Literature DB >> 18549256

Evaluation of three principally different intact protein prefractionation methods for plasma biomarker discovery.

Maria Pernemalm1, Lukas M Orre, Johan Lengqvist, Pernilla Wikström, Rolf Lewensohn, Janne Lehtiö.   

Abstract

The aim of this study was to evaluate three principally different top-down protein prefractionation methods for plasma: high-abundance protein depletion, size fractionation and peptide ligand affinity beads, focusing in particular on compatibility with downstream analysis, reproducibility and analytical depth. Our data clearly demonstrates the benefit of high-abundance protein depletion. However, MS/MS analysis of the proteins eluted from the high-abundance protein depletion column show that more proteins than aimed for are removed and, in addition, that the depletion efficacy varies between the different high-abundance proteins. Although a smaller number of proteins were identified per fraction using the peptide ligand affinity beads, this technique showed to be both robust and versatile. Size fractionation, as performed in this study, focusing on the low molecular weight proteome using a combination of gel filtration chromatography and molecular weight cutoff filters, showed limitations in the molecular weight cutoff precision leading detection of high molecular weight proteins and, in the case of the cutoff filters, high variability. GeLC-MS/MS analysis of the fractionation methods in combination with pathway analysis demonstrates that increased fractionation primarily leads to high proteome coverage of pathways related to biological functions of plasma, such as acute phase reaction, complement cascade and coagulation. Further, the prefractionation methods in this study induces limited effect on the proportion of tissue proteins detected, thereby highlighting the importance of extensive or targeted downstream fractionation.

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Year:  2008        PMID: 18549256     DOI: 10.1021/pr700821k

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  16 in total

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