ArticleOpen forum infectious diseases2021
Use of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Analysis of Serum Peptidome to Classify and Predict Coronavirus Disease 2019 Severity.
Article in Open forum infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Transthyretin is a novel innate immune effector against Gram negative bacteria.PLoS pathogens · 2026Article
- Article
- Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva.Journal of clinical medicine · 2022Article
- Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling.Computer methods and programs in biomedicine · 2021Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClassification 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.
methodsWe 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.
resultsWe 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.
conclusionsWe 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.
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