Proteomics

Dataset Information

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Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data


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.

INSTRUMENT(S): Bruker Daltonics timsTOF series

ORGANISM(S): Homo Sapiens (human) Sus Scrofa Domesticus (domestic Pig)

TISSUE(S): Wound Fluid

SUBMITTER: Fredrik Forsberg  

LAB HEAD: Artur Schmidtchen

PROVIDER: PXD048892 | Pride | 2024-07-13

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
formatted_pig_design.csv Csv
human_design.csv Csv
human_sample_1.mgf Mgf
human_sample_1.mzid.gz Mzid
human_sample_1.zip Other
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