A Facile LC-MS Method for Profiling Cholesterol and Cholesteryl Esters in Mammalian Cells and Tissues

脂类学 胆固醇酯 胆固醇 化学 生物化学 脂质代谢 质谱法 新陈代谢 液相色谱-质谱法 色谱法 脂蛋白
作者
Aakash Chandramouli,Siddhesh S. Kamat
出处
期刊:Biochemistry [American Chemical Society]
卷期号:63 (18): 2300-2309 被引量:4
标识
DOI:10.1021/acs.biochem.4c00160
摘要

Cholesterol is central to mammalian lipid metabolism and serves many critical functions in the regulation of diverse physiological processes. Dysregulation in cholesterol metabolism is causally linked to numerous human diseases, and therefore, in vivo, the concentrations and flux of cholesterol and cholesteryl esters (fatty acid esters of cholesterol) are tightly regulated. While mass spectrometry has been an analytical method of choice for detecting cholesterol and cholesteryl esters in biological samples, the hydrophobicity, chemically inert nature, and poor ionization of these neutral lipids have often proved a challenge in developing lipidomics compatible liquid chromatography-mass spectrometry (LC-MS) methods to study them. To overcome this problem, here, we report a reverse-phase LC-MS method that is compatible with existing high-throughput lipidomics strategies and capable of identifying and quantifying cholesterol and cholesteryl esters from mammalian cells and tissues. Using this sensitive yet robust LC-MS method, we profiled different mammalian cell lines and tissues and provide a comprehensive picture of cholesterol and cholesteryl esters content in them. Specifically, among cholesteryl esters, we find that mammalian cells and tissues largely possess monounsaturated and polyunsaturated variants. Taken together, our lipidomics compatible LC-MS method to study this lipid class opens new avenues in understanding systemic and tissue-level cholesterol metabolism under various physiological conditions.
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
万能图书馆应助XXXXX采纳,获得10
1秒前
西出阳关完成签到,获得积分10
1秒前
Hello应助obsession采纳,获得10
1秒前
xuzhu0907完成签到,获得积分10
1秒前
薯条桃桃完成签到 ,获得积分10
1秒前
柒柒完成签到,获得积分10
2秒前
呆呆完成签到,获得积分10
2秒前
明理的又柔完成签到 ,获得积分10
2秒前
v0id应助syz采纳,获得10
3秒前
挞挞黄发布了新的文献求助10
3秒前
xjiang001完成签到,获得积分10
3秒前
水123完成签到,获得积分10
4秒前
4秒前
jazz完成签到,获得积分10
4秒前
4秒前
xcx完成签到 ,获得积分10
4秒前
5秒前
朵朵完成签到,获得积分10
5秒前
5秒前
William完成签到 ,获得积分10
6秒前
琪丸发布了新的文献求助10
7秒前
lei.qin完成签到 ,获得积分10
7秒前
xyz完成签到,获得积分10
7秒前
驽马十驾完成签到,获得积分10
7秒前
酱子完成签到 ,获得积分10
7秒前
7秒前
7秒前
积极向上完成签到,获得积分10
7秒前
8秒前
英俊青旋完成签到 ,获得积分10
8秒前
尤珩完成签到,获得积分10
8秒前
无钱完成签到 ,获得积分10
9秒前
云鹤发布了新的文献求助10
9秒前
nn应助wll采纳,获得10
9秒前
xjiang017完成签到,获得积分10
9秒前
Freening完成签到,获得积分10
10秒前
呆萌致远发布了新的文献求助20
10秒前
坡坡发布了新的文献求助10
10秒前
dde应助朵朵采纳,获得20
10秒前
10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
An Introduction to Foreign Language Learning and Teaching 750
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Les chinois de jakarta: temples et vie collective 500
The fast track to determining transfer functions of linear circuits: The student guide 500
What is the Future of Psychotherapy in Digital Age? Technology, AI Bots, and Psychotherapy after Covid 444
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 7628129
求助须知:如何正确求助?哪些是违规求助? 9202533
关于积分的说明 19731512
捐赠科研通 7197860
什么是DOI,文献DOI怎么找? 3273926
关于科研通互助平台的介绍 2436244
邀请新用户注册赠送积分活动 2270100