Glycoproteomic analysis is critical for understanding the effects of glycosylation in biological systems, yet comprehensive analysis remains challenging due to low abundance and structural complexity. HILIC is widely used for glycopeptide enrichment, but current manual workflows introduce variability and limit throughput. Here, we present an automated, HILIC-based enrichment strategy combined with glyco-PASEF® on timsTOF Ultra 2, enabling high-throughput glycopeptide profiling with improved reproducibility and sensitivity. This workflow has been applied to various samples, including plasma, cell (HeLa and K562) and tissue lysates.
HILIC-based enrichment resulted in a substantial increase in glycopeptide and glycan identifications across all biological matrices tested. In plasma samples, glycopeptide structural matches (gPSM) increased approximately 8-fold in citrate-plasma (from 629 to 4891) and 5.4-fold in EDTA-plasma (from 600 to 3217), with corresponding increases in glycopeptides (~6-fold in citrate-, ~3.8-fold in EDTA-plasma) and glycans (~2.6-fold in citrate-, ~2.1-fold in EDTA-plasma). Similarly, in HeLa and K562 cell lysates, gPSMs increased 9-fold (798 to 6942) and 7.3-fold (313 to 2283), respectively, with glycopeptides increasing 7.5-fold (637 to 4763) and 6.4-fold (253 to 1628) in, and glycans increasing 2.7-fold (50 to 136) and 3.3-fold (34 to 112), in HeLa and K562-lysates, respectively.
Stepped-energy CID resulted in increases in gPSMs and glycopeptide identifications across all sample types compared to single energy CID. The improvement was most pronounced in HeLa, where stepped CID increased gPSM counts by 15%, while plasma samples showed a 10% higher gPSM yield. Additionally, stepped CID improved glycan fragmentation efficiency, leading to the identification of a greater number of structurally informative glycan fragments, particularly for sialylated glycans.
Reproducibility was evaluated across three replicates for each sample type, with standard deviations consistently below 15%, demonstrating the robustness of the workflow. Future efforts will focus on further CID optimization and expanding the workflow to additional tissue and disease-relevant samples.