title: A systematic analysis of eluted fraction of plasma post immunoaffinity depletion: implications in biomarker discovery.
abstract: Plasma is the most easily accessible source for biomarker discovery in clinical proteomics. However, identifying potential biomarkers from plasma is a challenge given the large dynamic range of proteins. The potential biomarkers in plasma are generally present at very low abundance levels and hence identification of these low abundance proteins necessitates the depletion of highly abundant proteins. Sample pre-fractionation using immuno-depletion of high abundance proteins using multi-affinity removal system (MARS) has been a popular method to deplete multiple high abundance proteins. However, depletion of these abundant proteins can result in concomitant removal of low abundant proteins. Although there are some reports suggesting the removal of non-targeted proteins, the predominant view is that number of such proteins is small. In this study, we identified proteins that are removed along with the targeted high abundant proteins. Three plasma samples were depleted using each of the three MARS (Hu-6, Hu-14 and Proteoprep 20) cartridges. The affinity bound fractions were subjected to gelC-MS using an LTQ-Orbitrap instrument. Using four database search algorithms including MassWiz (developed in house), we selected the peptides identified at <1% FDR. Peptides identified by at least two algorithms were selected for protein identification. After this rigorous bioinformatics analysis, we identified 101 proteins with high confidence. Thus, we believe that for biomarker discovery and proper quantitation of proteins, it might be better to study both bound and depleted fractions from any MARS depleted plasma sample.
authors.affiliation: GN Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology, Council of Scientific and Industrial Research, Delhi, India.
authors.forename: Amit Kumar
authors.initials: AK
authors.lastname: Yadav
authors.forename: Gourav
authors.initials: G
authors.lastname: Bhardwaj
authors.forename: Trayambak
authors.initials: T
authors.lastname: Basak
authors.forename: Dhirendra
authors.initials: D
authors.lastname: Kumar
authors.forename: Shadab
authors.initials: S
authors.lastname: Ahmad
authors.forename: Ruby
authors.initials: R
authors.lastname: Priyadarshini
authors.forename: Ashish Kumar
authors.initials: AK
authors.lastname: Singh
authors.forename: Debasis
authors.initials: D
authors.lastname: Dash
authors.forename: Shantanu
authors.initials: S
authors.lastname: Sengupta
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000737:chemistry / Q000378:metabolism*; D002853:Chromatography, Liquid / Q000379:methods; D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D004591:Electrophoresis, Polyacrylamide Gel / Q000379:methods; D006801:Humans; D007070:Immunoglobulin A / Q000737:chemistry; D007074:Immunoglobulin G / Q000737:chemistry; D013058:Mass Spectrometry / Q000379:methods; D010455:Peptides / Q000737:chemistry; D011506:Proteins / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods; D053719:Tandem Mass Spectrometry / Q000379:methods; D013696:Temperature
chemical_list: D015415:Biomarkers; D007070:Immunoglobulin A; D007074:Immunoglobulin G; D010455:Peptides; D011506:Proteins