Development of Isotope Labeling Liquid Chromatography
Mass Spectrometry for Mouse Urine Metabolomics: Quantitative Metabolomic
Study of Transgenic Mice Related to Alzheimer’s Disease
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Abstract
Because of a limited volume of urine
that can be collected from
a mouse, it is very difficult to apply the common strategy of using
multiple analytical techniques to analyze the metabolites to increase
the metabolome coverage for mouse urine metabolomics. We report an
enabling method based on differential isotope labeling liquid chromatography
mass spectrometry (LC–MS) for relative quantification of over
950 putative metabolites using 20 μL of urine as the starting
material. The workflow involves aliquoting 10 μL of an individual
urine sample for <sup>12</sup>C-dansylation labeling that target amines
and phenols. Another 10 μL of aliquot was taken from each sample
to generate a pooled sample that was subjected to <sup>13</sup>C-dansylation
labeling. The <sup>12</sup>C-labeled individual sample was mixed with
an equal volume of the <sup>13</sup>C-labeled pooled sample. The mixture
was then analyzed by LC–MS to generate information on metabolite
concentration differences among different individual samples. The
interday repeatability for the LC–MS runs was assessed, and
the median relative standard deviation over 4 days was 5.0%. This
workflow was then applied to a metabolomic biomarker discovery study
using urine samples obtained from the TgCRND8 mouse model of early
onset familial Alzheimer’s disease (FAD) throughout the course
of their pathological deposition of beta amyloid (Aβ). It was
showed that there was a distinct metabolomic separation between the
AD prone mice and the wild type (control) group. As early as 15–17
weeks of age (presymptomatic), metabolomic differences were observed
between the two groups, and after the age of 25 weeks the metabolomic
alterations became more pronounced. The metabolomic changes at different
ages corroborated well with the phenotype changes in this transgenic
mice model. Several useful candidate biomarkers including methionine,
desaminotyrosine, taurine, N1-acetylspermidine, and 5-hydroxyindoleacetic
acid were identified. Some of them were found in previous metabolomics
studies in human cerebrospinal fluid or blood samples. This work illustrates
the utility of this isotope labeling LC–MS method for biomarker
discovery using mouse urine metabolomics