View
78
Download
2
Category
Tags:
Preview:
DESCRIPTION
My collection for metabolomics articles.
Citation preview
ORIGINAL ARTICLE
Development of a gas chromatography/mass spectrometry basedmetabolomics protocol by means of statistical experimental design
Anders P. H. Danielsson • Thomas Moritz •
Hindrik Mulder • Peter Spegel
Received: 11 August 2010 / Accepted: 28 January 2011 / Published online: 11 February 2011
� Springer Science+Business Media, LLC 2011
Abstract Metabolomics is a growing research field
where new protocols are rapidly developed and new
applications discovered. Common applications include
biomarker discovery and elucidation of drug metabolism.
However, the development of such protocols rarely
includes a systematic optimization followed by validation
with real samples. Here a GC/MS-based protocol using
methoximation followed by silylation with N-tert-butyldi-
methylsilyl-N-methyltrifluoroacetamide (MTBSTFA) for
analysis of blood plasma metabolites is thoroughly devel-
oped and optimized from derivatization to detection with
statistical design of experiments (DOE). Validation was
performed with blood plasma samples and proved the
methodology to be efficient, rapid and reliable with a total
of 51 analyses performed in 24 h, with linear responses,
low detection limits and good precision. The obtained
chromatograms were much cleaner, due to the absence of
glucose overloading, and the data was found to drift less
with MTBSTFA derivatisation than with MTBSTFA
derivatisation.
Keywords Metabolomics � Gas chromatography �Mass spectrometry � MTBSTFA � Blood plasma � DOE
1 Introduction
Metabolomics aims for an unbiased quantification of all
metabolites in a biological sample. As the metabolite levels
reflects both genetic and environmental effects, metabolo-
mics is an exceptionally useful technique for discovery of
potential biomarkers for environmental factors, medical
treatments, and diseases (Chorell et al. 2009; Fiehn 2002).
However, the metabolome comprises a very large and
heterogeneous group of metabolites with concentrations
differing several orders of magnitude. Thus, great care
must be taken in the development of metabolomics meth-
ods to attain a wide selectivity, high efficiency and high
sample throughput while at the same time avoid introduc-
ing excessive biases.
To date, no single analytical method exists that is
capable of simultaneously measuring all members of the
metabolome. Several techniques, including nuclear mag-
netic resonance spectroscopy (NMR) (Zhang et al. 2008),
near-infrared spectroscopy (NIR) (Cozzolino et al. 2006),
gas chromatography (GC) (Fiehn 2008; Jiye et al. 2005),
liquid chromatography (LC) (Zelena et al. 2009), and
capillary electrophoresis (CE) (Lapainis et al. 2009), with
the latter three being coupled to mass spectrometry (MS),
have been applied in metabolomics. Out of these tech-
niques, the combination of a chromatographic separation
with mass spectrometric detection offers a somewhat
higher sensitivity than the pure spectroscopic techniques,
although sample preparation generally becomes more
complicated. Among these techniques, GC/MS, applied in
the present investigation, offers the highest separation
Electronic supplementary material The online version of thisarticle (doi:10.1007/s11306-011-0283-6) contains supplementarymaterial, which is available to authorized users.
A. P. H. Danielsson � H. Mulder � P. Spegel (&)
Unit of Molecular Metabolism, CRC 91:12, Entrance 72,
UMAS, Lund University Diabetes Centre, Clinical Research
Centre, SE-205 02 Malmo, Sweden
e-mail: Peter.spegel@med.lu.se
A. P. H. Danielsson
Analytical Chemistry, Faculty of Engineering LTH,
Lund University, Lund, Sweden
T. Moritz
Umea Plant Science Center, Swedish University of Agricultural
Sciences, Umea, Sweden
123
Metabolomics (2012) 8:50–63
DOI 10.1007/s11306-011-0283-6
efficiency and also yields the cleanest chromatograms due
to the absence of interfering peaks from peptides, proteins
and other large non-volatile substances.
Metabolite derivatisation is an important element of GC/
MS-based metabolomics. Great care must be taken in the
development of derivatisation conditions, as it will influ-
ence both the limit of detection (LOD), the sensitivity, and
the selectivity of the method. The most common derivati-
zation procedure applied to GC/MS-based metabolomics
involves a two-step reaction, comprising a methoximation
step and a silylation step (Gullberg et al. 2004). Methoxi-
mation reduces the number of sugar tautomers, thus
reducing the number of peaks generated from a single
sugar metabolite and thereby enhances both separation and
quantification (Asres and Perreault 1997; Fiehn et al. 2000;
Schweer 1982). Additionally, methoximation protects
a-ketoacids from decarboxylation (Fiehn et al. 2000;
Tam and Normanly 1998). The silylation reagent most
commonly applied in metabolomics, N-methyl-N-tri-
methylsilyltrifluoroacetamide (MSTFA), yields volatile
trimethylsilyl (TMS) derivatives of a vast number of
metabolites including large multifunctional metabolites
such as sugars and their derivatives (Begley et al. 2009;
Danielsson et al. 2010; Gullberg et al. 2004). Besides
MSTFA, N-tert-butyldimethylsilyl-N-methyltrifluoroaceta-
mide (MTBSTFA), has also been applied in metabolomics.
The large size of this silylation reagent reduces both the
yield and the volatility of glucose resulting in a complete
elimination of glucose and other large carbohydrates from
the analysis. In blood plasma analysis, glucose is found at
high concentrations which generally severely overloads the
column and complicating detection and quantification of
other metabolites eluted in the same retention interval.
Furthermore, the tert-butyldimethylsilyl (TBDMS) deriv-
atives have been shown to offer both improved hydrolytic
stability and improved thermal stability over the corre-
sponding TMS derivatives (Rodrıguez et al. 2003; Yu et al.
2007) and generate an intense [M-57]? mass fragment
(Fiehn et al. 2000) providing additional information for
identification.
Although MTBSTFA derivatization has several unique
features in comparison to MSTFA derivatization in meta-
bolomics analysis, the derivatization and chromatographic
parameters for this derivatization reagent have not yet been
thoroughly optimized. In contrast, there are several devel-
oped metabolomics protocols based on MSTFA (Begley
et al. 2009; Danielsson et al. 2010; Gullberg et al. 2004;
Jiye et al. 2005). These protocols are aimed at plant cells
(Gullberg et al. 2004), adherent cell cultures (Danielsson
et al., 2010) and blood plasma (Begley et al. 2009; Jiye
et al. 2005).
In the present investigation, a metabolomics protocol for
blood plasma analysis based on methoximation and
MTBSTFA derivatization followed by GC/MS is devel-
oped and optimized using statistical design of experiments
(DOE) (Araujo and Brereton 1996a, b, c). DOE is a multi-
variate statistical optimization tool that enables efficient
experimental planning and identification of both linear
effects, interaction effects and higher order non-linear
effects with a minimum number of experiments. The two-
step derivatization method, injection onto the GC, the
chromatographic settings and the mass spectrometer set-
tings were optimized aiming at enhancing the limit of
detection (LOD), the sample throughput and the peak
capacity. Finally, the developed method was applied to a
set of blood plasma samples, and the performance and
validity of the developed protocol was assessed.
2 Experimental
2.1 Chemicals
Methoxyamine hydrochloride, N-tert-butyldimethylsilyl-
N-methyltrifluoroacetamide and N-methyl-N-trimethylsi-
lyltrifluoroacetamide were from Aldrich (Steinhein,
Germany). Pyridine, heptane, methanol, methyl stearate and
the alkane standard mixtures (C8–C20 and C21–C40) were
from Fluka (Buchs, Switzerland). The stable isotope-labeled
internal standards; 13C3–15N alanine, 13C4-succinate, 13C6–
phenylalanine, 13C3-serine, 2H7-cholesterol, 13C16-palmitate
and 13C5-a-ketoisovalerate were from Cambridge Isotope
Laboratories, Inc. (Andover, MA). The stable isotope-
labeled standards; 13C9–15N-tyrosine and 13C18-oleate were
from Isotec (Sigma-Aldrich, St. Louis, MA). Water was
purified using a Purelab Ultra water purification system
(Elga, Gothenburg, Sweden). The metabolites used in the
study were all purchased from Sigma-Aldrich.
2.1.1 Blood samples
The subjects, four males and two females, were all Cauca-
sian and non-obese (body mass index 22.4 ± 2.4 ranging
from 18 to 24). They were 33.8 ± 6.9 (ranging from 28 to
46) years of age, non-smokers and all healthy. None were
taking any prescribed medication. Blood samples were
drawn after an overnight fast and the subjects were all sitting
down during the whole procedure. Blood samples were
immediately centrifuged at ?4�C and plasma was separated.
The blood plasma was subsequently pooled and frozen on
dry ice. The samples were kept at -80�C until analysis.
2.2 Instrumentation
GC/MS was performed on an Agilent 6890N gas chro-
matograph (Agilent, Atlanta, GA) equipped with an
Development of a Metabolomic Protocol 51
123
Agilent 7683B autosampler (Agilent) and coupled to a
Leco Pegasus III TOFMS electron impact, time-of-flight
mass spectrometer (Leco Corp.). Two columns were used
in the study, a 10 m (ID 180 lm, phase thickness 0.18 lm)
and a 30 m (ID 250 lm, phase thickness 0.25 lm), both
DB5-MS (J&W Scientific, Folsom, CA). The initial
method employed a splitless injection with the injector
temperature set to 270�C with a purge vent time of 60 s.
The flow rate through the column was 1 ml/min, with a
three-step temperature program starting with an initial
isocratic temperature of 70�C kept for 2 min, followed by a
gradient rate of 30�C/min reaching a final temperature of
320�C kept for a duration of 5 min. The ionization voltage
was set at 70 eV and the mass spectra were recorded
between 50 and 800 m/z, throughout the study. Data were
acquired using the Leco ChromaTof software v. 3.31 (Leco
Corp.). Retention indexes (RIs) were calculated based
on the elution times of a homologous series of n-alkanes
(C8-C40).
2.3 Metabolite cocktail
36 metabolite standards were derivatized and analyzed
individually to construct a database (NIST MS Search 2.0)
containing retention indexes and mass spectra. The
metabolites were selected to cover a broad range of func-
tionalities, metabolic pathways, and retention indexes.
Next, a polar and a non-polar model mixture were created
from the 36 metabolite standards, isotope labeled internal
standards and methyl stearate. The combination of both
these standard mixtures is herein referred to as the
metabolite cocktail (Table 1) and is used throughout the
model optimizations conducted in this paper.
2.4 Raw data pre-treatment and experimental designs
Peak integration was performed either directly in Leco
ChromaTof 3.31 or after export as NetCDF files to
MATLAB 7.0 (Mathworks, Natich, MA) using a hierar-
chical multivariate curve resolution (H-MCR) script
(Jonsson et al. 2006). Peak identification was performed
with NIST MS Search 2.0 using mass spectra and retention
index from several libraries. For the TBDMS derivatives
the database constructed here was used and all metabolites
were quantified using only their [M-57]? mass fragment
(Table 1). For the TMS derivatives, the NIST mass spectra
library, a library developed at the Max Planck Institute in
Golm (http://csbdb.mpimp-golm.mpg.de/), and libraries
developed in house, both at Umea Plant Science Centre
(UPSC) and Lund University Diabetes Centre (LUDC)
were used.
Experimental designs were created in ModdeTM 8.01
(Umetrics, Umea, Sweden). All responses were centered
and scaled to unit variance (UV) and projections to latent
structures (PLS) were used to calculate the models. Prior to
evaluation, the models were optimized in a five-step
procedure aimed at reaching the highest possible cross-
validated predictive power (Q2Y), which compares the
cross-validated residuals to the total residual for the model
(Wold 1978). In the first step, normalization, when appli-
cable, of the responses was performed. In the second step,
responses deviating from the normal distribution were
transformed, to avoid erroneous influence on the model. In
the third step, responses with a replicate variance larger
than half of the total variance of the response were con-
sidered irreproducible and were therefore deleted. In the
fourth step responses exhibiting considerable lack-of-fit,
i.e. responses where the model error variance was equal or
larger than the replicate variance, were deleted. In the fifth
and final step, linear factors, factor interactions and qua-
dratic factors which reduced Q2Y were identified. These
factors and interactions that mainly modeled noise were
considered insignificant for the model and consequently
deleted. The models were evaluated from coefficient and
contour plots.
Orthogonal projections to latent structures (OPLS) was
performed on centered and UV-scaled data in Simca P?
12.0 (Umetrics, Umea, Sweden).
2.5 Derivatization design
A D-optimal design with a quadratic model consisting of 8
factors was constructed to explore the MTBSTFA deriva-
tization conditions using the metabolite cocktail as sample
(Table 2). Specifically, the temperature, duration and sol-
vent composition used for both methoximation and silyla-
tion were varied. The aim was to obtain the maximum yield
of metabolite derivatives, and minimum number of arti-
facts in the shortest possible derivatization time.
In total 45 runs were performed including three center
point runs. The responses were the peak areas for the
metabolites in the metabolite cocktail, normalized to the
peak area of the underivatizable methyl stearate, and
the number of artifact peaks detected by Leco ChromaTof
in the chromatograms. The artifact peaks were quantified as
the number of non-metabolite peaks between the first and
last eluted metabolite.
2.6 Injection design
A three-level full factorial design with a quadratic model
was generated to find the optimum settings for the injector
using the metabolite cocktail as sample (Table 2). The
design included 2 factors and the responses consisted of the
peak areas of the metabolites in the cocktail. The injection
volume was left unchanged at 1 ll throughout the study, to
52 A. P. H. Danielsson et al.
123
Table 1 Metabolite and isotope
labeled standard derivatives
investigated in the present study
with their [M-57]? fragments
and retention indexes (RIs)
a Underivatizable standardb Quantification mass
Analyte Abbreviation RI [M-57]?
Pyruvate MEOX TBDMS PYR 1254.7 174
2-ketobutyrate MEOX TBDMS 2KBA 1305.5 18813C5-a-ketoisovalerate MEOX TBDMS peak1 AKIX1 1321.6 20713C5-a-ketoisovalerate MEOX TBDMS peak2 AKIX2 1336 207
a-ketoisovalerate MEOX TBDMS peak1 AKI1 1321.6 202
a-ketoisovalerate MEOX TBDMS peak 2 AKI2 1336 202
Lactate 2TBDMS LAC 1493.1 261
Alanine 2TBDMS ALA 1541.4 26013C3-15N alanine 2TBDMS ALAX 1541.5 264
Glycine 2TBDMS GLY 1562.1 246
Valine 2TBDMS VAL 1662.4 288
Leucine 2TBDMS LEU 1702.1 302
Isoleucine 2TBDMS ILE 1735.7 302
Threonine 2TBDMS THR2 1747.5 29013C4-succinate 2TBDMS SUCX 1761.1 293
Succinate 2TBDMS SUC 1761.1 289
Proline 2TBDMS PRO 1775.6 286
Undecanoate TBDMS FA11 1783.8 243
Dodecanoate TBDMS (Laurate) FA12 1884.3 257
Trans-4-hydroxyproline 2TBDMS T4HP2 1970.1 302
Methionine 2TBDMS MET 1976.2 320
Serine 2TBDMS SER 1998.5 390
Alpha ketoglutarate MEOX 2TBDMS AKG 2011.7 346
Threonine 3TBDMS THR3 2032.4 404
Phenylalanine 2TBDMS PHE 2103.5 336
Malate 3TBDMS MAL 2117.8 419
Methyl Stearatea MEST 2134.5 298b
Trans-4-hydroxyproline 3TBDMS T4HP3 2196 416
Cysteine 3TBDMS CYS 2217.1 406
Phosphoenolpyruvate 3TBDMS PEP 2239.6 453
Hexadecanoate TBDMS (Palmitate) FA16 2288.3 313
Heptadecanoate TBDMS (Margarate) FA17 2389.5 325
Glyceraldehyde 3-phosphate MEOX 3TBDMS GA3P 2346.5 484
Dihydroxyacetone phosphate MEOX 3TBDMS peak1 DHAP1 2370.2 484
Dihydroxyacetone phosphate MEOX 3TBDMS peak2 DHAP2 2390.2 484
Lysine 3TBDMS LYS 2390.4 431
(9Z)-Octadec-9-enoate TBDMS (Oleate) FA18U 2468.6 339
Histidine 3TBDMS HIS 2609.6 440
Citrate 4TBDMS CIT 2632.2 591
Isocitrate 4TBDMS ISOC 2647.2 591
3-Phosphoglycerate 4TBDMS 3PGA 2647.6 585
Tryptophan 2TBDMS TRP 2708.9 375
Serotonin 2TBDMS SERO 2720.5 347
N-Acetyl 5-hydroxytryptamine TBDMS NAHT 2775.7 275
Tricosanoate TBDMS FA23 2999.2 411
L-5 Hydroxytryptophan 3TBDMS L5HT 3187.6 505
Cholesterol TBDMS CHO 3493.8 443
Development of a Metabolomic Protocol 53
123
minimize liner contamination that is expected from
excessive injection of non-metabolite matrix from complex
biological samples.
2.7 Gas chromatography design
A three-level central composite design (CCF) with a qua-
dratic model was calculated for each column. The two
models were then merged to facilitate the interpretation.
For each column 5 factors and 8 responses were investi-
gated using the metabolite cocktail as sample (Table 2).
The aim was to optimize the separation efficiency, peak
symmetry, detection limits and analysis time. To achieve
this, all parameters of the temperature program were
optimized, including the initial temperature and its duration
and the temperature gradient rate. The final temperature
and its isocratic duration were maintained at 320�C and
5 min, respectively, due to their insignificant influence on
the selected responses. In addition, also the volumetric flow
rate was varied to minimize band broadening and hence
optimize the separation efficiency.
The injector purge vent time was included also in this
design to allow for the investigation of sample loading
effects on the chromatographic performance.
To obtain a representative response for the separation
efficiency, the peak capacity was calculated from the peak
width at 10% of the peak height (w0.1) for three analytes
spanning a broad retention window, valine 2TBDMS,
Table 2 Summary of the models for derivatization and GC/MS optimization
Study Design and model Factors Low High Responses
Methoximation D-optimal design, quadratic model Amount Acetonitrile
Methoximation (MAC)a0% 25% Peak areas
Artifact peaks
Methoximation Temperature
(MTE)
20�C 80�C
Methoximation Duration
(MDU)
1 h 17 h
Silylation Amount Heptane in Silylation
(SHP)b0% 75%
Amount Acetonitrile in
Silylation (SAC)b0% 75%
Amount MTBSTFA in
Silylation (STB)b25% 100%
Silylation Duration (SDU) 0.5 h 4 h
Silylation Temperature (STE) 20�C 100�C
Injection Three-level full factorial design,
quadratic model
Injector Temperature (IJT) 200�C 320�C Peak areas
Injector Purge Vent Time
(PVT)
5 s 115 s
Chromatography Central composite face (CCF) design,
quadratic model
Injector Purge vent time (PVT) 45 s 115 s Peak capacity for valine (PxVc)
Initial Gradient Temperature
Duration (ITD)
2 min 6 min Peak capacity for cholesterol
(PxCc)
Initial Gradient Temperature
(ITE)
60�C 90�C Peak capacity for (9Z)-Octadec-
9-enoate (PxOc)
Temperature Gradient Rate
(TGR)
10�C/
min
40�C/
min
Average peak capacity (PxMc)
Volumetric Flow Rate (VFR) 1 ml/
min
3 ml/
min
Total analysis time (Txc)
Sample throughput per 24 h
(STxc)
Asymmetry factor for valine
(AxVc)
Peak height for cholesterol
(HxC)c
Mass
spectrometry
Three-level full factorial design,
quadratic model
Data Acquisition rate (ACQ) 10 Hz 50 Hz [M-57] ? Peak area
Ion source temperature (IST) 130�C/
min
250�C/
min
Ratio of low and high m/z-
fragment
a Pyridine is used as the additional solvent in the methoximation stepb Formulation factorc x refers to the column length and has a value of either 10 or 30 m
54 A. P. H. Danielsson et al.
123
oleate TBDMS and cholesterol TBDMS, representing the
early, intermediate and late portion of the chromatogram,
respectively, according to Eq. 1. (Pous-Torres et al. 2008)
Pc ¼ 1þtrðchoÞ � trðvalÞ
w0:1ð1Þ
The elution times for cholesterol TBDMS (tr(chol)) and
valine 2TBDMS (tr(val)) defined the retention window.
Additionally an average peak capacity was calculated.
The asymmetry factor (Kirkland, 1977) (As) for valine
2TBDMS was included as a response as this peak was
observed to suffer from asymmetric band broadening,
whereas this type of peak distortion was weaker or absent
for later eluting metabolites. As was determined according
to Eq. 2,
As ¼B10%
A10%ð2Þ
where A10% and B10% are the distances at 10% of the peak
height, measured from a line perpendicular to the baseline
from the peak apex, to the peak front and back respectively.
The noise level was not found to be significantly
affected by the parameter settings and therefore the peak
height of cholesterol TBDMS was included as a response
reflecting the method detection limit. Cholesterol TBDMS
was chosen as it is the last eluted metabolite derivative in
our cocktail and should therefore be most affected by the
longitudinal band broadening which is of primary concern
in GC separations.
2.8 Mass spectrometer design
A three-level full factorial design with a quadratic model
was generated to find the optimal settings of the mass
spectrometer using the metabolite cocktail as sample
(Table 2). The responses studied were the area of the
[M-57]? fragment peak and the ratio between the [M-57]?
fragment peak area and a low weight high intensity frag-
ment peak area. Peak areas of the [M-57]? response were
normalized using the [M-57]? peak area showing a close to
zero reproducibility as it was assumed that for such a peak
the instrumental variability considerably overpowered the
systematic variation caused by the varied factors.
2.9 Method validation
Pooled blood plasma from 6 healthy individuals was
diluted to relative concentrations of 0.1, 0.2, 0.4, 0.6, 0.8
and 1.0 (v/v, plasma/plasma ? water). A total of 12 mea-
surements from four preparations measured in triplicates
were performed at each concentration to assess both
instrumental and preparation variability. The total volume
of diluted plasma in each extraction tube was 100 ll. Prior
to extraction 3.75 lg of each stable isotope labeled stan-
dard were added to each of the 24 tubes. Extraction of the
metabolites was performed according to a previously
developed protocol (Jiye et al. 2005). To each tube 900 ll
methanol/water mixture (8:1 v/v) was added and the sam-
ples were rapidly mixed and kept on ice for 10 min. All
tubes were then vigorously extracted using a multitube
vortexer (VX-2500 Multi Tube Vortexer, VWR, West
Chester, PA) operating at 30 Hz for 3 min. Subsequent the
extraction tubes were centrifuged for 10 min at 175309g at
4�C. 200 ll of the supernatant was then transferred to a GC
vial and evaporated to dryness (miVac Duo concentrator;
Genevac, Ipswich, UK). Samples were derivatized and
analyzed with the optimized protocol developed here.
The results were evaluated with regards to linear range,
intra-day precision, limit of detection (LOD) and limit of
quantification (LOQ). The peak areas were normalized to
the peak areas of the corresponding stable isotope labeled
standard, if available. If no corresponding stable isotope
labeled standard was available, a stable isotope-labeled
standard with similar properties was selected (only per-
formed for determination of linear range and precision).
From the normalized peak areas, a linear model was cre-
ated and analyzed in Modde 8.0.1. The models were tested
for lack-of-fit and also cross-validated to thoroughly
determine the linear range. The precision was calculated as
the average relative standard deviation (RSD) for 10
sample runs at 2 different concentrations; 0.1 and 0.4 (v/v,
plasma/plasma ? water).
The LOD and LOQ were estimated as 3 and 10 times,
respectively, the standard deviation for the signal-to-noise
ratio (S/N) for a 10 times diluted sample of pooled blood
plasma.
2.10 Sample drift comparison
Unnormalized data from the method validation runs were
further analyzed using OPLS to relate the peak area to the
sample run order. An OPLS model was accordingly created
for blood plasma samples derivatized with MSTFA
according to a protocol developed elsewhere (Jiye et al.
2005) aiming at comparing the drift pattern using these two
derivatization protocols.
3 Results and discussion
3.1 Derivatization
Initially, a PLS model was calculated from the normalized
peak areas of all metabolites in the cocktail. In the loading
plot the metabolites were found to cluster roughly
Development of a Metabolomic Protocol 55
123
according to their chemical functionality. However, the
model predictive power, Q2Y, was poor which probably
was due to the heterogeneity of the responses. Guided by
the chemical functionality of the metabolites and their
positions in the loading plot, groups of metabolites show-
ing similar behavior were constructed and modeled. All
models are summarized in Supplementary material S1.
From the linear effects of the three studied methoxi-
mation step factors, it was obvious that the settings of the
methoximation duration and temperature had the largest
influence on the derivatization yield (Supplementary
material S1). For most metabolites, the methoximation
duration and temperature were negatively correlated to the
peak areas. Furthermore, the methoximation temperature
was positively correlated to the number of artifact peaks.
Generally, addition of acetonitrile to the methoximation
solution did not improve the yield.
Next, the linear effects in the silylation step were
investigated. It was found that the most influential factors
were the silylation duration and temperature. The silylation
temperature was positively correlated with 3-phospho-
glycerate 4TBDMS and several amine containing analytes,
whereas it was negatively correlated with succinate
2TBDMS, containing two carboxylic acid functionalities.
The silylation duration was positively correlated with
cysteine 4TBDMS, cholesterol TBDMS and serotonin
2TBDMS but was negatively correlated with isocitrate
4TBDMS, lactate 2TBDMS, succinate 2TBDMS and fatty
acid TBDMS esters. Thus, a low silylation time and tem-
perature was beneficial for carboxylic acids, whereas the
opposite improves the yield of amines. The composition of
the silylation solvent was generally insignificant.
Next, the contour plots were evaluated to characterize the
effects of significant factor interactions and quadratic
effects. For the modifications of the methoximation solvent,
the a-keto acids pyruvate MEOX TBDMS, 2-ketobutyrate
MEOX TBDMS and a-ketoglutarate MEOX 2TBDMS
showed a negative correlation to the interaction between the
added amount of acetonitrile and the methoximation tem-
perature. A moderate temperature of 40–55�C and no ace-
tonitrile was optimal. Interestingly, the amount of
acetonitrile in the methoximation solvent also showed a
dependence on the silylation temperature for both leucine
2TBDMS and the number of artifact peaks. The yield for
leucine 2TBDMS and the number of artifacts increased with
a low fraction of acetonitrile and a high silylation temper-
ature. Only trans-4-hydroxyproline 3TBDMS benefitted
from a high fraction acetonitrile. Although there are a few
benefits of using more complex reaction mixtures, the
absence of strong effects motivates their removal as a much
simplified method will be the result. Therefore, the amount
of pyridine in the methoximation step and the amount of
MTBSTFA in the silylation step was set to 100%.
The interaction between the methoximation time and
temperature was significant for numerous metabolites and
the contour plots generally showed that the yield was
Fig. 1 Contour plots describing
the effect of methoximation
temperature and duration on the
normalized peak areas (as
numerical values in the plots)
for (a) 2-ketobutyrate MEOX
TBDMS, (b) a-keto isovalerate
MEOX TBDMS,
(c) phosphoenolpyruvate
3TBDMS, and (d) histidine
3TBDMS. These contour plots
clearly illustrate the complexity
involved in optimizing a
derivatization method for
metabolome analyses
56 A. P. H. Danielsson et al.
123
increased with settings in the lower range for both factors.
However, there were some exceptions. As noted above the
a-keto acids are sensitive to the settings of methoximation
temperature and only a-ketoisovalerate MEOX TBDMS
benefited from a lower setting while the rest in this group
had local maxima for the methoximation temperature in
the range of 35–70�C (Fig. 1a, b). Phosphoenolpyruvate
3TBDMS followed the general trend with a substantial
decrease in peak area with higher settings of the methox-
imation time and temperature (Fig. 1c). Even so, the
methoximation temperature was set to room temperature,
approximately 20�C, which was optimal for most metab-
olites and resulted in only moderate deviations from the
optimum yield for some of the a-keto acids.
With respect to the methoximation settings, the majority
of metabolites benefited from a short duration, whereas two
amino acids with amine-containing side chains, histidine
3TBDMS (Fig 1d) and tryptophan 2TBDMS, were found
to have maxima between 5 and 10 h. The settings of the
methoximation step has previously been shown to affect
also metabolites lacking aldehyde or ketone functionality.
(Gullberg et al. 2004) Speculatively, this effect may be
related to the solubility of the amino acids in the deriva-
tization solvents and reagents. The methoximation duration
was set to 4 h, which allowed for a sufficient yield also for
histidine 3TBDMS and tryptophan 2TBDMS.
Evaluation of the interactions of the silylation temper-
ature with other factors supports the employment of a high
silylation temperature. This factor was therefore set to
100�C, which is within the range of previous studies that
have reported 60–120�C (Birkemeyer et al. 2003; Buscher
et al. 2009; Ewald et al. 2009; Fiehn et al. 2000). The
silylation duration was set to 170 min, which allowed for
sufficient yield of amines, carboxylic acids and cholesterol
TBDMS.
Quantification of metabolites yielding several different
derivatives is difficult as the derivatives may have very
different properties and the ratio between derivatives may
vary with time (Kanani and Klapa 2007). However, of all
investigated metabolites, only two generates multiple
derivatives in the form of unsilylated and monosilylated
amines. The levels of the unsilylated amines were very low
and likely not affecting the quantification of these amino
acids to any greater extent. For the same reason, these
derivatives could not be accurately modeled due to their
low signal-to-noise ratios. With MSTFA, monosilylated
and disilylated amines are commonly observed. The partial
derivatization of threonine and trans-4-hydroxyproline
observed in the present investigation is probably caused by
steric hindrance from hydroxyl bound TBDMS which
decreases the yield of derivatization of the amine group.
The present models support this order of derivatization,
with carboxylic acids generally reacting fastest, followed
by hydroxyls and amines. Thus, in comparison with TMS
derivatization, TBDMS derivatization may reduce both
the formation of multiple derivatives and reduce the
-0,8
-0,6
-0,4
-0,2
-0,0
0,2
0,4
0,6
0,8
-0,2 0,0 0,2 0,4 0,6 0,8 1,0
wc[
2]
wc[1]
IJT
PVT
PVT*PVT
IJT*PVT
Fig. 2 PLS loading plot from
the injection study, showing the
influence of the factors (filledtriangle) and factor interactions
(filled diamond) on the
metabolite derivatives peak
areas (filled circle). The left
contour plot shows the behavior
of the thermally unstable
phosphoenolpyruvate 3TBDMS
and the right contour plot shows
the behavior of the late eluting
cholesterol TBDMS.
PVT = purge vent time and
IJT = injector temperature
Development of a Metabolomic Protocol 57
123
desilylation rate, due to its higher resistance to hydrolysis
(Rodrıguez et al. 2003; Yu et al. 2007).
3.2 Injection
A PLS model was calculated using all metabolite peak
areas as responses. The model yielded a total explained
variation of 95% (R2Y = 0.954) and a cross-validated
predictive power of 93% (Q2Y = 0.934) with 0.846 B
R2Y B 0.982 and 0.933 B Q2Y B 0.967 for all individual
responses (Supplementary material S2).
The loading scatter plot for the first two PLS compo-
nents (Fig. 2), the contour plots and the coefficient plot for
each of the analytes showed that, for all analytes the peak
area was positively correlated with the purge vent time. At
this point, an injector purge vent time of 115 s was chosen
as optimum and possible column overloading effects will
be assessed in the optimization of the GC-settings.
The effect of increased injector temperature was for
most analytes positively correlated to the yield and there
was also a clear trend towards an increasingly positive
effect of injector temperature with increasing retention
time. For the three latest eluting analytes, having
low volatility, the injector temperature was in fact the
dominating effect. However, for phosphoenolpyruvate
3TBDMS, threonine 2TBDMS and trans-4-hydroxyproline
2TBDMS, an increase in injector temperature decreased
the peak areas, suggesting a degradation of these com-
pounds at higher temperatures. This is illustrated in Fig. 2
for phosphoenolpyruvate 3TBDMS in comparison to cho-
lesterol TBDMS. For these amino acids only the partially
silylated 2TBDMS derivatives demonstrated this behavior
in contrast to their more abundant 3TBDMS derivatives,
clearly showing the improved thermal stability of silylated
amines over unsilylated amine.
The difference in yield in the investigated temperature
interval was for some analytes very large. Increasing the
injector temperature from 200 to 320�C increased the
peak area of cholesterol TBDMS and decreased the peak
area for phosphoenolpyruvate 3TBDMS with 90 and
80%, respectively. An injector temperature of 270�C was
found being a good compromise, resulting in an
approximately 45% reduction in peak area of these two
analytes with the remaining metabolites close to their
maximum values.
3.3 Chromatography
A PLS model was calculated, displaying a total explained
variation of 93% (R2Y = 0.932) and a cross-validated
predictive power of 88% (Q2Y = 0.884) with 0.719 B
R2Y B 0.989 and 0.564 B Q2Y B 0.979 for all studied
responses (Supplementary material S3).
It was found that an increase in injector purge vent time
greatly reduced the peak capacity for the shorter column,
whereas the effect on the longer column was much smaller
(Fig. 3). The main factors affecting the peak asymmetry
factor on the 10 m column were purge vent time and flow
rate, whereas these factors were insignificant on the 30 m
column. It was also observed that the range for the peak
asymmetry factor (Kirkland 1977) calculated for valine
2TBDMS was much greater on the 10 m column (0.4 B
As B 1.4) than on the 30 m column (0.7 B As B 1.2). The
peak height of cholesterol TBDMS was increased for both
columns primarily by an increase in temperature gradient
rate and/or purge vent time. However, improving this
response by increasing these parameters would severely
reduce the peak capacity and yield asymmetric peaks,
especially on the shorter column. Although the 10 m col-
umn may generate faster analyses, only a 17% increase in
the number of analyses in 24 h was obtained compared to
the 30 m column.
Fig. 3 Contour plots describing the influence of the injector purge
vent time and temperature gradient rate on the average peak capacity
for the 30 m column (a) and 10 m column (b). All factors not
presented in the plots are assigned center values
58 A. P. H. Danielsson et al.
123
Due to a greatly higher average peak capacity, superior
peak symmetry and decreased sensitivity to the factor
settings only the 30 m column was further optimized. The
purge vent time was set to 115 s, to optimize the chro-
matography for a high sample load.
The flow rate and the gradient rate affected both the
analysis time and the peak capacity. The effect of the flow
rate on the analysis time was moderate whereas its effect
on peak capacity was very pronounced. The flow rate was
consequently set to the lower setting of 1 ml/min yielding
high peak capacity and only slightly slower analysis.
The interaction between the temperature gradient rate
and the injector purge vent time was significant for the
peak capacity. At higher gradient rates, an increased purge
vent time decreases the peak capacity, whereas at a gra-
dient rate lower than 25�C/min, this effect is much less
pronounced. The gradient rate was consequently set to
25�C/min.
Increasing the initial temperature duration decreased the
peak capacity for the early portion of the chromatogram
while it was increased for the late portion of the chromato-
gram, suggesting an influence on the sample zone focusing.
However, as the average peak capacity was unaffected, the
initial isothermal duration was set to the lowest investigated
value, 2 min to increase the sample throughput.
The initial temperature was negatively correlated with
analysis time and the peak capacity for the early portion of
the chromatogram, but was positively correlated with the
peak capacity for the late portion of the chromatogram. At
the highest investigated temperature, 2 min could be cut off
the analysis time due to decreased retention during the initial
isothermal period. The interaction between the initial tem-
perature and the temperature gradient rate was significant
for the analysis time, but the contour plot showed that at the
chosen gradient rate of 25�C/min, the setting of the initial
temperature was unimportant. The interaction between the
initial temperature and the purge vent time was significant
for peak asymmetry for valine 2TBDMS, but as the peak
asymmetry factor for valine 2TBDMS was acceptable
regardless of the settings, this effect was not further con-
sidered. The same interaction also reduced the peak capacity
for the intermediate and late portion of the chromatogram,
whereas it was insignificant for the early part. A low setting
of the initial temperature could therefore be motivated.
In conclusion, the 30 m capillary column was optimal
together with a 115 s purge vent time, a flow rate of 1 ml/min,
an initial temperature of 60�C, an initial isothermal time of
2 min, and a gradient rate of 25�C/min. With these settings,
51 analyses, including an oven equilibration time of 10 min,
can be performed in 24 h. In comparison, the maximum
number of analyses achievable with the 30 m column is 68.
However, with the fastest method the average peak capacity is
severely decreased to 119 compared to the proposed method
with which a peak capacity of 220 is achievable.
-0,8
-0,6
-0,4
-0,2
-0,0
0,2
0,4
0,6
0,8
-0,2 -0,1 -0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0
wc[
2]
wc[1]
IST
Acq
Acq*Acq
IST*Acq
Late eluting
Early eluting
Fig. 4 PLS loading plot of the
model for the normalized peak
area of the [M-57]? fragments.
The plot shows that all
responses are positively
correlated with the ion source
temperature (IST), whereas the
three latest eluting metabolites
are strongly and negatively
correlated with the acquisition
rate (ACQ). The contour plots
of cholesterol TBDMS and
dihydroxyacetone phosphate
MEOX 3TBDMS illustrates the
different behavior of these two
clusters
Development of a Metabolomic Protocol 59
123
3.4 Mass spectrometry
The [M-57]? fragment of a-ketoisovalerate MEOX
TBDMS peak 1 was used for normalization as it was found
to have a very low reproducibility of 0.005, indicating that
the random error greatly overpowered the effects of the
factors. The model obtained after normalization yielded
R2Y = 0.686 and Q2Y = 0.582 with 0.507 B R2Y B
0.930 and 0.400 B Q2Y B 0.904 for all studied responses
(Supplementary material S4). It was found that the ion
source temperature was positively correlated with the
[M-57]? fragment peak areas of all metabolite derivatives
(Fig. 4). The acquisition rate had a low influence on the
majority of responses. However, for the late eluting
metabolites, this factor became increasingly important with
a negative contribution to the responses. This effect is
probably related to the increased peak width with
increasing elution times.
Although the ion source temperature should be kept high
according to the above results, it is likely to affect the
fragmentation pattern. To study whether this occurs in the
present investigation, a ratio of the unnormalized [M-57]?
fragment, and a low m/z-fragment of the metabolite
derivatives was modeled. A lower value of this response
indicates a more excessive fragmentation. The model
yielded R2Y = 0.887 and Q2Y = 0.822 with 0.598 B
R2Y B 0.999 and 0.401 B Q2Y B 0.999 for all studied
responses (Supplementary material S5). Interestingly, the
PLS loading plot shows distinct clustering of early, inter-
mediate and late eluting metabolites (Fig. 5).
The acquisition rate affected the ratio for the early and
late eluting metabolites, whereas this effect was insignifi-
cant for the metabolites with intermediate elution times.
The ratio was furthermore, for all metabolites, declining
with increasing ion source temperature. In general, proto-
cols for GC/MS-based metabolomics apply an ion source
temperature ranging between 200 and 250�C and increas-
ing the temperature further might hamper the construction
of common mass spectra databases. For these reasons, the
ion source temperature was set to 250�C. The acquisition
rate was set to 20 Hz.
3.5 Method validation
To properly assess the linearity criteria a lack-of-fit test,
which compares the pure error (random error) to the model
error, i.e. the error in the linear fit (Araujo, 2009), was
included in the linear range determination. For a truly
linear response, these errors are of the same magnitude.
However, if the model error is significantly larger than the
pure error, the model has lack-of-fit and the values do not
strictly follow a linear function. For some of the chosen
metabolites, lack-of-fit due to curvature was detected
(Table 3). The curvature was detected at the highest
Fig. 5 PLS loading plot of the
peak area ratio between low and
high m/z fragments. Contour
plots of alpha ketoisovalerate
MEOX TBDMS (early eluting),
dihydroxyacetone phosphate
MEOX 3TBDMS (intermediate
eluting) and cholesterol
TBDMS (late eluting) illustrate
the different behavior of the
three clusters
60 A. P. H. Danielsson et al.
123
concentration and was therefore concluded to be due to
sample overloading. For these analytes the linear range was
accordingly decreased. All metabolites investigated were
found to be linear at a relative plasma concentration B0.8
(v/v, plasma/plasma ? water). Each linear function was
also cross-validated to further ensure that the response is
truly linear in the determined range.
The LOD and LOQ were estimated for the analytes for
which a stable isotope-labeled internal standard was available
(Table 3). The LOD and LOQ were found to be in the range
of 0.05 and 2.8 lM and 0.17 and 9.3 lM, respectively, for all
metabolites except for the latest eluting metabolite, choles-
terol, having a LOD and LOQ of 14.1 and 47 lM, respec-
tively. The RSD of the peak areas, reflecting the precision of
the method, was below 5% for most analytes, although some
metabolites exhibited comparatively high standard deviations
especially at the lowest concentration (Table 3). This is most
likely due to that the LOD for these metabolites are approa-
ched at high dilutions of the plasma. Cholesterol, on the other
hand, being highly abundant in the plasma has a higher pre-
cision at the lowest plasma concentration. Speculatively, this
distinct behavior of cholesterol results from a slight over-
loading at higher plasma concentrations.
A chromatogram, illustrated both by the TIC and over-
laid SIMs of the investigated metabolites, illustrates the
high intensity of the [M-57]? fragments that greatly
facilitates the identification and quantification of the
metabolites (Fig. 6).
3.6 Sample drift comparison
Finally, an orthogonal projections to latent structures
(OPLS) model was calculated to relate the metabolite peak
area to the run order. The model had one predictive and 5
orthogonal components and yielded R2Y = 0.95 and
Q2Y = 0.81. The model indicated that mainly metabolites
containing carboxylic acid and hydroxyl functionalities
were drifting, with a slight decrease with the run order. The
drift was overall very small, as indicated by cholesterol
TBDMS possessing the strongest decay with run order
(Area = -0.002(run order) ? 1.5, R2 = 0.02). Likewise,
a model was calculated on replicates of the same set of
plasma samples derivatized with MSTFA (Gullberg et al.
2004; Jiye et al. 2005). This model required one predictive
and two orthogonal components, yielding R2Y = 0.90 and
Q2Y = 0.76. In that model, several metabolite peak areas
were found to increase with run order. Among these
metabolites, amino acids with disilylated amines were
over-represented indicating a progressing silylation.
Glycine 3TMS, having the least sterically hindered amine,
was found having the strongest positive loading, and a plot
of the raw data indicated a pronounced alteration in peak
area over time (Area = 0.11(run order) ? 0.1, R2 = 0.57).
Thus, it is evident that the application of a bulkier silyla-
tion reagent reduces the number of disilylated amines
and that this in turn reduces the drift in the metabolomics
data.
Table 3 Linear range, LOD and precision calculated from a set of blood plasma samples
Metabolite R2 Q2 Linear rangea Internal standard LOD (lM) LOQ (lM) %RSD (0.1)c %RSD (0.4)c
Alanine 0.990 0.989 0.1–0.8 13C3-15N alanine 0.11 0.37 0.8 0.4
Cholesterol 0.973 0.971 0.1–1.0 2H7-cholesterol 14.1 47 1.0 5.6
Citric acid 0.987 0.986 0.1–0.8 13C4-succinic acid NAb NAb 6.6 4.6
Cysteine 0.949 0.943 0.1–0.8 13C3-serine NAb NAb 27.8 6.5
Glycine 0.958 0.953 0.1–0.8 13C5-a-ketoisovaleric acid NAb NAb 12.2 4.4
Isoleucine 0.976 0.973 0.1–0.8 13C3-15N alanine NAb NAb 10.6 2.3
Isocitric acid 0.950 0.946 0.1–1.0 13C4-succinic acid NAb NAb 16.1 6.4
Lactic acid 0.964 0.961 0.1–1.0 13C4-succinic acid NAb NAb 5.2 3.3
Leucine 0.980 0.978 0.1–0.8 13C3-15N alanine NAb NAb 9.6 2.3
Lysine 0.958 0.953 0.1–0.8 13C9-15N-tyrosine NAb NAb 7.4 3.1
Methionine 0.951 0.946 0.1–0.8 13C3-serine NAb NAb 9.2 2.2
Oleic acid 0.961 0.958 0.1–0.8 13C18-oleic acid 2.80 9.3 6.1 2.7
Phenylalanine 0.995 0.995 0.1–1.0 13C6-phenylalanine 0.05 0.17 1.4 0.5
Serine 0.995 0.995 0.1–1.0 13C3-serine 0.05 0.17 1.2 0.5
Threonine 0.957 0.953 0.1–0.8 13C3-serine NAb NAb 9.5 2.7
Tyrosine 0.989 0.988 0.1–1.0 13C9-15N-tyrosine 0.07 0.23 2.9 1.4
Valine 0.962 0.958 0.1–0.8 13C3-15N alanine NAb NAb 6.3 1.2
a The linear range is expressed as relative concentrations 0.1, 0.4 and 1.0 (v/v, plasma/plasma ? water)b No stable isotope labeled internal standard availablec Precision expressed as relative standard deviation calculated at relative concentrations 0.1 and 0.4 (v/v, plasma/plasma ? water)
Development of a Metabolomic Protocol 61
123
4 Conclusion
Optimizing a metabolomics protocol is complicated by the
heterogeneity and vastly differing concentration ranges of
the metabolites. In an effort to cope with this obstacle when
optimizing a metabolomics protocol, sets of metabolites
were selected that spanned a large portion of the metabo-
lome. Thus, the individual metabolites investigated here
should not be considered being single metabolites but
rather representatives of whole groups of metabolites
having similar properties. This means that the metabolite
cocktail, although containing 36 unique metabolites, is a
less complex model of a real plasma sample, where several
hundred metabolites normally are detected. Moreover,
generating the whole set of responses from all of the
metabolites would result in very large data sets, which
would complicate both data generation and interpretation.
For this reason, parts of the optimization were only per-
formed on a selected set of representative metabolites. The
optimization therefore does not deal with specific co-elu-
tion problems that commonly are encountered in meta-
bolomics analyses. Nevertheless, responses related to the
overall method performance, such as peak capacity, anal-
ysis time, efficiency, and detection limits, still are valuable
measures, which represent the ability of the method to
handle real metabolomics samples.
With DOE, the influence of all major parameters in the
metabolomics protocol, spanning derivatization and GC/
MS analysis, could be investigated. The results clearly
reflected the heterogeneity of the metabolome, with several
metabolites showing inversely correlating responses. The
response surfaces calculated with DOE were extremely
beneficial in finding the optimal parameters resulting in
sufficient recovery of all metabolites. Although a general
metabolomics protocol is presented, the models calculated
here may also serve as guidelines to aid the selection of
parameters for targeted metabolomics, also known as
metabolite profiling. The proposed protocol for blood
plasma analysis, based on MTBSTFA derivatization, also
offers a different selectivity to that of the more frequently
used protocols based on MSTFA. Thus, it may serve both
as a standard operating procedure for some applications
and as a complementary approach for other metabolomics
protocols based on MSTFA. The optimal protocol found in
the present investigation suggested that methoximation
should be performed at 20�C for 4 h in 100% pyridine. The
silylation should be performed at 100�C for 170 min with
pure MTBSTFA added the vial. Injection of the samples
should be performed at 270�C, employing an injector purge
vent time of 115 s. The chromatography should be per-
formed on a 30 m capillary column with a flow rate of
1 ml/min, an initial temperature of 60�C, an initial iso-
thermal time of 2 min, and a gradient rate of 25�C/min.
The ion source temperature should be 250�C, with an
acquisition rate of 20 Hz for the mass spectrometer.
Acknowledgments This work was supported by grants from Swedish
Research Council (14196-06-3), the Crafoord Foundation, Lars Hierta,
Fredrik and Ingrid Thuring, Ake Wiberg, Albert Pahlsson, O.E. and
Edla Johansson Foundations, Knut and Alice Wallenberg Foundation,
and the Royal Physiographic Society. Support from Inga and John Hain
Foundation to PS is acknowledged.
References
Araujo, P. (2009). Key aspects of analytical method validation and
linearity evaluation. Journal of Chromatography B, 877,
2224–2234.
Araujo, P. W., & Brereton, R. G. (1996a). Experimental design I.
Screening. Trends in Analytical Chemistry, 15, 26–31.
Fig. 6 Chromatogram from pooled blood plasma illustrating the high
intensity of the [M-57]? fragments from the metabolites. a TIC
(b) SIMs from the [M-57]? fragments of the investigated metabolites
62 A. P. H. Danielsson et al.
123
Araujo, P. W., & Brereton, R. G. (1996b). Experimental design II.
Optimization. Trends in Analytical Chemistry, 15, 63–70.
Araujo, P. W., & Brereton, R. G. (1996c). Experimental design III.
Quantification. Trends in Analytical Chemistry, 15, 156–163.
Asres, D. D., & Perreault, H. (1997). Monosaccharide permethylation
products for gas chromatography - mass spectrometry: How
reaction conditions can influence isomeric ratios. CanadianJournal of Chemistry, 75, 1385–1392.
Begley, P., Francis-McIntyre, S., Dunn, W. B., et al. (2009).
Development and performance of a gas chromatography-time-
of-flight mass spectrometry analysis for large-scale nontargeted
metabolomic studies of human serum. Analytical Chemistry, 81,
7038–7046.
Birkemeyer, C., Kolasa, A., & Kopka, J. (2003). Comprehensive
chemical derivatization for gas chromatography-mass spectrom-
etry-based multi-targeted profiling of the major phytohormones.
Journal of Chromatography A, 993, 89–102.
Buscher, J. M., Czernik, D., Ewald, J. C., Sauer, U., & Zamboni, N.
(2009). Cross-platform comparison of methods for quantitative
metabolomics of primary metabolism. Analytical Chemistry, 81,
2135–2143.
Chorell, E., Moritz, T., Branth, S., Antti, H., & Svensson, M. B.
(2009). Predictive metabolomics evaluation of nutrition-modu-
lated metabolic stress responses in human blood serum during
the early recovery phase of strenuous physical exercise. Journalof Proteome Research, 8, 2966–2977.
Cozzolino, D., Flood, L., Bellon, J., Gishen, M., & Lopes, M. D. B.
(2006). Combining near infrared spectroscopy and multivariate
analysis as a tool to differentiate different strains of Saccharo-
myces cerevisiae: A metabolomic study. Yeast, 23, 1089–1096.
Danielsson, A. P. H., Moritz, T., Mulder, H., & Spegel, P. (2010).
Development and optimization of a metabolomic method for
analysis of adherent cell cultures. Analytical Biochemistry, 404,
30–39.
Ewald, J. C., Heux, S. p., & Zamboni, N. (2009). High-throughput
quantitative metabolomics: Workflow for cultivation, quenching,
and analysis of yeast in a multiwell format. Analytical Chem-istry, 81, 3623–3629.
Fiehn, O. (2002). Metabolomics—the link between genotypes and
phenotypes. Plant Molecular Biology, 48, 155–171.
Fiehn, O. (2008). Extending the breadth of metabolite profiling by gas
chromatography coupled to mass spectrometry. Trends inAnalytical Chemistry, 27, 261–269.
Fiehn, O., Kopka, J., Trethewey, R. N., & Willmitzer, L. (2000).
Identification of uncommon plant metabolites based on calcu-
lation of elemental compositions using gas chromatography and
quadrupole mass spectrometry. Analytical Chemistry, 72,
3573–3580.
Gullberg, J., Jonsson, P., Nordstrom, A., Sjostrom, M., & Moritz, T.
(2004). Design of experiments: An efficient strategy to identify
factors influencing extraction and derivatization of Arabidopsisthaliana samples in metabolomic studies with gas chromatogra-
phy/mass spectrometry. Analytical Biochemistry, 331, 283–295.
Jiye, A., Trygg, J., Gullberg, J., et al. (2005). Extraction and GC/MS
analysis of the human blood plasma metabolome. AnalyticalChemistry, 77, 8086–8094.
Jonsson, P., Johansson, E. S., Wuolikainen, A., et al. (2006). Predictive
metabolite profiling applying hierarchical multivariate curve
resolution to GC-MS Data-A potential tool for multi-parametric
diagnosis. Journal of Proteome Research, 5, 1407–1414.
Kanani, H. H., & Klapa, M. I. (2007). Data correction strategy for
metabolomics analysis using gas chromatography-mass spec-
trometry. Metabolic Engineering, 9, 39–51.
Kirkland, J. J. (1977). Sampling and extra-column effects in high-
performance liquid chromatography; influence of peak skew on
plate count calculations. Journal of Chromatographic Science,15, 303–316.
Lapainis, T., Rubakhin, S. S., & Sweedler, J. V. (2009). Capillary
electrophoresis with electrospray ionization mass spectrometric
detection for single-cell metabolomics. Analytical Chemistry, 81,
5858–5864.
Pous-Torres, S., Baeza-Baeza, J. J., Torres-Lapasio, J. R., & Garcıa-
Alvarez-Coque, M. C. (2008). Peak capacity estimation in
isocratic elution. Journal of Chromatography A, 1205, 78–89.
Rodrıguez, I., Quintana, J. B., Carpinteiro, J., Carro, A. M., Lorenzo,
R. A., & Cela, R. (2003). Determination of acidic drugs in
sewage water by gas chromatography-mass spectrometry as tert.-
butyldimethylsilyl derivatives. Journal of Chromatography A,985, 265–274.
Schweer, H. (1982). Gas chromatography–mass spectrometry of
aldoses as O-methoxime, O-2-methyl-2-propoxime and O-n-
butoxime pertrifluoroacetyl derivatives on OV-225 with meth-
ylpropane as ionization agent: I. Pentoses. Journal of Chroma-tography A, 236, 355–360.
Tam, Y. Y., & Normanly, J. (1998). Determination of indole-3-
pyruvic acid levels in Arabidopsis thaliana by gas chromatog-
raphy-selected ion monitoring-mass spectrometry. Journal ofChromatography A, 800, 101–108.
Wold, S. (1978). Cross-validatory estimation of the number of
components in factor and principal components models. Tech-nometrics, 20, 397–405.
Yu, Z., Peldszus, S., & Huck, P. M. (2007). Optimizing gas
chromatographic-mass spectrometric analysis of selected phar-
maceuticals and endocrine-disrupting substances in water using
factorial experimental design. Journal of Chromatography A,1148, 65–77.
Zelena, E., Dunn, W. B., Broadhurst, D., et al. (2009). Development
of a robust and repeatable UPLC-MS method for the long-term
metabolomic study of human serum. Analytical Chemistry, 81,
1357–1364.
Zhang, S., Nagana Gowda, G. A., Asiago, V., Shanaiah, N., Barbas,
C., & Raftery, D. (2008). Correlative and quantitative 1H NMR-
based metabolomics reveals specific metabolic pathway distur-
bances in diabetic rats. Analytical Biochemistry, 383, 76–84.
Development of a Metabolomic Protocol 63
123
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Recommended