Dataset Information

Glycan family analysis for deducing N-glycan topology from single MS.

ABSTRACT:

Motivation

In the past few years, mass spectrometry (MS) has emerged as the premier tool for identification and quantification of biological molecules such as peptides and glycans. There are two basic strategies: single-MS, which uses a single round of mass analysis, and MS/MS (or higher order MS(n)), which adds one or more additional rounds of mass analysis, interspersed with fragmentation steps. Single-MS offers higher throughput, broader mass coverage and more direct quantitation, but generally much weaker identification. Single-MS, however, does work fairly well for the case of N-glycan identification, which are more constrained than other biological polymers. We previously demonstrated single-MS identification of N-glycans to the level of 'cartoons' (monosaccharide composition and topology) by a system that incorporates an expert's detailed knowledge of the biological sample. In this article, we explore the possibility of ab initio single-MS N-glycan identification, with the goal of extending single-MS, or primarily-single-MS, identification to non-expert users, novel conditions and unstudied tissues.

Results

We propose and test three cartoon-assignment algorithms that make inferences informed by biological knowledge about glycan synthesis. To test the algorithms, we used 71 single-MS spectra from a variety of tissues and organisms, containing more than 2800 manually annotated peaks. The most successful of the algorithms computes the most richly connected subgraph within a 'cartoon graph'. This algorithm uniquely assigns the correct cartoon to more than half of the peaks in 41 out of the 71 spectra.

SUBMITTER: Goldberg D

PROVIDER: S-EPMC2732296 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Glycan family analysis for deducing N-glycan topology from single MS.

Goldberg David D Bern Marshall M North Simon J SJ Haslam Stuart M SM Dell Anne A

Bioinformatics (Oxford, England) 20081210 3

<h4>Motivation</h4>In the past few years, mass spectrometry (MS) has emerged as the premier tool for identification and quantification of biological molecules such as peptides and glycans. There are two basic strategies: single-MS, which uses a single round of mass analysis, and MS/MS (or higher order MS(n)), which adds one or more additional rounds of mass analysis, interspersed with fragmentation steps. Single-MS offers higher throughput, broader mass coverage and more direct quantitation, but ...[more]

PMID: 19073587

Similar Datasets

Project description:Over the past few decades, essential role of glycosylation in protein functioning has become widely recognized, rapidly advancing glycan analysis techniques. Because free glycan's lack chromophore or fluorophore properties, and do not ionize well, they are often derivatized to facilitate their separation or detection, and to enhance the sensitivity of the analysis. Released glycan's are usually derivatized using a fluorescent tag, which enables their optical detection in LC profiling. Some fluorescent labels can also promote ionization efficiency, thus facilitating MS detection. For this reason, there is a need to design fluorophores that will contribute more to the fluorescence and ionization of glycan's and the need to quantify these contributions to improve glycan analysis methods. In this paper we focused on negative MS mode as these methods are more informative than methods involving positive MS mode, allowing for a less ambiguous elucidation of detailed glycan structures. Additionally, traditional glycan labels in negative mode MS usually result with diminished sensitivity compared to positive mode, thus making selection of appropriate label even more important for successful high-throughput analysis. Therefore, eleven fluorescent labels of different chemo-physical properties were chosen to study the influence of label hydrophobicity and presence of a negative charge on glycan ionization in negative MS mode. N-glycans released from IgG sample were labeled with one of the eleven labels, purified with HILIC-SPE and analyzed with HILIC-UPLC-FLR-MS. To make evaluation of studied labels performance more objective, analysis was performed in two laboratories and at two mobile phase pH (4.4 and 7.4). Although there was a notable trend of more hydrophobic labels having bigger signal intensities in one laboratory, we observed no such trend in the other laboratory. The results show that MS parameters and intrinsic configuration of the spectrometer have even bigger effect on the final ESI response of the labeled-glycan ionization in negative MS mode that the labels themselves. With this in mind, further research and development of fluorophores that will be suitable for high-throughput glycan analysis in the negative MS mode are proposed.

Project description:BackgroundGlycosylation is an important modification to proteins that plays a significant role in biological processes. Glycan structures are characterized by liquid chromatography (LC) combined with mass spectrometry (MS), but data interpretation of LC/MS and MS/MS data can be time-consuming and arduous when analyzed manually. Most of glycan analysis requires dedicated glycobioinformatics tools to process MS data, identify glycan structure, and display the results. However, software tools currently available are either too costly or heavily focused on academic applications, limiting their use within the biopharmaceutical industry for implementing the standardized LC/MS glycan analysis in high-throughput manner. Additionally, few tools provide the capability to generate report-ready annotated MS/MS glycan spectra.ResultsHere, we present a MATLAB-based app, GlyKAn AZ, which can automate data processing, glycan identification, and customizable result displays in a streamlined workflow. MS1 and MS2 mass search algorithms along with glycan databases were developed to confirm the fluorescent labeled N-linked glycan species based on accurate mass. A user-friendly graphical user interface (GUI) streamlines the data analysis process, making it easy to implement the software tool in biopharmaceutical analytical laboratories. The databases provided with the app can be expanded through the Fragment Generator functionality which automatically identifies fragmentation patterns for new glycans. The GlyKAn AZ app can automatically annotate the MS/MS spectra, yet this data display feature remains flexible and customizable by users, saving analysts' time in generating individual report-ready spectra figures. This app accepts both OrbiTrap and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS data and was successfully validated by identifying all glycan species that were previously identified manually.ConclusionsThe GlyKAn AZ app was developed to expedite glycan analysis while maintaining a high level of accuracy in positive identifications. The app's customizable user inputs, polished figures and tables, and unique calculated outputs set it apart from similar software and greatly improve the current manual analysis workflow. Overall, this app serves as a tool for streamlining glycan identification for both academic and industrial needs.

Dataset Information

Glycan family analysis for deducing N-glycan topology from single MS.

Motivation

Results

Publications

Glycan family analysis for deducing N-glycan topology from single MS.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets