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Glycans are vital biomolecules with diverse functions in biological processes. Mass spectrometry (MS) has become the most widely employed technology for glycomics studies. However, in the traditional data-dependent acquisition mode, only a subset of the abundant ions during MS1 scans are isolated and fragmented in subsequent MS2 events, which reduces reproducibility and prevents the measurement of low-abundance glycan species. Here, we reported a new method termed 6-plex m d SUGAR isob a ric-labeling g uide fi n gerprint embedd i ng (MAGNI), to achieve multiplexed, quantitative, and targeted glycan analysis. The glycan peak signature was embedded by a triplicate-labeling strategy with a 6-plex md SUGAR tag, and using ultrahigh-resolution mass spectrometers, the low-abundance glycans that carry the mass fingerprints can be recognized on the MS1 spectra through an in-house developed software tool, MAGNIFinder. These embedded unique fingerprints can guide the selection and fragmentation of targeted precursor ions and further provide rich information on glycan structures. Quantitative analysis of two standard glycoproteins demonstrated the accuracy and precision of MAGNI. Using this approach, we identified 304 N-glycans in two ovarian cancer cell lines. Among them, 65 unique N-glycans were found differentially expressed, which indicates a distinct glycosylation pattern for each cell line. Remarkably, 31 N-glycans can be quantified in only 1 × 10 3 cells, demonstrating the high sensitivity of our method. Taken together, our MAGNI method offers a useful tool for low-abundance N-glycan characterization and is capable of determining small quantitative differences in N-glycan profiling. Therefore, it will be beneficial to the field of glycobiology and will expand our understanding of glycosylation.