微流控
蛋白质组
质谱法
色谱法
样品制备
数字微流体
轨道轨道
化学
蛋白质组学
材料科学
溶解
串联质谱法
纳米技术
生物化学
基因
物理化学
电润湿
电极
作者
Jan Leipert,Andreas Tholey
出处
期刊:Lab on a Chip
[The Royal Society of Chemistry]
日期:2019-01-01
卷期号:19 (20): 3490-3498
被引量:56
摘要
While LC-MS-based proteomics with high nanograms to micrograms of total protein has become routine, the analysis of samples derived from low cell numbers is challenged by factors such as sample losses, or difficulties encountered with the manual manipulation of small liquid volumes. Digital microfluidics (DMF) is an emerging technique for miniaturized and automated droplet manipulation, which has been proposed as a promising tool for proteomic sample preparation. However, proteome analysis of samples prepared on-chip by DMF has previously been unfeasible, due to incompatibility with down-stream LC-MS instrumentation. To overcome these limitations, we here developed protocols for bottom-up LC-MS based proteomics sample preparation of as little as 100 mammalian cells on a commercially available digital microfluidics device. To this end, we developed effective cell lysis conditions optimized for DMF, as well as detergent-buffer systems compatible with downstream proteolytic digestion on DMF chips and subsequent LC-MS analysis. A major step was the introduction of the single-pot, solid-phase-enhanced sample preparation (SP3) approach on-chip, which allowed the removal of salts and anti-fouling polymeric detergents, thus rendering sample preparation by DMF compatible with LC-MS-based proteome analysis. Application of DMF-SP3 to the proteome analysis of Jurkat T cells led to the identification of up to 2500 proteins from approximately 500 cells, and up to 1200 proteins from approximately 100 cells on an Orbitrap mass spectrometer, emphasizing the high compatibility of DMF-SP3 with low protein input and minute volumes handled by DMF. Taken together, we demonstrate the first sample preparation workflow for proteomics on a DMF chip device reported so far, allowing the sensitive analysis of limited biological material.
科研通智能强力驱动
Strongly Powered by AbleSci AI