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Targeted proteomics in biomarker validation: Detection and quantification of proteins using a multi‐dimensional peptide separation strategy
Author(s) -
Immler Dorian,
Greven Simone,
Reinemer Peter
Publication year - 2006
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200500659
Subject(s) - biomarker , biomarker discovery , analyte , proteomics , computer science , quantitative proteomics , computational biology , chromatography , chemistry , biology , biochemistry , gene
Authentic biomarkers, distilling the essence of a complex, functionally significant process in a mammalian system into a precise, physicochemical measurement have been implicated as a tool of increasing importance for drug discovery and development. However, even in spite of recent technological advances, validating a new biomarker candidate, where generation of suitable antibodies is required, is still a long‐lasting task. Methods to accelerate initial validation by MS approaches have been suggested, but all methods described so far are associated with serious drawbacks, finally leading to non‐generic methods of detection and quantification. Moreover, when complex body fluids are used as samples, efficient debulking strategies are crucial to open a window of analytical sensitivity in the ng/mL range, where many diagnostically relevant analytes are present. Here we report the proof‐of‐principle of a multi‐dimensional strategy for accelerated initial validation of biomarker candidates by MS, which promises to be generally applicable, sensitive and quantitative. The method presented employs a combination of electrophoretic and chromatographic steps on the peptide level, followed by MS quantification using isotopically labeled synthetic peptides as internal standards. Our proposed workflow includes up to four dimensions, finally resulting in a desired LOD sufficient to detect and quantify diagnostically relevant analytes from complex samples. Although the current state of the method only represents a starting point for further validation and development, it reveals great potential in biomarker validation.

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