Immuno- μSARS2 Chip: A Peptide-based microarray to assess COVID-19 prognosis based on immunological fingerprints

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• dc.contributor.author Guercetti, Julian
• dc.contributor.author Alorda, Marc
• dc.contributor.author Sappia, Luciano
• dc.contributor.author Galve, Roger
• dc.contributor.author Duran-Corbera, Macarena
• dc.contributor.author Pulido, Daniel
• dc.contributor.author Berardi, Ginevra
• dc.contributor.author Royo, Miriam
• dc.contributor.author Lacoma, Alicia
• dc.contributor.author Muñoz, José
• dc.contributor.author Padilla, Eduardo
• dc.contributor.author Castañeda, Silvia
• dc.contributor.author Sendra, Elena
• dc.contributor.author Horcajada Gallego, Juan Pablo
• dc.contributor.author Gutierrez-Galvez, Agustín
• dc.contributor.author Marco Sola, Santiago
• dc.contributor.author Salvador, J-Pablo
• dc.contributor.author Marco, M-Pilar
• dc.date.accessioned 2025-05-05T06:25:18Z
• dc.date.available 2025-05-05T06:25:18Z
• dc.date.issued 2025
• dc.description.abstract A multiplexed microarray chip (Immuno-μSARS2) aiming at providing information on the prognosis of the COVID-19 has been developed. The diagnostic technology records information related to the profile of the immunological response of patients infected by the SARS-CoV-2 virus. The diagnostic technology delivers information on the avidity of the sera against 28 different peptide epitopes and 7 proteins printed on a 25 mm2 area of a glass slide. The peptide epitopes (12-15 mer) derived from structural proteins (Spike and Nucleocapsid) have been rationally designed, synthesized, and used to develop Immuno-μSARS2 as a multiplexed and high-throughput fluorescent microarray platform. The analysis of 755 human serum samples (321 from PCR+ patients; 288 from PCR- patients; 115 from prepandemic individuals and classified as hospitalized, admitted to intensive-care unit (ICU), and exitus) from three independent cohorts has shown that the chips perform with a 98% specificity and 91% sensitivity identifying RT-PCR+ patients. Computational analysis utilized to correlate the immunological signatures of the samples analyzed indicate significant prediction rates against exitus conditions with 82% accuracy, ICU admissions with 80% accuracy, and 73% accuracy over hospitalization requirement compared to asymptomatic patients' fingerprints. The miniaturized microarray chip allows simultaneous determination of 96 samples (24 samples/slide) in 90 min and requires only 10 μL of sera. The diagnostic approach presented for the first time here could have a great value in assisting clinicians in decision-making based on the information provided by the Immuno-μSARS2 regarding progression of the disease and could be easily implemented in diagnostics of other infectious diseases.
• dc.format.mimetype application/pdf
• dc.identifier.citation Guercetti J, Alorda M, Sappia L, Galve R, Duran-Corbera M, Pulido D, et al. Immuno- μSARS2 Chip: A Peptide-based microarray to assess COVID-19 prognosis based on immunological fingerprints. ACS Pharmacol Transl Sci. 2025 Feb 21;8(3):871-84. DOI: 10.1021/acsptsci.4c00727
• dc.identifier.doi http://dx.doi.org/10.1021/acsptsci.4c00727
• dc.identifier.issn 2575-9108
• dc.identifier.uri http://hdl.handle.net/10230/70282
• dc.language.iso eng
• dc.publisher American Chemical Society (ACS)
• dc.relation.ispartof ACS Pharmacol Transl Sci. 2025 Feb 21;8(3):871-84
• dc.rights This publication is licensed under CC-BY 4.0 (http://creativecommons.org/licenses/by/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Microarray
• dc.subject.keyword High-throughput
• dc.subject.keyword Serological signature
• dc.subject.keyword Peptide epitopes
• dc.subject.keyword Multiplexation
• dc.subject.keyword Machine learning
• dc.subject.keyword Clinical diagnostic
• dc.subject.keyword Severity prediction
• dc.subject.keyword SARS-CoV-2
• dc.title Immuno- μSARS2 Chip: A Peptide-based microarray to assess COVID-19 prognosis based on immunological fingerprints
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion