Microfabricated Modules for Sample Handling, Sample Concentration and Flow Mixing: Application to Protein Analysis by Tandem Mass Spectrometry

1999 ◽  
Vol 121 (1) ◽  
pp. 7-12 ◽  
Author(s):  
D. Figeys ◽  
R. Aebersold

The comprehensive analysis of biological systems requires a combination of genomic and proteomic efforts. The large-scale application of current genomic technologies provides complete genomic DNA sequences, sequence tags for expressed genes (EST’s), and quantitative profiles of expressed genes at the mRNA level. In contrast, protein analytical technology lacks the sensitivity and the sample throughput for the systematic analysis of all the proteins expressed by a tissue or cell. The sensitivity of protein analysis technology is primarily limited by the loss of analytes, due to adsorption to surfaces, and sample contamination during handling. Here we summarize our work on the development and use of microfabricated fluidic systems for the manipulation of minute amounts of peptides and delivery to an electrospray ionization tandem mass spectrometer. New data are also presented that further demonstrate the potential of these novel approaches. Specifically, we describe the use of microfabricated devices as modules to deliver femtomole amounts of protein digests to the mass spectrometer for protein identification. We also describe the use of a microfabricated module for the generation of solvent gradients at nl/min flow rates for gradient chromatography-tandem mass spectrometry. The use of microfabricated fluidic systems reduces the risk of sample contamination and sample loss due to adsorption to wetted surfaces. The ability to assemble dedicated modular systems and to operate them automatically makes the use of microfabricated systems attractive for the sensitive and large-scale analysis of proteins.

Author(s):  
Haipeng Wang

Protein identification (sequencing) by tandem mass spectrometry is a fundamental technique for proteomics which studies structures and functions of proteins in large scale and acts as a complement to genomics. Analysis and interpretation of vast amounts of spectral data generated in proteomics experiments present unprecedented challenges and opportunities for data mining in areas such as data preprocessing, peptide-spectrum matching, results validation, peptide fragmentation pattern discovery and modeling, and post-translational modification (PTM) analysis. This article introduces the basic concepts and terms of protein identification and briefly reviews the state-of-the-art relevant data mining applications. It also outlines challenges and future potential hot spots in this field.


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