Dataset Information

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.The raw data alongside code for easily loading and processing the data and creating relevant figures is available at https://github.com/Fredan97/porcine-peptidomics-data.

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

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

Hartman Erik E Forsberg Fredrik F Kjellström Sven S Petrlova Jitka J Luo Congyu C Scott Aaron A Puthia Manoj M Malmström Johan J Schmidtchen Artur A

Nature communications 20240820 1

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 defini ...[more]

PMID: 39164298

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