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Multiplexed discrimination of SARS-CoV-2 variants via plasmonic-enhanced fluorescence in a portable and automated device

Nature Biomedical Engineering volume 7, pages 1636–1648 (2023)Cite this article

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Abstract

Portable assays for the rapid identification of lineages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to aid large-scale efforts in monitoring the evolution of the virus. Here we report a multiplexed assay in a microarray format for the detection, via isothermal amplification and plasmonic-gold-enhanced near-infrared fluorescence, of variants of SARS-CoV-2. The assay, which has single-nucleotide specificity for variant discrimination, single-RNA-copy sensitivity and does not require RNA extraction, discriminated 12 lineages of SARS-CoV-2 (in three mutational hotspots of the Spike protein) and detected the virus in nasopharyngeal swabs from 1,034 individuals at 98.8% sensitivity and 100% specificity, with 97.6% concordance with genome sequencing in variant discrimination. We also report a compact, portable and fully automated device integrating the entire swab-to-result workflow and amenable to the point-of-care detection of SARS-CoV-2 variants. Portable, rapid, accurate and multiplexed assays for the detection of SARS-CoV-2 variants and lineages may facilitate variant-surveillance efforts.

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Fig. 1: FEMMAN achieves single-RNA copy sensitivity with Asy-RT–RPA for the detection of SARS-CoV-2 and the simultaneous discrimination of viral variants with SNV distinction.
Fig. 2: Comprehensive identification of SARS-CoV-2 lineages by FEMMAN.
Fig. 3: SARS-CoV-2 detection and viral-lineage discrimination of clinical samples with FEMMAN.
Fig. 4: Point-of-care surveillance of SARS-CoV-2 variants with a FEMMAN portable device.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are available for research purposes from the corresponding authors on reasonable request. Source data are provided with this paper.

References

  1. Harvey, W. T. et al. SARS-CoV-2 variants, spike mutations and immune escape. Nat. Rev. Microbiol. 19, 409–424 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Yamasoba, D. et al. Neutralisation sensitivity of SARS-CoV-2 omicron subvariants to therapeutic monoclonal antibodies. Lancet Infect. Dis. 22, 942–943 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Ke, H., Chang, M. R. & Marasco, W. A. Immune evasion of SARS-CoV-2 Omicron subvariants. Vaccines 10, 1545 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Cao, Y. et al. BA.2.12.1, BA.4 and BA.5 escape antibodies elicited by Omicron infection. Nature 608, 593–602 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. He, P. et al. SARS-CoV-2 Delta and Omicron variants evade population antibody response by mutations in a single spike epitope. Nat. Microbiol. 7, 1635–1649 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Amrute, J. M. et al. Cell specific peripheral immune responses predict survival in critical COVID-19 patients. Nat. Commun. 13, 882 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Angela, L. & Rasmussen, S. V. P. SARS-CoV-2 transmission without symptoms. Science 371, 1206–1207 (2021).

    Article  Google Scholar 

  8. Brito, A. F. et al. Global disparities in SARS-CoV-2 genomic surveillance. Nat. Commun. 13, 7003 (2022).

  9. Peddu, V. et al. Metagenomic analysis reveals clinical SARS-CoV-2 infection and bacterial or viral superinfection and colonization. Clin. Chem. 66, 966–972 (2020).

    Article  PubMed  Google Scholar 

  10. Neopane, P., Nypaver, J., Shrestha, R. & Beqaj, S. S. SARS-CoV-2 variants detection using TaqMan SARS-CoV-2 mutation panel molecular genotyping assays. Infect. Drug Resist. 14, 4471–4479 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Wang, H. et al. Multiplex SARS-CoV-2 genotyping reverse transcriptase PCR for population-level variant screening and epidemiologic surveillance. J. Clin. Microbiol. 59, e0085921 (2021).

    Article  PubMed  Google Scholar 

  12. Caza, M. et al. Evaluation of the clinical and analytical performance of the Seegene allplex™ SARS-CoV-2 variants I assay for the detection of variants of concern (VOC) and variants of interests (VOI). J. Clin. Virol. 144, 104996 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Broughton, J. P. et al. CRISPR-Cas12-based detection of SARS-CoV-2. Nat. Biotechnol. 38, 870–874 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Joung, J. et al. Detection of SARS-CoV-2 with SHERLOCK one-pot testing. N. Engl. J. Med. 383, 1492–1494 (2020).

    Article  PubMed  CAS  Google Scholar 

  15. Fozouni, P. et al. Amplification-free detection of SARS-CoV-2 with CRISPR-Cas13a and mobile phone microscopy. Cell 184, 323–333.e9 (2021).

    Article  PubMed  CAS  Google Scholar 

  16. Ackerman, C. M. et al. Massively multiplexed nucleic acid detection with Cas13. Nature 582, 277–282 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Welch, N. L. et al. Multiplexed CRISPR-based microfluidic platform for clinical testing of respiratory viruses and identification of SARS-CoV-2 variants. Nat. Med. 28, 1083–1094 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Moitra, P. et al. Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning. Biosens. Bioelectron. 208, 114200 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Beduk, D. et al. ‘All In One’ SARS-CoV-2 variant recognition platform: machine learning-enabled point of care diagnostics. Biosens. Bioelectron. X 10, 100105 (2022).

    PubMed  PubMed Central  CAS  Google Scholar 

  20. Moitra, P., Alafeef, M., Dighe, K., Frieman, M. B. & Pan, D. Selective naked-eye detection of SARS-CoV-2 mediated by N gene targeted antisense oligonucleotide capped plasmonic nanoparticles. ACS Nano 14, 7617–7627 (2020).

    Article  PubMed  CAS  Google Scholar 

  21. Alafeef, M., Dighe, K., Moitra, P. & Pan, D. Rapid, ultrasensitive, and quantitative detection of SARS-CoV-2 using antisense oligonucleotides directed electrochemical biosensor chip. ACS Nano 14, 17028–17045 (2020).

    Article  PubMed  CAS  Google Scholar 

  22. Kumblathan, T. et al. An efficient method to enhance recovery and detection of SARS-CoV-2 RNA in wastewater. J. Environ. Sci. 130, 139–148 (2023).

    Article  Google Scholar 

  23. Pang, B. et al. Isothermal amplification and ambient visualization in a single tube for the detection of SARS-CoV-2 using loop-mediated amplification and CRISPR technology. Anal. Chem. 92, 16204–16212 (2020).

    Article  PubMed  CAS  Google Scholar 

  24. Yousefi, H. et al. Detection of SARS-CoV-2 viral particles using direct, reagent-free electrochemical sensing. J. Am. Chem. Soc. 143, 1722–1727 (2021).

    Article  PubMed  CAS  Google Scholar 

  25. Zhang, T. et al. A paper-based assay for the colorimetric detection of SARS-CoV-2 variants at single-nucleotide resolution. Nat. Biomed. Eng. 6, 957–967 (2022).

    Article  PubMed  CAS  Google Scholar 

  26. Kuno, A. et al. Evanescent-field fluorescence-assisted lectin microarray: a new strategy for glycan profiling. Nat. Methods 2, 851–856 (2005).

    Article  PubMed  CAS  Google Scholar 

  27. Shrivastav, A. M., Cvelbar, U. & Abdulhalim, I. A comprehensive review on plasmonic-based biosensors used in viral diagnostics. Commun. Biol. 4, 70 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Zhang, B., Kumar, R. B., Dai, H. & Feldman, B. J. A plasmonic chip for biomarker discovery and diagnosis of type 1 diabetes. Nat. Med. 20, 948–953 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Zhang, B. et al. Diagnosis of Zika virus infection on a nanotechnology platform. Nat. Med. 23, 548–550 (2017).

    Article  PubMed  CAS  Google Scholar 

  30. Liu, T. et al. Quantification of antibody avidities and accurate detection of SARS-CoV-2 antibodies in serum and saliva on plasmonic substrates. Nat. Biomed. Eng. 4, 1188–1196 (2020).

    Article  PubMed  CAS  Google Scholar 

  31. Koh, B. et al. Visible to near-infrared fluorescence enhanced cellular imaging on plasmonic gold chips. Small 12, 457–465 (2016).

    Article  PubMed  CAS  Google Scholar 

  32. Cosar, B. et al. SARS-CoV-2 mutations and their viral variants. Cytokine Growth Factor Rev. 63, 10–22 (2021).

    PubMed  PubMed Central  Google Scholar 

  33. Xie, X. et al. Emerging SARS-CoV-2 B.1.621/Mu variant is prominently resistant to inactivated vaccine-elicited antibodies. Zool. Res. 42, 789–791 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Muik A. et al. Omicron BA.2 breakthrough infection enhances cross- neutralization of BA.2.12.1 and BA.4/BA.5. Sci. Immunol. 7, eade2283 (2022).

  35. Subissi, L. et al. An early warning system for emerging SARS-CoV-2 variants. Nat. Med. 28, 1110–1115 (2022).

    Article  PubMed  CAS  Google Scholar 

  36. Larremore, D. B. et al. Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening. Sci. Adv. 7, eabd5393 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Gootenberg, J. S. et al. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science 356, 438–442 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Huang, Y., Yang, C., Xu, X. F., Xu, W. & Liu, S. W. Structural and functional properties of SARS-CoV-2 spike protein: potential antivirus drug development for COVID-19. Acta Pharmacol. Sin. 41, 1141–1149 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Gobeil, S. M. et al. Effect of natural mutations of SARS-CoV-2 on spike structure, conformation, and antigenicity. Science 373, eabi6226 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Yao, H. et al. Molecular architecture of the SARS-CoV-2 virus. Cell 183, 730–738.e13 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Segal, E. et al. A genomic code for nucleosome positioning. Nature 442, 772–778 (2006).

    Article  Google Scholar 

  42. Islam, M. S., Aryasomayajula, A. & Selvaganapathy, P. R. A review on macroscale and microscale cell lysis methods. Micromachines 8, 83 (2017).

    Article  Google Scholar 

  43. Gao, Y. et al. A flexible multiplexed immunosensor for point-of-care in situ wound monitoring. Sci. Adv. 7, eabg9614 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Li, C., Ying, T., Dimitrov, D. S. & Wu, Y. Counter changes with changelessness: cope with SARS-CoV-2 immune evasion by targeting cryptic epitopes. Life Med. 1, 24–26 (2022).

    Article  Google Scholar 

  45. Sun, C., Xie, C., Bu, G. L., Zhong, L. Y. & Zeng, M. S. Molecular characteristics, immune evasion, and impact of SARS-CoV-2 variants. Sig. Transduct. Target. Ther. 7, 202 (2022).

    Article  CAS  Google Scholar 

  46. Gandhi, S. et al. De novo emergence of a remdesivir resistance mutation during treatment of persistent SARS-CoV-2 infection in an immunocompromised patient: a case report. Nat. Commun. 13, 1547 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Heilmann, E. et al. SARS-CoV-2 3CLpro mutations selected in a VSV-based system confer resistance to nirmatrelvir, ensitrelvir, and GC376. Sci. Transl. Med. 15, eabq7360 (2022).

    Article  Google Scholar 

  48. Birnie, E. et al. Development of resistance-associated mutations after sotrovimab administration in high-risk individuals infected with the SARS-CoV-2 Omicron variant. JAMA 328, 1104–1107 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Ren, S. Y., Wang, W. B., Gao, R. D. & Zhou, A. M. Omicron variant (B.1.1.529) of SARS-CoV-2: mutation, infectivity, transmission, and vaccine resistance. World J. Clin. Cases 10, 1–11 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Xia, S., Wang, L., Zhu, Y., Lu, L. & Jiang, S. Origin, virological features, immune evasion and intervention of SARS-CoV-2 Omicron sublineages. Sig. Transduct. Target. Ther. 7, 241 (2022).

    Article  CAS  Google Scholar 

  51. Su, Y. et al. Multiple early factors anticipate post-acute COVID-19 sequelae. Cell 185, 881–895.e20 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Grant, R. et al. When to update COVID-19 vaccine composition. Nat. Med. https://doi.org/10.1038/s41591-023-02220-y (2023).

  53. Daher, R. K., Stewart, G., Boissinot, M. & Bergeron, M. G. Recombinase polymerase amplification for diagnostic applications. Clin. Chem. 62, 947–958 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Dovgan, I., Kolodych, S., Koniev, O. & Wagner, A. 2-(maleimidomethyl)-1,3-dioxanes (MD): a serum-stable self-hydrolysable hydrophilic alternative to classical maleimide conjugation. Sci. Rep. 6, 30835 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Wang, W. & Vaughn, M. W. Morphology and amine accessibility of (3-aminopropyl) triethoxysilane films on glass surfaces. Scanning 30, 65–77 (2008).

    Article  PubMed  Google Scholar 

  56. Deb-Choudhury, S., Plowman, J. E. & Harland, D. P. Isolation and analysis of keratins and keratin-associated proteins from hair and wool. Methods Enzymol. 568, 279–301 (2016).

    Article  PubMed  CAS  Google Scholar 

  57. Qian, J. et al. An enhanced isothermal amplification assay for viral detection. Nat. Commun. 11, 5920 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Matic, N. et al. Rapid detection of SARS-CoV-2 variants of concern, including B.1.1.28/P.1, British Columbia, Canada. Emerg. Infect. Dis. 27, 1673–1676 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Daher, R. K., Stewart, G., Boissinot, M., Boudreau, D. K. & Bergeron, M. G. Influence of sequence mismatches on the specificity of recombinase polymerase amplification technology. Mol. Cell. Probes 29, 116–121 (2015).

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

We thank members of the Yongye Liang laboratory, as well as S. Zhang, J. Ding, D. Jin and Z. Dong, for helpful discussions; Y. He, B. Wang, S. Zeng, J. Xu, W. Wang, X. Li, J. Sheng, Y. Wang, H. Wang, Z. Jiang, W. Xie, M. Cheng, M. Zhai, Y. Tian, J. Ming, X. Gong and Z. Lin for the collection of negative samples; F. Yang and X. Zhao for assistance in SEM imaging; and B. Mi, N. Nuo, D. Yuan, T. Tao, A. Li and H. Pang for their contributions to this work. B.Z. and Ying Liu disclose support for the research described in this study from the National Natural Science Foundation of China (grant nos. 22274069, 22304070), from Guangdong Basic and Applied Basic Research Foundation (grant no. 2022A1515011408) and from the Shenzhen Science and Technology programme project (grant no. JCYJ20180504165657443). Ying Liu and P.S. disclose support for the research described in this study from the Shenzhen Science and Technology programme project (grant no. JCYJ20210324104007020). B.Z., X.J., Y.L. and D.W. disclose support for the research described in this study from Guangdong Provincial Key Laboratory of Advanced Biomaterials (grant no. 2022B1212010003). X.J. discloses support for the research described in this study from the National Key R&D Program of China (grant nos. 2021YFF1200100, 2018YFA0902600 and 2020YFA0908900) and technical support from SUSTech Core Research Facilities. J. Yuan and Y.Y. disclose support for the research described in this study from the National Science and Technology Major Project (grant no. 2022YFB3207205) and from the Shenzhen High-level Hospital Construction Fund (grant no. SZGSP011).

Author information

Author notes

  1. These authors contributed equally: Ying Liu, Yang Yang, Guanghui Wang, Dou Wang.

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Advanced Biomaterials, Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China

    Ying Liu, Guanghui Wang, Dou Wang, Jiahu Tang, Tingzhen He, Jintao Zheng, Ruibin Hu, Yiyi Liu, Ziyi Xu, Dan Niu, Jiahui Lv, Jingkai Yang, Hongjun Xiao, Shuang He, Zhongrong Tang, Xingyu Jiang & Bo Zhang

  2. Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Infectious Disease Department, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China

    Yang Yang, Shuai Wu, Yan Liu & Jing Yuan

  3. Guangzhou Municipal and Guangdong Provincial Key Laboratory of Molecular Target and Clinical Pharmacology, the NMPA and State Key Laboratory of Respiratory Disease, School of Pharmaceutical Sciences and the Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, China

    Pan-Lin Shao

  4. Nirmidas Biotech, Inc., Palo Alto, CA, USA

    Meijie Tang

  5. Department of Chemistry, Stanford University, Stanford, CA, USA

    Hongjie Dai

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  1. Ying Liu
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Contributions

B.Z. and Ying Liu conceived the study and designed the experiments. Ying Liu, G.W., T.H., D.N., R.H. and Z.T. performed the assay experiments. J. Yuan, Y.Y., S.W., Yan Liu, P.-L.S., H.X. and J. Yang collected the clinical samples, Y.Y. performed the viral RNA extraction and thermal lysis of clinical samples. X.J., D.W., J.Z., J.T, Yiyi Liu and Z.X. designed and implemented the point-of-care part of this work. J.T. and S.H. performed bioinformatics analysis. J.L. performed SEM imaging. Ying Liu, J.T., M.T., H.D. and B.Z. analysed the data and wrote the manuscript. All authors participated in the analysis of the experimental data and contributed to the results and discussion.

Corresponding authors

Correspondence to Meijie Tang, Xingyu Jiang, Jing Yuan, Hongjie Dai or Bo Zhang.

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Competing interests

H.D. is a scientific adviser and consultant for Nirmidas Biotech Inc. and contributed to this work in that capacity independent of his Stanford work. The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Dipanjan Pan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Constructing a DNA microarray on a modified pGOLD substrate.

a, Schematic of surface chemical modification on pGOLD substrate and construction of FEMMAN chip with N501N-probe (Supplementary Table 2). b, Detailed molecular schematic of DNA detection without amplification. c, IRDye800 fluorescence image of a typical titration curve for N501N-target-20 nt (Supplementary Table 4) detection from 2 nM to 2 pM on bare pGOLD substrate, cysteamine modified pGOLD substrate, PEG (Mw = 1,000) modified pGOLD substrate, PEG (Mw = 5,000) modified pGOLD substrate, BSA modified pGOLD substrate and glass substrate. d, Fluorescence quantification of DNA titration on the six substrates. e, Optimization of binding buffer in amplicon capturing. f, Schematic and corresponding IRDye800 fluorescence images of 20 nt DNA target (N501N-target-20 nt in Supplementary Table 4) detection on FEMMAN chip. DNA probes in Type 1 (Type 1-probe in Supplementary Table 2) and Type 2 (N501N-probe in Supplementary Table 2) had reverse direction for DNA target hybridization. The spacer containing 30 Adenosine bases (dA30) was inserted between DNA probe and thiol group in Type 3 (Type 3-probe in Supplementary Table 2) and dA10 in Type 4 (N501N-probe in Supplementary Table 2). g, Schematic and corresponding IRDye800 fluorescence images of amplicon detection on FEMMAN chip. The forward primer used for Type 5 and Type 8 was N501-RPA-PF1, for Type 6 and Type 7 was N501-RPA-PF2; The reverse primer used for Type 6 and Type 8 was N501-RPA-PR1, for Type 5 and Type 7 was N501-RPA-PR2 (primer sequence in Supplementary Table 1, target sequence in Supplementary Table 4). h, Fluorescence quantification comparison between Type1 & Type 2, and Type 3 & Type 4. i, Fluorescence quantification of Type 5, Type 6, Type 7, and Type 8. Probe concentration optimization for Asy-RT-RPA in FEMMAN assay for j, images and k, statistical results. N = 3 technical replicates, bars represent mean ± s.d.

Source data

Extended Data Fig. 2 FEMMAN workflow at the molecular scale, and titration curve for SARS-CoV-2 detection.

a, IRDye800 fluorescence images of a typical titration curve for a 22 nt DNA target (Target A in Supplementary Table 4) detection from 2 nM to 2 fM on pGOLD substrate, glass substrate and evaporated Au film with Probe A (Supplementary Table 2). b, Fluorescence quantification of DNA titration on the three substrates for 4 DNA sequences (Probe A, B, C & D in Supplementary Table 2, Target A, B, C & D in Supplementary Table 4). c, Detailed molecular schematic of SARS-CoV-2 viral RNA detection using FEMMAN assay. d, Sequences of the DNA probes and primers for N501 target site. The target site is highlighted in green. e, IRDye800 fluorescence images of a typical titration curve for SARS-CoV-2 wild type viral RNA detection by FEMMAN from 5,000 copies/reaction to 5 copies/rxn on pGOLD and glass substrate. f, Background subtracted fluorescence quantification of viral RNA titration on pGOLD for N501N, N501Y and NTC probes. n = 3 technical replicates, bars represent mean ± s.d.

Source data

Extended Data Fig. 3 Multiplexed DNA sensing on the FEMMAN chip.

a, Schematic of SARS-CoV-2 viral RNA detection using FEMMAN assay. b, DNA probe layout on FEMMAN chip (sequences listed in Supplementary Table 2). c, IRDye800 fluorescence images for multiplexed DNA microarray when different combination of DNA targets (Target A, B, C & D in Supplementary Table 4) at 200 fM/20 fM were probed. d, Fluorescence image and e, signal quantification of DNA hybridization selectivity test for single base pair (Probe C-1 mismatch) and double base pair mismatch (Probe C-2 mismatch). f, Sequences of the DNA probes and primers for N501 target site. The target site is highlighted in green. g, IRDye800 fluorescence images of a typical titration curve for SARS-CoV-2 wild type viral RNA detection by FEMMAN from 5,000 copies/rxn to 5 copies/rxn on pGOLD. h, Background subtracted fluorescence quantification of viral RNA titration on pGOLD for N501N, N501Y and NTC probes. n = 3 technical replicates, bars represent mean ± s.d.

Source data

Extended Data Fig. 4 Construction and characterization of the FEMMAN chip.

a, Layout of DNA array on FEMMAN chip and photograph of a standard pGOLD chip and a standard detection set with 4 chips. b, Top-view SEM image of pGOLD chip (randomly selected). c, Background subtracted logic by the software. A: Feature pixels. B: Background pixels. C: 2 pixel exclusion region. d, Extinction spectra of pGOLD chip (red), 20 nm AuNPs (blue) overlaid with the excitation and emission of IRDye800 dye (orange for excitation and purple for emission). e, Fluorescence images of Cy5 on pGOLD substrate at (1) 200 pM, (2) 20 pM, (3) 2 pM, and (4) 200 fM compared to a quartz substrate at (5) 200 pM and (6) 20 pM. The Cy5 fluorophore density at 20 pM on both the (7) plasmonic substrate and (8) the quartz substrate was comparable. f, Single Cy5 blinking events were observed on the 20 pM pGOLD substrate using a lower 658 nm laser power (60 mW) while representative photobleaching curves on both the g, pGOLD and h, quartz substrate was obtained using a high 658 nm laser power (120 mW) and indicated a ~ 50 × enhancement factor on the pGOLD substrate. i, Schematic of SARS-CoV-2 viral RNA detection using FEMMAN assay. j, Layout of DNA array for SARS-CoV-2 panel with 3 sites (G142-145, K417 and L452, Sequences listed in Supplementary Table 2). k, IRDye800 fluorescence images for synthetic RNA of SARS-CoV-2 wild type and Omicron subvariants detection by FEMMAN assay with triplex RPA. Block in top left corner represent G142-Y145 target site, block in top right corner represent K417 target site, block in right bottom corner represent L452 target site, and block in bottom corner represent positive control and negative control (H1N1).

Source data

Extended Data Fig. 5 SARS-CoV-2 lineage identification by FEMMAN with a serially diluted lentivirus sample.

a, Schematic of SARS-CoV-2 viral RNA detection using FEMMAN assay. Fluorescence intensity statistics of G142-Y145, K417 and L452 site for b, Alpha variant, G142-Y145 site for c, Delta variant, d, Delta Plus variant, e, Mu variant, f, Omicron BA.1 variant, g, Omicron BA.2 variant, h, Omicron BA.2.12.1 variant and i, Omicron BA.5 variant, K417 site for SARS-CoV-2 j, Beta variant, k, Gamma variant, n, Omicron BA.1 variant, o, Omicron BA.2 variant, r, Omicron BA.2.12.1 variant and s, Omicron BA.5 variant, L452 site for l, Delta variant, m, Lambda variant, p, Delta Plus variant, q, Omicron BA.2 variant, t, Omicron BA.2.12.1 variant and u, Omicron BA.5 variant, detected by FEMMAN from 100,000 copies/rxn or 10,000 copies/rxn to 1 copies/rxn., measured by ddPCR. n = 3 technical replicates, bars represent mean ± s.d.

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Extended Data Fig. 6 Positive and negative assessments of clinical samples.

The dotted line represents fluorescence intensity threshold of 3 × s.d. above blank, which is the mean value of all blank samples in each chip. a, fluorescence signal of G142-Y145, K417 and L452 loci for all clinical samples, Dots in black for uninfected individuals, grey for non-SARS-CoV-2 HVs-infected patients, dark red for Alpha (b), green for Beta (c), blue for Delta (d), yellow for Omicron BA.1 (e), purple for Omicron BA.2 (f), orange for Omicron BA.5 (g), red for SARS-CoV-2 wild type (h).

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Extended Data Fig. 7 SARS-CoV-2 detection and viral-lineage discrimination of thermally lysed clinical samples.

a, Schematic for SARS-CoV-2 detection and viral-lineage identification using FEMMAN. b, FEMMAN assay achieved single-molecule sensitivity post amplification for SARS-CoV-2 detection of 12 lineages without viral RNA extraction. c, Mu variant identification in G142-Y145 site by FEMMAN from 10,000 copies/rxn to 1 copy/rxn. d, Ct value of lentivirus titration from 100 copies/rxn to 100,000 copies/rxn. e, Amplification plot of lentivirus titration from 100 copies/rxn to 100,000 copies/rxn. f, Concordance between thermal lysis (up) and RNA extraction (below) for 12 positive samples obtained in both forms. g, Signal proportion calculation of Y144del in G142-Y145 site, K417N in K417 site, and L452R in L452 site for 12 positive samples with thermal lysed nasopharyngeal swab samples. n = 3 technical replicates, two-tailed Student’s t test; *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001, precise figures were listed in data file, bars represent mean ± s.d.

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Extended Data Fig. 8 Point-of-care design of the FEMMAN assay, and stability of the reagents.

a, Schematic of point-of-care FEMMAN DNA-chip design with Cy5 fluorophore. b, Alpha variant identification in G142-Y145 site by FEMMAN from 10,000 copies/rxn to 1 copy/rxn using Cy5 labelled primer. Stability of Asy-RT-RPA reagents and DNA chip used in FEMMAN for c, IRDye800-SA staining and d, Cy5 labelled primer. The reagents and DNA chip were stored in room temperature for 0 day, 7 days, 14 days, 21 days, and 30 days (from left to right in each column). n = 3 technical replicates, bars represent mean ± s.d.

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Extended Data Fig. 9 Design of the microfluidic DNA chip and of the control processes.

a, Schematic of point-of-care FEMMAN DNA-chip. b, the front (left) and back (right) schematics. c, Control processes design. The initial state of valves 1 and 2 are closed while valve 3 is opened (1). In (2), valve 1 was opened, valves 2 and 3 were closed, and the liquid in chamber a is sucked into chamber c. In (3), valve 1, 2 and 3 were closed. In (4), valve 2 was opened, the 1 and 3 were closed, liquid in chamber b was sucked into the chamber c and mixed with the liquid in chamber c. In (5), valve 3 was open, valves 1 and 2 were closed, and the liquid in chamber c was driven into chamber d and incubated for 1 h at room temperature. In (6), valve 3 was opened, valves 1 and 2 were closed, the peristaltic pump drives the washing buffer to wash the DNA-chip in chamber d, and finally, the waste flowed into the waste chamber e. d, Schematic of synthesis processes for microfluidic DNA chip. e, FEMMAN point-of-care device realized 100 copies/rxn (10 copies/µL) lentivirus detection using fluorescence requisition system. f, Schematic of array on DNA-chip in point-of-care device. g, Image of 100 copies/rxn (10 copies/µL) lentivirus detection using fluorescence requisition system in tool-box sized portable device. SARS-CoV-2 detection at 100 copies/rxn (10 copies/µL) can be clearly visualized by the fluorescence acquisition system. h, Images of point-of-care device and automated microfluidic DNA chip.

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Extended Data Fig. 10 Versatility and scalability of FEMMAN.

a, Schematic of panel design for FEMMAN assay. b, Development of SARS-CoV-2 panel V1 to future Vx.

Supplementary information

Supplementary Information

Supplementary discussion, figures, tables, references and video captions.

Reporting Summary

Supplementary Video 1

Animated illustration of the operational dynamics of the FEMMAN device.

Supplementary Video 2

Fluidic flow within the microfluidic chip under unidirectional control.

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Liu, Y., Yang, Y., Wang, G. et al. Multiplexed discrimination of SARS-CoV-2 variants via plasmonic-enhanced fluorescence in a portable and automated device. Nat. Biomed. Eng 7, 1636–1648 (2023). https://doi.org/10.1038/s41551-023-01092-4

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