MALDI-TOF MS analysis of nasal swabs for the characterization of patients infected with SARS-CoV-2 Omicron

Rui Song, Dandan Li, Xiaohua Hao, Qian Lyu, Qingwei Ma, Xiaoyou Chen, Liang Qiao

VIEW ›› 2024, Vol. 5 ›› Issue (3) : 20240015-11.

PDF
VIEW ›› 2024, Vol. 5 ›› Issue (3) : 20240015-11. DOI: 10.1002/VIW.20240015
RESEARCH ARTICLE

MALDI-TOF MS analysis of nasal swabs for the characterization of patients infected with SARS-CoV-2 Omicron

Author information +
History +

Abstract

With the ongoing mutation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leading to various variants, there is an urgent need for new diagnostic methods for SARS-CoV-2 infection. The existing nucleic acid test and antigen test suffer from long assay time and low sensitivity, respectively. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based nasal swabs analysis have been demonstrated as a promising technique in SARS-CoV-2 infection screening. However, the applicability of the technique in the different variants of SARS-CoV-2 is uncertain.Given the prevalence of the Omicron variant since 2022, we developed a MALDI-TOFbased diagnosis method with nasal swab samples to detect the infection by this variant. We collected 325 SARS-CoV-2-positive and 221 SARS-CoV-2-negative nasal swab samples, and the molecular mass fingerprints were acquired from the samples by MALDI-TOF MS. Using a random forest machine learning classification model to analyze the molecular mass fingerprints MALDI-TOF mass spectra, the accuracy of 97%, false negative rate of 0%, and false positive rate of 7.6% were achieved for the diagnosis of SARS-CoV-2 infection. Combining the MALDI-TOF analysis with top-down proteomics, we identified four potential protein biomarkers, that is, humanin-like 4, thymosin beta-10, thymosin beta-4 and statherin, in the nasal swab for the diagnosis of coronavirus disease 2019. It was further found that the four protein biomarkers can also differentiate the SARS-CoV-2 original strains infection and Omicron strains infection. These results suggest that the MALDI-TOF MS-based nasal swab analysis holds effective diagnostic capabilities of SARS-CoV-2 infection, and shows promising potential for global application and extension to other infectious diseases.

Keywords

diagnosis / machine learning / MALDI-TOF / SARS-CoV-2

Cite this article

Download citation ▾
Rui Song, Dandan Li, Xiaohua Hao, Qian Lyu, Qingwei Ma, Xiaoyou Chen, Liang Qiao. MALDI-TOF MS analysis of nasal swabs for the characterization of patients infected with SARS-CoV-2 Omicron. VIEW, 2024, 5(3): 20240015‒11 https://doi.org/10.1002/VIW.20240015

References

[1]
S. H. Safiabadi Tali, J. J. LeBlanc, Z. Sadiq, O. D. Oyewunmi, C. Camargo, B. Nikpour, N. Armanfard, S. M. Sagan, S. Jahanshahi-Anbuhi, Clin. Microbiol. Rev. 2021, 34, e00228.
CrossRef Google scholar
[2]
J. Dinnes, J. J. Deeks, S. Berhane, M. Taylor, A. Adriano, C. Davenport, S. Dittrich, D. Emperador, Y. Takwoingi, J. Cunningham, S. Beese, J. Domen, J. Dretzke, L. Ferrante di Ruffano, I. M. Harris, M. J. Price, S. Taylor-Phillips, L. Hooft, M. M. Leeflang, M. D. McInnes, R. Spijker, A. Van den Bruel, Cochrane Database Syst. Rev. 2021, 3, Cd013705.
[3]
L. Yan, J. Yi, C. Huang, J. Zhang, S. Fu, Z. Li, Q. Lyu, Y. Xu, K. Wang, H. Yang, Q. Ma, X. Cui, L. Qiao, W. Sun, P. Liao, Anal. Chem. 2021, 93, 4782.
CrossRef Google scholar
[4]
B. Shen, X. Yi, Y. Sun, X. Bi, J. Du, C. Zhang, S. Quan, F. Zhang, R. Sun, L. Qian, W. Ge, W. Liu, S. Liang, H. Chen, Y. Zhang, J. Li, J. Xu, Z. He, B. Chen, J. Wang, H. Yan, Y. Zheng, D. Wang, J. Zhu, Z. Kong, Z. Kang, X. Liang, X. Ding, G. Ruan, N. Xiang, X. Cai, H. Gao, L. Li, S. Li, Q. Xiao, T. Lu, Y. Zhu, H. Liu, H. Chen, T. Guo, Cell 2020, 182, 59.
CrossRef Google scholar
[5]
Y. Zhu, H. H. Girault, VIEW 2023, 4, 20220042.
[6]
F. M. Nachtigall, A. Pereira, O. S. Trofymchuk, L. S. Santos, Nat. Biotechnol. 2020, 38, 1168.
CrossRef Google scholar
[7]
N. G. Davies, S. Abbott, R. C. Barnard, C. I. Jarvis, A. J. Kucharski, J. D. Munday, C. A. B. Pearson, T. W. Russell, D. C. Tully, A. D. Washburne, T. Wenseleers, A. Gimma, W. Waites, K. L. M. Wong, K. van Zandvoort, J. D. Silverman, K. Diaz-Ordaz, R. Keogh, R. M. Eggo, S. Funk, M. Jit, K. E. Atkins, W. J. Edmunds, Science 2021, 372, eabg3055.
[8]
S. N. Slavov, J. S. L. Patané, R. D. S. Bezerra, M. Giovanetti, V. Fonseca, A. J. Martins, V. L. Viala, E. S. Rodrigues, E. V. Santos, C. R. S. Barros, E. C. Marqueze, B. Santos, F. Aburjaile, R. M. Neto, D. B. Moretti, R. Haddad, R. T. Calado, J. P. Kitajima, E. Freitas, D. Schlesinger, L. C. Junior de Alcantara, M. C. Elias, S. C. Sampaio, S. Kashima, D. T. Covas, J. Med. Virol. 2021, 93, 6782.
[9]
N. R. Faria, T. A. Mellan, C. Whittaker, I. M. Claro, D. D. S. Candido, S. Mishra, M. A. E. Crispim, F. C. S. Sales, I. Hawryluk, J. T. McCrone, R. J. G. Hulswit, L. A. M. Franco, M. S. Ramundo, J. G. de Jesus, P. S. Andrade, T. M. Coletti, G. M. Ferreira, C. A. M. Silva, E. R. Manuli, R. H. M. Pereira, P. S. Peixoto, M. U. G. Kraemer, N. Gaburo, Jr., C. D. C. Camilo, H. Hoeltgebaum, W. M. Souza, E. C. Rocha, L. M. de Souza, M. C. de Pinho, L. J. T. Araujo, F. S. V. Malta, A. B. de Lima, J. D. P. Silva, D. A. G. Zauli, A. C. S. Ferreira, R. P. Schnekenberg, D. J. Laydon, P. G. T. Walker, H. M. Schlüter, A. L. P. Dos Santos, M. S. Vidal, V. S. Del Caro, R. M. F. Filho, H. M. DosSantos, R. S. Aguiar, J. L. Proenç-Modena, B. Nelson, J. A. Hay, M. Monod, X. Miscouridou, H. Coupland, R. Sonabend, M. Vollmer, A. Gandy, C. A. Prete, Jr., V. H. Nascimento, M. A. Suchard, T. A. Bowden, S. L. K. Pond, C. H. Wu, O. Ratmann, N. M. Ferguson, C. Dye, N. J. Loman, P. Lemey, A. Rambaut, N. A. Fraiji, M. Carvalho, O. G. Pybus, S. Flaxman, S. Bhatt, E. C. Sabino, Science 2021, 372, 815.
CrossRef Google scholar
[10]
P. Mlcochova, S. A. Kemp, M. S. Dhar, G. Papa, B. Meng, I. Ferreira, R. Datir, D. A. Collier, A. Albecka, S. Singh, R. Pandey, J. Brown, J. Zhou, N. Goonawardane, S. Mishra, C. Whittaker, T. Mellan, R. Marwal, M. Datta, S. Sengupta, K. Ponnusamy, V. S. Radhakrishnan, A. Abdullahi, O. Charles, P. Chattopadhyay, P. Devi, D. Caputo, T. Peacock, C. Wattal, N. Goel, A. Satwik, R. Vaishya, M. Agarwal, A. Mavousian, J. H. Lee, J. Bassi, C. Silacci-Fegni, C. Saliba, D. Pinto, T. Irie, I. Yoshida, W. L. Hamilton, K. Sato, S. Bhatt, S. Flaxman, L. C. James, D. Corti, L. Piccoli, W. S. Barclay, P. Rakshit, A. Agrawal, R. K. Gupta, Nature 2021, 599, 114.
[11]
H. Shuai, J. F. Chan, B. Hu, Y. Chai, T. T. Yuen, F. Yin, X. Huang, C. Yoon, J. C. Hu, H. Liu, J. Shi, Y. Liu, T. Zhu, J. Zhang, Y. Hou, Y. Wang, L. Lu, J. P. Cai, A. J. Zhang, J. Zhou, S. Yuan, M. A. Brindley, B. Z. Zhang, J. D. Huang, K. K. To, K. Y. Yuen, H. Chu, Nature 2022, 603, 693.
CrossRef Google scholar
[12]
Y. Pan, L. Wang, Z. Feng, H. Xu, F. Li, Y. Shen, D. Zhang, W. J. Liu, G. F. Gao, Q. Wang, Lancet 2023, 401, 664.
CrossRef Google scholar
[13]
N. Bray, W. Sopwith, M. Edmunds, H. Vansteenhouse, J. D. M. Feenstra, P. Jacobs, K. Rajput, A. M. O’Connell, M. L. Smith, P. Blomquist, D. Hatziioanou, R. Elson, R. Vivancos, E. Gallagher, M. J. Wigglesworth, A. Dominiczak, S. Hopkins, I. R. Lake, Lancet Microbe. 2024, 5, e173.
CrossRef Google scholar
[14]
X. Shao, Y. Huang, G. Wang, VIEW 2023, 4, 20220032.
[15]
S. Gibb, K. Strimmer, Bioinformatics 2012, 28, 2270.
CrossRef Google scholar
[16]
J. Chong, D. S. Wishart, J. Xia, Curr. Protoc. Bioinformatics 2019, 68, e86.
[17]
a) K. Xiao, F. Yu, Z. Tian, J. Proteomics 2017, 152, 41; b) L. Li, Z. Tian, Rapid Commun. Mass Spectrom. 2013, 27, 1267.
CrossRef Google scholar
[18]
a) J. Ma, T. Chen, S. Wu, C. Yang, M. Bai, K. Shu, K. Li, G. Zhang, Z. Jin, F. He, H. Hermjakob, Y. Zhu, Nucleic Acids Res. 2019, 47, D1211; b) T. Chen, J. Ma, Y. Liu, Z. Chen, N. Xiao, Y. Lu, Y. Fu, C. Yang, M. Li, S. Wu, X. Wang, D. Li, F. He, H. Hermjakob, Y. Zhu, Nucleic Acids Res. 2022, 50, D1522.
CrossRef Google scholar
[19]
a) H. Su, X. Li, L. Huang, J. Cao, M. Zhang, V. Vedarethinam, W. Di, Z. Hu, K. Qian, Adv. Mater. 2021, 33, e2007978; b) A. S. Kulkarni, L. Huang, K. Qian, J. Mater. Chem. B 2021, 9, 3622; c) S. Sun, W. Liu, J. Yang, H. Wang, K. Qian, Angew. Chem. Int. Ed Engl. 2021, 60, 11310.
CrossRef Google scholar
[20]
a) J. G. Greener, S. M. Kandathil, L. Moffat, D. T. Jones, Nat. Rev. Mol. Cell Biol. 2022, 23, 40; b) C. J. Haug, J. M. Drazen, N. Engl. J. Med. 2023, 388, 1201.
CrossRef Google scholar
[21]
J. J. Deeks, J. Dinnes, Y. Takwoingi, C. Davenport, R. Spijker, S. Taylor-Phillips, A. Adriano, S. Beese, J. Dretzke, L. Ferrante di Ruffano, I. M. Harris, M. J. Price, S. Dittrich, D. Emperador, L. Hooft, M. M. Leeflang, A. Van den Bruel, Cochrane Database Syst. Rev. 2020, 6, Cd013652.
[22]
S. L. McKay, F. A. Tobolowsky, E. D. Moritz, K. M. Hatfield, A. Bhatnagar, S. P. LaVoie, D. A. Jackson, K. D. Lecy, J. Bryant-Genevier, D. Campbell, B. Freeman, S. E. Gilbert, J. M. Folster, M. Medrzycki, P. L. Shewmaker, B. Bankamp, K. W. Radford, R. Anderson, M. D. Bowen, J. Negley, S. C. Reddy, J. A. Jernigan, A. C. Brown, L. C. McDonald, P. K. Kutty, Ann. Intern. Med. 2021, 174, 945.
CrossRef Google scholar
[23]
A. M. Carabelli, T. P. Peacock, L. G. Thorne, W. T. Harvey, J. Hughes, S. J. Peacock, W. S. Barclay, T. I. de Silva, G. J. Towers, D. L. Robertson, Nat. Rev. Microbiol. 2023, 21, 162.
[24]
J. C. Tran, L. Zamdborg, D. R. Ahlf, J. E. Lee, A. D. Catherman, K. R. Durbin, J. D. Tipton, A. Vellaichamy, J. F. Kellie, M. Li, C. Wu, S. M. Sweet, B. P. Early, N. Siuti, R. D. LeDuc, P. D. Compton, P. M. Thomas, N. L. Kelleher, Nature 2011, 480, 254.
CrossRef Google scholar
[25]
R. Yu, Y. Mao, K. Li, Y. Zhai, Y. Zhang, S. Liu, Y. Gao, Z. Chen, Y. Liu, T. Fang, M. Zhao, R. Li, J. Xu, W. Chen, Mediators Inflamm. 2021, 2021, 9979032.
[26]
X. Zhang, D. Ren, L. Guo, L. Wang, S. Wu, C. Lin, L. Ye, J. Zhu, J. Li, L. Song, H. Lin, Z. He, Breast Cancer Res. 2017, 19, 15.
[27]
E. J. Helmerhorst, G. Traboulsi, E. Salih, F. G. Oppenheim, J. Proteome Res. 2010, 9, 5413.
CrossRef Google scholar
[28]
A. M. Cole, P. Dewan, T. Ganz, Infect. Immun. 1999, 67, 3267.
CrossRef Google scholar
[29]
a) B. Ghafouri, K. Irander, J. Lindbom, C. Tagesson, M. Lindahl, J. Proteome Res. 2006, 5, 330; b) L. M. Benson, C. J. Mason, O. Friedman, H. Kita, H. R. Bergen, D. A. Plager, J. Sep. Sci. 2009, 32, 44.
CrossRef Google scholar
[30]
C. F. Zuccato, A. S. Asad, A. J. Nicola Candia, M. F. Gottardo, M. A. Moreno Ayala, M. S. Theas, A. Seilicovich, M. Candolfi, Expert Opin. Ther. Targets 2019, 23, 117.
CrossRef Google scholar
[31]
W. Zhang, D. Li, B. Xu, L. Xu, Q. Lyu, X. Liu, Z. Li, J. Zhang, W. Sun, Q. Ma, L. Qiao, P. Liao, Front. Immunol. 2022, 13, 956369.

RIGHTS & PERMISSIONS

2024 2024 The Authors. View published by Shanghai Fuji Technology Consulting Co., Ltd, authorized by Professional Community of Experimental Medicine, National Association of Health Industry and Enterprise Management (PCEM) and John Wiley & Sons Australia, Ltd.
PDF

Accesses

Citations

Detail

Sections
Recommended

/