tetano
Editor, Senior Moderator
Open Forum Infect Dis
. 2021 May 2;8(6)
fab222.
doi: 10.1093/ofid/ofab222. eCollection 2021 Jun.
Use of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Analysis of Serum Peptidome to Classify and Predict Coronavirus Disease 2019 Severity
Rosa M Gomila[SUP] 1 [/SUP], Gabriel Martorell[SUP] 1 [/SUP], Pablo A Fraile-Ribot[SUP] 2 3 [/SUP], Antonio Doménech-Sánchez[SUP] 3 4 [/SUP], Miguel Albertí[SUP] 4 [/SUP], Antonio Oliver[SUP] 2 3 [/SUP], Mercedes García-Gasalla[SUP] 3 5 [/SUP], Sebastián Albertí[SUP] 1 3 4 [/SUP]
Affiliations
Abstract
Background: Classification and early detection of severe coronavirus disease 2019 (COVID-19) patients is required to establish an effective treatment. We tested the utility of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) to classify and predict the severity of COVID-19.
Methods: We used MALDI-TOF MS to analyze the serum peptidome from 72 patients with COVID-19 (training cohort), clinically classified as mild (28), severe (23), and critical (21), and 20 healthy controls. The resulting matrix of peak intensities was used for Machine Learning (ML) approaches to classify and predict COVID-19 severity of 22 independent patients (validation cohort). Finally, we analyzed all sera by liquid chromatography mass spectrometry (LC-MS/MS) to identify the most relevant proteins associated with disease severity.
Results: We found a clear variability of the serum peptidome profile depending on COVID-19 severity. Forty-two peaks exhibited a log fold change ≥1 and 17 were significantly different and at least 4-fold more intense in the set of critical patients than in the mild ones. The ML approach classified clinical stable patients according to their severity with 100% accuracy and correctly predicted the evolution of the nonstable patients in all cases. The LC-MS/MS identified 5 proteins that were significantly upregulated in the critical patients. They included the serum amyloid protein A2, which probably yielded the most intense peak detected by MALDI-TOF MS.
Conclusions: We demonstrate the potential of the MALDI-TOF MS as a bench to bedside technology to aid clinicians in their decision making regarding patients with COVID-19.
Keywords: COVID-19; MALDI-TOF; machine learning; serum peptidome.
. 2021 May 2;8(6)
fab222.doi: 10.1093/ofid/ofab222. eCollection 2021 Jun.
Use of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Analysis of Serum Peptidome to Classify and Predict Coronavirus Disease 2019 Severity
Rosa M Gomila[SUP] 1 [/SUP], Gabriel Martorell[SUP] 1 [/SUP], Pablo A Fraile-Ribot[SUP] 2 3 [/SUP], Antonio Doménech-Sánchez[SUP] 3 4 [/SUP], Miguel Albertí[SUP] 4 [/SUP], Antonio Oliver[SUP] 2 3 [/SUP], Mercedes García-Gasalla[SUP] 3 5 [/SUP], Sebastián Albertí[SUP] 1 3 4 [/SUP]
Affiliations
- PMID: 34109258
- PMCID: PMC8135799
- DOI: 10.1093/ofid/ofab222
Abstract
Background: Classification and early detection of severe coronavirus disease 2019 (COVID-19) patients is required to establish an effective treatment. We tested the utility of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) to classify and predict the severity of COVID-19.
Methods: We used MALDI-TOF MS to analyze the serum peptidome from 72 patients with COVID-19 (training cohort), clinically classified as mild (28), severe (23), and critical (21), and 20 healthy controls. The resulting matrix of peak intensities was used for Machine Learning (ML) approaches to classify and predict COVID-19 severity of 22 independent patients (validation cohort). Finally, we analyzed all sera by liquid chromatography mass spectrometry (LC-MS/MS) to identify the most relevant proteins associated with disease severity.
Results: We found a clear variability of the serum peptidome profile depending on COVID-19 severity. Forty-two peaks exhibited a log fold change ≥1 and 17 were significantly different and at least 4-fold more intense in the set of critical patients than in the mild ones. The ML approach classified clinical stable patients according to their severity with 100% accuracy and correctly predicted the evolution of the nonstable patients in all cases. The LC-MS/MS identified 5 proteins that were significantly upregulated in the critical patients. They included the serum amyloid protein A2, which probably yielded the most intense peak detected by MALDI-TOF MS.
Conclusions: We demonstrate the potential of the MALDI-TOF MS as a bench to bedside technology to aid clinicians in their decision making regarding patients with COVID-19.
Keywords: COVID-19; MALDI-TOF; machine learning; serum peptidome.