Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints

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 technolog...

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Autores: Guercetti, Julian, Alorda, Marc, Sappia, Luciano, Galve, Roger, Duran-Corbera, Macarena, Pulido, Daniel, Berardi, Ginevra, Royo, Miriam, Lacorna, Alicia, Muñoz Gutiérrez, José, Padilla, Eduardo, Castañeda, Silvia, Sendra, Elena, Horcajada Gallego, Juan Pablo, Gutiérrez Gálvez, Agustín, Marco Colás, Santiago, Salvador, J. Pablo, Marco, M. Pilar
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/220776
Acceso en línea:https://hdl.handle.net/2445/220776
Access Level:acceso abierto
Palabra clave:COVID-19
Aprenentatge automàtic
Pèptids
Machine learning
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spelling Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological FingerprintsGuercetti, JulianAlorda, MarcSappia, LucianoGalve, RogerDuran-Corbera, MacarenaPulido, DanielBerardi, GinevraRoyo, MiriamLacorna, AliciaMuñoz Gutiérrez, JoséPadilla, EduardoCastañeda, SilviaSendra, ElenaHorcajada Gallego, Juan PabloGutiérrez Gálvez, AgustínMarco Colás, SantiagoSalvador, J. PabloMarco, M. PilarCOVID-19Aprenentatge automàticPèptidsCOVID-19Machine learningPeptidesA 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.2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/220776Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1021/acsptsci.4c00727American Chemical Society, 2025, vol. 8, num.3https://doi.org/10.1021/acsptsci.4c00727cc-by (c) Guercetti, Julian et al., 2025http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2207762026-05-27T06:46:51Z
dc.title.none.fl_str_mv Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
title Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
spellingShingle Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
Guercetti, Julian
COVID-19
Aprenentatge automàtic
Pèptids
COVID-19
Machine learning
Peptides
title_short Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
title_full Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
title_fullStr Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
title_full_unstemmed Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
title_sort Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints
dc.creator.none.fl_str_mv Guercetti, Julian
Alorda, Marc
Sappia, Luciano
Galve, Roger
Duran-Corbera, Macarena
Pulido, Daniel
Berardi, Ginevra
Royo, Miriam
Lacorna, Alicia
Muñoz Gutiérrez, José
Padilla, Eduardo
Castañeda, Silvia
Sendra, Elena
Horcajada Gallego, Juan Pablo
Gutiérrez Gálvez, Agustín
Marco Colás, Santiago
Salvador, J. Pablo
Marco, M. Pilar
author Guercetti, Julian
author_facet Guercetti, Julian
Alorda, Marc
Sappia, Luciano
Galve, Roger
Duran-Corbera, Macarena
Pulido, Daniel
Berardi, Ginevra
Royo, Miriam
Lacorna, Alicia
Muñoz Gutiérrez, José
Padilla, Eduardo
Castañeda, Silvia
Sendra, Elena
Horcajada Gallego, Juan Pablo
Gutiérrez Gálvez, Agustín
Marco Colás, Santiago
Salvador, J. Pablo
Marco, M. Pilar
author_role author
author2 Alorda, Marc
Sappia, Luciano
Galve, Roger
Duran-Corbera, Macarena
Pulido, Daniel
Berardi, Ginevra
Royo, Miriam
Lacorna, Alicia
Muñoz Gutiérrez, José
Padilla, Eduardo
Castañeda, Silvia
Sendra, Elena
Horcajada Gallego, Juan Pablo
Gutiérrez Gálvez, Agustín
Marco Colás, Santiago
Salvador, J. Pablo
Marco, M. Pilar
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv COVID-19
Aprenentatge automàtic
Pèptids
COVID-19
Machine learning
Peptides
topic COVID-19
Aprenentatge automàtic
Pèptids
COVID-19
Machine learning
Peptides
description 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.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/220776
url https://hdl.handle.net/2445/220776
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1021/acsptsci.4c00727
American Chemical Society, 2025, vol. 8, num.3
https://doi.org/10.1021/acsptsci.4c00727
dc.rights.none.fl_str_mv cc-by (c) Guercetti, Julian et al., 2025
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Guercetti, Julian et al., 2025
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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