Bioinformatic Tools in Crop Improvement - Crop Improvement: New Approaches and Modern Techniques - page 88

InstrumentsinMetabolomics
Many technological advances have recently been made in instrumentation related
to metabolomics. Metabolomics data starts with the acquisition of metabolic finger-
prints using these analytical instruments (Fiehn et al. 2000 ; Roessner et al. 2001 ;
Fernie et al. 2004 ). Methods for sample separation, such as gas chromatography
(GC), high-performance or ultra performance liquid chromatography (LC) and cap-
illary electrophoresis (CE) are used, in conjunction with various types of MS. CE-
MS is especially effective, because it is a highly-sensitive method for separating
and analysing biological molecules (Ramautar et al. 2009 ). Detailed below, are the
URL web pages for explanation, methodology and instruments used in metabolo-
mics, and outlined in Table 2.9 .
QMS and TOF MS
QMS is especially usefull in metabolomics. TOF MS are also well regarded for use
in metabolomics. Triple Q (QqQ) MS (a tandem-type MS) and Q-TOF (a hybrid
type MS) are also in common use.
FT-ICR MS
Methods that do not involve pre-separation of samples can be used, e.g. FT-ICR
MS, are often employed, allowing for MS analysis even on quite crude plant prepa-
rations (Werner et al. 2008 ).
NMR Based Methods
NMR-based methods are used in metabolomic analysis (Dixon et al. 2006 ; Schripse-
ma 2009 ). These methods can be broadly classified into solution NMR and insolu-
ble or solid-state NMR, according to sample solubility. High-resolution (hr)-MAS
techniques can generate suitable metabolic fingerprints from insoluble samples
and solid-state preparations (Bertocchi and Paci 2008 ). One-dimensional NMRis
where protons (1 H) are observed (1 H-NMR), however more detailed analyses
and metabolite identification and/or flux analysis can be obtained with other nuclei,
particularly 13 C and 15 N (Kikuchi et al. 2004 ; Sekiyama and Kikuchi 2007 ).
Processed sets of data are subsequently used to identify metabolites corresponding
to each spectrum signal by searching against standard compound databases. In non-
target analyses, spectrum data sets that include spectra of unknown compounds are
subjected to statistical analyses, such as multivariate analysis (Sect. 5.1.8 below), to
mine data for biological significance (Tikunov et al. 2005 ). In target analyses, spec-