N-linked (N-) glycoproteomics of urinary exosomes. [Corrected].

Epithelial cells lining the urinary tract secrete urinary exosomes (40-100 nm) that can be targeted to specific cells modulating their functionality. One potential targeting mechanism is adhesion between vesicle surface glycoproteins and target cells. This makes the glycopeptide analysis of exosomes important. Exosomes reflect the physiological state of the parent cells; therefore, they are a good source of biomarkers for urological and other diseases. Moreover, the urine collection is easy and noninvasive and urinary exosomes give information about renal and systemic organ systems. Accordingly, multiple studies on proteomic characterization of urinary exosomes in health and disease have been published. However, no systematic analysis of their glycoproteomic profile has been carried out to date, whereas a conserved glycan signature has been found for exosomes from urine and other sources including T cell lines and human milk. Here, we have enriched and identified the N-glycopeptides from these vesicles. These enriched N-glycopeptides were solved for their peptide sequence, glycan composition, structure, and glycosylation site using collision-induced dissociation MS/MS (CID-tandem MS) data interpreted by a publicly available software GlycopeptideId. Released glycans from the same sample was also analyzed with MALDI-MS. We have identified the N-glycoproteome of urinary exosomes. In total 126 N-glycopeptides from 51 N-glycosylation sites belonging to 37 glycoproteins were found in our results. The peptide sequences of these N-glycopeptides were identified unambiguously and their glycan composition (for 125 N-glycopeptides) and structures (for 87 N-glycopeptides) were proposed. A corresponding glycomic analysis with released N-glycans was also performed. We identified 66 unique nonmodified N-glycan compositions and in addition 13 sulfated/phosphorylated glycans were also found. This is the first systematic analysis of N-glycoproteome of urinary exosomes.

Novel data analysis tool for semiquantitative LC-MS-MS2 profiling of N-glycans.

Despite recent technical advances in glycan analysis, the rapidly growing field of glycomics still lacks methods that are high throughput and robust, and yet allow detailed and reliable identification of different glycans. LC-MS-MS(2) methods have a large potential for glycan analysis as they enable separation and identification of different glycans, including structural isomers. The major drawback is the complexity of the data with different charge states and adduct combinations. In practice, manual data analysis, still largely used for MALDI-TOF data, is no more achievable for LC-MS-MS(2) data. To solve the problem, we developed a glycan analysis software GlycanID for the analysis of LC-MS-MS(2) data to identify and profile glycan compositions in combination with existing proteomic software. IgG was used as an example of an individual glycoprotein and extracted cell surface proteins of human fibroblasts as a more complex sample to demonstrate the power of the novel data analysis approach. N-glycans were isolated from the samples and analyzed as permethylated sugar alditols by LC-MS-MS(2), permitting semiquantitative glycan profiling. The data analysis consisted of five steps: 1) extraction of LC-MS features and MS(2) spectra, 2) mapping potential glycans based on feature distribution, 3) matching the feature masses with a glycan composition database and de novo generated compositions, 4) scoring MS(2) spectra with theoretical glycan fragments, and 5) composing the glycan profile for the identified glycan compositions. The resulting N-glycan profile of IgG revealed 28 glycan compositions and was in good correlation with the published IgG profile. More than 50 glycan compositions were reliably identified from the cell surface N-glycan profile of human fibroblasts. Use of the GlycanID software made relatively rapid analysis of complex glycan LC-MS-MS(2) data feasible. The results demonstrate that the complexity of glycan LC-MS-MS(2) data can be used as an asset to increase the reliability of the identifications.

De novo glycan structure search with the CID MS/MS spectra of native N-glycopeptides.

The aim of our study is to automatically analyze the glycan and peptide structures of N-glycopeptides without a need to release glycans from the glycopeptides. Our wet laboratory raw data represent a series of MS/MS mass spectra obtained from a reverse-phase liquid chromatography run of size-exclusion-enriched tryptic-digested glycopeptides from glycoproteins. The MS/MS spectra are first analyzed in order to identify glycosylated peptides and N-glycan monosaccharide compositions present on each glycopeptide. We further developed a Branch-and-Bound algorithm to search de novo N-glycan structures, i.e., monosaccharide compositions and their ordered sequences from native glycopeptides. Our de novo algorithm is based on iterative growth and selection of a population of glycan structures and it does not use databases of known glycan structures. We validate the algorithm with (i) in silico-generated spectra, with or without deteriorating deletions, (ii) with a purified glycoprotein transferrin, and (iii) with a complex mixture of N-glycopeptides enriched from human plasma. Our Branch-and-Bound algorithm depicted glycan structures from all the above-mentioned three input data types. Due to the large diversity of glycan structures, the results typically contained several proposed structures matching almost equally well to the spectra. In conclusion, this algorithm automatically identifies glycopeptides and their structures from the MS/MS spectra and thus greatly reduces the number of possible glycan structures from the vast amount of potential ones.

N-glycoproteomics - an automated workflow approach.

Glycan decorations dictate protein functions and thus have crucial importance in life sciences. Previously glycoprotein analysis was mainly focused on the analysis of the liberated glycans allowing detailed structural, but lacking positional information. Analysis of intact glycopeptides required purified glycoproteins and manual interpretation of spectra. We developed an approach where mixtures of native glycopeptides were analyzed with tandem mass spectrometry and the spectra were analyzed with automated in silico workflows. The latter included combination of the original spectra, generation of a human N-glycopeptide library, matching the glycopeptide spectra to the theoretical peptide fragments, scoring the observations, predicting the glycan composition, which were then matched against the observed spectra, statistical validation of the results with target-decoy filtering, and finally the calculation of glycan structures. We verified this approach with the 150 serotransferrin glycopeptide spectra, where we automatically generated 10(5) putative interpretations from >10(9) theoretical glycopeptides. After scoring 62 glycopeptide spectra obtained validated interpretation with concomitant amino acid sequences, glycan compositions, and structures. When applying this method to an unknown mixture of human plasma glycoproteins we identified 80 glycopeptides with their glycan compositions or structures. Instead of weeks and months of interpretation work of mass spectrometry files our automated workflow can be executed in few hours and provide information concomitantly from both the amino acid and glycan moieties of intact glycopeptides in mixtures. No advanced computational skills were needed to use these preformed and tested workflows. In case users want to add complexity to the analysis they are allowed to alter all parameters and rebuild the workflows.