Step-Wise Assessment and Optimization of Sample Handling Recovery Yield for Nanoproteomic Analysis of 1000 Mammalian Cells

化学 样品制备 样品(材料) 蛋白质组学 双肌酸测定 工作流程 色谱法 再现性 样本量测定 纳米技术 生物化学 计算机科学 材料科学 统计 基因 数据库 数学
作者
Ruilin Wu,Sansi Xing,Maryam Badv,Tohid F. Didar,Yu Lu
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:91 (16): 10395-10400 被引量:18
标识
DOI:10.1021/acs.analchem.9b02092
摘要

Protein and peptide adhesion is a major factor contributing to sample loss during proteomic sample preparation workflows. Sample loss often has detrimental effects on the quality of proteomic analysis by compromising protein identification and data reproducibility. When starting with a low sample amount, only the most abundant proteins can be identified, which often offers little insights for biological research. Although the general idea about severe sample loss from low amount of starting material is widely presumed in the proteomics field, quantitative assessment on the impact of sample loss has been poorly investigated. In the present study, we have quantitatively assessed sample loss during each step of a conventional in-solution sample preparation workflow using bicinchoninic acid (BCA) and targeted LC/MS/MS protein and peptide assays. According to our assessment, for starting materials of ∼1000 mammalian cells, surface adhesion, along with desalting and speed-vacuum drying steps, all contribute heavily to sample loss, in particular for low-abundance proteins. With this knowledge, we have adapted slippery liquid infused porous surface (SLIPS) treatment, commercial LoBind tubes, and in-line desalting during sample processing. With these improvements, we were able to use a conventional in-solution sample handling method to identify on average 829 proteins with 1000 U2OS osteosarcoma cells (∼100 ng) with 75-min LC/MS/MS runs, an 11-fold increase in protein identification. Our optimized in-solution workflow is straightforward and also much less equipment- and technique-demanding than other advanced sample preparation protocols in the field.
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
啊打吧发布了新的文献求助50
2秒前
3秒前
李爱国应助真实的夜南采纳,获得10
5秒前
大个应助强健的月饼采纳,获得10
5秒前
alielie完成签到,获得积分10
6秒前
完美世界应助提速狗采纳,获得100
7秒前
8秒前
快乐发布了新的文献求助10
8秒前
9秒前
9秒前
无敌鱼发布了新的文献求助30
12秒前
13秒前
852应助快乐采纳,获得10
19秒前
Singularity应助无敌鱼采纳,获得10
20秒前
顾矜应助无敌鱼采纳,获得10
20秒前
21秒前
柠檬酸盐汽水完成签到,获得积分10
22秒前
25秒前
27秒前
小Li发布了新的文献求助10
28秒前
28秒前
提速狗完成签到,获得积分10
30秒前
嘿嘿嘿完成签到,获得积分10
30秒前
31秒前
迅速的花生完成签到,获得积分10
32秒前
啊打吧完成签到,获得积分10
34秒前
清水发布了新的文献求助10
34秒前
提速狗发布了新的文献求助100
34秒前
34秒前
一叶知秋完成签到,获得积分10
35秒前
36秒前
冷哲宇应助zyfzyf采纳,获得10
36秒前
嘿嘿嘿发布了新的文献求助10
36秒前
37秒前
flow完成签到,获得积分10
37秒前
温琼林完成签到 ,获得积分10
38秒前
辣子肉发布了新的文献求助10
39秒前
1.1发布了新的文献求助10
41秒前
yiyi131发布了新的文献求助10
42秒前
44秒前
高分求助中
【本贴是提醒信息,请勿应助】请在求助之前详细阅读求助说明!!!! 20000
One Man Talking: Selected Essays of Shao Xunmei, 1929–1939 1000
The Three Stars Each: The Astrolabes and Related Texts 900
Yuwu Song, Biographical Dictionary of the People's Republic of China 800
Multifunctional Agriculture, A New Paradigm for European Agriculture and Rural Development 600
Challenges, Strategies, and Resiliency in Disaster and Risk Management 500
Bernd Ziesemer - Maos deutscher Topagent: Wie China die Bundesrepublik eroberte 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2481747
求助须知:如何正确求助?哪些是违规求助? 2144344
关于积分的说明 5469639
捐赠科研通 1866860
什么是DOI,文献DOI怎么找? 927886
版权声明 563039
科研通“疑难数据库(出版商)”最低求助积分说明 496404