Interlaboratory study on differential analysis of protein glycosylation by mass spectrometry: the ABRF glycoprotein research multi-institutional study 2012

Abstract

One of the principal goals of glycoprotein research is to correlate glycan structure and function. Such correlation is necessary in order for one to understand the mechanisms whereby glycoprotein structure elaborates the functions of myriad proteins. The accurate comparison of glycoforms and quantification of glycosites are essential steps in this direction. Mass spectrometry has emerged as a powerful analytical technique in the field of glycoprotein characterization. Its sensitivity, high dynamic range, and mass accuracy provide both quantitative and sequence/structural information. As part of the 2012 ABRF Glycoprotein Research Group study, we explored the use of mass spectrometry and ancillary methodologies to characterize the glycoforms of two sources of human prostate specific antigen (PSA). PSA is used as a tumor marker for prostate cancer, with increasing blood levels used to distinguish between normal and cancer states. The glycans on PSA are believed to be biantennary N-linked, and it has been observed that prostate cancer tissues and cell lines contain more antennae than their benign counterparts. Thus, the ability to quantify differences in glycosylation associated with cancer has the potential to positively impact the use of PSA as a biomarker. We studied standard peptide-based proteomics/glycomics methodologies, including LC-MS/MS for peptide/glycopeptide sequencing and label-free approaches for differential quantification. We performed an interlaboratory study to determine the ability of different laboratories to correctly characterize the differences between glycoforms from two different sources using mass spectrometry methods. We used clustering analysis and ancillary statistical data treatment on the data sets submitted by participating laboratories to obtain a consensus of the glycoforms and abundances. The results demonstrate the relative strengths and weaknesses of top-down glycoproteomics, bottom-up glycoproteomics, and glycomics methods.

Fig. 1.

Agglomerative hierarchal clustering of N-glycans profiles for PSA and PSA high isoform from the participating labs. The dendrogram illustrates by clustering (X axis) and height (Y axis) the degree of similarity of profile of different N-glycans of PSA and PSA high Isoform detected by different labs. The main clusters reported are: (A), (B), (C), (D), and sub-clustering could be distinguished (C₁), (C₂).

Fig. 2.

Average relative intensity of each compound per cluster for the major, intermediate and minor N-glycans. Clusters A (Lab 8) and B (lab22) showed a complete difference with the other participating laboratories in term of repartition for PSA and PSA high isoform. Lab 8 reported Hex₄HexNac₃dHex₁Neu₁ as the major compound (> 60%) for the PSA sample, while clusters C and D reported the same compounds with an intensity lower than 15%. Lab 8 did not report any of the major N-glycans that the other labs observed for PSA High Isoform and PSA except the Neu₁dHex₁Hex₄HexNac₃. Cluster B for PSA and PSA high Isoform presented higher intensity for N-glycans Hex₅HexNac₃Neu₂ and Hex₅HexNac₄dHex₁Neu₂ than the other clusters C and D. Only in cluster C were intermediate N-glycans detected. In cluster A, the average relative intensity of compound dHex₁Hex₅HexNac₄ is 23.5% while it is lower than 6% for all the other clusters. (b) Minor N-glycans. The main difference is observed for the compound Hex₆HexNac₄NeuAc₁ of the PSA high isoform that is detected by cluster A with a relative intensity of 68% compared to less than 1% for the other clusters.

Fig. 3.

Comparison of robustness for bottom-up, top-down and PNGAse F release methods used by the participating laboratories. (A) Data from 22 participating laboratories were included in the permutation tests. (B) Data from consensus cluster C were used in the permutation tests. The red lines show the difference between the average standard deviations for the two methods in each plot. The p-values correspond to double the area of bars to the left of the red line in each plot.

Fig. 4.

Heat map of the consensus data. Agglomerative hierarchical clustering results were processed based on the differences of relative intensity values for glycan compositions between PSA and PSA high isoform. Orange color indicates a null difference of relative intensity between PSA and PSA high isoform, yellow indicates a positive difference, red indicates a negative difference. The N-glycan compositions of cluster 7 (80% of the total compounds) are similar between PSA and PSA high isoform. Compositions are more intense in PSA high isoform than PSA for the cluster 1 (Hex₅HexNAc₄dHexNeuAc₂, Hex₅HexNAc₄dHexNeuAc), cluster 3 (Hex₄HexNAc₅dHexNeuAc₂), cluster 4 (NeuAc₁Hex₅HexNAc₄NeuAc), cluster 6 (Hex₅HexNAc₄dHex, NeuAc₂Hex₅HexNAc₄NeuAc₂). Compositions are less intense in PSA high isoform than PSA for cluster 2 (Hex₄HexNAc₄dHexNeuAc, Hex₄HexNAc₃dHexNeuAc, Hex₆HexNAc₃NeuAc), cluster 5 (Hex₄HexNAc₄NeuAc, Hex₅HexNAc₂), cluster 8 (Hex₃Hex₅dHexNeuAc, Hex₄Hex₃NeuAc). In order to confirm the significance of the differences in N-glycan composition abundances between PSA and PSA high isoform, a W-test was used and revealed 8 significant compositions (indicated in bold).

Fig. 5.

Differential profile of N-glycan profilederived from the consensus data (Cluster C). A W-test was employed in order to determine 8 N-glycans (p value <0.0008) significantly different between PSA and PSA high isoform. The significant N-glycans are marked by **.