A Highly Efficient and Visualized Method for Glycan Enrichment by Self-Assembling Pyrene Derivative Functionalized Free Graphene Oxide

Protein glycosylation plays key roles in many biological processes, such as cell growth, differentiation, and cell–cell recognition. Therefore, global structure profiling of glycans is very important for investigating the biological significance and roles of glycans in disease occurrence and development. Mass spectrometry (MS) is currently the most powerful technique for structure analysis of oligosaccharides, but the limited availability of glycan/glycoproteins from natural sources restricts the wide adoption of this technique in large-scale glycan profiling. Though various enrichment methods have been developed, most methods relay on the weak physical affinity between glycans and adsorbents that yields insufficient enrichment efficiency. Furthermore, the lack of monitoring the extent/completeness of enrichment may lead to incomplete enrichment unless repeated sample loading and prolonged incubation are adopted, which limits sample handling throughput. Here, we report a rapid, highly efficient, and visualized approach for glycan enrichment using 1-pyrenebutyryl chloride functionalized free graphene oxide (PCGO). In this approach, glycan capturing is achieved by reversible covalent bond formation between the hydroxyl groups of glycans and the acyl chloride groups on graphene oxide (GO) introduced by π–π stacking of 1-pyrenebutyryl chloride on the GO surface. The multiple hydroxyl groups of glycans lead to cross-linking and self-assembly of free PCGO sheets into visible aggregation within 30 s, therefore achieving simple visual monitoring of the enrichment process. Improved enrichment efficiency is achieved by the large specific surface area of free PCGO and heavy functionalization of highly active 1-pyrenebutyryl chloride. Application of this method in enrichment of standard oligosaccharides or <i>N</i>-glycans released from glycoproteins results in remarkably increased MS signal intensity (approximately 50 times), S/N, and number of glycoform identified

Summary of identified N-glycosites.

<p>A) The number of unique N-glycosites identified and the percentage of N-glycopeptides from all of the identified peptides in each cell line. B) Overlap of N-glycosites between the different enrichment methods. C) Overlap of the N-glycosites and proteins between the different cell lines. D) Number of N-glycosites identified per protein.</p

Bioinformatics analysis of identified N-glycoproteins.

<p>A) Cellular component annotation of identified N-glycoproteins. B) Biological functions of differentially expressed N-glycoproteins.</p

Validation of the differential expression of two selected N-glycoproteins.

<p>A) Ten micrograms secretome protein samples were separated on SDS-PAGE gels, transferred to PVDF membranes, and probed with anti-FN1 or FAT1 antibodies. B) The de-glycosylation of the same amount of secreted proteins from MHCC97L and HCCLM3 cells was performed with PNGase F cleavage for 12 h. Proteins were separated on SDS-PAGE and analyzed by western blotting.</p

Overview of the experimental workflow.

<p>A) The secretome was collected from the conditioned medium. B) N-glycosylated peptides were enriched using hydrazide chemistry and zic-HILIC methods. First, proteins were digested using FASP, and then the N-glycosylated peptides were captured using two methods, followed by de-glycosylation using PNGase F and LC-MS-MS analysis. C) Label-free quantitative analysis.</p

Network view of the up-regulated N-glycoproteins in HCCLM3 cells.

<p>A) The networks of the top 3 liver-related diseases. B) The number of related genes and the <i>p</i>-value of the top 3 liver-related diseases that are indicated in A). C) Cellular motility network. (The proteins with higher expression in HCCLM3 cells are in red (Ratio > 2), whereas the other proteins that were generated from the IPA database are not colored.).</p