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|Title:||Towards the application of proteomics in renal disease diagnosis|
|Authors:||Vidal Jr., B.C.|
I-Hong Hsu, S.
|Citation:||Vidal Jr., B.C., Bonventre, J.V., I-Hong Hsu, S. (2005-11). Towards the application of proteomics in renal disease diagnosis. Clinical Science 109 (5) : 421-430. ScholarBank@NUS Repository. https://doi.org/10.1042/CS20050085|
|Abstract:||Proteomics is widely envisioned as playing a significant role in the translation of genomics to clinically useful applications, especially in the areas of diagnostics and prognostics. In the diagnosis and treatment of kidney disease, a major priority is the identification of disease-associated biomarkers. Proteomics, with its high-throughput and unbiased approach to the analysis of variations in protein expression patterns (actual phenotypic expression of genetic variation), promises to be the most suitable platform for biomarker discovery. Combining such classic analytical techniques as two-dimensional gel electrophoresis with more sophisticated techniques, such as MS, has enabled considerable progress to be made in cataloguing and quantifying proteins present in urine and various kidney tissue compartments in both normal and diseased physiological states. Despite these accomplishments, there remain a number of important challenges that will need to be addressed in order to pave the way for the universal acceptance of proteomics as a clinically relevant diagnostic tool. We discuss issues related to three such critical developmental tasks as follows: (i) completely defining the proteome in the various biological compartments (e.g. tissues, serum and urine) in both health and disease, which presents a major challenge given the dynamic range and complexity of such proteomes; (ii) achieving the routine ability to accurately and reproducibly quantify proteomic expression profiles; and (iii) developing diagnostic platforms that are readily applicable and technically feasible for use in the clinical setting that depend on the fruits of the preceding two tasks to profile multiple disease biomarkers. © 2005 The Biochemical Society.|
|Source Title:||Clinical Science|
|Appears in Collections:||Staff Publications|
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