STOPCovid.v2 had a detection limit similar to an RT-qPCR cycle threshold (Ct) of 40.3 (Figs. S10D and S10E).

Positive and negative predictive values, sensitivity and specificity of STOPCovid.v2 for detection of SARS-CoV-2 in nasopharyngeal samples.

In blind tests at an external laboratory at the University of Washington, we tested 202 SARS-CoV-2-positive and 200 SARS-CoV-2-negative nasopharyngeal swabs obtained from patients. These samples were prepared by adding 50 μl of swabs obtained from patients with Covid-19 to a clean swab, in accordance with the recommendation of the Food and Drug Administration for simulation of whole swabs for control applications (see section Methods in appendix). This test showed that STOPCovid.v2 had a sensitivity of 93.1% and a specificity of 98.5% (Figures 1B and 1C, Fig. S11A and table 1). STOPCovid.v2 false negative samples had RT-qPCR Ct values ​​greater than 37. Positive samples were detected in 15 to 45 minutes. Finally, we used fresh, dry, anterior nasal cones (collected according to the recommendations of the CDC) to validate STOPCovid.v2, and we correctly identified 5 positive samples (Ct values, 19 to 36) and 10 negative samples (Fig. S11B through S11E) . A detailed protocol for STOPCovid.v2 can be found in the supplementary appendix. The simplified format of STOPCovid.V2 is suitable for use in low-complexity clinical laboratories.

Julia Joung, BS
Alim Ladha, BS
Massachusetts Institute of Technology, Cambridge, MA

Makoto Saito, Ph.D.
Broad Institute of MIT and Harvard, Cambridge, MA

Nam-Gyun Kim, Ph.D.
University of Washington, Seattle, WA

Ann E. Woolley, MD, MPH
Brigham and Women’s Hospital, Boston, MA

Michael Segel, Ph.D.
Broad Institute of MIT and Harvard, Cambridge, MA

Robert PJ Barretto, Ph.D.
Kallyope, New York, NY

Amardeep Ranu, BS
DynamiCare Health, Boston, MA

Rhiannon K. Macrae, Ph.D.
Guilhem Faure, Ph.D.
Broad Institute of MIT and Harvard, Cambridge, MA

Eleonora I. Ioannidi, BS
Rohan N. Krajeski, BS
Massachusetts Institute of Technology, Cambridge, MA

Robert Bruneau, BS
Meei-Li W. Huang, Ph.D.
University of Washington, Seattle, WA

Xu G. Yu, MD
Ragon Institute of MGH, MIT and Harvard, Cambridge, MA

Jonathan Z. Li, MD
Brigham and Women’s Hospital, Boston, MA

Bruce D. Walker, MD
Ragon Institute of MGH, MIT and Harvard, Cambridge, MA

Deborah T. Hung, MD, Ph.D.
Broad Institute of MIT and Harvard, Cambridge, MA

Alexander L. Greninger, MD, Ph.D.
University of Washington, Seattle, WA

Keith R. Jerome, MD, Ph.D.
Fred Hutchinson Cancer Research Center, Seattle, WA

Jonathan S. Gootenberg, Ph.D.
Omar O. Abudayyeh, Ph.D.
Massachusetts Institute of Technology, Cambridge, MA
[email protected], [email protected]

Feng Zhang, Ph.D.
Broad Institute of MIT and Harvard, Cambridge, MA
[email protected]

Supported by a scholarship (1F31-MH117886, to Mrs Joung) from the National Institutes of Health (NIH); a scholarship (to Dr. Saito) from the Swiss National Science Foundation; a scholarship (to Dr. Gootenberg and Abudayyeh) from the McGovern Institute for Brain Research at the Massachusetts Institute of Technology; scholarships (to Dr. Gootenberg, Abudayyeh, and Zhang) from the Patrick J. McGovern Foundation and the Massachusetts Consortium on Pathogen Readiness Evergrande Covid-19 Response Fund; and grants (to Dr. Zhang) from the NIH (1R01-MH110049 and 1DP1-HL141201), the Mathers Foundation, the Howard Hughes Medical Institute, the Open Philanthropy Project, and James and Patricia Poitras and Robert Metcalfe.

Information forms provided by the authors can be found with the full text of this letter on NEJM.org

This letter was published on September 16, 2020 on NEJM.org.

Joung and Ladha and Drs. Gootenberg, Abudayyeh and Zhang contributed equally to this letter.

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