Biology Reference
In-Depth Information
Experimental Design
Metabonomics involves the analysis of differ-
ent biological matrices such as blood, urine, feces,
cerebrospinal
Experimental design
fluid, saliva, cells,
and tissues. The choice of matrix usually depends
on the research question and ease of obtaining
the sample. Because many metabonomic investi-
gations are clinically translational in nature, urine
and blood are being analyzed most frequently.
19
Being integrative bio
fluid, bile, lymph
Sample preparation
GC/MS data acquisition
uids, both blood and
urinary metabolic pro
u-
enced by physiological conditions and dietary
intake such that subtle metabolic changes related
to disease or drug-induced toxicity may be
masked. In selected cases, speci
les are dynamic and in
c tissue or a bio-
logical matrix in proximity with a speci
Data pre-processing
c tissue
may be considered.
7
Nevertheless, urinary
metabonomics remains widely adopted as it is
non-volume-restricted and offers the valuable
possibilities of monitoring time-related metabolic
perturbations.
20
Proper collection and storage of
samples have to be incorporated into the study
design, as these processes ensure sample quality
and validity of data.
4,21,22
Data processing
Model validation
Sample Preparation
Sample preparation depends on the type of
sample being analyzed and usually involves
extraction and derivatization. For serum and
plasma, which are rich in protein, protein precip-
itation by organic solvent is usually the
Screening of marker
metabolites
rst
step; for urine, which contains high concen-
trations of urea, incubation with urease is
performed to remove its chromatographic inter-
ference, which may mask low-intensity peaks
and reduce the risk of column overloading
or peak distortion; tissues are usually homoge-
nized prior to sample extraction.
4
Extraction
procedure is performed with the aim of iso-
lating the largest possible number of metabolites.
Present within the metabolome are metabolites
such as amino acids, organic acids, amines,
sugars, nucleotides, fatty acids, and steroids,
which span huge polarity and molecular weight
Metabolite
identification
Confirmation of
biomarkers
FIGURE 1
GC/MS-based metabolic profiling workflow.
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