Huang Jiangming, Jiang Biyun, Liu Mingqi, Yang Pengyuan, Cao Weiqian
The 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.
Department of Chemistry, Fudan University, Shanghai, China.
Front Chem. 2021 Jul 28;9:707738. doi: 10.3389/fchem.2021.707738. eCollection 2021.
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.
基于基质辅助激光解吸电离质谱（MALDI-MS）的聚糖同位素标记方法已在定量糖组学中得到有效且广泛的应用。然而，由于生物信息学工具不完善，对MALDI-MS产生的数据进行解读既不准确又繁琐。在这项工作中，我们展示了gQuant，这是一种用于处理基于MALDI-MS的聚糖同位素标记数据的自动化工具。gQuant采用了一套专门的算法进行设计，以提高定量数据处理的效率、准确性和便利性。在参考数据集上进行测试时，gQuant显示出快速的处理速度，它能够在几分钟内搜索模型糖蛋白的聚糖数据，并且比手动分析报告的结果更多。报告的定量比率与1:10至10:1范围内的实验聚糖混合比率匹配良好。此外，gQuant是完全开源的，用Python编写代码，大多数操作系统都支持，并且具有用户友好的界面。用户可以轻松地根据特定的实验设计对gQuant进行调整，例如特定的聚糖数据库、不同的衍生化类型和相对定量设计，因此可以促进基于MALDI-MS的稳定同位素标记在临床样本分析中的快速糖组学定量。
