Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry

被引:376
作者
Villanueva, J
Philip, J
Entenberg, D
Chaparro, CA
Tanwar, MK
Holland, EC
Tempst, P
机构
[1] Mem Sloan Kettering Canc Ctr, Prot Ctr, New York, NY 10021 USA
[2] Mem Sloan Kettering Canc Ctr, Dept Surg, New York, NY 10021 USA
[3] Mem Sloan Kettering Canc Ctr, Dept Neurol, New York, NY 10021 USA
[4] Mem Sloan Kettering Canc Ctr, Program Mol Biol, New York, NY 10021 USA
[5] Mem Sloan Kettering Canc Ctr, Canc Biol & Genet Program, New York, NY 10021 USA
关键词
D O I
10.1021/ac0352171
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
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 (similar to50 muL) 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.
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页码:1560 / 1570
页数:11
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