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Open Access Technical Note

An automated plasma protein fractionation design: high-throughput perspectives for proteomic analysis

Claudia Boccardi12, Silvia Rocchiccioli1, Antonella Cecchettini13, Alberto Mercatanti1 and Lorenzo Citti1*

Author affiliations

1 Institute of Clinical Physiology-CNR, Via Moruzzi 1, 56124 Pisa, Italy

2 Center for Nanotechnology Innovation @NEST, Istituto Italiano di Tecnologia, Piazza San Silvestro 12, 56127 Pisa, Italy

3 Department of Human Morphology and Applied Biology, University of Pisa, Via Roma 55 Pisa, Italy

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Citation and License

BMC Research Notes 2012, 5:612  doi:10.1186/1756-0500-5-612

Published: 1 November 2012

Abstract

Background

Human plasma, representing the most complete record of the individual phenotype, is an appealing sample for proteomics analysis in clinical applications. Up to today, the major obstacle in a proteomics study of plasma is the large dynamic range of protein concentration and the efforts of many researchers focused on the resolution of this important drawback.

Findings

In this study, proteins from pooled plasma samples were fractionated according to their chemical characteristics on a home-designed SPE automated platform. The resulting fractions were digested and further resolved by reversed-phase liquid chromatography coupled with MALDI TOF/TOF mass spectrometry. A total of 712 proteins were successfully identified until a concentration level of ng/mL. Pearson correlation coefficient was used to test reproducibility.

Conclusions

Our multidimensional fractionation approach reduced the analysis time (2 days are enough to process 16 plasma samples filling a 96-well plate) over the conventional gel-electrophoresis or multi-LC column based methods. The robotic processing, avoiding contaminants or lack of sample handling skill, promises highly reproducible specimen analyses (more than 85% Pearson correlation). The automated platform here presented is flexible and easily modulated changing fractioning elements or detectors.

Keywords:
Human plasma; Proteomics; Automated platform; LC-MALDI; High-throughput analysis