DIGE Analysis of ProteoMiner™ Fractionated Serum/Plasma Samples

The discovery of clinically relevant biomarkers using gel-based proteomics has proven extremely challenging, principally because of the large dynamic range of protein abundances in biofluids such as blood and the fact that only a small number of proteins constitute the vast majority of total blood protein mass. Various separation, depletion, enrichment, and quantitative developments coupled with improvements in gel-based protein quantification technologies, specifically fluorescence two-dimensional difference gel electrophoresis (2D-DIGE), have contributed to significant improvements in the detection and identification of lower abundance proteins. One of these enrichment technologies, ProteoMiner, is the focus of this chapter. The ProteoMiner technology utilizes hexapeptide bead library with huge diversity to bind and enrich low-abundance proteins but at the same time suppresses the concentration of high-abundance proteins in subsequent analysis.

Enriching for Low-Abundance Serum Proteins Using ProteoMiner™ and Protein-Level HPLC

The mammalian immune system acts to protect the body from harmful diseases ranging from cancer to infection. Differentially expressed proteins as a result of such an immune response can shed light on the mechanism of disease or serve as biomarkers. These biomarkers can be used in a diagnostic capacity or as correlates of protection following vaccination. Protein levels in the circulatory system are considered representative of the system as a whole, making serum an ideal matrix for surveilling immune responses. However, serum proteomics using mass spectrometry is extremely challenging due to the complexity of the matrix and the dynamic range of protein concentration. This chapter will describe two orthogonal enrichment strategies that can be used sequentially or in isolation to improve the identification of low-abundance serum proteins by mass spectrometry.

DIGE Analysis of ProteoMiner(TM) Fractionated Serum/Plasma Samples

The discovery of clinically relevant biomarkers using gel-based proteomics has proven extremely challenging, principally because of the large dynamic range of protein abundances in biofluids such as blood and the fact that only a small number of proteins constitute the vast majority of total blood protein mass. Various separation, depletion, enrichment, and quantitative developments coupled with improvements in gel-based protein quantification technologies, specifically difference gel electrophoresis (DIGE), have contributed to significant improvements in the detection and identification of lower abundance proteins. One of these enrichment technologies, Proteominer, will be the focus of this chapter. The Proteominer technology a utilizes hexapeptide bead library with huge diversity to bind and enrich low-abundance proteins but at the same time suppressing the concentration of high-abundance proteins in subsequent analysis.

Capturing and identification of differentially expressed fucome by a gel free and label free approach

This research reports a proof-of-concept that describes an instrumental approach that is gel free and label free at both the separation and mass spectrometry ends for the capturing and identification of differentially expressed proteins (DEPs) in diseases, e.g., cancers. The research consists of subjecting/processing equalized and non-equalized (i.e., untreated) disease-free and hepatocellular carcinoma (HCC) human sera via a multicolumn platform for capturing/fractionating human serum fucome. The equalization was performed via the combinatorial peptide ligand library (CPLL) beads technology that ensured narrowing the protein concentration range, thus allowing the detection of low abundance proteins. The equalized and non-equalized disease-free and HCC sera were first fractionated online onto two lectin columns specific to fucose, namely Aleuria aurantia lectin (AAL) and Lotus tetragonolobus agglutinin (LTA) followed by the online fractionation of the lectin captured fucome by reversed phase chromatography. The online desalted fractions were first subjected to trypsinolysis and then to liquid chromatography-mass spectrometry (LC-MS/MS) analysis. In comparison with untreated serum, the CPLL treated serum is superior in terms of the total number of identified DEPs, which reflected an increased number of DEPs in a wide abundance range. The DEPs in HCC serum were found to be 70 and 40 in both LTA and AAL fractions for the serum treated by CPLL and untreated serum, respectively. In addition, the platform combined with the CPLL treatment was accomplished with virtually no sample loss and dilution as well as with no experimental biases and sample labeling when comparing the diseased-free and cancer sera using LC-MS/MS.

Targeting deeper the human serum fucome by a liquid-phase multicolumn platform in combination with combinatorial peptide ligand libraries

Combinatorial peptide ligand library (CPLL) was evaluated as an off line step to narrow the differences of protein concentration in human serum prior to the capturing of human fucome from disease-free and breast cancer sera by a multicolumn platform via lectin affinity chromatography (LAC) followed by the fractionation of the captured glycoproteins by reversed phase chromatography (RPC). Two monolithic lectin columns specific to fucose, namely Aleuria aurantia lectin (AAL) and Lotus tetragonolobus agglutinin (LTA) columns were utilized to capture the fucome, which was subsequently fractionated by RPC yielding desalted fractions in volatile acetonitrile-rich mobile phase, which after vacuum evaporation were subjected to tryptic digestion prior to LC-MS/MS analysis. AAL has a strong affinity towards core fucosylated N-glycans and has a weak binding towards fucose in the outer arm while LTA can bind to glycans having fucose present in the outer arm. The combined strategy consisting of the CPLL, multicolumn platform and LC-MS/MS analysis permitted the identification of the differentially expressed proteins (DEPs) in breast cancer serum yielding 58 DEPs in both the LTA and AAL fractions with 6 DEPs common to both lectins. 17 DEPs were of the low abundance type, 16 DEPs of the borderline abundance type, 4 DEPs of the medium abundance type and 15 DEPs of the high abundance type. The remaining 6 DEPs are of unknown concentration. Only proteins exhibiting 99.9% protein identification probability, 95% peptide identification probability, and a minimum of 5 unique peptides were considered in finding the DEPs via scatterplots.