Rapid and Accurate Detection of SARS-CoV-2 Using an iPad-Controlled, High-Throughput, Portable, and Multiplex Hive-Chip Platform (HiCube)

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Abstract

Rapid and accurate detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the most effective measures to control the coronavirus disease 2019 (COVID-19) pandemic. However, there is still lack of an ideal detection platform capable of high sample throughput, portability, and multiplicity. Herein, by combining Hive-Chip (capillary microarray) and reverse transcriptional loop-mediated isothermal amplification (RT-LAMP), we developed an iPad-controlled, high-throughput (48 samples at one run), portable (smaller than a backpack), multiplex (monitoring 8 gene fragments in one reaction), and real-time detection platform for SARS-CoV-2 detection. This platform is composed of a portable Hive-Chip device ( HiCube; 32.7 × 29.7 × 20 cm, 5 kg), custom-designed software, and optimized Hive-Chips. RT-LAMP primers targeting seven SARS-CoV-2 genes (S, E, M, N, ORF1ab, ORF3a, and ORF7a) and one positive control (human RNase P) were designed and prefixed in the Hive-Chip. On-chip RT-LAMP showed that the limit of detection (LOD) of SARS-CoV-2 synthetic RNAs is 1 copy/μL, and there is no cross-reaction among different target genes. The platform was validated by 100 clinical samples of SARS-CoV-2, and the results were highly consistent with those of the traditional real-time PCR assay. In addition, on-chip detection of 6 other respiratory pathogens showed no cross-reactivity. Overall, our platform has great potential for fast, accurate, and on-site detection of SARS-CoV-2.

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A new coronavirus associated with human respiratory disease in China

Fan Wu, Su Zhao, Bin Yu … (2020)

Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.

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CRISPR-Cas12–based detection of SARS-CoV-2

James Broughton, Xianding Deng, Guixia Yu … (2020)

An outbreak of betacoronavirus SARS-CoV-2 began in Wuhan, China in December 2019. COVID-19, the disease associated with infection, rapidly spread to produce a global pandemic. We report development of a rapid (<40 min), easy-to-implement and accurate CRISPR-Cas12-based lateral flow assay for detection of SARS-CoV-2 from respiratory swab RNA extracts. We validated our method using contrived reference samples and clinical samples from US patients, including 36 patients with COVID-19 infection and 42 patients with other viral respiratory infections. Our CRISPR-based DETECTR assay provides a visual and faster alternative to the US CDC SARS-CoV-2 real-time RT-PCR assay, with 95% positive predictive agreement and 100% negative predictive agreement.. SARS-CoV-2 in patient samples is detected in under an hour using a CRISPR-based lateral flow assay.

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Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis

Mayara Lisboa Bastos, Gamuchirai Tavaziva, Syed Kunal Abidi … (2020)

Abstract Objective To determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (covid-19). Design Systematic review and meta-analysis. Data sources Medline, bioRxiv, and medRxiv from 1 January to 30 April 2020, using subject headings or subheadings combined with text words for the concepts of covid-19 and serological tests for covid-19. Eligibility criteria and data analysis Eligible studies measured sensitivity or specificity, or both of a covid-19 serological test compared with a reference standard of viral culture or reverse transcriptase polymerase chain reaction. Studies were excluded with fewer than five participants or samples. Risk of bias was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using random effects bivariate meta-analyses. Main outcome measures The primary outcome was overall sensitivity and specificity, stratified by method of serological testing (enzyme linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), or chemiluminescent immunoassays (CLIAs)) and immunoglobulin class (IgG, IgM, or both). Secondary outcomes were stratum specific sensitivity and specificity within subgroups defined by study or participant characteristics, including time since symptom onset. Results 5016 references were identified and 40 studies included. 49 risk of bias assessments were carried out (one for each population and method evaluated). High risk of patient selection bias was found in 98% (48/49) of assessments and high or unclear risk of bias from performance or interpretation of the serological test in 73% (36/49). Only 10% (4/40) of studies included outpatients. Only two studies evaluated tests at the point of care. For each method of testing, pooled sensitivity and specificity were not associated with the immunoglobulin class measured. The pooled sensitivity of ELISAs measuring IgG or IgM was 84.3% (95% confidence interval 75.6% to 90.9%), of LFIAs was 66.0% (49.3% to 79.3%), and of CLIAs was 97.8% (46.2% to 100%). In all analyses, pooled sensitivity was lower for LFIAs, the potential point-of-care method. Pooled specificities ranged from 96.6% to 99.7%. Of the samples used for estimating specificity, 83% (10 465/12 547) were from populations tested before the epidemic or not suspected of having covid-19. Among LFIAs, pooled sensitivity of commercial kits (65.0%, 49.0% to 78.2%) was lower than that of non-commercial tests (88.2%, 83.6% to 91.3%). Heterogeneity was seen in all analyses. Sensitivity was higher at least three weeks after symptom onset (ranging from 69.9% to 98.9%) compared with within the first week (from 13.4% to 50.3%). Conclusion Higher quality clinical studies assessing the diagnostic accuracy of serological tests for covid-19 are urgently needed. Currently, available evidence does not support the continued use of existing point-of-care serological tests. Study registration PROSPERO CRD42020179452.

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Journal

Journal ID (nlm-ta): ACS Sens

Journal ID (iso-abbrev): ACS Sens

Journal ID (publisher-id): se

Journal ID (coden): ascefj

Title: ACS Sensors

Publisher: American Chemical Society

ISSN (Electronic): 2379-3694

Publication date (Electronic): 24 April 2023

Electronic Location Identifier: acssensors.3c00031

Affiliations

[† ]Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University , Shanghai 200240, China

[‡ ]School of Biomedical Engineering, Shanghai Jiao Tong University , Shanghai 200240, China

[§ ]CAS Key Laboratory of Special Pathogens and Biosafety, Centre for Biosafety Mega-Science, Wuhan Institute of Virology, Chinese Academy of Sciences , Wuhan 430071, China

[∥ ]University of Chinese Academy of Science , Beijing 100049, China

[⊥ ]Sports & Medicine Integrative Innovation Center (SMIC), Capital University of Physical Education and Sports , Beijing 100191, China

[# ]Department of Biomedical Engineering, School of Medicine, Tsinghua University , Beijing 100084, China

[¶ ]National Institutes for Food and Drug Control , Beijing 102629, China

[∇ ]Perfect Diagnosis Biotechnology (ZhenCe) Co., Ltd. , Shanghai 200240, China

Author notes

[* ]Email: hpwei@ 123456wh.iov.cn .

[* ]Email: taosc@ 123456sjtu.edu.cn .

Author information

Yuanshou Zhu https://orcid.org/0000-0003-2711-1790

Ning Shao https://orcid.org/0000-0003-2534-1277

Peng Liu https://orcid.org/0000-0003-3000-3168

Hongping Wei https://orcid.org/0000-0002-9948-8880

Sheng-ce Tao https://orcid.org/0000-0002-9210-1823

Article

DOI: 10.1021/acssensors.3c00031

PMC ID: 10152401

PubMed ID: 37093957

SO-VID: cb662adb-49bd-4631-b133-f214e1c5d139

License:

This article is made available via the PMC Open Access Subset for unrestricted RESEARCH re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

History

Date received : 06 January 2023

Date accepted : 12 April 2023

Funding

Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;

Award ID: 31970130

Funded by: Ministry of Science and Technology of the People''s Republic of China, doi 10.13039/501100002855;

Award ID: 2022YFF0710105

Funded by: Ministry of Science and Technology of the People''s Republic of China, doi 10.13039/501100002855;

Award ID: 2020YFE0202200

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document-id-old-9 se3c00031

document-id-new-14 se3c00031

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Keywords: sars-cov-2,hive-chip platform,rt-lamp,high-throughput and multiplex detection

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Keywords: sars-cov-2, hive-chip platform, rt-lamp, high-throughput and multiplex detection

Rapid and Accurate Detection of SARS-CoV-2 Using an iPad-Controlled, High-Throughput, Portable, and Multiplex Hive-Chip Platform (HiCube)

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Novel Coronavirus Disease COVID-19

Most cited references 27

A new coronavirus associated with human respiratory disease in China

CRISPR-Cas12–based detection of SARS-CoV-2

Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis

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