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Boost-DiLeu: Enhanced Isobaric N,N-Dimethyl Leucine Tagging Strategy for Comprehensive Quantitative Glycoproteomic Analysis

ABSTRACT: Intact glycopeptide analysis has been of great interest because it can elucidate glycosylation site information and glycan structural composition at the same time. However, mass-spectrometry (MS)-based glycoproteomic analysis is hindered by the low stoichiometry of glycosylation and poor ionization efficiency of glycopeptides. Due to the relatively large amounts of starting materials needed for enrichment, identification and quantification of intact glycopeptides in some size-limited biological systems are especially challenging. To overcome these limitations, here we developed an improved boosting strategy to enhance N,N-dimethyl leucine tagging-based quantitative glycoproteomic analysis, termed as Boost-DiLeu. With integration of one-tube sample processing workflow and high pH fractionation, 3514 quantifiable N-glycopeptides were identified from 30 µg HeLa cell tryptic digests with reliable quantification performance. Furthermore, this strategy was applied to human cerebrospinal ﬂuid (CSF) samples to differentiate N-glycosylation profiles between Alzheimer’s disease patients and healthy donors. The results revealed processes and pathways affected by dysregulated N-glycosylation in AD, including platelet degranulation, cell adhesion, and extracellular matrix, which highlighted the involvement of N-glycosylation aberrations in AD pathogenesis. Moreover, co-expression network analysis (WGCNA) showed 3 modules of glycoproteins, one of which are associated with AD phenotype. Our results demonstrated the feasibility of using this strategy for comprehensive glycoproteomic analysis of size-limited clinical samples. Taken together, we developed and optimized a strategy for enhanced comprehensive quantitative intact glycopeptide analysis with DiLeu labeling, which is especially promising for identifying novel therapeutic targets or biomarkers in sample size limited models or systems.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid, Epithelial Cell, Hela Cell

DISEASE(S): Alzheimer's Disease

SUBMITTER: Danqing Wang

LAB HEAD: Lingjun Li

PROVIDER: PXD029269 | Pride | 2022-09-08

REPOSITORIES: Pride

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			Action	DRS
	20200728_0_HeLa_Boost_HILIC_HCD_30ug_9ul_1.msf	Msf
	20200728_0_HeLa_Boost_HILIC_HCD_30ug_9ul_1.raw	Raw
	20200728_0_HeLa_Boost_HILIC_HCD_30ug_9ul_2.msf	Msf
	20200728_0_HeLa_Boost_HILIC_HCD_30ug_9ul_2.raw	Raw
	20200728_1_HeLa_Boost_HILIC_HCD_330ug_9ul_1.msf	Msf

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Project description:Protein O-glycosylation has long been recognized to be closely associated with many diseases, particularly with tumor proliferation, invasion and metastasis. The ability to efﬁciently proﬁle the variation of O-glycosylation in large-scale clinical samples provides an important approach for the development of biomarkers for cancer diagnosis and for therapeutic response evaluation. Therefore, mass spectrometry (MS)-based techniques for high throughput, in-depth and reliable elucidation of protein O-glycosylation in large clinical cohorts are in high demand. However, the wide existence of serine and threonine residues in the proteome and the tens of mammalian O-glycan types lead to extremely large searching space composed of millions of theoretical combinations of peptides and O-glycans for intact O-glycopeptide database searching. As a result, exceptionally long time is required for database searching which is a major obstacle in O-glycoproteome studies of large clinical cohorts. More importantly, due to the low abundance and poor ionization of intact O-glycopeptides and the stochastic nature of data-dependent MS2 acquisition, substantially elevated missing data levels are inevitable as the sample number increases, which undermines the quantitative comparison across samples. Therefore, we report a new MS data processing strategy that integrates glycoform-specific database searching, reference library-based MS1 feature matching and MS2 identification propagation for fast identification, in-depth and reproducible label-free quantification of O-glycosylation of human urinary proteins. This strategy increases the database searching speeds by up to 20-fold and leads to a 30-40% enhanced intact O-glycopeptide quantification in individual samples with an obviously improved reproducibility. In total, we obtained quantitative information for 1068 intact O-glycopeptides across 36 healthy human urine samples with a 30-40% reduction in the amount of missing data. This is currently the largest dataset of urinary O-glycoproteome and demonstrates the application potential of this new strategy in large-scale clinical investigations.

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Boost-DiLeu: Enhanced Isobaric N,N-Dimethyl Leucine Tagging Strategy for Comprehensive Quantitative Glycoproteomic Analysis

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