ArticleCommunications biology2024
Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-Omics Analysis of PBMCs Revealed Distinct Biological Differences Between Hemodialysis and Peritoneal Dialysis.International journal of molecular sciences · 2026Article
- Immunopathogenesis and Cytokine Pathways in Reactive Infectious Mucocutaneous Eruption (RIME) in Pediatric Population: Infectious Triggers and Molecular Insights.Clinical reviews in allergy & immunology · 2026Review
- Quantitative protein analysis and glycan profiling of umbilical cord plasma and corresponding maternal plasma samples.Scientific reports · 2026Article
- Multiomics approaches in Kawasaki disease: insights into pathogenesis and emerging directions for diagnosis and treatment.Clinical and experimental pediatrics · 2026Article
- Mass spectrometry combined with machine learning identifies novel protein signatures as demonstrated with multisystem inflammatory syndrome in children.Scientific reports · 2025Article
- Fever in Children with Cancer: Pathophysiological Insights Using Blood Transcriptomics.International journal of molecular sciences · 2025Article
- Children with Post COVID-19 Multisystem Inflammatory Syndrome Display Unique Pathophysiological Metabolic Phenotypes.Journal of proteome research · 2025Article
- Ontology-guided clustering enables proteomic analysis of rare pediatric disorders.EMBO molecular medicine · 2025Article
- Omics data integration analysis identified new biological insights into chronic antibody-mediated rejection (CAMR).Journal of translational medicine · 2025Article
- Proteomic Signatures of Multisystem Inflammatory Syndrome in Children (MIS-C) Associated with COVID-19: A Narrative Review.Children (Basel, Switzerland) · 2024Review
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Authors and funding
16 authors.
Funding
Abstract
Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.
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Registered trials
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