Evaluation and optimization of quantitative analysis of cofactors from yeast by liquid chromatography/mass spectrometry

辅因子 化学 酵母 色谱法 质谱法 萃取(化学) 生物化学
作者
Jungyeon Kim,In‐Ho Jung,Yu Eun Cheong,Kyoung Heon Kim
出处
期刊:Analytica Chimica Acta [Elsevier BV]
卷期号:1211: 339890-339890 被引量:4
标识
DOI:10.1016/j.aca.2022.339890
摘要

Cofactors play pivotal roles in catabolism and anabolism in all living organisms. Many studies have investigated the concentration of cofactors in living organisms to understand their metabolic status, which can be used to produce valuable chemicals or to understand the pathophysiology of diseases. Among various analytical platforms, liquid chromatography/mass spectrometry (LC/MS) is the most frequently used method for the quantification of cofactors. Several studies have reported various analytical methods for cofactors using LC/MS. However, the lack of optimal LC/MS methods makes it challenging to analyze various cofactors simultaneously. In addition, the method of extracting cofactors from cells needs to be optimized because conventional protocols probably have low extraction efficiency, which makes it difficult to reflect the actual concentration of cofactors in cells. In this study, we systematically compared various analytical methods and suggested optimal methods for the analysis of cofactors using LC/MS. In addition, we systematically compared quenching methods and extraction solvents and suggested optimal methods for the extraction of cofactors from Saccharomyces cerevisiae. The optimized methods can be used as standard protocols for LC/MS analysis and the extraction of cofactors from S. cerevisiae.
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
无花果应助科研通管家采纳,获得10
1秒前
Richard发布了新的文献求助10
1秒前
orixero应助rr采纳,获得10
1秒前
小蘑菇应助科研通管家采纳,获得20
1秒前
隐形曼青应助大豪采纳,获得10
1秒前
SciGPT应助vampire采纳,获得10
1秒前
12454应助科研通管家采纳,获得10
1秒前
所所应助科研通管家采纳,获得10
1秒前
1秒前
元谷雪应助科研通管家采纳,获得10
1秒前
1秒前
mLI应助科研通管家采纳,获得10
1秒前
打打应助科研通管家采纳,获得10
2秒前
会飞的猪发布了新的文献求助10
2秒前
天天快乐应助简隋英采纳,获得10
2秒前
无极微光应助科研通管家采纳,获得20
2秒前
科目三应助科研通管家采纳,获得10
2秒前
2秒前
2秒前
贝贝应助科研通管家采纳,获得10
2秒前
NexusExplorer应助科研通管家采纳,获得10
2秒前
2秒前
Akim应助科研通管家采纳,获得10
2秒前
3秒前
3秒前
汉堡包应助科研通管家采纳,获得10
3秒前
3秒前
3秒前
NexusExplorer应助科研通管家采纳,获得30
3秒前
lee_someone完成签到,获得积分10
3秒前
SciGPT应助科研通管家采纳,获得10
3秒前
完美世界应助科研通管家采纳,获得10
4秒前
4秒前
xing_xing应助科研通管家采纳,获得20
4秒前
4秒前
Lucas应助热心犀牛采纳,获得10
4秒前
共产主义战士应助iris采纳,获得10
4秒前
贝贝完成签到 ,获得积分10
4秒前
5秒前
alan20完成签到,获得积分10
5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
2016 Venous Blood Study (VBS) (Final V3.0) 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
核安全综合知识2024版 500
Photothermal Science and Techniques 500
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 7703605
求助须知:如何正确求助?哪些是违规求助? 9261926
关于积分的说明 20034182
捐赠科研通 7279168
什么是DOI,文献DOI怎么找? 3294620
关于科研通互助平台的介绍 2449862
邀请新用户注册赠送积分活动 2301412