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Article

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

Josep Villanueva,^†^‡ John Philip,^† David Entenberg,^§ Carlos A. Chaparro,^† Meena K. Tanwar,^# Eric C. Holland,^# and Paul Tempst*^†^‡

Protein Center, Research Engineering, Department of Surgery, Department of Neurology, Molecular Biology Program, and Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10021

Anal. Chem., 2004, 76 (6), pp 1560–1570

DOI: 10.1021/ac0352171

Publication Date (Web): February 14, 2004

†

Protein Center.

‡

Molecular Biology Program.

Research Engineering.

Department of Surgery.

Department of Neurology.

Cancer Biology and Genetics Program.

To whom correspondence should be addressed: (e-mail) p-tempst@mskcc.org; (phone) (212) 639-8923.

Abstract

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.