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DIGITAL.CSIC
Dataset . 2025 . Peer-reviewed
Data sources: DIGITAL.CSIC
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[Dataset] Immuno-μSARS2 Chip: A Peptide-Based Microarray to Assess COVID-19 Prognosis Based on Immunological Fingerprints

Authors: Guercetti, Julian; Alorda, Marc; Sappia, Luciano; Galvé, Roger; Duran-Corbera, Macarena; Pulido, Daniel; Berardi, Ginevra; +11 Authors

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

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.

Peer reviewed

Country
Spain
Related Organizations
Keywords

Clinical diagnostic, SARS-CoV-2, Multiplexation, Machine learning, High-throughput, Microarray, Serological signature, http://metadata.un.org/sdg/3, Severity prediction, Ensure healthy lives and promote well-being for all at all ages, Peptide epitopes

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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