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In comparison with the profiling analyses of other molecular levels of cellu-
lar function, the main asset of transcriptomics derives from the relative simplicity
of its subject: mRNA is a polymer of only four different subunits, unlike proteins
that are composed of 20 different amino acids and have various 3D structures and
metabolites that have great chemical diversity. Thus, a single method of extraction
and detection can theoretically identify and quantify every transcript in a tissue
sample. As a result, transcriptomic studies tend to identify at least one order of
magnitude more gene products than proteome studies (Baginsky 2009 ; Deyholos
2010 ). Moreover, the protein and metabolic levels are highly dynamic and environ-
ment sensitive. Currently, microarrays (Pease et al. 1994 ; Schena et al. 1995 ) have
been the main platform used for transcriptomic studies in plants and in general in
most biological systems. Microarrays have proven to be a reliable technological
platform for the study of gene expression patterns, because of their relatively high
sensitivity, specificity, accuracy, throughput, and cost-efficiency. However, array-
based technologies are limited to the analysis of known transcripts. This limitation
can be bypassed with transcriptome analysis based on the next-generation (“deep”)
sequencing platforms (Wang et al. 2009 ), which have not yet gained adequate mo-
mentum in plant physiology studies. Recently, Mizuno et al. ( 2010 ) conducted a
study on the transcriptional effects of salinity stress on rice using both RNA deep se-
quencing and microarrays. RNA sequencing predicted the expression of more than
3000 transcripts not previously annotated by the Rice Annotation Project. Some of
the unannotated genes were differentially expressed in response to salinity stress
(Mizuno et al. 2010 ).
Metabolomic analyses provide the link between gene expression and the meta-
bolic phenotype, the latter being very sensitive to the physiological responses caused
by environmental perturbations on the plants. It has been estimated that about tens
of thousand primary and secondary metabolism intermediates (metabolites) occur
in the plant kingdom (Fiehn 2002 ). The metabolite concentration profile is affected
and also affects the metabolic reaction rates, being thus a fingerprint of the meta-
bolic state of the cells and tissues. Most metabolites act as regulatory molecules of
protein functions and interactions, their accurate quantification being of additional
importance for deciphering the molecular mechanisms that impose the physiology
of the plants under specific conditions. Because of the chemical diversity of me-
tabolites, metabolomic analysis is subject to analytical constraints that limit the
number of metabolites that can be identified and quantified in a single sample. Cur-
rently, there is no extraction protocol and technological platform that can detect and
quantify the total metabolome. Most often, extraction protocols of polar and semi-
polar compounds are used in the metabolomics studies, as they capture a larger
chemical diversity range. The most common technological platforms used for me-
tabolome analysis are liquid or gas chromatography coupled with mass spectrome-
try (LC–MS or GC–MS), capillary electrophoresis coupled with mass spectrometry
(CE–MS), and nuclear magnetic resonance (NMR) spectroscopy. Each platform
has certain analytical limitations and a single platform can detect only a fraction of
the total metabolome. The combined use of multiple analytical techniques, if avail-
able, can increase the fraction of the observable metabolome. Depending on the
tissue, such a protocol and analytical technique will extract the components of the
M.-E. P. Papadimitropoulos and M. I. Klapa