Among the developed techniques, MALDI-MS-based methods have shown high feasibility, efficiency and speed in quantitative glycan analysis and have been widely used. For example, a metabolic labeling strategy was introduced for in vivo labeling and glycan quantitation ( Orlando et al., 2009) chemical labeling strategies using different isotope labeling reagents, such as 2-aminobenzoic acid, aniline, arginine, and 1-phenyl-3-methyl-5-pyrazolone, were developed for glycan derivatization and quantitation ( Prien et al., 2010 Albrecht et al., 2017 Cai et al., 2015 Smith et al., 2017 Wang et al., 2017) and enzymatic 18O labeling was also utilized for glycan relative quantification ( Zhang et al., 2015 Cao et al., 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). Therefore, precision quantitation of glycans is greatly needed to better understand glycan functionality, measure different glycan levels and potentially discover glycan biomarkers.
It has also been reported that aberrant glycosylation has a substantial impact on host-cell reactions, infectious diseases such as tuberculosis, the progression of tumors ( Lu et al., 2016), autoimmune diseases ( Eakin et al., 2016) and prostate cancer ( Shah et al., 2015). 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).
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. 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. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1.
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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. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate.
MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics.
3NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China.2Department of Chemistry, Fudan University, Shanghai, China.1The Fifth People’s Hospital, Fudan University, and the Shanghai Key Laboratory of Medical Epigenetics, The International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai, China.Jiangming Huang 1,2, Biyun Jiang 1, Mingqi Liu 1, Pengyuan Yang 1,2 and Weiqian Cao 1,3*
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