ArticleNature communications2024
Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Extracellular matrix and proteolysis: mechanisms driving irreversible changes and shaping cell behavior.The FEBS journal · 2026Review
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- Rapid peptide analysis in dried bloodspots to identify novel markers for newborn screening for congenital hypothyroidism.Scientific reports · 2026Article
- Degradation graphs reveal hidden proteolytic activity in peptidomes.PLoS computational biology · 2026Article
- Single-Position Peptide Clustering for Peptidomics Reveals Novel Disease Biomarkers and Dysregulated Proteolytic Characteristics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- Mass spectrometry peptidomics data from infected and uninfected porcine wounds.Scientific data · 2025Article
- Article
- Editorial: Advancements in translational models: bridging basic infection research and clinical applications.Frontiers in medicine · 2025Article
- Article
- Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.
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Registered trials
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