基于气相色谱-质谱联用技术的水稻代谢轮廓分析方法的建立

开展了基于衍生化气相色谱-质谱联用的水稻代谢物分析方法的研究。采用D-最优试验设计对代谢物的提取溶剂进行优化,考察了水、甲醇、乙腈和异丙醇的提取效率,通过多元统计分析评价提取效能和溶剂配比的相关性,最终确立以80%(v/v)甲醇/水作为代谢物提取的最适溶剂。在此基础上对该方法的分析性能进行评价,发现绝大多数代谢物(＞90%)具有良好的精密度、重现性和稳定性(相对标准偏差小于30%),且占总峰面积88.0%的代谢物的响应值与其浓度间呈线性关系(相关系数＞0.9)。采用气相色谱-质谱联用方法从水稻种子中共鉴定出86个代谢物,涵盖糖、氨基酸、有机酸、甾体等多类浓度差异大的物质,适合于水稻的代谢表型差异研究。

Development of a gas chromatography-mass spectrometry method for the metabolomic study of rice(Oryza sativa L.) grain

An analytical strategy for the metabolic profiling of rice grain was developed based on gas chromatography-mass spectrometry (GC-MS). For the purpose of obtaining abundant metabolite information, sample preparation step prior to instrumental analysis is necessary to be optimized. D-optimal experimental design was applied to optimize the extraction solvent. Four solvents, including water, methanol, isopropanol and acetonitrile, and their combinations were evaluated for the extraction efficiency using multivariate statistical analysis (partial least square regression). The count of resolved peaks and the sum of peak areas were taken as the evaluation indexes. Methanol/water (80:20, v/v) mixture was highly efficient for rice metabolites and was selected as the suitable solvent formulation. Then, the analytical characteristics of the method were measured. More than 90% of the metabolites had satisfactory precisions, reproducibilities and stabilities (relative standard deviations (RSDs)＜30%). Most of the detected metabolites (about 88.0% of total peak area) showed good linear responses. With the optimized analytical protocol, 315 metabolites were detected in rice and 86 of which were structurally identified by searching in the NIST 08/Wiley standard mass spectral library, covering carbohydrates, amino acids, organic acids, steroids and so on which showed a broad coverage of metabolite data. The established method is expected to be useful for the metabolomic studies of rice.