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    Quantitative mass spectrometry-based techniques for clinical use: biomarker identification and quantification.

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    Authors
    Simpson, Kathryn L
    Whetton, Anthony D
    Dive, Caroline
    Affiliation
    Paterson Institute for Cancer Research, University of Manchester, Wilmslow Road, Manchester, M20 4BX, United Kingdom. KSimpson@PICR.man.ac.uk
    Issue Date
    2009-05-01
    
    Metadata
    Show full item record
    Abstract
    The potential for development of personalised medicine through the characterisation of novel biomarkers is an exciting prospect for improved patient care. Recent advances in mass spectrometric (MS) techniques, liquid phase analyte separation and bioinformatic tools for high throughput now mean that this goal may soon become a reality. However, there are challenges to be overcome for the identification and validation of robust biomarkers. Bio-fluids such as plasma and serum are a rich source of protein, many of which may reflect disease status, and due to the ease of sampling and handling, novel blood borne biomarkers are very much sought after. MS-based methods for high throughput protein identification and quantification are now available such that the issues arising from the huge dynamic range of proteins present in plasma may be overcome, allowing deep mining of the blood proteome to reveal novel biomarker signatures for clinical use. In addition, the development of sensitive MS-based methods for biomarker validation may bypass the bottleneck created by the need for generation and usage of reliable antibodies prior to large scale screening. In this review, we discuss the MS-based methods that are available for clinical proteomic analysis and highlight the progress made and future challenges faced in this cutting edge area of research.
    Citation
    Quantitative mass spectrometry-based techniques for clinical use: biomarker identification and quantification. 2009, 877 (13):1240-9 J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.
    Journal
    Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences
    URI
    http://hdl.handle.net/10541/67943
    DOI
    10.1016/j.jchromb.2008.11.023
    PubMed ID
    19058768
    Type
    Article
    Language
    en
    ISSN
    1873-376X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jchromb.2008.11.023
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research
    Clinical and Experimental Pharmacology Group
    School of Cancer and Imaging Sciences

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