Abstract
MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. When tested on the reference data set, gQuant showed a fast processing speed, as it was able to search the glycan data of model glycoproteins in a few minutes and reported more results than the manual analysis did. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1. In addition, gQuant is fully open-source and is coded in Python, which is supported by most operating systems, and it has a user-friendly interface. gQuant can be easily adapted by users for specific experimental designs, such as specific glycan databases, different derivatization types and relative quantitation designs and can thus facilitate fast glycomic quantitation for clinical sample analysis using MALDI-MS-based stable isotope labeling.
Highlights
Protein glycosylation plays significant roles in many biological and physiological processes, including cell adhesion, sperm fusion, and protein folding, as well as in protein half-life (Hart and Copeland, 2010; Xu and Ng, 2015)
A total of 419 glycan compositions were recorded in the mammalian glycan database, with maximum hexose (Hex), N-acetylhexosamine (HexNAc), N-acetylneuraminic acid/ N-glycolylneuraminic acid (NeuAc/NeuGc), and fucose values of 12, 7, 4, and 5, respectively, and 344 entries were recorded in the human glycan database (No NeuGc)
We designed and implemented an automated glycan quantitation tool, gQuant, in this work. gQuant is capable of automatically and efficiently processing quantitative glycan mass spectrometry data and reporting all matched glycans and quantitation ratios. gQuant was embedded in N-glycan databases for human (No NeuGc)- or mammalian (Containing NeuGc)-sourced samples
Summary
Protein glycosylation plays significant roles in many biological and physiological processes, including cell adhesion, sperm fusion, and protein folding, as well as in protein half-life (Hart and Copeland, 2010; Xu and Ng, 2015). Many efforts have been made to develop mass spectrometry (MS)-based glycan quantitation techniques due to the excellent qualitative ability, sensitivity and high throughput of mass spectrometry (Wuhrer, 2013; Cao et al, 2020). MALDI-MSbased methods have shown high feasibility, efficiency and speed in quantitative glycan analysis and have been widely used. MALDI-MS-based glycan quantitation data have mostly been processed manually. This work is tedious and requires expert knowledge of protein glycosylation. This drawback greatly impedes the development of quantitative glycomics and the understanding of protein glycosylation
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