Abstract
It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.
Highlights
It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements
These include surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS)2 [7], liquid chromatography tandem mass spectrometry (LC-MS/MS) of serum proteome digests (8 –10), two- or three-dimensional protein separation analyzed by differential fluorescent staining [11, 12], fractionation of the serum proteome on surface-modified magnetic beads followed by matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) [13], and combinations and variations of these approaches
The objective of the method is the generation of reproducible peptide patterns representing the serum proteome, leading to the detection of peptides that discriminate between related groups of proteomes and the subsequent identification of these discriminatory peptides
Summary
HPLC grade reagents were purchased from Fisher Scientific (Pittsburgh, PA, USA). For quantitative analysis of peptides using LC-MS, an ESI-QTOF mass spectrometer (Waters, Beverly, MA) was used In both systems, peptides isolated from 5 l of serum sample using the glycopeptide capture method were injected into a home-made peptide trap packed with Magic C18 resin (Michrome Bioresources, Auburn, CA) using a FAMOS autosampler (DIONEX, Sunnyvale, CA) and passed through a 10-cm ϫ 75m-inner diameter microcapillary HPLC column packed with Magic C18 resin (Michrome Bioresources). The software tools use LC-MS data generated by ESI-QTOF analysis of formerly N-linked glycopeptides from serum samples and sequentially perform the following tasks to determine peptides that are of different abundance in cancer and normal mice, respectively. Peptide alignment was facilitated by the following factors: i) the glycopeptide capture procedure significantly simplifies the sample complexity, ii) the high mass accuracy achieved in the ESI-QTOF instrument, and iii) the optimized HPLC system that produced highly consistent and reproducible peptide patterns.
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