Serum Peptide Profiling by Magnetic Particle-Assisted, Automated Sample Processing and MALDI-TOF Mass Spectrometry
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- 作者：Josep Villanueva ; John Philip ; David Entenberg ; Carlos A. Chaparro ; Meena K. Tanwar ; Eric C. Holland ; and Paul Tempst
- 刊名：Analytical Chemistry
- 出版年：2004
- 出版时间：March 15, 2004
- 年：2004
- 卷：76
- 期：6
- 页码：1560 - 1570
- 全文大小：381K
- 年卷期：v.76,no.6(March 15, 2004)
- ISSN：1520-6882
文摘
Human serum contains a complex array of proteolyticallyderived peptides (serum peptidome) that may provide acorrelate of biological events occurring in the entireorganism; 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, automatedtechnology platform for the simultaneous measurementof serum peptides that is simple, scalable, and generateshighly reproducible patterns. Peptides are captured andconcentrated using reversed-phase (RP) batch processingin a magnetic particle-based format, automated on a liquidhandling robot, and followed by a MALDI TOF massspectrometric readout. The protocol is based on a detailedinvestigation of serum handling, RP ligand and eluantselection, small-volume robotics design, an optimizedspectral acquisition program, and consistent peak extraction plus binning across a study set. The improvedsensitivity and resolution allowed detection of 400 polypeptides (0.8-15-kDa range) in a single droplet (~50 
L)of serum, and almost 2000 unique peptides in largersample sets, which can then be analyzed using commonmicroarray data analysis software. A pilot study indicatedthat sera from brain tumor patients can be distinguishedfrom controls based on a pattern of 274 peptide masses.This, in turn, served to create a learning algorithm thatcorrectly predicted 96.4% of the samples as either normalor diseased.
L)of serum, and almost 2000 unique peptides in largersample sets, which can then be analyzed using commonmicroarray data analysis software. A pilot study indicatedthat sera from brain tumor patients can be distinguishedfrom controls based on a pattern of 274 peptide masses.This, in turn, served to create a learning algorithm thatcorrectly predicted 96.4% of the samples as either normalor diseased.