GlycanDIA Enables Deep Capture and Precise Quantification of Low-Abundance Glycans
March 3, 2026
In the field of glycobiology, comprehensively and accurately analyzing the complex and diverse glycans on cell surfaces has always been key to revealing their biological functions. Traditional mainstream methods are like fishing, only able to catch a few of the most abundant targets, while a large amount of information on low-abundance glycans is inevitably missed. This poses a huge obstacle to studying trace samples (such as clinical biopsies) or new targets with extremely low abundance (such as glycosylated RNA).
On August 1, 2025, a study published in Nature, titled "Development and application of GlycanDIA workflow for glycomic analysis," brought about a paradigm shift. Researchers developed a data-independent acquisition mass spectrometry workflow called GlycanDIA. It can unbiasedly fragment and analyze all glycans at once, achieving a leap forward in identification depth, quantitative accuracy, and sensitivity for detecting low-abundance molecules.
Inherent Limitations of Traditional Analysis
Currently, Glycomics primarily relies on Data-dependent Acquisition (DDA) Mass Spectrometry technology. Its working principle is that in each scan, the mass spectrometer only selects a few of the most abundant precursor ions for fragmentation analysis.
This selective approach has two major core drawbacks:
- Undetected Low-Abundance Molecules: A large number of medium- and low-abundance glycan ions are not selected, preventing the acquisition of secondary fragmentation spectra and leading to failed identification.
- Large Quantitative Fluctuations: The ion intensity of the primary mass spectrum (MS1) is easily affected by co-eluting impurities, resulting in poor reproducibility of quantitative results.
When faced with novel research objects like glycosylated RNA (glycoRNA), which has extremely low glycan content (only about 20 picomoles per microgram of RNA), the limitations of the DDA method are particularly prominent, severely hindering exploration in this field.
How GlycanDIA Achieves Comprehensive Analysis
To overcome the limitations, the research team innovatively applied the increasingly mature data-independent acquisition (DIA) strategy from Proteomics to glycomics.
Core Principle Innovation:
- DDA (Traditional): Uses a narrow window (approximately 1-2 m/z) to select and fragment a single high-abundance ion, thereby obtaining the exclusive MS2 spectrum of that ion.
- GlycanDIA (New Method): Uses a wide window (e.g., 24 m/z) for comprehensive acquisition, simultaneously fragmenting all ions within the window, ultimately obtaining a mixed MS2 spectrum containing fragment information from all ions.
To accurately decode the information of individual glycans from the complex mixed spectra, the team developed a dual data analysis strategy:
- MS1-Centric Strategy: Knowing the theoretical mass of the glycan, its chromatographic peak is first located in MS1, and then the characteristic fragment ions of the glycan are extracted from the MS2 spectrum of the corresponding DIA window for verification and identification.
- MS2-Centric Strategy: Starting from characteristic fragment ions in the MS2 spectrum (e.g., sialic acid fragment m/z 292.10), the mass window containing its precursor ion is traced back to discover and identify the glycan.
A Tailored Acquisition Scheme for Glycan Chains
The key to the successful application of DIA lies in parameter optimization. The research team conducted a systematic exploration:
- Chromatographic Selection: A porous graphitized carbon column was used, which excels at separating glycan isomers and reducing co-elution, laying the foundation for DIA analysis.
- Fragmentation Energy: An optimized normalized collision energy of 20% was determined, achieving the best balance between generating abundant sequence fragments and retaining important large fragments.
- Window Design: By analyzing the mass distribution of a large number of glycan chains, 600-1800 m/z was determined as the main acquisition range. After comparing various DIA schemes, an interleaved window scheme with a width of 24 m/z was selected, ensuring sufficient fragment ion specificity and providing approximately 10 data points for each chromatographic peak, ensuring high quantitative accuracy.
Automated Search Engine GlycanDIA Finder
To process massive amounts of DIA data, the team developed a user-friendly automated search engine, GlycanDIA Finder. Its workflow includes:
- Parameter filtering of raw spectra.
- Matching at the MS1 and MS2 levels using the input glycan library.
- Employing an iterative decoy search algorithm to estimate the false discovery rate, ensuring reliable identification.
- Integrating a glycan product generator that predicts theoretical glycan fragments for subsequent analysis.
This software achieves high-throughput, automated analysis from raw data to qualitative and quantitative results, significantly lowering the barrier to entry.
Completely Surpassing Traditional DDA Methods
In a direct comparison with traditional DDA methods, GlycanDIA demonstrates comprehensive advantages:
- Increased Identification Depth: Analyzing standard N-glycan samples, GlycanDIA consistently identified over 270 glycans (including isomers), while DDA identified only about 200. GlycanDIA covers all glycans that DDA can identify.
- Significantly Improved Quantitative Accuracy: The average coefficient of variation for technical replicates is less than 5%, far superior to DDA. Even with samples diluted a thousandfold, most glycans maintain good quantitative linearity.
- Superior Sensitivity And Stability: In 10 consecutive analyses of complex samples spiked with extremely low amounts of isotope-labeled standard glycans, DDA missed at least one standard in 60% of the analyses, while GlycanDIA successfully identified all standards in all ten analyses, with more stable quantitative results.
High Versatility and Ability to Distinguish Isomers and Modifications
The power of GlycanDIA is not limited to N-Glycans:
- Broad Applicability: Successfully applied to the analysis of O-glycans and Human Milk Oligosaccharides, requiring only adjustment of the mass range.
- Distinguishing Isomers: Combining chromatographic separation with MS2 fragmentation patterns, it can distinguish compositional isomers (e.g., Fuc+NeuGc vs. Hex+NeuAc) and structural isomers (e.g., 2'-FL vs. 3-FL HMO; different sialic acid linkages α2,3 vs. α2,6).
- Discovery of Rare Modifications: Thanks to its comprehensive nature, it can directly detect acetylated sialic acid-modified glycans at abundances three orders of magnitude lower than common sialic acids, without enrichment.
Fig. 1 GlycanDIA provides valuable insights into glycomic analysis. (Xie, et al. 2025)
First In-Depth Mapping of RNA N-Glycan Profiles
The research team applied GlycanDIA to a previously inaccessible area—the analysis of N-glycans on glycosylated RNA.
Through in-depth analysis of RNA from HEK 293T and HeLa cells, they made groundbreaking discoveries:
- Successful Microanalysis: Using only 5 μg of small RNA (previous studies required 25 μg), nearly a hundred N-glycans were identified, significantly improving sensitivity.
- Unique Rna Glycan Characteristics: Compared to protein N-glycans from the same cell source, the glycan profile on RNA is significantly different. RNA glycans have a higher proportion of sialylated and Fucosylated Glycans, while the proportion of high-mannose type glycans is lower.
- Follows Classic Synthesis Pathways: In HCT116 cells with a fucose synthesis defect, there were almost no fucosylated glycans on RNA; however, after exogenous addition of fucose, RNA glycans could be re-fucosylated through a salvage synthesis pathway, proving that their biosynthesis shares some enzymatic mechanisms with protein glycans.
- Tissue Specificity: Analysis of RNA glycans from different mouse tissues (heart, brain, spleen, lung, colon) revealed distinct tissue-specific compositions. For example, fucosylated glycans accounted for over 75% in the brain, while the proportion of high-mannose type glycans was higher in the heart; the sialylation pattern (ratio of Neu5Ac vs Neu5Gc) also varied depending on the tissue.
These findings not only demonstrate the powerful capabilities of GlycanDIA in analyzing extremely low-abundance glycans, but also systematically reveal for the first time the richness, uniqueness, and potential biological significance of glycosylated RNA modifications.
Summary and Outlook
The establishment of the GlycanDIA workflow represents a significant advancement in glycomics analytical methodology. Through the DIA strategy, optimized acquisition schemes, and accompanying bioinformatics tools, it addresses the core limitations of traditional methods in terms of detection breadth, quantitative accuracy, and sensitivity for low-abundance glycans.
Its advantages can be summarized as follows:
- Capturing more low-abundance glycans and modifications.
- Providing high-precision quantification based on MS2 fragment ions.
- Reducing data loss due to the randomness of ion selection.
- Applicable to various types of glycans and capable of re-analyzing existing data.
Although there is still room for improvement in precisely resolving glycan linkage patterns, GlycanDIA undoubtedly provides a powerful tool for exploring cutting-edge fields such as glycosylated RNA, disease biomarker discovery, and trace clinical sample analysis. It allows us to glimpse the previously overlooked dark matter of the glycan world and will undoubtedly propel glycobiology to play a more important role in life science and medical research.
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Reference
- Xie, Y., et al. (2025). Development and application of GlycanDIA workflow for glycomic analysis. Nature communications, 16(1), 7075. DOI: 1038/s41467-025-61473-y.
