Knapp J.S., Cabrera W.L. Metabolomics: Metabolites, Metabonomics, and Analytical Technologies
Подождите немного. Документ загружается.
Metabolomic Profile and Fractal Dimensions in Breast Cancer Cells 117
of migation and cell division in the absence of tumor formation. Dev Dynam, 233,
1560-1570.
[133] Postovit, L. M., Maragaryan, N. V., Seftore, E. A., et al. (2008). Human embryonic
stem cell microenvironment suppress the tumorigenic phenotype of aggressive cancer
cells. Proc Natl Acad Sci USA, 105, 4329-4334.
[134] Meadows, A. L., Kong, B., Berdichevsky, M., Roy, S., Rosiva, R., Blanch, H. W. &
Clark, D. S. (2008). Metabolic and Morphological Differences between Rapidly
Proliferative Cancerous and Normal Breast Epithelial Cells. Biotechnol. Prog., 24, 334-
341.
[135] Virchow, R. L. K. (1859). Cellular pathology. special ed. London, UK, John Churchill;
1978, 204-7.
[136] Bizzarri, M. (2008). Consequences of space exploration for mankind. G Ital Nefrol.,
25(6), 686-689.
[137] Boonstra, J. (1999). Growth factor-induced signal transduction in adherent mammalian
cells is sensitive to gravity FASEB, 13, S35-S42.
[138] Carmeliet, G. & Bouillon, R. (1999). The effect of microgravity on morphology and
gene expression of osteoblasts in vitro FASEB, 13, S129-134.
[139] Pourati, J., Maniotis, A., Speigel, D., Schaffer, J. L., Butler, J. P., Fredberg, J. J.,
Ingber, D. E., Stamenovic, D. & Wang, N. (1998). Is cytoskeletal tension a major
determinant of cell deformability in adherent endothelial cells? Am. J. Physiol., 274,
C1283-C1289.
[140] Wang, N., Tytell, J. D. & Ingber, D. E. (2009). Mechanotransduction at a distance:
mechanically coupling the extracellular matrix with the nucleus. Nat Rev Mol Cell
Biol., 10(1), 75-82.
[141] Chen, C. S., Mrksich, M., Huang, S., Withesides, G. M. & Ingber D. E. (1997).
Geometric control of cell life and death. Science, 276, 1425-1428.
[142] Ingber, D. E. (1999). How cells (might) sense microgravity. FASEB, 13, S3-S15.
[143] Mandelbrott, B. B. (1982). The fractal geometry of the Nature, W.H. Freeman. New
York;.
[1
44] Rosai, J. (2001). The continuing role of morphology in the molecular age. Modern
Path., 14, 258-260.
[145] Rangayyan, R. M. & Nguyen, T. M. (2007). Fractal analysis of contours of breast
masses in mammograms. J. Dig. Imag., 20(3), 223-237.
[146] Rangayyan, R. M., El-Faramawy, N. M., Desautels, J. E. L. & Alim, O. A. (1997).
Measures of acutance and shape for classification of breast tumours. IEEE Trans Med
Imag., 16(6), 799-810.
[147] Mandelbrot, B. B. (1975). Stochastic models for the Earth's relief, the shape and the
fractal dimension of the coastlines, and the number-area rule for islands. Proc. Nat.
Acad. Sci.U.S.A., 72, 3825-3828.
[148] Cutting, J. E. & Garvin, J. J. (1987). Fractal curves and complexity. Percept.
Psicophys., 42, 365-370.
[149] Smith, T. G., Lange, G. D. & Marks, W. B. (1996). Fractal methods and results in
cellular morphology - dimensions, lacunarity and multifractals. J. Neurosci Methods.,
69, 123-136.
[150] Losa, G. A., Merlini, D., Nonnenmacher, T. F. & Weibel, E. R. (2002). (Eds.) Fractals
in Biology and Medicine, Birkhauser Verlag. Basel;.
Mariano Bizzarri, Fabrizio D’Anselmi, Mariacristina Valerio et al. 118
[151] Baish, J. W. & Jain, R. K. (2000). Fractals and Cancer. Cancer Res., 60, 3683-3688.
[152] Cross, S. S. (1997). Fractals in pathology. J. Pathol., 182, 1-8.
[153] Cross, S. S., McDonagh, A. J. G., Stephenson, T. J., et al. (1995). Fractal and integer-
dimensional analysis of pigmented skin lesions. Am J Dermatol., 17, 374-378.
[154] Claridge, E., Hall, P. N., Keefe, M., et al. (1992). Shape analysis for classification of
malignant melanoma. J Biomed Eng., 14, 229-324.
[155] Gazit, Y., Berk, D. A., Leunig, M., Baxter, L. T. & Jain, R. K. (1995). Scale-invariant
behavior and vascular network formation in normal and tumour tissue. Phys Rev Lett.,
75, 2428-2431.
[156] Michaelson, J. S., Cheongsiatmoy, J. A., Dewey, F., et al. (2005). Spread of human
cancer cells occurs with probabilities indicative of a nongenetic mechanism. Br J.
Cancer., 93, 1244-1249.
[157] Tracqui, P. (2009). Biophysical model of tumor growth. Rep. Prog. Phys., 72, 1-30.
[158] Landini, G. & Rippin, J. W. (1996). How important is tumour shape? Quantification of
the epithelial connective tissue interface in oral lesions using local connected fractal
dimension analysis. J Pathol., 179, 210-217.
[159] Folkman, J. & Moscona, A. (1978). Role of cell shape in growth control. Nature, 273,
345-349.
[160] Ingber, D. E. (2005). Mechanical control of tissue growth: function follows form. Proc
Natl Acad Sci USA, 102(33), 11571-11572.
[161] Scheck, F. (1990). Mechanics, Springer. Verlag, Heidelberg, Germany;.
[162] Toussaint, O. & Schneider, E. D. (1998). The thermodynamics and evolution of
complexity in biological systems Comp. Biochem. Physiol.
, 12
0, 3-9.
[163] Ingber, D. E. (2008). Can cancer be reversed by engineering the tumour
microenvironment? Sem Cancer Biol., 18, 356-364.
[164] D’Anselmi, F., Valerio, M., Cucina, A., Galli, L., Proietti, S., Dinicola, S., Pasqualato,
A., Manetti, C., Ricci, G., Giuliani, A., Bizzarri, M. Metabolism and cell shape in
cancer: a fractal analysis. Int J Biochem Cell Biol. (in press).
[165] Bowie, J. E. & Young, I. T. (1977). An analysis technique for biological shape. Acta
Cytol., 21, 739-746.
[166] Cesar, R. M. Jr. & Costa, L. & da F. (1997). The application and assessment of
multiscale Bending Energy for Morphometric characterization of neural cells. Rev. Sci.
Instrum., 68, 2177-2186.
[167] Castleman, K. R. (1996). Digital Image Processing, Prentice-Hall. NJ, Engelewood
Cliffs;.
[168] Lelièvre, S. A., Weaver, V. M., Nickerson, J. A., Larabell, C. A., Bhaumik, A.,
Petersen, O. W. & Bissell, M. J. (1998). Tissue phenotype depends on reciprocal
interactions between the extracellular matrix and the structural organization of the
nucleus. Proc Natl Acad Sci U S A., 95, 14711-14716.
[169] Thomas, C. H., Collier J. H., Sfeir C. S. & Healy, K. E. (2002). Engineering gene
expression and protein synthesis by modulation of nuclear shape. Proc Natl Acad Sci U
S A, 99, 1972-1977.
[170] Guilak, F. (1995). Compression-induced changes in the shape and volume of the
chondrocyte nucleus. J Biomech., 28, 1529 -1541.
[171] Zink, D., Fischer, A. H. & Nickerson, J. A. (2004). Nuclear structure in cancer cells.
Nat Rev Cancer, 4, 677- 687.
Metabolomic Profile and Fractal Dimensions in Breast Cancer Cells 119
[172] Paszek, M. J., Zahir, N., Johnson, K. R., Lakins, J. N., Rozenberg, G. I., Gefen, A.,
Reinhart-King, C. A., Margulies, S. S., Dembo, M., Boettiger, D., Hammer, D. A. &
Weaver, V. M. (2005). Tensional homeostasis and the malignant phenotype. Cancer
Cell, 8, 241-254.
[173] Wolf, K. & Friedl, P. (2006). Molecular mechanisms of cancer cell invasion and
plasticity. Br J Dermatol., 154, 11-15.
[174] Dahl, K. N., Ribeiro, A. J. S. & Lammerding, J. (2008). Nuclear Shape, Mechanics, and
Mechanotransduction. Circ Res., 102, 1307-1318.
[175] Prigogine, I. & Wiame, J. M. (1946). Biologie et Thermodynamique des phenomenes
irreversibles. Experientia, 2, 451-453.
[176] Zotin, A. I. (1990). Thermodynamic bases of biological processes: physiological
reactions and adaptations. Walter de Gruyter. Berlin,.
[177] Zotin, A. A. & Zotin A. I. (1997). Phenomenological theory of ontogenesis. Int J Dev
Biol., 41, 917-921.
[178] DeBerardinis, R., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. (2008). The
Biology of Cancer: Metabolic Reprogramming Fuels Cell Growth and Proliferation.
Cell Metabolism, 7, 11-20.
[179] Cascante, M., Boros, L. G., Comin-Anduix, B., de Atauri, P., Centelles, J. J. & Lee, P.
W. N. (2002). Metabolic control analysis in drug discovery and disease. Nat. Biotech.,
20, 243-249.
In: Metabolomics: Metabolites, Metabonomics… ISBN: 978-1-61668-006-0
Editors: J.S. Knapp and W.L. Cabrera, pp. 121-161 © 2011 Nova Science Publishers, Inc.
Chapter 3
FROM METABOLIC PROFILING TO METABOLOMICS:
FIFTY YEARS OF INSTRUMENTAL AND
METHODOLOGICAL IMPROVEMENTS
Chiara Cavaliere, Eleonora Corradini, Patrizia Foglia,
Riccardo Gubbiotti, Roberto Samperi and Aldo Laganà
*
SAPIENZA Università di Roma, Rome, Italy
Abstract
Molecular biology has recently concentrated on the determination of multiple gene-expression
changes at the RNA level (transcriptomics), and into determination of multiple protein
expression changes (proteomics). Similar developments have been taking place at metabolite
small-molecule level, leading to the increasing expansion in studies now termed
metabolomics. This approach can be used to provide comprehensive and simultaneous
systematic profiling of metabolite levels in biofluids and tissues, and their systematic and
temporal changes. Analysis of metabolites is not a new field; long prior to the development of
the various ‘‘omics’’ approaches, the simultaneous analysis of the plethora of metabolites
seen in biological fluids had been carried out largely, but historically it has been limited to
relatively small numbers of target analytes. However, the realization that metabolic pathways
do not act in isolation but rather as part of an extensive network has led to the need for a more
holistic approach to metabolite analysis.
The main analytical techniques employed for metabolomics studies are based on NMR
spectroscopy and mass spectrometry (MS), that, in turn, can be considered complementary
each other. Neverthless, MS measurement following chromatographic separation offers the
best combination of sensitivity and selectivity, so it is central to most metabolomics
approaches. Either gas chromatography after chemical derivatization, or liquid
chromatography (LC), with the newer method of ultrahigh-performance LC being used
increasingly, can be adopted. Capillary electrophoresis coupled to MS has also shown some
promises. Analyte detection by MS in complex mixtures is not as universal as for NMR and
quantitation can be impaired by variable ionization and ion-suppression effects. A LC
chromatogram is generated with MS detection, usually using electrospray ionization (ESI),
* E-mail address: aldo.lagana@uniroma1.it. Phone: +39-06-49913679 Fax: +39-06-490631. Dipartimento di
Chimica, SAPIENZA Università di Roma, Box n° 34 - Roma 62, Piazzale Aldo Moro 5, 00185 Rome, Italy.
(Corresponding author)
Chiara Cavaliere, Eleonora Corradini, Patrizia Foglia et al. 122
and both positive- and negative-ion chromatograms can be recorded. The utilization of nano-
ESI can reduce ionization suppression effects due to the increased ionization efficiency. Mass
analyzer able to produce high mass resolution, mass accuracy, and tandem MS, such as
quadrupole-time-of-flight (Q-TOF) or high-resolution ion trap instruments, are employed.
Direct infusion (DI)-MS/MS using Fourier transform ion cyclotron resonance mass
spectrometers provides a sensitive, high-throughput method for metabolic fingerprinting.
Unfortunately, DI-MS analysis is particularly susceptible to ionization suppression arising
from competitive ionization. In metabolomics, matrix assisted laser desorption-ionization
(MALDI) has largely been confined to the targeted analysis of high-molecular weight
metabolites due to the substantial signals generated by the matrix in the low-molecular-weight
region (<1,000 m/z). Recent advancements in laser desorption techniques include desorption-
ionization MS from porous silicon chips and matrices that have minimal background signals
in the low-molecular-weight region. These offer new opportunities for the utilization of
MALDI ionization in metabolite screening and fingerprinting employing MALDI-TOF/TOF.
However, the technique is still subject to ion suppression and yields poor quantitative
detection. Desorption ESI (DESI), a new ambient, soft-ionization technique that combines
features from both ESI and desorption-ionization methods, allows the direct analysis of
animal and plant tissues. However, DESI experimental conditions typically require
optimization for each sample type, so time must be invested initially in optimizing the
experimental parameters.
Introduction
In the post-genomic era, the attention of molecular biology has concentrated more and
more on the determination of multiple gene-expression changes at the RNA level,
(transcriptomics), the determination of multiple protein expression changes in a cell or tissue
(proteomics), and at the small-molecule metabolite level (metabolomics). The general aim of
these techniques is to gain new insights and a better understanding of the biological
functioning of a cell or organism [1,2]. Their practical applications include virtually all
aspects of the system biology of cellular (and also sub-cellular) compartments, and complex
organisms, ranging from the identification of differences between certain sets of organisms
(e.g., differences in genotypes) to the identification of differences between subjects affected
by various diseases and disease-free controls, or the elucidation of factors that influence
biochemical events following external stimuli such as exposure to environmental toxins or
other stressors.
The main limitation associated with interpreting transcriptomics and proteomics is often
the difficulty of relating observed gene-expression fold changes or protein-level (not activity)
changes to conventional disease and pharmaceutically relevant end-points. In other words,
changes in the transcriptome and proteome do not always result in altered biochemical
phenotypes (the metabolome) [3,4]. It has been suggested that metabolomics, among the
‘omics’ technologies, may in fact provide the most “functional” information [3]. The
metabolome can be considered as the final stage in the chain of events from genes to
metabolism, and the metabolic phenotype is the most direct reflection of the actual state of a
biological system. Metabolites have a well-defined function in the life of the biological
system and are also contextual [5], reflecting the surrounding environment. Thus, quantitative
global analysis of endogenous metabolites from cells, tissues, fluids, etc. is becoming an
integral part of functional genomics effort [4,6,7] as well as a tool for discovering diagnostic
biomarkers [8-11].
From Metabolic Profiling to Metabolomics 123
As reported by D. Ryan and K. Robards [12], terminology relating to metabolomics has
been (and is still) controversial. The term “metabolome” was first used by Olivier et al. in
1998 [13] to describe the entire set of metabolites synthesized by an organism, on analogy of
“genome” and “proteome”. More recently, this definition has been limited to “the quantitative
complement of all of the low molecular weight molecules present in cells in a particular
physiological or developmental state” [14].
The term “metabolomics” was coined by O. Fiehn
and defined as a comprehensive analysis in which all metabolites of a biological system were
identified and quantified [15]. The confusion in the terminology arises from the similar term
“metabonomics”, which was coined earlier by J.K. Nicholson et al. [16]. Later,
metabonomics has been described as a subset of metabolomics [17] which, in contrast, seeks
to measure those metabolites which change in response to a stimulus of one sort or another.
Although the authors who first used the term metabonomics accepted Fiehn’s distinction [18],
the two terms have been often used interchangeably. Whatever the accepted definition is, the
two fields employ similar methodologies and have the common aim of analyzing the
metabolome. Hence, most of the literature supports the use of the term metabolomics to
describe a comprehensive, non-targeted analytical approach that is universally applicable to
identify and quantify all metabolites of a biological system and the term metabonomics will
no longer be used in this chapter.
Metabolomics is a rapidly maturing field: it is increasingly being applied to study
biological systems including microorganisms [19,20],
plants [21,22],
mammals [23-26],
and
environment [27,28]. Metabolomes are complex systems composed of hundreds or thousands
of metabolites (1,168 for yeast [29], 200,000 for plants in the total vegetable kingdom [17],
>6,500 for mammals [30]) with a wide range of physical and chemical properties,
and a large
dynamic range. The study of these systems requires an integrated approach or metabolome
pipeline [31] and a number of strategies are applied [32,33].
From an analytical perspective,
metabolomics is a huge analytical challenge that needs the ability to perform high-throughput
experiments with relatively low operating costs after the initial investment for the purchase of
the necessary instruments [32].
Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the
primary analytical techniques used for the identification and quantification of a large set of
metabolites present in a given biological system [34-39]. Each technology shows some
advantages and they are essentially complementary [37,38]. Although
1
H and
13
C NMR are
capable of measuring most aspects of the metabolome, the extremely large dynamic range
typically encountered in biological systems added to the difficulties in coupling NMR to
chromatography, are a drawback for NMR since important aspects of the metabolome
composition may potentially go unmeasured. Thus, because of its high sensitivity, the
capability to analyze highly complex samples (especially when hyphenated with
chromatographic separations) and a good dynamic range throughout its measurement, the
MS-based approaches have started to take the leadership in metabolomic research. This
chapter will be focused only on MS based methodologies.
Because of the huge amount of data produced by a single metabolomics experiment,
computational tools are crucial for analyzing the data. A visual inspection of the results
can
quickly reveal errors, so it is often needed to validate the output of analysis. On the other
hand, comparing such complex multi-dimensional data manually to find corresponding
signals is a tedious task, as each experiment usually consists of thousands of individual scans,
each containing hundreds or even thousands of distinct signals. Long before the development
Chiara Cavaliere, Eleonora Corradini, Patrizia Foglia et al. 124
of the various ‘‘omics’’ approaches, the simultaneous analysis of the plethora of metabolites
seen in biological fluids had been carried out largely by MS and it was shown that these
complex data sets could be interpreted using multi-variate statistics [40]. Now MS instrument
software could be used for most of these tasks, but this has several drawbacks
. Instrument
software is generally expensive, and most of the instrument softwares cannot import data
from other instruments of other brands. Therefore, different software has to be used for
different data sets. A viable alternative could be freely available, open source viewers (see for
example [41]). In parallel, bioinformaticians have attempted to compute the size of the
metabolome on the basis of genome information [42]; however, such approaches are
considerably restrained by the quality of genome annotation. To gain ultimate value from
large-scale experiments the data and associated metadata that they produce need to be
incorporated in databases. Large databases of NMR spectra are already available, while the
situation for MS is not so advanced; however, recent years have brought major developments
and a large number of web-based tools that can be of great help in the interpretation of
chromatography/MS data are now currently available.
Origins and Development: Looking Back
Analysis of metabolites is not a new field; but historically it has been limited to relatively
small numbers of target analytes as in the study of a particular metabolic pathway. However,
in the long run, the realization that metabolic pathways do not act in isolation but rather as
part of an extensive network has led to the need for a more holistic approach to metabolite
analysis. Historical approaches in MS-based methods include metabolite profiling, metabolite
fingerprinting, and target analysis. Metabolite profiling involves the identification and
quantitation of a predefined set of metabolites of known or unknown identity and belonging
to a selected metabolic pathway [15,43]. The aim of metabolite fingerprinting is the rapid
classification of numerous samples using multivariate statistics, typically without
differentiation of individual metabolites or their quantitation. Target analysis is limited
exclusively to the qualitative and quantitative analysis of a particular metabolite or
metabolites. As a result, only a very small fraction of the metabolome is focused upon, signals
from all other components being ignored [44]. Because of their nature, these approaches
provide a restrictive non-comprehensive view of the metabolome. Nevertheless, metabolite
profiling represents the oldest and most established approach and can be considered the
precursor for metabolomics.
The concept that individuals might have a “metabolic pattern” that would be reflected in
the constituents of their biological fluids was first developed and tested by Roger Williams
and his associates during the late 1940s and early 1950s. Utilizing data from over 200,000
paper chromatograms, many run with techniques developed in his own laboratory for this
purpose, Williams was able to show convincingly that the taste thresholds and the excretion
patterns for a variety of substances varied greatly from individual to individual [45]. The
work of Williams and his group, however, was apparently not duplicated by others,
hence his
ideas about the utility of metabolic pattern analysis remained essentially dormant until the late
1960s, when gas chromatography (GC) [46-49] and liquid chromatography (LC) [50,51] were
sufficiently advanced to allow such studies to be carried out with considerably less effort. The
concept of metabolite fingerprint as specie-specific GC profile reflecting different metabolic
From Metabolic Profiling to Metabolomics 125
patterns was reported the first time by R. Kuntzman in 1966 [52]. The concept of metabolic
profiles was introduced finally by E.C. and M.G. Horning [53,54], who coined the term to
refer to qualitative and quantitative analyses of complex mixtures of physiological origin
“metabolic profiles are multicomponent GC analyses that define or describe metabolic
patterns for a group of metabolically or analytically related metabolites.” At the beginning;
researchers involved in the field were not aware of the difference between metabolite (or
metabolic) profiling and metabolic fingerprint, owing the enormous difficulties in obtaining
quantitative data, difficulties now solved with the development of computerized data
handling.
From an instrumental point of view, although metabolomics is a relatively new term, its
origin can be traced back to 1956-59, when Golay presented his lecture on the “Theory of
chromatography in open and coated tubular column with round or rectangular cross-sections”
at symposium on GC held in Amsterdam [55], Zlatkis developed Golay’s idea [56], Beynon
realized the high resolution MS of organic compounds [57,58], and Gohlke coupled GC with
a Time of Flight (ToF) mass spectrometer [59]. Almost from the birth of GC, people involved
in organic MS saw the potential advantage of separating a complex mixture into its
components followed by structural analysis by MS. As investigation toke place it was evident
that GC-MS was different from both GC and MS. Three major hurdles had to be overcome: i)
the large amount of gas leaving the column (working with packed columns), while MS
separates the ions in high vacuum condition; ii) the need for rapid mass spectral acquisition;
iii) the enormous amount of data collected during a GC-MS analysis.
The first problem was solved by a device named “jet separator” [60] that eliminates
selectively most of the carrier gas. Undoubtedly the open tubular columns were much more
amenable for GC-MS, owing the much smaller flow-rate, but their use become popular only
in the mid ’70s, after Horning’s group developed a method for the preparation of
thermostable capillary columns which provided extremely high resolution [54]. When GC-
MS was at its beginning, magnetic sector mass spectrometer did not have a rapid data
acquisition capability as the ToF instruments which, on the other hand, presented other
problems, and low resolution. The first magnetic sector instrument built as a GC-MS was
commercially available in the mid ’60s; although the problem of rapid acquisition was solved,
this instrument did not tackle the issue of the amount of data acquired in a GC-MS analysis. It
used a light beam oscilloscope to record the mass spectra that were manually selected for
recording. Minicomputers were also developed in the mid ’60s, and in few years the
automated collection of GC-MS data became possible. The contribution of Hites and
Biemann [61-63]
was particularly important. The generation of several different types of
chromatograms was made possible by processing the m/z signal and its intensity recorded by
the data system: the reconstructed total ion current, the specific m/z value current and the MS
spectrum recorded in a certain scan time.
GC-MS began to become a routine technology with the introduction of quadrupolar
instruments. Quadrupole technology, which includes the transmission quadrupole (TQ) and
the quadrupole ion trap (QIT), was explored by Paul [64]. The first GC-QTMS was developed
in the late ’60s and rapidly substituted the magnetic sector based instruments [65], owing to
the simplicity of operation and the continued advancement of the data station. The TQ mass
spectrometer was very well suited for GC-MS: compared to a magnetic sector it was smaller
in size and much more suitable for rapid scanning and electronic control. The limit of TQ
instruments was (and still is) the fact that they do not consent high resolution and accurate
Chiara Cavaliere, Eleonora Corradini, Patrizia Foglia et al. 126
mass measurements, since, throughout the scan range, ions with a certain m/z are resolved
from those of m/z +1. The coupling of high resolution (HR) GC to HR mass spectrometry
(HR-MS) was realized for the first time by the Burlingam’s group in 1975 with a double
focalization magnetic-electrostatic analyzer [66]. Although the QIT analyzer was born from
the same Paul research that produced TQ, its history is much more complicated owing the
intrinsic complexity of the ion motion inside it, and the first GC-QITMS was commercially
viable at the end of 1985 [67]. QITs were initially developed as low-resolution, low-mass
detectors for use with GC, but their performance has been considerably improved and several
versions are available with enhanced resolution. The use of QIT was promoted by their
capability of operating in multiple MS (MS
n
). In the meanwhile, there had been a significant
hardware development with the introduction by Yost and Henke in 1978 [68] of the triple
quadrupole (QqQ),
that included two quadrupole mass analyzers (Q) separated by a
quadrupolar not-separating collision cell (q). Precursor ions separated by the first Q were
converted to fragments by collision with a gas molecule in q, and the second Q separates the
ions produced. Tandem MS (MS
2
), and MS
n
will become the key of compound identification
after separation by high performance LC (HPLC).
Figure 1. General scheme for the analysis of urinary steroid conjugates including group separation.
XAD2: Amberlite XAD2 poly(styrene-divynilbenzene) resin. SE-LH-20: Sulphoethyl Sephadex LH-20
cation exchange resin. DEAP-LH-20: Diethylaminohydroxypropyl Sephadex-LH-20. Seventy-seven
different deconjugated steroid metabolites (51 glucuronides, 44 monosulphates, and 22 disulphates)
were detected by GC-MS from 25 mL male urine as metoxime-trimetylsylil derivates. (Reproduced
from reference 74 by permission.)
Early applications of metabolic profiling by GC and GC-MS were in the field of volatile
metabolites in serum, plasma, urine, and breath [69-72];
steroidal hormones and their
metabolites in plasma and urine [53,73,74]; organic acids in urine [75-81]; amino acids in
urine [82]; aliphatic alcohols [83].
However, some attempts were also made to obtain
multiclass metabolic profiles, [54,66,77]
by using sample preparation/fractionation and a
suitable derivatization step for non-volatile compounds. Almost the whole body of literature
concerned the detection of human disease, whereas studies regarding plants were rather rare
Enzyme
hydrolysis
Enzyme
hydrolysis
Enzyme
hydrolysis
Solvolysis
Solvolysis
Derivatise
Derivatise
Derivatise
Derivatise
Bicarbonate
wash
Bicarbonate
wash
EtOAc - phase
Water - phase
EtOAc - phase
Water - phase
Derivatise
Enzyme
hydrolysis