A recent study published in Engineering introduces GlycoPro, a novel high-throughput sample-processing platform that aims to transform the field of multi-glycosylation-omics analysis.
Glycosylation, a crucial post-translational protein modification, plays a vital role in various biological functions. In the context of cancer research, analyzing glycosylation patterns can potentially lead to the discovery of new biomarkers for early detection and diagnosis. However, current methods for processing complex biological samples in multi-glycosylation-omics applications face limitations, such as low throughput and the inability to integrate the analysis of different glycosylation-related biomolecules.
The GlycoPro platform addresses these challenges by integrating multiple steps including protein extraction, desalting, digestion, derivatization, and enrichment into a single, efficient workflow. It utilizes a 96-well plate format, enabling the processing of up to 384 samples in a single day. This represents a significant improvement in throughput compared to existing techniques.
The researchers demonstrated the platform’s effectiveness through comprehensive profiling of N-glycopeptides, O-glycopeptides, and N/O-glycans in human serum. From as little as 2 μL of serum, the GlycoPro platform could identify over 3300 N-glycopeptides and 3500 O-glycopeptides, with high reproducibility (correlation coefficients exceeding 0.98 across technical replicates). In glycomics analysis, it could identify 193 N-glycans and 71 O-glycans from 2 μL of serum.
One of the most significant applications of the GlycoPro platform is in breast cancer research. The researchers processed serum samples from breast cancer patients and healthy controls. By analyzing the N-glycans in these samples, they identified a panel of five N-glycan biomarkers. A machine-learning model based on these biomarkers achieved a sensitivity of 88.24% and a specificity of 78.95% in distinguishing between malignant and non-malignant states, with an area under the receiver operating characteristic curve (AUC) of 0.89.
The GlycoPro platform not only offers a more efficient way to analyze glycosylation-related biomolecules but also has the potential to be applied to other glycosylation-related diseases. However, the researchers note that further validation in larger, independent cohorts is needed before the findings can be translated into clinical diagnostics. Future research will also focus on expanding the platform’s capabilities and refining the biomarker panel. This new platform is a significant step forward in the field of glycosylation-omics, potentially opening up new avenues for disease diagnosis and treatment.
The paper “GlycoPro: A High-Throughput Sample-Processing Platform for Multi-Glycosylation-Omics Analysis,” is authored by Xuejiao Liu, Yue Meng, Bin Fu, Haoru Song, Bing Gu, Ying Zhang, Haojie Lu. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.01.011. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.