TY - JOUR
T1 - Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19
AU - Lazari, Lucas Cardoso
AU - de Rose Ghilardi, Fabio
AU - Rosa-Fernandes, Livia
AU - Assis, Diego M.
AU - Nicolau, José Carlos
AU - Santiago, Veronica Feijoli
AU - Dalçóquio, Talia Falcão
AU - Angeli, Claudia B.
AU - Bertolin, Adriadne Justi
AU - Marinho, Claudio R.F.
AU - Wrenger, Carsten
AU - Durigon, Edison Luiz
AU - Siciliano, Rinaldo Focaccia
AU - Palmisano, Giuseppe
N1 - Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2021/8
Y1 - 2021/8
N2 - SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.
AB - SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.
UR - http://www.scopus.com/inward/record.url?scp=85109398392&partnerID=8YFLogxK
U2 - 10.26508/LSA.202000946
DO - 10.26508/LSA.202000946
M3 - Article
C2 - 34168074
AN - SCOPUS:85109398392
SN - 2575-1077
VL - 4
SP - 1
EP - 12
JO - Life Science Alliance
JF - Life Science Alliance
IS - 8
M1 - e202000946
ER -