High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry

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 Nlinked 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. Molecular & Cellular Proteomics 4: 144 -155, 2005.There is growing interest in testing the hypothesis that the serum 1 proteome contains protein biomarkers that are useful for classifying the physiological or pathological status of an individual. Such markers are expected to be useful for the prediction, detection, and diagnosis of disease as well as to follow the efficacy, toxicology, and side effects of drug treatment (1). The idea of reading diagnostic or prognostic signatures from human body fluids is neither new nor original. Early attempts using high resolution two-dimensional gel electrophoresis were described more than 2 decades ago (2-4). Renewed interest in this idea has emerged due to recent advances in proteomic technologies (5), intriguing initial results from analyzing serum protein patterns using mass spectrometry (1), and the clinical validation and use of a number of diagnostic disease markers including CA125 for ovarian cancer, prostate-specific antigen for prostate cancer, and carcinoembryonic antigen for colon, breast, pancreatic, and lung cancer (6).A number of new approaches that differ from the traditional two-dimensional gel electrophoresis method for the discovery of protein biomarkers in serum have recently been described (1). These include surface-enhanced laser desorption ionization mass spectrometry (SELDI-MS) 2 (7), liquid chromatography tandem mass spectrometry (LC-MS/MS) of serum proteome diges...

Solid-phase extraction of N-linked glycopeptides

“…For glycopeptide capture using hydrazide chemistry, peptides containing either N-linked or O-linked oligosaccharides are conjugated to a solid support covalently. Non-glycosylated peptides can be removed by extensive washing before the release of glycopeptides; therefore, the glycopeptides can be specifically enriched (over 90% enrichment 6 ). The specific release of different types of glycopeptides from solid support can be achieved by varying glycosidases or chemicals.…”

“…SPEG has been successfully applied to the quantitative analysis of glycopeptides from complex mixtures, including serum/plasma and other body fluids 5,6,[11][12][13][14][15] , the detection of glycoproteins secreted or shed into the culture medium by cells, the detection of membrane proteins and cell-surface proteins from cells and tissues 16,17 and for comparing glycoproteins in the extracellular matrix of normal and disease tissues 18 . Several high-abundance plasma proteins including the most abundant plasma protein, albumin, do not appear to contain any N-glycosites and are therefore transparent to the method, allowing for more efficient identification and quantification of the lower abundance glycoproteins in blood 6 .…”

“…Several high-abundance plasma proteins including the most abundant plasma protein, albumin, do not appear to contain any N-glycosites and are therefore transparent to the method, allowing for more efficient identification and quantification of the lower abundance glycoproteins in blood 6 . By combining quantitative analysis of N-glycosites with methods that determine relative protein changes in different proteomes, such as the cysteine tagging method 19 , the occupancy of individual N-glycosites and changes therein can also be determined.…”

Nanomechanical identification of liquid reagents in a microfluidic channel

“…The current state of the art techniques for detection of molecules dissolved in liquid are surface plasmon resonance 17 (SPR), silicon nanowires 18 (SNW), suspended microchannel resonators 19 (SMR), quartz crystal monitors 20 (QCM) and liquid chromatography 21 (LC). Except for SMR, all of the above stated techniques need liquids in microliters to perform an analysis.…”

Setup for human sera MALDI profiling: The case of rhEPO treatment

“…So far, to overcome this issue, an adequate fractionation step prior proteomic analysis is mandatory in order to remove high-abundant components. Several prefractionation methodologies have been introduced such as ultracentrifugation [4,16], solid-phase extraction (SPE) columns [17][18][19][20][21], size fractionation [22], derivatized beads [23,24], combinatorial peptide ligand libraries [25], and specific enrichment techniques [26][27][28][29]. Moreover, the high cost, the scarce selectivity, or long operation times, impose the development of new depletion systems based on multiple affinity columns [30,31].…”