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Comprehensive analysis of small molecule metabolites (30-1500 Da)
is a challenging task for quality control. Metabolites are found in very
different concentrations in complex biological matrices, from which
they have to be extracted without compromising the structural
integrity and relative abundances. There are metabolites which are
transformed extremely rapidly if enzymatic activity is not stopped
completely at the time of sample collection, such as the ratio of the
energy metabolites ATP to ADP. Similarly, redox carriers such as NADH and NADPH are
very sensitive to oxidative degradation during sample preparation. Consequently, quality
control in metabolomics means more than just taking care of chromatographic or mass
spectrometry parameters. Quality control is an attitude towards gaining reliable data,
rather than an automatic procedure implemented in instrument software.
The first issue critical to obtaining valid metabolomic data is understanding the question
behind a study. This means that communication with the partners of the metabolomic
laboratory is an essential part of any metabolomic study. Most often, at least one other
partner will be involved in a study (e.g. another laboratory focused on understanding the
effect of a particular genetic alteration in an organ-
ism), and these partners may already have hypotheses
on specific metabolic pathways that should be pur-
sued. These hypotheses may then lead to suggestions
for analytical procedures. For example, many second-
ary metabolites are easier to analyze by LC/MS meth-
ods whereas most primary metabolites can readily be
quantified by GC/MS procedures. Therefore, commu-
nication with the partners should focus on the
chemical classes of compounds that should be target-
ed. It is also critical for the analytical laboratory to understand that unbiased analysis of
mass spectrometric data sets does not constitute metabolomics. A multivariate statistical
differentiation of ‘test’ versus ‘control’ samples is meaningless if no identified metabolites
can be reported that allow biological interpretation! Unidentified signals in metabolite
analysis are as useless as unscored peptide peaks in proteomic experiments. Metabolomics
is not a number game of detection of m/z features, but must be regarded as an extension of
classical target-driven analytical chemistry. Only if the quantification and identification of
known compounds empowers biological interpretations, can unknown peaks be further
investigated and pulled into statistical tests.
There is a fundamental problem associated with metabolomics analyses, that is, the lack
of clean up steps. If metabolomics means a comprehensive analysis of a wide range of
small molecules, varying in molecular size, functional moieties, lipophilicity, volatility, or
other physicochemical parameters, then the analytical laboratory faces tough choices. One
option is to employ a variety of fractionation steps, but this can cause biases in metabolite
coverage, require a number of different analytical procedures (raising the subsequent chal-
lenge of integrating the data sets), and also may result in analyte loss or degradation.
Alternatively, the whole extract is subjected to one or several analytical methods; howev-
er, certain matrix components may lead to deterioration of analytical quality. In such
cases, literally dirt is injected into the instrument! It is critical, therefore, to acknowledge
that each matrix type requires validation and that procedures that worked for microbial
organisms may be very inadequate formore complex samples such as blood plasma. For exam-
ple, nonvolatile material will remain in the liner and other parts of the injector in GC/MS
systems, causing problems with cross-contamination, progressing pyrolysis of material,
and ultimately the formation of adsorptive materials, or catalytically active sites, in the
injector system. Therefore, frequent liner changes are highly recommended.
Correspondingly, for LC/MS procedures, matrix components may be irreversibly
adsorbed onto stationary phases, giving rise to similar challenges as described for GC/MS.
Additionally, the soft electrospray ionization in LC/MS is a more selective or vulnerable
Continued on page 23
Metabolomics is not
a numbers game of
detection; it is an
extension of classical
target-driven
analytical chemistry.
Quality Control inMetabolomics
Oliver Fiehn, UC Davis Genome Center
Editorial
Quality Control in Metabolomics
. . . . . . . .
2
Environmental
Increase Sample Throughput for Complex
Drinking Water Pesticides
. . . . . . . . . . . . . . .
3
One Stop Shop for EPA Method 535
. . . . .
6
Breaking Down? Improve BDE-209
Response
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
Increase Polycyclic Aromatic
Hydrocarbon Sample Throughput
. . . . . .
10
Characterizing all 136 Tetra- to
Octachlorinated Dioxins and Furans
. . . .
12
Clinical/Forensics/Toxicology
Assure LC/MS/MS System Performance
for Drug Analyses
. . . . . . . . . . . . . . . . . . . . .
14
Pharmaceutical
Separating NSAIDs through
Aromatic Selectivity
. . . . . . . . . . . . . . . . . . . .
16
Bioanalytical
Easily Resolve Oxytocin PEGylation
Reaction Products
. . . . . . . . . . . . . . . . . . . . .
18
Foods, Flavors & Fragrances
Rapid Screening Method for
Carbamates in Orange Oil
. . . . . . . . . . . . . .
19
Using Thermal Desorption to
Enhance Aroma Profiling by GC/MS
. . . .
20
Tech Tip
Under Pressure? Reduce System Stress
by Backflushing your HPLC Column
. . . . .
22
Restek Trademarks
Allure, CarboPrep, Press-Tight, Resprep, Restek logo, Rtx, Rxi.
Other Trademarks
Dacthal (Amvac Chemical Corp.), API 3200 (Applied
Biosystems), Cliquid,TurboIonSpray,Turbo V (Applied
Biosystems/MDS SCIEX Instruments MDS, Inc.), Unique (Leco
Corporation), Parker (Parker Intangibles LCC Ltd.), SEQUEST
(University of Washington), Upchurch Scientific (Upchurch
Scientific, Inc.), Valco (Valco Instruments Company, Inc.), PEEK
(WhitfordWorldwide Co.)
in this issue
2008.02
Editorial