Simultaneous metabolomics and lipidomics analysis based on novel heart-cutting two-dimensional liquid chromatography-mass spectrometry

代谢组学 脂类学 脂质体 化学 色谱法 代谢组 代谢物 液相色谱-质谱法 质谱法 重复性 鞘磷脂 生物化学
作者
Shuangyuan Wang,Lina Zhou,Zhichao Wang,Xianzhe Shi,Guowang Xu
出处
期刊:Analytica Chimica Acta [Elsevier BV]
卷期号:966: 34-40 被引量:62
标识
DOI:10.1016/j.aca.2017.03.004
摘要

Increasing metabolite coverage by combining data from different platforms or methods can improve understanding of related metabolic mechanisms and the identification of biomarkers. However, no one method can obtain metabolomic and lipidomic information in a single analysis. In this work, aiming at collecting comprehensive information on metabolome and lipidome in a single analytical run, we developed an on-line heart-cutting two-dimensional liquid chromatography-mass spectrometry (2D-LC-MS) method. Complex metabolites from biological samples are divided into two fractions by using a precolumn. The first fraction is directly transferred and subjected to metabolomics analysis. Most lipids are retained on the precolumn until the mobile phases for lipidomics flow through; then they are subjected to lipidomics analysis. Up to 447 and 289 metabolites in plasma, including amino acids, carnitines, bile acids, free fatty acids, lyso-phospholipids, phospholipids, sphingomyelins etc. were identified within 30 min in the positive mode and negative mode, respectively. A comparison of the newly developed method with the conventional metabolomic and lipidomic approaches showed that approximately 99% features obtained by the two conventional methods can be covered with this 2D-LC method. Analytical characteristics evaluation showed the method had a wide linearity range, high sensitivity, satisfactory recovery and repeatability. These results demonstrate that this method is reliable, stable and well qualified in metabolomics analysis, particularly for large-scale metabolomics studies with small amount of samples.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
wzzz发布了新的文献求助10
刚刚
刚刚
刚刚
球球发布了新的文献求助10
刚刚
孙皓阳发布了新的文献求助10
1秒前
lieason发布了新的文献求助10
1秒前
XZZH发布了新的文献求助200
1秒前
2秒前
Clara完成签到,获得积分10
2秒前
2秒前
春和景明发布了新的文献求助10
2秒前
3秒前
12完成签到,获得积分20
3秒前
3秒前
yi发布了新的文献求助10
4秒前
4秒前
老实皮卡丘完成签到,获得积分10
4秒前
4秒前
4秒前
5秒前
无极微光应助吉祥高趙采纳,获得20
5秒前
小蘑菇应助HY采纳,获得10
5秒前
希望天下0贩的0应助小菜采纳,获得10
5秒前
5秒前
小蘑菇应助Sepvvvvirtue采纳,获得10
5秒前
孙奕完成签到,获得积分20
5秒前
微醺发布了新的文献求助10
5秒前
烟花应助wzzz采纳,获得10
5秒前
hhxx发布了新的文献求助10
6秒前
太阳雨发布了新的文献求助10
6秒前
科研通AI6.4应助mdd采纳,获得10
6秒前
小二郎应助mdd采纳,获得10
6秒前
離殇发布了新的文献求助10
7秒前
7秒前
凝聚各方发布了新的文献求助10
7秒前
superbada完成签到,获得积分10
8秒前
豆豆完成签到,获得积分10
8秒前
8秒前
传奇3应助虞不见王采纳,获得10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Cognitive Psychology in a Changing World 800
内視鏡的に摘除しえた十二指腸乳頭部腫瘍の2例 660
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Neuroscience of Language 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7684627
求助须知:如何正确求助?哪些是违规求助? 9248117
关于积分的说明 19950992
捐赠科研通 7257536
什么是DOI,文献DOI怎么找? 3288865
关于科研通互助平台的介绍 2446044
邀请新用户注册赠送积分活动 2292986