Cell surface glycosylation is crucial for cell recognition, signaling, and tumor development. Abnormal glycosylation is linked to tumor initiation, progression, metastasis, and immune evasion. Traditional analysis methods, like mass spectrometry, require extensive preprocessing, losing in situ data, while fluorescent labeling faces limitations in specificity and accuracy. Recently, a study in Nature Communications introduced surface plasmon resonance imaging (SPRi) for label-free glycosylation kinetic analysis at the single-cell level, advancing glycobiology research significantly.
Glycosylation analysis has traditionally used population-level data, masking cellular heterogeneity. Single-cell analysis reveals more accurate glycosylation patterns, which can vary between individual cells. Real-time monitoring of these changes aids in understanding disease progression and therapeutic responses.
Glycosylation's complexity stems from its non-template biosynthesis, resulting in diverse structures that play key roles in cell communication, immune recognition, and disease progression. Altered glycosylation is linked to tumorigenesis, metastasis, and immune evasion, such as α-2,6 sialic acid in liver cancer and β-1,6 N-acetylglucosamine in tumor invasion. Current methods, like mass spectrometry and fluorescence labeling, face limitations in in situ analysis and quantification, often masking cell-to-cell heterogeneity. SPRi technology overcomes these challenges, offering a label-free, real-time, high-resolution glycosylation analysis at the single-cell level.
Cutting-edge research points out that next-generation glycosylation analysis technologies must meet three core requirements: single-cell resolution to analyze cell heterogeneity, in situ detection to preserve the natural membrane environment, and real-time dynamic monitoring to acquire functional binding parameters. The innovative application of SPRi technology perfectly meets these needs, with its label-free, mass-sensitive properties allowing for real-time monitoring of molecular binding processes, thus opening up new avenues for glycobiology research.
Fig. 1: Schematic diagram of in-situ glycosylation profiling using lectin-glycan kinetic quantification analysis with single-cell plasmonic imaging1,4.
SPRi technology enables real-time, direct observation of glycosylation at single-cell surfaces, offering unmatched sensitivity and specificity compared to conventional methods that rely on indirect measurements or complex sample preparations.
The SPRi system used in this study couples a prism and utilizes a 690 nm LED light source, capturing reflected intensity with a 20× zoom lens, achieving a spatial resolution of ~1μm. This allows for monitoring the glycosylation status of hundreds of single cells simultaneously. The system uses p-polarized light to excite plasmon resonance on a gold film surface. When cell surface glycans bind to lectins, it induces a local refractive index change, altering the resonance condition for real-time detection.
The study selected three representative lectins: WGA (which binds GlcNAc and Neu5Ac), SBA (which binds GalNAc and Gal), and ConA (which binds Man and Glc). Each lectin specifically recognizes two different glycan structures, laying the foundation for multiplexed glycan analysis.
This detailed design enables unprecedented accuracy in detecting subtle changes in glycosylation patterns that may occur at the single-cell level, providing new insights into glycosylation's role in health and disease.
Traditional lectin binding analyses use simple "one-to-one" models, but these don't account for the multivalent nature of lectins. This study introduced a "one-to-two" binding model, overcoming the challenges of quantifying lectin interactions with multiple glycans. By mathematical modeling, the researchers identified independent kinetic parameters, revealing distinct binding dynamics between lectins and different glycans, offering new insights into their recognition mechanisms.
To validate the reliability of the method, the research team conducted multiple validation experiments. A solution competition experiment confirmed that GlcNAc inhibits WGA binding significantly more than Neu5Ac, consistent with the higher affinity observed in the kinetic analysis. Using mannose as a negative control showed no significant change in binding signals, confirming the specificity of the interaction.
Enzyme treatment experiments provided further evidence: neuraminidase treatment significantly reduced WGA binding signals, confirming the specific contribution of Neu5Ac; while treatment with protein deglycosylation enzyme II eliminated most binding signals, verifying that the observed signals originated from specific glycosylation interactions.
Fig. 2: The representative kinetic curve results obtained from WGA binding to HeLa cells2,4.
The research team quantitatively analyzed the expression and binding characteristics of six glycans at the single-cell level in HeLa cells. GlcNAc showed the highest expression (~1.17×104/μm2), while Gal had the lowest (~8.03×102/μm2), with nearly a tenfold difference. Significant glycosylation heterogeneity was observed even within the same cell line, with glucose expression varying up to six times between individual cells. This variability extended to kinetic properties, with binding and dissociation rates differing across cells, reflecting variations in glycan conformation and accessibility. These findings offer insights into tumor cell adaptation and drug resistance.
WGA recognition was upregulated in all cancer cells, highlighting the role of sialylation in oral cancer progression. SBA binding increased in DOK and Cal-27 cells but decreased in metastatic HSC-3 cells, suggesting a negative correlation between Gal/GalNAc expression and metastasis. ConA binding showed higher Man/Glc expression in DOK and HSC-3 cells compared to Cal-27, indicating complex regulation. Principal component analysis revealed that glycosylation patterns could distinguish progression stages, with the first three components explaining over 80% of the variance, suggesting their potential as tumor staging biomarkers.
Fig. 3: Glycosylation alteration of different oral cancer cells during malignant transformation3,4.
The conversion of advanced single-cell glycosylation analysis techniques into reliable research and clinical tools faces several key challenges:
The cross-reactivity of lectins is an inherent characteristic, and there is currently a lack of systematic approaches to verify the exact binding profiles of each lectin. This requires structurally defined high-purity glycans as reference standards.
The absence of unified quantitative standards for glycosylation in the field makes it difficult to compare and integrate results from different laboratories. Structurally defined synthetic glycans are fundamental to establishing such standards.
Natural-source glycans face issues of batch-to-batch variability and supply instability, making it challenging to meet the demands for large-scale screening and diagnostic development.
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