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. 2022 Apr 20;63(5):480–489. doi: 10.3349/ymj.2022.63.5.480

Evaluation of CRISPR-Based Assays for Rapid Detection of SARS-CoV-2: A Systematic Review and Meta-Analysis

Pei-Ying Huang 1,2,*, Xin Yin 1,3,*, Yue-Ting Huang 1,2, Qi-Qing Ye 1,3, Si-Qing Chen 1,2, Xun-Jie Cao 1,4, Tian-Ao Xie 4, Xu-Guang Guo 1,4,5,6,✉
PMCID: PMC9086695  PMID: 35512751

Abstract

Purpose

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the pathogen of coronavirus disease 2019. Diagnostic methods based on the clustered regularly interspaced short palindromic repeats (CRISPR) have been developed to detect SARS-CoV-2 rapidly. Therefore, a systematic review and meta-analysis were performed to assess the diagnostic accuracy of CRISPR for detecting SARS-CoV-2 infection.

Materials and Methods

Studies published before August 2021 were retrieved from four databases, using the keywords “SARS-CoV-2” and “CRISPR.” Data were collected from these publications, and the sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic curve was plotted for analysis with MetaDiSc 1.4. The Stata 15.0 software was used to draw Deeks’ funnel plots to evaluate publication bias.

Results

We performed a pooled analysis of 38 independent studies shown in 30 publications. The reference standard was reverse transcription-quantitative PCR. The results indicated that the sensitivity of CRISPR-based methods for diagnosis was 0.94 (95% CI 0.93–0.95), the specificity was 0.98 (95% CI 0.97–0.99), the PLR was 34.03 (95% CI 20.81–55.66), the NLR was 0.08 (95% CI 0.06–0.10), and the DOR was 575.74 (95% CI 382.36–866.95). The area under the curve was 0.9894.

Conclusion

Studies indicate that a diagnostic method based on CRISPR has high sensitivity and specificity. Therefore, this would be a potential diagnostic tool to improve the accuracy of SARS-CoV-2 detection.

Keywords: CRISPR-based assays, detection, SARS-CoV-2, sensitivity, specificity

INTRODUCTION

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the pathogen of the coronavirus disease 2019 (COVID-19), and it has caused more than 1.45 million deaths worldwide by November 30, 2020.1 Patients infected with SARS-CoV-2 may exhibit symptoms such as shortness of dyspnea, high fever, and pneumonia, which are fatal for vulnerable individuals.2 Coronavirus-infected inpatients are more likely to develop acute respiratory failure, pulmonary embolism, or septic shock, resulting in death.3 Moreover, with the sharply increasing number of infected people and limited assays currently, the development of efficient, rapid, accurate, and sensitive SARS-CoV-2 sensing tools is urgent for public health in the world.4

Molecular tests and serological tests have been implemented for COVID-19 diagnosis to detect viral RNA and anti-SARS-CoV-2 immunoglobulins, respectively.5 For molecular diagnostic tests, the collection of upper nasopharyngeal swabs is recommended by the US Centers for Disease Control and Prevention. So far, reverse transcription-quantitative PCR (RT-qPCR) has widely been used as the reference standard for the detection of viral RNA in SARS-CoV-2.6,7,8,9 However, it requires well-trained personnel and advanced equipment, which limits the application of RT-qPCR, especially in resource-constrained developing countries.10,11,12 Metagenomic next-generation sequencing is another molecular test to identify SARS-CoV-2, but the sensitivity of this method is restricted by the influence of the human host background.13 On the other hand, the serology tests, including immunochromatographic analysis and enzyme-linked immunosorbent assay (ELISA), are not sufficiently accurate in detecting SARS-CoV-2.4 In addition, asymptomatic patients are considered to play a major role in the spread of the virus.14 These factors increase the need for effective, cheap, and rapid alternative methods.4

The clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated proteins (Cas) system shows strong collateral activity against single-stranded RNA and DNA targets through molecular immune mechanisms, providing highly accurate methods of nucleic acid detection.15 The mechanism of the detection system is the specific binding and cleavage activity of CRISPR-Cas. Once the primers for reverse transcription loop-mediated isothermal amplification or reverse transcription recombinase polymerase amplification recognize the specific regions of the SARS-CoV-2 genome, the targeted nucleic acid is amplified at a constant temperature. The guide RNAs then target SARS-CoV-2 E, N, or Orf1ab amplicons with the base-pairing pattern at attomolar sensitivity, ensuring the amplified nucleotide cleaved by the Cas nuclease accurately. The target nucleotide is finally identified on the detection platform with fluorescence tracking.16,17 Therefore, CRISPR is a more efficient and suitable point-of-care diagnostic method than RT-qPCR, considering its sequence-specific detection method and isothermal amplification approaches.18,19,20

In this study, we conducted a systematic review and meta-analysis to assess the diagnostic accuracy of CRISPR in detecting SARS-CoV-2 infection, evaluate the quality of available evidence, and perform an in-depth analysis regarding the related research.

MATERIALS AND METHODS

Search strategy and source

This study was conducted according to the PRISMA guidelines.21 We selected four databases, including PubMed, Embase, Cochrane Library, and Web of Science, and searched for data using “SARS-CoV-2” and “CRISPR” as keywords. All of the scientific papers were published before August 2021, without language restriction. All synonyms of the above-mentioned keywords were also included in the search formula for more comprehensive literature.

Study screening and selection

The retrieved publications were independently selected by four researchers. Based on the predetermined inclusion and exclusion criteria, data were extracted by analyzing the titles, abstracts, and full texts of the studies. All disagreements were resolved through discussion and consultation.

Inclusion and exclusion criteria

The publications that met all of the following criteria were included based on preset conditions: 1) the investigators’ experimental objectives included the role of CRISPR in the diagnosis of COVID-19 infection; 2) the study type was a diagnostic accuracy test, and the diagnostic accuracy was evaluated by comparing the index to be tested with the standard reference method; and 3) the data provided by the study could identify true positive (TP), false positive (FP), true negative (TN), false negative (FN), or sensitivity and specificity.

Exclusion criteria were as follows: 1) studies that were animal experiments; 2) studies where the reference method was not mentioned; 3) letters, conference abstracts, reviews, editorials, or erratum; and 4) duplicated publications or those with no description of the available data.

Data extraction

The EndNoteX9 software was used for file management and data extraction from articles. Excel standardized electronic data entry form was used to pool the required information, including the author’s name, publishing year, study type, sample size, reference standard, and indicators. In addition, the diagnostic features of SARS-CoV-2 were extracted along with TP, FP, TN, and FN. We reviewed the extracted information, resolved all disagreements through negotiated discussion, and excluded duplicate data.

Quality assessment standard

Four investigators evaluated the quality of the included studies independently in accordance with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) guidelines,22 regarding four main steps: case selection, trial to be assessed, reference standard, and case process and progress. The assessment of all four components was applied to analyze the risk of bias, while the assessment of the first three components was applied for the evaluation of clinical applicability. Issues with other iconic study designs were included in the risk of biased judgments, which were related to the potential for judicial bias. Responses of “Yes,” “No,” or “Indeterminate” corresponded to a risk of bias rating of “Low,” “High,” or “Indeterminate,” based on the questions included in each section.

Data analysis

We used the MetaDiSc 1.4 software (Ramony Cajal Hospital, Madrid, Spain) for statistical analysis following standard methods, and used the Stata 15.0 software (StataCorp LLC, College Station, TX, USA) to draw Deeks’ funnel plot and test funnel plot symmetry as well as publication bias. Spearman correlation coefficient and Cochran-Q were performed to analyze the heterogeneity of the included data, and a fixed-effects model or random-effects model was selected based on the result value. The sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated and analyzed by drawing a forest plot using MetaDiSc 1.4. The effect value and its 95% confidence interval (CI) were shown in the forest plot. In addition, the area under the curve (AUC) was calculated using the summary receiver operating characteristic (SROC) curve to obtain the specificity and sensitivity. Then, the total efficiency of CRISPR was assessed using diagnostic odds ratio (DOR) and AUC. The Review Manager 5.3 software (The Nordic Cochrane Centre, Copenhagen, Denmark) was used to evaluate the quality of the included studies.

RESULTS

Summary of the included studies

After searching through all four literature databases, we obtained 547 related documents, from which 374 were selected after the removal of duplicated publications. A total of 156 studies were removed for their uncorrelated titles or “CRISPR detection” not mentioned in their abstracts. We read through the text afterwards, and 188 studies were excluded for various reasons. Finally, 30 articles were selected with a total of 38 groups of data (Fig. 1).6,10,16,17,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48 The effect-indicator proposed in each literature was involved in the composition of the data extracted. Table 1 shows the characteristics of these studies in detail.

Fig. 1. Flow diagram of study identification and inclusion.

Fig. 1

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Table 1. Characteristics of Included Studies about CRISPR Detection of SARS-CoV-2.

Author Year Geographical distribution of virus Patients (n) Sample source Type of cas enzyme Gene site Readout mode TP FP FN TN
Patchsung, et al. (1)6 2020 Thailand 154 Nasopharyngeal swabs Cas13a N Lateral flow assays 71 0 10 73
Patchsung, et al. (2)6 2020 Thailand 154 Nasopharyngeal swabs Cas13a N Fluorescence reader 78 0 3 73
Huang, et al.10 2020 America 29 Nasal swabs Cas12a N Fluorescence reader 15 4 0 10
Wang, et al.23 2020 China 31 Nasal swabs Cas12a E Fluorescence reader 16 0 0 15
Joung, et al.24 2020 America 402 Nasal swabs Cas12b N Fluorescence reader 188 3 14 197
Broughton, et al. (a1)16 2020 America 82 Nasopharyngeal swabs Cas12 N\E Lateral flow assays 9 0 1 12
Broughton, et al. (a2)16 2020 America 82 Nasopharyngeal swabs Cas12 N\E Fluorescence reader 37 0 3 42
Broughton, et al. (b1)25 2020 America 21 Nasopharyngeal swabs Cas12 E Fluorescence reader 10 0 0 11
Broughton, et al. (b2)25 2020 America 21 Nasopharyngeal swabs Cas12 N Fluorescence reader 9 0 1 11
Chen, et al.17 2020 China 10 Respiratory swabs Cas12a N\E Lateral flow assays\Fluorescence reader 7 0 0 3
Ding, et al.26 2020 America 28 Respiratory swabs Cas12a N Fluorescence reader 8 0 0 20
Ma, et al.27 2020 China 24 Nasopharyngeal swabs Cas12a E Fluorescence reader 13 0 0 11
Arizti-San, et al.28 2020 America 50 Nasopharyngeal swabs Cas13 N Fluorescence reader 27 0 3 20
Mayuramart, et al.29 2021 Thailand 164 Nasopharyngeal and/or throat swabs Cas12a S Fluorescence reader 51 0 2 111
Nimsamer, et al. (1)30 2021 Thailand 107 Nasopharyngeal and/or throat swab Cas12a N1 Fluorescence reader 41 0 3 63
Nimsamer, et al. (2)30 2021 Thailand 107 Nasopharyngeal and/or throat swab Cas12a N2 Fluorescence reader 42 6 2 57
Nimsamer, et al. (3)30 2021 Thailand 107 Nasopharyngeal and/or throat swab Cas12a E Fluorescence reader 43 10 1 53
Nimsamer, et al. (4)30 2021 Thailand 107 Nasopharyngeal and/or throat swab Cas12a S Fluorescence reader 42 0 2 63
Ning, et al. (1)31 2021 America 103 Nasal swabs Cas12a O Fluorescence reader (Smartphone) 27 0 0 76
Ning, et al. (2)31 2021 America 103 Nasal swabs Cas12a O Fluorescence reader (Plate reader) 27 1 0 75
Ooi, et al.32 2021 Singapore 75 Nasopharyngeal swabs Cas12a S Lateral flow assays 37 0 8 30
Rauch, et al.33 2021 America 218 Nasopharyngeal swabs Cas13 N Fluorescence reader 63 3 2 150
Samacoits, et al.34 2021 Thailand 115 Nasal swabs Cas12a N Fluorescence reader 45 5 7 58
Brandsma, et al.35 2021 Netherlands 378 Nasopharyngeal swabs, bronchoalveolar lavage and sputum Cas12 N Lateral flow assays 144 10 11 213
Chen, et al.36 2021 America 27 Nasopharyngeal swabs Cas12a N Fluorescence reader 11 0 0 16
Curti, et al.37 2021 Argentina 210 Nasopharyngeal swabs Cas12 N Fluorescence reader 105 1 0 104
Ding, et al.38 2021 America 32 Clinical swabs Cas12a N1 Fluorescence reader 12 0 0 20
Jiang, et al. (1)39 2021 China 41 Nasopharyngeal and throat swabs Cas12a N Colorimetric analysis 21 0 0 20
Jiang, et al. (2)39 2021 China 41 Nasopharyngeal and throat swabs Cas12a O Colorimetric analysis 21 0 0 20
Lee, et al.40 2021 Korea 20 Nasopharyngeal and oropharyngeal swabs and sputum Cas12a N Fluorescence reader 10 0 0 10
Sun, et al.41 2021 China 54 Pharyngeal swabs Cas12a O Fluorescence reader 6 0 0 48
Pang, et al.42 2020 Canada 100 Respiratory swabs Cas12a N\E Fluorescence reader 47 0 3 50
Liu, et al.43 2021 China 25 Nasal swabs Cas12a O\N Fluorescence reader 20 0 0 5
Li, et al.44 2021 China 649 Oropharyngeal and sputum swabs Cas13a N Lateral flow assays 243 3 25 378
Tian, et al.45 2021 China 40 Nasopharyngeal swabs Cas13a O\N Fluorescence reader 20 0 0 20
Wang, et al.46 2021 China 50 Respiratory swabs Cas12a S Fluorescence reader 26 0 0 24
Xiong, et al.47 2021 China 64 Nasopharyngeal swabs Cas9 E\O Lateral flow assays 34 0 1 29
Zhu, et al.48 2021 China 114 Respiratory swabs Cas12a O\N Lateral flow assays 37 0 0 77
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CRISPR, clustered regularly interspaced short palindromic repeats; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; Cas, CRISPR-associated proteins; FN, false negative; FP, false positive; TN, true negative; TP, true positive; N, nucleocapsid protein gene; E, envelope protein gene; S, spike protein gene; O, open reading frame 1 ab.

*The reference standard of the included studies was reverse transcription-quantitative PCR.

Methodological quality evaluation

The quality of the included studies was evaluated by analyzing the data in terms of case selection, index detection, reference standard, and case process and progress using Review Manager 5.3. Fig. 2A summarizes the results of the QUADAS-2 assessment, and Fig. 2B shows the independent quality assessment of each study. The results indicated that for case selection, seven studies had a risk of bias due to the unclear case-control study design and the unknown inclusion of consecutive or randomized case conditions. In the index test field, three studies were at higher risk since the interpretation of the index test results was made when the reference standard results were known. Both the reference standard field and the flow rate and time were considered to have a low risk of bias.

Fig. 2. Quality evaluation results for each study included in the meta-analysis. (A) Risk of bias and applicability concerns summary. (B) Quality evaluation of the included studies.

Fig. 2

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Merged analysis results

Overall, the sensitivity of CRISPR in the diagnosis of COVID-19 was 0.94 (95% CI 0.93–0.95, I2=52.8%) (Fig. 3A), and the specificity was 0.98 (95% CI 0.97–0.99, I2=65.0%) (Fig. 3B). As shown in the chart in Fig. 3C, the AUC was 0.9894. The PLR was 34.03 (95% CI 20.81–55.66, I2=66.0%) (Fig. 4A), and the NLR was 0.08 (95% CI 0.06–0.10, I2=14.0%) (Fig. 4B). The value of the pooled DOR was 575.74 (95% CI 382.36–866.95) (Fig. 4C).

Fig. 3. Forest plots for CRISPR-based SARS-CoV-2 detection methods. (A) Forest plots for combined sensitivity. (B) Forest plots for combined specificity. (C) The SROC of SARS-CoV-2 infections detected by CRISPR. CRISPR, clustered regularly interspaced short palindromic repeats; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; CI, confidence interval; SROC, summary receiver operating characteristic; AUC, area under the curve; SE, standard error.

Fig. 3

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Fig. 4. Forest plots for CRISPR-based SARS-CoV-2 detection methods. (A) Forest plots for combined positive likelihood ratio. (B) Forest plots for combined negative likelihood ratio. (C) Forest plots for combined diagnostic OR. CRISPR, clustered regularly interspaced short palindromic repeats; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; LR, likelihood ratio; df, degree of freedom; CI, confidence interval; OR, odds ratio.

Fig. 4

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Analysis of threshold effect

In the threshold effect analysis, the Spearman correlation coefficient was 0.024, and the p-value was 0.888 (p>0.05). Moreover, the SROC curve (Fig. 3C) did not have a “shoulder arm” distribution. Therefore, we concluded that there was no threshold effect in the included studies.

Heterogeneity analysis of non-threshold effect

A forest map was used to plot the ratio following a random pattern. The heterogeneity in non-threshold effects was low (Fig. 4C): Cochran-Q=38.80, p=0.3884 (p>0.05), inconsistency=4.6% (inconsistency<50%).

Publication bias

The Deeks’ funnel plot (Fig. 5) was made using the Stata 15.0 software to identify publication bias in the included publications (p=0.457>0.1), and it showed no potential publication bias for the included studies.

Fig. 5. Deeks’ funnel plot asymmetry test to evaluate publication bias of CRISPR. CRISPR, clustered regularly interspaced short palindromic repeats; ESS, effective sample size.

Fig. 5

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DISCUSSION

Currently, RT-qPCR assays are the recommended molecular diagnostic tools to detect COVID-19 infection.7,8 However, they come with a high demand for equipment and skillful lab technicians.10 In contrast, the CRISPR-based nucleic acid detection platforms have the combined advantages of conventional RNA-targeting technologies, and a fluorescence readout or a lateral-flow readout can be used to analyze the results in an hour, with a setup time of less than 15 min.49 The simplicity of operation, remarkable specificity, and high efficiency make CRISPR-based diagnostics the new avenues for sensitive, robust, and rapid detection of viral pathogens.

In this study, we performed a pooled analysis, and the results of the systematic review and meta-analysis indicated that CRISPR had an overall sensitivity of 0.94 (95% CI 0.93–0.95) and an overall specificity of 0.98 (95% CI 0.97–0.99) in detecting SARS-CoV-2. The value of the pooled DOR was 575.74. The AUC was 0.9894 and was close to 1. Based on these results, we can infer that the diagnosis of COVID-19 by using CRISPR was highly accurate.

Several studies have evaluated the accuracy of immunochromatographic analysis and ELISA for SARS-CoV-2 detection.50 A test strip for the detection of SARS-CoV-2 IgG/IgM-combined antibody based on immunochromatography has been developed by Liao, et al.,51 with a sensitivity of 92.9% and a specificity of 98.7%. However, the antibody-positive rate in the first week of infection was only 77.3% and reached 100% on day 9. Another study reported by Beavis, et al.52 evaluated an ELISA assay to detect SARS-CoV-2 IgA and IgG antibodies. The sensitivity of IgA ELISA was 82.9% and the specificity was 88.4%, while the sensitivity of IgG ELISA was 67.1% and the specificity was 97.7%. Although these assays are fast and easy to operate50,53 compared to CRISPR, infection-generated antibodies are detectable at later stages in the disease, which is not conducive to early disease screening.51,54 In addition, if the sample is heat-inactivated, the effective concentration of the antibody would decrease and probably give false-negative results.55 Meanwhile, according to Beavis, et al.,52 ELISA assay tended to have a lower sensitivity and specificity compared to CRISPR. Therefore, CRISPR is a valid and appropriate instrument for detecting SARS-CoV-2.

Furthermore, to minimize the sources of heterogeneity, this study implemented strict criteria for the inclusion and exclusion of the studies. In the threshold effect analysis, the Spearman correlation coefficient was found to be 0.024 (<0.6) and the p-value was 0.888 (p>0.05), which indicated the lack of threshold effect in the included studies. However, I2 values of pooled sensitivity (52.8%), specificity (65.0%), PLR (66.0%), which exceeded 50%, suggested the presence of heterogeneity from non-threshold effects. Subgroup analysis was performed to investigate the heterogeneity caused by different types of Cas enzyme used, Cas12 and Cas13, but no statistically significant results were obtained. Instead, we found that gene targets and readout modes might be the possible sources of underlying heterogeneity.6,10,23 Moreover, the Deeks’ funnel plot (p=0.457>0.1) indicated that no publication bias was possibly subsistent.

The present systematic review and meta-analysis also had several limitations. First, we only extracted data from the literature published in the four select English databases, and ignored some negative results without statistical significance or unpublished data. This may lead to defects in the comprehensiveness of the current study and more publication bias. Second, the detection capability of the reference methods may not necessarily be more reliable than that of CRISPR. The reference methods could also provide false-positive results, thereby leading to underestimation of the specificity of the CRISPR method. Finally, there were no remarkable changes in subgroup analyses. This study can be improved with the accumulation of more clinical data in the future. With more COVID-19 cases being reported every day worldwide, there may be more studies supporting our theory and, at the same time, having important implications for the diagnosis of COVID-19.

In summary, CRISPR has proven to be a rapid, sensitive, and specific method to detect SARS-CoV-2. It can provide reliable information for clinical laboratory tests and contribute to point-of-care diagnostics where simplicity and cost-effectiveness are needed. This technology is expected to become the major auxiliary diagnostic method for COVID-19 in the near future.

ACKNOWLEDGEMENTS

This study was supported by Guangzhou Medical University (No. 2019A018) and the Third Clinical School of Guangzhou Medical University (No. 2018B088).

Footnotes

The authors have no potential conflicts of interest to disclose.

AUTHOR CONTRIBUTIONS:
  • Conceptualization: Xu-Guang Guo.
  • Data curation: Pei-Ying Huang.
  • Formal analysis: Pei-Ying Huang and Xin Yin.
  • Investigation: Pei-Ying Huang, Xin Yin, Yue-Ting Huang, and Qi-Qing Ye.
  • Methodology: Xu-Guang Guo.
  • Project administration: Pei-Ying Huang.
  • Resources: Xu-Guang Guo.
  • Software: Pei-Ying Huang, Xun-Jie Cao, and Tian-Ao Xie.
  • Supervision: Pei-Ying Huang.
  • Validation: Pei-Ying Huang and Xin Yin.
  • Visualization: Pei-Ying Huang and Xin Yin.
  • Writing—original draft: all authors.
  • Writing—review & editing: Pei-Ying Huang, Xin Yin, and Si-Qing Chen.
  • Approval of final manuscript: all authors.

DATA AVAILABILITY

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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Data Availability Statement

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