The Use of 2D-LC-MS/MS in Disease Characterization and Global Proteomics
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Date
2006-06
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Publisher
The Ohio State University
Abstract
Proteins occupy more than 50% of the dry weight of the average human cell. The
proteins present in a given cell at a given time reflect the function and specialization of
that cell. In addition to the specialization of cells within the body leading to various
normal protein states, protein ‘sub-states’ can be induced by various conditions such as
infections or cancer [9,21]. These disease-induced protein sub-states result in both
qualitative and quantitative differences in both ribosomal production of proteins and posttranslational
modification of proteins [2]. Each disease-induced sub-state therefore has
its own protein profile or signature. For diseases such as cancer, which can easily
progress undetected and whose manifestations have grave consequences, statistical
comparisons of a patient’s protein profile with many cancer-state protein profiles can be
made for early detection. Two-dimensional-liquid chromatography-tandem mass
spectrometry (2D-LC-MS/MS) holds much promise in the analysis of such diseaseinduced
protein profiles. 2D-LC-MS/MS uses two means of separation prior to online
mass analysis, whereas one-dimensional (1D) methods use only one means of separation.
The increased separation of a 2D system allows for much more complete mass analysis of
proteins and results in the identification and characterization of many more proteins and
modifications than with a 1D system [15]. By comparing patients’ protein profiles with a
comprehensive proteomic database, 2D-LC-MS/MS could serve as the critical step in an
efficient early-detection method for diseases such as cancer. Before protein profiles can
be accurately compared and linked with diseases, a standardized technique must be used,
and mechanisms that account for experimental variations must be implemented. The 2DLC-
MS/MS technique proposed here is both very useful and can be standardized easily.
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Keywords
Cancer, bioinformatics, proteomics, LC-MS