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Serum Peptide Profiling by Magnetic Particle-Assisted, Automated Sample Processing and MALDI-TOF Mass Spectrometry

机译:磁性粒子辅助自动样品处理和MALDI-TOF质谱分析血清肽

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摘要

Human serum contains a complex array of proteolytically derived peptides (serum peptidome) that may provide a correlate of biological events occurring in the entire organism; for instance, as a diagnostic for solid tumors (Petricoin, E. F.; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. Lancet 2002, 359, 572-577). Here, we describe a novel, automated technology platform for the simultaneous measurement of serum peptides that is simple, scalable, and generates highly reproducible patterns. Peptides are captured and concentrated using reversed-phase (RP) batch processing in a magnetic particle-based format, automated on a liquid handling robot, and followed by a MALDI TOF mass spectrometric readout. The protocol is based on a detailed investigation of serum handling, RP ligand and eluant selection, small-volume robotics design, an optimized spectral acquisition program, and consistent peak extraction plus binning across a study set. The improved sensitivity and resolution allowed detection of 400 polypeptides (0.8-15-kDa range) in a single droplet (~50 μL) of serum, and almost 2000 unique peptides in larger sample sets, which can then be analyzed using common microarray data analysis software. A pilot study indicated that sera from brain tumor patients can be distinguished from controls based on a pattern of 274 peptide masses. This, in turn, served to create a learning algorithm that correctly predicted 96.4% of the samples as either normal or diseased.
机译:人血清包含一系列复杂的蛋白水解衍生肽(血清肽组),可以提供整个生物体中发生的生物学事件的相关性。例如,作为实体瘤的诊断剂(EF,Petricoin; AM Ardekani; BA,Hitt,BA; Levine,PVA; Fusaro,VA; Steinberg,SM; Mills,GB; Simone,C。; Fishman,DA; Kohn,EC ; Liotta,L. Lancet 2002,359,572-577)。在这里,我们描述了一种同时测量血清肽的新颖,自动化的技术平台,该平台简单,可扩展且可产生高度可重复的模式。使用反相(RP)批处理以基于磁性粒子的格式捕获并浓缩肽,在液体处理机器人上自动进行,然后进行MALDI TOF质谱读数。该协议基于对血清处理,RP配体和洗脱液选择,小体积机器人设计,优化的光谱采集程序以及在整个研究集中一致的峰提取和装箱的详细研究。灵敏度和分辨率的提高允许在单滴血清(〜50μL)中检测400种多肽(0.8-15 kDa范围),并在较大的样品集中检测近2000种独特的肽,然后可以使用常见的微阵列数据分析进行分析软件。一项前瞻性研究表明,基于274种肽的模式,可以将脑肿瘤患者的血清与对照区分开。反过来,这有助于创建一种学习算法,可以正确预测96.4%的样本为正常或患病。

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