Abstract

Several point-of-care RNA detection technologies that do not require special instruments exist, including reverse transcription–recombinase polymerase amplification (RT–RPA) and reverse transcription-loop-mediated isothermal amplification (RT-LAMP). RT–RPA and RT-LAMP are highly sensitive methods, but can suffer from nonspecific amplification under isothermal conditions, leading to false-positive results when used for viral RNA detections. Stringency of detection by these isothermal amplification methods can be improved by incorporating an additional sequence-specific detection module, such as hybridization-based fluorescent-oligonucleotide probes.

CRISPR-based diagnostic methods,   such as nucleic acid detection with crispr-cas13a, which use collateral cleavage activity of bystander nucleic acid probes of RNA-guided CRISPR-associated Cas12/Cas13 nucleases, are typically used in combination with RT-LAMP or RT–RPA isothermal amplification methods. Primers for RT-LAMP or RT–RPA can be designed for specific viral RNA sequences, and the amplification methods are used before the Cas enzyme detection step, to multiply detection signals from the specific nucleic acid sequences. Since both the nucleic acid amplification step and the CRISPR–Cas detection step require sequence specificity to trigger signal amplification, CRISPR Cas diagnostic methods are highly sensitive (attomolar level) and highly specific (down to single-nucleotide level). Readout of Cas-mediated nucleic acid probe cleavage can be done by fluorescence detection or using the lateral-flow strip method; the latter is advantageous as the strips are portable and the results can be read by eye and easily quantified by smartphones. In contrast to hybridization-based detection methods, CRISPR–Cas-based detection can be performed in the same vessel, simultaneously with isothermal amplification, greatly simplifying the operational procedure upon testing and reducing the risk of contamination. However, there is a trade-off between operational simplicity and the test performance: the easier-to-perform one-pot CRISPR diagnostic protocols are less sensitive than the two-step variant, where amplification and CRISPR-mediated detection steps are performed sequentially.

Here, we report clinical validation of the two-step CRISPR Cas13-based SHERLOCK system for sensitive and specific detection of SARS-CoV-2 viral RNA. We performed SHERLOCK CRISPR Cas13a detection on a totalof 534 clinical samples. We first characterized the test characteristics on 154 clinical samples, 81 of which were positive for SARS-CoV-2, and then used the test in real diagnostic settings for pre-operation assessment for an additional 380 clinical samples. We challenged the testing procedures with clinical samples with diverse threshold cycle (Ct) values (11–37), as well as samples from asymptomatic cases. Overall, we found that the SHERLOCK detection systemof SARS-CoV-2 RNA extracted from nasopharyngeal and throat swabs of infected patients in Thailand was 100% specific and 96% sensitive with the fluorescence readout, and 88% sensitive with the lateral-flow readout. These characteristics are comparable to the accuracy and the performances of other point-of-care genetic tests but have no requirement for specialized instruments. Within the characterized limit of detection (LoD) of the method (approximately 42 copiesper reaction, corresponding to a Ct of 33.5), SHERLOCK has 100% specificity and100% sensitivity with the fluorescence readout, and 97% sensitivity with the lateral-flow readout. To facilitate the potential use of SHERLOCK in testing settings with limited resources where there is an increased risk of ribonuclease (RNase) contamination, we designed the lateral-flow detection to contain an internal control for RNase contamination and demonstrated multiplex detection of SARS-CoV-2 RNA and RNase presence in a single lateral-flow strip.

Fig.1.

SHERLOCK detection of SARS-CoV-2 relies onRT–RPA to isothermally amplify viral gene segments of interest, followed by CRISPR Cas mediated detection of the amplified genes (in this case, using LwaCas13a), shown previously to confer the highest sensitivity in SHERLOCK-type detection). The detection of the amplified gene sequences by CRISPR Cas triggers collateral cleavage of reporter molecules for fluorescence or lateral-flow measurements (Fig. 1). We first designed and tested a total off our RPA-primer pairs and the corresponding CRISPR RNAs (crRNAs) targeting the spike (S), nucleoprotein (N) and replicase polyprotein 1ab (Orf1ab) genes of SARS-CoV-2. Two of the primer pairs (and crRNAs), for S and Orf1ab genes, have previously been validated with synthetic RNAs. The other two primer pairs and crRNAs, targeting the N gene and another region of Orf1ab, were designed to match gene regions used in the standard RT–qPCR assay for SARS-CoV-2 detection at Siriraj Hospital. All of the RPA primers and crRNA sequences were designed to be specific and selective towards SARS-CoV-2 viral RNA, and to minimize off-target affinity towards other common human coronaviruses .

Analysis of LoD and specificity

We first determined the LoD of SHERLOCK-based detection of SARS-CoV-2 upon using different combinations of RPA primers and crRNAs for the four selected gene regions. We used total RNA extracted from cultured SARS-CoV-2 in Vero cells (clinical isolateh CoV-19/Thailand/Siriraj_5/2020; GISAID accession ID: EPI_ISL_447908) as our LoD standards, performed serial dilutions of the extracted RNA and measured their corresponding Ct values with RT–qPCR for the N gene to ensure we obtained clinically relevant Ct ranges. We then verified thatall four RPA primers and crRNA sets can be used effectively with the SHERLOCK assay to detect the presence of SARS-CoV-2 RNA. We found the detection of the S gene to be the most sensitive, under both lateral-flow strip and fluorescence readouts, with reproducible detection down to 10⁻⁶ dilution, corresponding to Ct of approximately 33.5 in an RT–qPCRassay and about 42 copies per reaction, as quantified by digital-droplet PCR. We note that the low sensitivity in N gene detectionis probably due to the longer N RPA amplicon we had designed; indeed, extending the RPA reaction time for the N gene to 1h increased the sensitivity of detection to match that of the S gene. We further demonstrated that SHERLOCK-based detection of the SARS-CoV-2 S gene is specific to COVI-19, with no cross-reactivity towards other common human coronaviruses, including human coronavirus OC43 (hCoV-OC43), hCoV-NL63 and hCoV-229E.

Validation of SHERLOCK-based detection of SARS-CoV-2 in clinical samples

We empirically tested the amount of RNA added to the RT–RPA reaction and the amount of RPA reaction transferred to the CRISPR Cas13a reaction, to optimize the detection signal from SARS-CoV-2 RNA in RNA extracts from nasopharyngeal swabs. Subsequent to assessing the performance of SHERLOCK usingthe S gene as the targeted sequence, we performed the SHERLOCK-based detection of SARS-CoV-2 on COVID-19 clinical samples and directly benchmarked the SHERLOCK assay performance against the standard RT–qPCR assay. To minimize patient-selection bias and to ensure our validation study reflected the full distribution of Ct values among patients positive for COVID-19, we included all available positive samples obtained from a defined swab-collection time window, between 3 March and 10 April 2020, at Siriraj Hospital. To minimize information bias, positive and negative samples were randomized before being given to study staff, and the SHERLOCK results were interpreted without knowledge of the RT–qPCR results. As RT–qPCR was performed before SHERLOCK for all samples (weused RNA left overs from RT–qPCR for SHERLOCK validation), we stored RNA samples at −80°Cto reduce degradation and always performed SHERLOCK validation experiments onsite at Siriraj Hospital, to eliminate the possibility of sample degradation during transport.

A validation study was conducted with a total of 154 total clinical samples, consisting of 81 RT–qPCR-verified COVID-19-positive samples (Ct of N gene ranging from 11–37) and 73 RT–qPCR-verified COVID-19-negative samples. We envisioned that readouts from the SHERLOCK method could be performed with either fluorescence or lateral-flow readout, depending on the setting and the throughput, with the fluorescence readout being preferred for higher-throughput assessment, and the lateral-flow readouts intended for point-of-care usage. Both detection methods were assessed in parallel on clinical samples.

Among the 154 clinical samples, we were able to identify all 73 negative COVID-19 samples by both fluorescence and lateral-flow strip readouts, 78 out of 81 positive COVID-19 samples by fluorescence readouts, and 71 out of 81 positive COVID-19 samples by lateral-flow strip readouts. Sampleswith Ct<32 were all detectable by both fluorescence and lateral-flow readouts, closely matching the LoD Ct (33.5) determined from cultured viral RNA. Beyond this LoD, we observed better sensitivity of detection using fluorescence readouts, with sample Ct as high as37 being detected, albeit at much lower signal-to-noise ratio. Detection of samples at higher Ct values (>32) with lateral flow was less robust, presumably due to a requirement for a critical concentration of the cleaved RNAreporter to accumulate sufficient gold nanoparticles to generate an observable colorimetric signal at the test line. Since not all of the high-Ct samplescan be detected by the SHERLOCK method, we suspected that at low levels oftarget RNA, amplification by RPA can become variable, and initial RNA input needs to be sufficiently high to ensure productive amplification and detection.Thus, we performed further empirical optimizations on the RPA step, and found that doubling components of the RPA reaction can boost the sensitivity of detection for samples with low viral load.

While almost all of our clinical samples were nasopharyngeal or throat swabs, we analysed three positive sputum samples(Ct for N of 26, 28 and 29), all of which could be detected by the SHERLOCK method under both readout modes. In addition, while 90% of samples were obtained from symptomatic patients, 10% (8 samples) of our 81 RT–qPCR-positive samples were from asymptomatic cases. We were able to detect SARS-CoV-2 in six out of eight asymptomatic cases using the SHERLOCK method. Indeed, two of the three patients who tested false negative with SHERLOCK with fluorescence readout were asymptomatic; all three false-negative samples didshow detectable fluorescent signal over background but did not pass the threshold we set. Samples were obtained from patients who had not taken antimicrobial or antiviral treatments, except for two samples, one from a patient undergoing treatment with lopinavir, ritonavir and chloroquine, and the other was from a patient treated with lopinavir, ritonavir, chloroquine and favipiravir.

Compared with RT–qPCR, we found SHERLOCK-based detection of the SARS-CoV-2 S gene in clinical samples to be 96% sensitive and 100% specific using the fluorescence readout, and 88% sensitiveand 100% specific using the lateral-flow readout . These correspond to100% positive predictive agreement (PPA) for either readout, 96% negative predictive agreement (NPA) for the fluorescence readout, and 88% NPA for the lateral-flowreadout. Within the determined LoD, SHERLOCK is 100% sensitive and 100% specific using the fluorescence readout, and 97% sensitive and 100% specificusing the lateral-flow readout. The diagnostic odds ratios (DORs) of SHERLOCK detection of SARS-CoV-2 are in the range of 6.91–9.94, among the highest when compared to DORs of other point-of-care genetic tests for SARS-CoV-2. The higher sensitivity and DOR when using fluorescence make this readout suitable for point-of-care and routinediagnostics, where a light source with appropriate filters and a smartphone camera can be used. The lateral-flow readout, with its ease of use and current sensitivity, may already be suitable for screening purposes—potentially outsideof diagnostic laboratory settings—before diagnostic confirmation.

Fig.5.

Multiplexing SHERLOCK detection of SARS-CoV-2 RNA with RNase-contamination detection on a lateral-flow strip

As SHERLOCK is based on nucleic acid detection, the presence of active nucleases, especially RNases, can obscure testing results. RNases can lead to RNA-input degradation, leading to false negatives, or if carried over to the CRISPR Cas detection step, can cleave RNA reporters and create false positives. The risk of RNase contamination is increased in resource-limited settings, where access to clean facilities and equipment to control RNases may be lacking. Current SHERLOCK protocols include RNase inhibitors and steps to inactivate nucleases and a negative control to ensure that there is no contamination leading to false-positive results (and a positive control to ensure functionality of the components). However, an in-strip confirmation could serve as an additional alert of the contamination.

Here we incorporated an internal RNase-contamination detection into the design of the SHERLOCK-based detection of RNAs. As LwaCas13a shows sequence preference for its collateral activity, we designed an RNase-responsive RNA reporter that is resistant to Cas13a cleavage, but remains susceptible to RNase I-, RNase A- and RNase T1-mediated cleavage (Fig. 5a). The RNA reporter is functionalised with digoxigenin (DIG) and fluorescein, allowing capture on a lateral-flow strip with anti-DIG antibody and detection with anti-fluorescein conjugated to gold nanoparticles (anti-Fl–NP). Combining the DIG–fluorescein-functionalised RNase reporter, the biotin–fluorescein-functionalised LwaCas13a reporter, and a lateral-flow stripcapable of detecting both DIG- and biotinylated analytes enables one-pot, simultaneous detection of both RNA target by SHERLOCK and RNase contamination. This multiplexed readout should enable easy differentiation of true-positive resultsfrom false results caused by RNase contamination.

We first optimized the loadings of the biotin–fluorescein SHERLOCK reporter and the DIG–fluorescein RNase reporter to ensure near-complete capture at their respective bands. We further adjusted the amount of anti-FI–NP on the lateral-flow strip to minimize the spillover of anti-FI–NP beyond the first streptavidin band in the case where no Cas-mediated cleavage of the biotin–fluorescein SHERLOCK reporter occurs. In a true-negative control (no RNA input and noRNase), only one strong coloured band at the control (C) line is produced when all anti-FI–NP binds to intact biotin–fluorescein SHERLOCK reporter (Fig. 5b).Specific SHERLOCK detection of the RNA target would cleave the biotin–fluorescein SHERLOCK reporter, allowing production of two coloured bandsat T2 (where anti-FI–NP binds to intact DIG–fluorescein RNase reporter) and T1(where anti-rabbit IgG binds to excess anti-FI–NP). RNase-mediated cleavage cleaves both reporters regardless of specific RNA input, resulting in onestrong band at the T1 line (where all anti-FI–NP binds).

We tested the multiplex detection ofSARS-CoV-2 RNA with SHERLOCK and RNase contamination (Fig. 5c). True-positive samples (+SARS-CoV-2 RNA, Ct≈27; −RNase) indeed produced two coloured bands at T1and T2, readily distinguishable from true-negative controls (main band at C)and RNase-contaminated samples (one band at T1). Analysis of serial dilutionsof SARS-CoV-2 RNA showed that the multiplex detection maintains the sensitivityof SARS-CoV-2 S gene detection when compared with the single-plex detection(Fig. 5d).

Source file

Clinical validation of a Cas13-based assay for the detection of SARS-CoV-2 RNA

Maturada Patchsung, Krittapas Jantarug, Archiraya Pattama, Kanokpol Aphicho, Surased Suraritdechachai, Piyachat Meesawat, Khomkrit Sappakhaw, Nattawat Leelahakorn, Theerawat Ruenkam, Thanakrit Wongsatit, Niracha Athipanyasilp, Bhumrapee Eiamthong, Benya Lakkanasirorat, Thitima Phoodokmai, Nootaree Niljianskul, Danaya Pakotiprapha, Sittinan Chanarat, Aimorn Homchan, Ruchanok Tinikul, Philaiwarong Kamutira, Kochakorn Phiwkaow, Sahachat Soithongcharoen, Chadaporn Kantiwiriyawanitch, Vinutsada Pongsupasa, Duangthip Trisrivirat, Juthamas Jaroensuk, Thanyaporn Wongnate, Somchart Maenpuen, Pimchai Chaiyen, Sirichai Kamnerdnakta, Jirawat Swangsri, Suebwong Chuthapisith, Yongyut Sirivatanauksorn, Chutikarn Chaimayo, Ruengpung Sutthent, Wannee Kantakamalakul, Julia Joung, Alim Ladha, Xin Jin, Jonathan S. Gootenberg, Omar O. Abudayyeh, Feng Zhang, Navin Horthongkham & Chayasith Uttamapinant