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.