Dataset Information

Optimized Workflow for In-depth N-glycosylation Analysis of 2D and 3D Breast Cancer Cell Cultures and Xenografted Tumors

ABSTRACT: Native intact N-glycopeptides analysis can access to a lot of useful information including N-glycosites, N-glycans and N-glycoproteins of original complex samples. The traditional sample preprocessing method is to analyze the tryptic intact N-glycopeptides enriched using hydrophilic materials or some new materials by LC-MS/MS. However, the number of identified intact N-glycopeptides was very limited using existing methods. Herein, we systematically compared the different analytical methods, including the use of different sources of trypsin, combinations of different proteases and different enrichment materials.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Yong Zhang

LAB HEAD: Yong Zhang

PROVIDER: PXD020254 | Pride | 2020-12-14

REPOSITORIES: Pride

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Publications

In-depth characterization and comparison of the N-glycosylated proteome of two-dimensional- and three-dimensional-cultured breast cancer cells and xenografted tumors.

Mao Yonghong Y Zhao Yang Y Zhang Yong Y Yang Hao H

PloS one 20201210 12

Native intact N-glycopeptide analysis can provide access to the comprehensive characteristics of N-glycan occupancy, including N-glycosites, N-glycan compositions, and N-glycoproteins for complex samples. The sample pre-processing method used for the analysis of intact N-glycopeptides usually depends on the enrichment of low abundance N-glycopeptides from a tryptic peptide mixture using hydrophilic substances before LC-MS/MS detection. However, the number of identified intact N-glycopeptides rem ...[more]

PMID: 33301522

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Project description:Large-scale identification of N-linked intact glycopeptides by liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) in human serum is challenging due to the wide dynamic range of serum protein abundances, the lack of a complete serum N-Glycan database and the existence of non-specifically digested peptides and numerous modifications. In this regard, a spectral library search method was presented for serum N-linked intact glycopeptides identification with target-decoy and motif-specific false discovery rate (FDR) control. Low-abundant glycoproteins were firstly separated from high-abundant proteins by acetonitrile (ACN) precipitation. After digestion, the N-linked intact glycopeptides were enriched by hydrophilic interaction liquid chromatography (HILIC) column and a portion of the enriched N-linked intact glycopeptides were processed by N-Glycosidase F (PNGase F) to generate de-glycopeptides. Both N-linked intact glycopeptides and de-glycopeptides were analyzed by LC-MS/MS. From N-linked de-glycopeptides datasets, 764 N-linked glycoproteins, 1,699 N-linked glycosites and 3,328 unique N-linked de-glycopeptides were identified. The spectra of these N-linked de-glycopeptides were utilized for N-linked de-glycopeptides library construction and identification of N-linked intact glycopeptides. Four types of N-linked glycosylation motifs (NXS/T/C/V, X≠P) were used to recognize the N-linked de-glycopeptides, and two different N-Glycan mass databases including 27 modified N-Glycan masses and 712 unmodified N-Glycan masses were combined and utilized during spectral library search for identification of N-linked intact glycopeptides. In total, from the N-linked intact glycopeptides datasets, 526 N-linked glycoproteins, 1,036 N-linked glycosites, 22,677 N-linked intact glycopeptides and 738 N-Glycan masses were identified under 1% FDR, representing the most in-depth serum N-glycoproteome identified by LC-MS/MS at N-linked intact glycopeptide level and N-linked de-glycopeptides level.

			Action	DRS
	2D-Proteomics_A_R1.raw	Raw
	2D-Proteomics_A_R2.raw	Raw
	2D-Proteomics_A_R3.raw	Raw
	2D-Proteomics_B_R1.raw	Raw
	2D-Proteomics_B_R2.raw	Raw