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An automated, sheathless capillary electrophoresis‐mass spectrometry platform for discovery of biomarkers in human serum
Author(s) -
Sassi Alexander P.,
Andel Frank,
Bitter HansMarcus L.,
Brown Michael P.S.,
Chapman Robert G.,
Espiritu Jeraldine,
Greenquist Alfred C.,
Guyon Isabelle,
HorchiAlegre Mariana,
Stults Kathy L.,
Wainright Ann,
Heller Jonathan C.,
Stults John T.
Publication year - 2005
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200410127
Subject(s) - chromatography , isotachophoresis , capillary electrophoresis , chemistry , mass spectrometry , capillary electrophoresis–mass spectrometry , electrospray , analytical chemistry (journal) , electrospray ionization , pattern recognition (psychology) , artificial intelligence , computer science , electrode , electrolyte
A capillary electrophoresis‐mass spectrometry (CE‐MS) method has been developed to perform routine, automated analysis of low‐molecular‐weight peptides in human serum. The method incorporates transient isotachophoresis for in‐line preconcentration and a sheathless electrospray interface. To evaluate the performance of the method and demonstrate the utility of the approach, an experiment was designed in which peptides were added to sera from individuals at each of two different concentrations, artificially creating two groups of samples. The CE‐MS data from the serum samples were divided into separate training and test sets. A pattern‐recognition/feature‐selection algorithm based on support vector machines was used to select the mass‐to‐charge ( m/z ) values from the training set data that distinguished the two groups of samples from each other. The added peptides were identified correctly as the distinguishing features, and pattern recognition based on these peptides was used to assign each sample in the independent test set to its respective group. A twofold difference in peptide concentration could be detected with statistical significance ( p ‐value < 0.0001). The accuracy of the assignment was 95%, demonstrating the utility of this technique for the discovery of patterns of biomarkers in serum.