PXD057799-1

PXD057799 is an original dataset announced via ProteomeXchange.

Title	Library-based virtual match-between-runs quantification scheme enabled by GlyPep-Quant dramatically enhances the identification of site-specific glycans and detection of occupancy ratio changes
Description	Glycosylation changes are closely related to various diseases like cancer. However, the quantitative analysis of the site-specific glycans at proteomics scale remains challenging because of the extremely low interpretation rate of glycopeptide spectra. Here, we present a new software tool, GlyPep-Quant, for the sensitive quantification and identification of site-specific glycans. By implementing a two-step elution profile extraction method for precise location of matched profiles, a well-trained machine learning model and target-decoy matching method for confidence assessment, GlyPep-Quant enabled quantification of 21.3%-173.3% more site-specific glycans without any missing values than cutting-edge quantitative tools like pGlycoQuant, MSFragger-Glyco, and Skyline. To fully utilize identified information from previous large-scale dataset, a virtual match-between-runs quantification scheme was developed by matching library MS1 features to new data and enabled the identification and quantification of over 8-fold more site-specific glycans than conventional identification-based quantifications. The enhanced coverage on site-specific glycans prompted the development of a novel glycoproteomic biomarker discovery method, which involves calculating the ratios of site-specific glycan abundances at the same glycosylation site, thereby minimizing the impact of individual variances of glycoprotein expression and experimental conditions on the detection of glycosylation pattern changes. By using this method, two pairs of site-specific glycan ratios on sites P01011-N127 and P08185-N96, were determined to be high-performance novel biomarkers to classify gastric cancer (GC) from healthy controls with areas under the curves exceeding 0.95. Moreover, the selected glycan abundance ratios performed well in distinguishing GC using an independent cohort quantified by the newly developed library-based strategy with diagnostic accuracy up to 85%, demonstrated the robustness of the quantification module and the value of the discovered marker candidates. It is anticipated that GlyPep-Quant is ready to be applied in a broader range of glycoproteomic research scenarios.
HostingRepository	PRIDE
AnnounceDate	2025-07-06
AnnouncementXML	Submission_2025-07-06_10:48:03.407.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Zheng Fang
SpeciesList	scientific name: Schizosaccharomyces pombe; NCBI TaxID: 4896; scientific name: Homo sapiens (Human); NCBI TaxID: 9606; scientific name: Saccharomyces cerevisiae (Baker's yeast); NCBI TaxID: 4932;
ModificationList	monohydroxylated residue; iodoacetamide derivatized residue
Instrument	Orbitrap Exploris 480

Revision	Datetime	Status	ChangeLog Entry
0	2024-11-12 08:12:36	ID requested
⏵ 1	2025-07-06 10:48:03	announced

10.1038/S41467-025-61673-6;

submitter keyword: Proteomics

Glycosylation

Site-Specific Glycan

Quantification

Match-Between-Runs

Mingliang Ye
contact affiliation	Dalian Institute of Chemical Physics, CAS
contact email	mingliang@dicp.ac.cn
lab head
Zheng Fang
contact affiliation	Dalian Institute of Chemical Physics, Chinese Academy of Sciences
contact email	zhengfang@dicp.ac.cn
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD057799
     1. Label: PRIDE project
     2. Name: Library-based virtual match-between-runs quantification scheme enabled by GlyPep-Quant dramatically enhances the identification of site-specific glycans and detection of occupancy ratio changes