Boost-DiLeu: Enhanced Isobaric N, N-Dimethyl Leucine Tagging Strategy for a 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 abundance and poor ionization efficiency of glycopeptides. Relatively large amounts of starting materials are needed for the enrichment, which makes the identification and quantification of intact glycopeptides from samples with limited quantity more challenging. To overcome these limitations, we developed an improved isobaric labeling strategy with an additional boosting channel to enhance N,N-dimethyl leucine (DiLeu) tagging-based quantitative glycoproteomic analysis, termed as Boost-DiLeu. With the integration of a 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 (AD) patients and non-AD 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, weighted gene coexpression network analysis (WGCNA) showed nine modules of glycopeptides, two of which were associated with the AD phenotype. Our results demonstrated the feasibility of using this strategy for in-depth glycoproteomic analysis of size-limited clinical samples. Taken together, we developed and optimized a strategy for the enhanced comprehensive quantitative intact glycopeptide analysis with DiLeu labeling, showing significant promise for identifying novel therapeutic targets or biomarkers in biological systems with a limited sample quantity.

Figure 1.

Workflow of Boost-DiLeu strategy. Proteins from biological samples were extracted, enzymatically digested, and labeled through one-tube sample preparation workflow. DiLeu 118d was used as a boosting channel. Samples were pooled after labeling for HILIC enrichment and HpH fractionation, followed by LC-MS/MS analysis.

Figure 2.

Comparison of sample preparation using conventional and one-tube sample processing workflow. (A) Identification number in glycoproteomic analysis. (B) Overlap of glycoproteins by the two methods as shown using Venn diagram.

Figure 3.

Comparison of different B/S ratios. (A) Experimental design: First three channels were labeled as study channels and DiLeu 118d channel was used as a boosting channel. (B) Reporter ion signal intensities at a 30x B/S ratio. (C) Identification number of GPSMs, total glycopeptides and quantifiable glycopeptides at different B/S ratios. (D) CV distribution of quantifiable glycopeptides.

Figure 4.

Comparison of different AGC settings. (A) Identification number of GPSMs, total glycopeptides and quantifiable glycopeptides with different AGC. (B) Distribution of actual ion injection time. (C) CV distribution of quantifiable glycopeptides. (D) Distribution of reporter ion signal intensities.

Figure 5.

Global glycoproteome mapping in HeLa cell line. (A) Identification number of GPSMs, total glycopeptides and quantifiable glycopeptides. (B) Overlap of unique glycopeptides identified in each fraction. (C) A glycoprotein-glycan network diagram that maps which glycans (outer nodes) modify which proteins (inner bar). Glycoproteins are sorted by the number of glycosites. Glycan nodes and their linkage to the proteins are colored according to the glycosylation type. (D) Distribution of reporter ion signal intensities across 12 channels. (E) CV distribution of quantifiable glycopeptides. (F) Pearson correlation of reporter ion intensities between three study channels.

Figure 6.

Site-specific quantitative glycoproteomic analysis of human CSF samples. (A) 18 aberrant N-glycopeptides in AD CSF samples with site-specific information (*p < 0.05, **p < 0.01, and ***p < 0.001). Each glycan structure only represents one possible glycan candidate due to the lack of structural information (N: HexNAc, H: Hex, F: Fucose, S: NeuAc). (B) WGCNA cluster dendrogram of 1172 glycopeptides. Nine modules with similar expression patterns were grouped via average linkage hierarchical clustering. (C) GO analysis of the red module glycopeptides clustered in WGCNA dendrogram. (D) GO analysis of the pink module glycopeptides clustered in WGCNA dendrogram.