Targeted Metabolomic Analysis of Head and Neck Cancer Cells Using High Performance Ion Chromatography Coupled with a Q Exactive HF Mass Spectrometer

化学 代谢组学 代谢物 苹果酸 色谱法 质谱法 再现性 富马酸 柠檬酸 分析化学(期刊) 生物化学
作者
Shen Hu,Junhua Wang,Eoon Hye Ji,Terri Christison,Linda Lopez,Yingying Huang
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:87 (12): 6371-6379 被引量:73
标识
DOI:10.1021/acs.analchem.5b01350
摘要

In this study, we have demonstrated a targeted metabolomics method for analysis of cancer cells, based on high-performance ion chromatography (IC) separation, Q Exactive HF MS for high-resolution and accurate-mass (HR/AM) measurement and the use of stable isotope-labeled internal standards for absolute quantitation. Our method offers great technical advantages for metabolite analysis, including exquisite sensitivity, high speed and reproducibility, and wide dynamic range. The high-performance IC provided fast separation of cellular metabolites within 20 min and excellent resolving power for polar molecules including many isobaric metabolites. The IC/Q Exactive HF MS achieved wide dynamic ranges of 5 orders of magnitude for six targeted metabolites, pyruvate, succinic acid, malic acid, citric acid, fumaric acid, and alpha-ketoglutaric acid, with R(2) ≈ 0.99. Using this platform, metabolites can be simultaneously quantified from low fmol/μL to nmol/μL levels in cellular samples. The high flow rate IC at 380 μL/min has shown excellent reproducibility for a large set of samples (150 injections), with minimal variations of retention time (SD < ± 0.03 min). In addition, the IC-MS-based approach acquires targeted and global metabolomic data in a same analytical run, and the use of stable isotope-labeled standards facilitates accurate quantitation of targeted metabolites in large-scale metabolomics analysis. This metabolomics approach has been successfully applied to analysis of targeted metabolites in head and neck cancer cells as well as cancer stem-like cells (CSCs), and the findings indicate that the metabolic phenotypes may be distinct between high and low invasive head and neck cancer cells and between CSCs and non-SCCs.
最长约 10秒,即可获得该文献文件

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
科研通AI5应助平常叫兽采纳,获得30
4秒前
5秒前
6秒前
8秒前
autumn发布了新的文献求助10
9秒前
酷波er应助duan采纳,获得10
9秒前
10秒前
10秒前
11发布了新的文献求助10
12秒前
mrjohn完成签到,获得积分10
13秒前
Xiang发布了新的文献求助10
15秒前
17秒前
LeichterL发布了新的文献求助10
17秒前
hh完成签到,获得积分10
19秒前
19秒前
will完成签到,获得积分20
19秒前
19秒前
Richardisme完成签到,获得积分10
19秒前
21秒前
酒酒完成签到,获得积分10
22秒前
22秒前
义气笑容完成签到,获得积分10
24秒前
朱佳慧完成签到,获得积分20
24秒前
斯文静竹发布了新的文献求助10
25秒前
幸福大白发布了新的文献求助10
26秒前
screct完成签到,获得积分10
27秒前
123完成签到,获得积分10
28秒前
123完成签到,获得积分10
30秒前
斯文静竹完成签到,获得积分10
31秒前
Billie完成签到,获得积分10
32秒前
CipherSage应助lp采纳,获得10
32秒前
childheart发布了新的文献求助10
33秒前
平常叫兽发布了新的文献求助30
33秒前
34秒前
34秒前
35秒前
热舞特发布了新的文献求助10
35秒前
37秒前
hobowei完成签到 ,获得积分10
38秒前
38秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Machine Learning Methods in Geoscience 1000
Resilience of a Nation: A History of the Military in Rwanda 888
Musculoskeletal Pain - Market Insight, Epidemiology And Market Forecast - 2034 500
Crystal Nonlinear Optics: with SNLO examples (Second Edition) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3734464
求助须知:如何正确求助?哪些是违规求助? 3278459
关于积分的说明 10009515
捐赠科研通 2995045
什么是DOI,文献DOI怎么找? 1643172
邀请新用户注册赠送积分活动 780986
科研通“疑难数据库(出版商)”最低求助积分说明 749183