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Metabolic fingerprinting of Schistosoma mansoni infection in mice urine with capillary electrophoresis
Author(s) -
GarcíaPérez Isabel,
Whitfield Philip,
Bartlett Ann,
Angulo Santiago,
LegidoQuigley Cristina,
HannaBrown Melissa,
Barbas Coral
Publication year - 2008
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.200800031
Subject(s) - electropherogram , capillary electrophoresis , analyte , chromatography , schistosoma mansoni , urine , electrophoresis , principal component analysis , computational biology , biology , chemistry , schistosomiasis , computer science , immunology , artificial intelligence , biochemistry , helminths
Schistosoma mansoni infection in mice has been fingerprinted using CE to study the capabilities of this technique as a diagnostic tool for this parasitic disease. Two modes of separation were used in generating the electrophoretic data, with each untreated urine sample the following methods were applied: (i) a fused‐silica capillary, operating with an applied potential of 18 kV, in micellar EKC (MEKC) and (ii) a polyacrylamide‐coated capillary, operating with an applied potential of −20 kV under zonal CZE conditions. By combining normal and reverse polarities in the data treatment we have extracted more information from the samples, which is a better approach for CE metabolomics. The traditional problems associated with variability in electrophoretic peak migration times for analytes were countered by using a dynamic programming algorithm for the electropherograms alignment. Principal component analyses of these aligned electropherograms and partial least square discriminant analysis (PLS‐DA) data are shown to provide a valuable means of rapid and sample classification. This approach may become an important tool for the identification of biomarkers, diagnosis and disease surveillance.