Application Note # ET-30
Metabolic profiling of Arabidopsis thaliana secondary
metabolites using a maXis impact
Abstract
In this study we profiled metabolite extracts of wild type
and mutant Arabidopsis thaliana seedlings impaired in
flavonoid biosynthesis. Multivariate data analysis revealed
that several compounds were absent in the mutant strains.
MS/MS and Pseudo-MS³ data acquired on a maXis impact
high-resolution Q-TOF instrument enabled unambiguous
elemental composition determination for precursor and
fragment ions.
Correlating this information with hypothetical compound
structures using the FragmentExplorer™ enabled the
tentative identification of a flavonoid-glucoside: quercetin3-O-rhamnoside-7-O-glucoside. The aglycon structure
could be verified by comparing Pseudo-MS³ spectra with
reference spectra of a quercetin standard. Interestingly,
querying an MS/MS library that was measured on a
micrOTOF-Q with the quercetin aglycon Pseudo-MS³
spectrum measured on the maXis impact returned
the same candidate hit. This result demonstrates the
applicability of shared MS/MS data acquired on different
Q-TOF instruments – even in different labs – for fast
de-replication workflows.
Authors
Dr. Aiko Barsch, Dr. Verena Tellström, Dr. Sven Meyer,
Dr. Gabriela Zurek, Bruker Daltonics GmbH, Bremen,
Germany
Keywords
Instrumentation and Software
Metabolomics
maXis impact
Pseudo-MS 3
DataAnalysis
Structure elucidation
CompoundCrawler
Structure confirmation
SmartFormula3D
Library Search
FragmentExplorer
Flavonoids
Library Editor
ProfileAnalysis
Introduction
Identification of differentiating compounds by PCA
It is estimated that plants produce several hundred
thousand different secondary metabolites, the majority
of which remain unknown. Recent progress in plant
metabolomics has driven interest in identifying the vast
number of unknown compounds, a task that has – over the
last decades – been addressed in natural product research.
Mass spectrometry has always played an important role
in structure elucidation of plant secondary metabolites.
The maXis impact provides an unparalleled combination
of the MS performance features required to analyze highly
complex samples. When used in conjunction with software
solutions tailored for metabolomics studies, this potent
combination perfectly addresses the needs for accurate
and comprehensive small molecule identification in
metabolomics and natural product research.
Flavonoids are a large group of phenolic metabolites
estimated to contain ~7000 different compounds [1].
Biological functions of these plant secondary metabolites
include plant signaling, protection against abiotic and biotic
stresses, and pigmentation. Flavonoid intake in human
nutrition has been linked to risk factors in several diseases
– such as cancer, osteoporosis and cardiovascular diseases
– and has consequently led to special interest in this group
of compounds [1]. The complexity of flavonoid structures is
based on several phenolic (isomeric) aglycon structures that
can be glycosylated and acylated at different positions [2].
In this proof of concept study we compared metabolic
profiles of A. thaliana Columbia-0 (Col-0) wild type and tt4
and tt7 (transparent testa) mutant plants, in which flavonoid
biosynthesis is affected. Chalcone synthase (chs) catalyzes
the first step in flavonoid biosynthesis. A. thaliana tt4
mutants, which lack this enzyme activity, contain none
of the flavonoids detected in wild type plants [3]. The tt7
mutant plants, affected in flavonoid 3-hydroxylase activity,
do not contain quercetin and isorhamnetine flavonoid
derivatives.
Experimental
Metabolic extracts from wild type Col-0 and tt4 and tt7 A.
thaliana seedlings were prepared as described in [4].
Dried extracts were dissolved in 1 µL 80% methanol /
~1 mg original extracted plant fresh weight. These samples
were diluted 1:10 with water before UHPLC-MS analysis
Figure 1: Arabidopsis thaliana extracts derived from wild type (Col)
and 2 mutants with impaired flavonoid productions (tt4, tt7) were
analyzed on a maXis impact (Q-TOF) following UHPLC separation
on a C18 column.
(A) PCA scores and loadings plot of extracted molecular features.
(B) EIC traces for a selected differentiating loading with
m/z = 611.161 plotted in ProfileAnalysis 2.1 show high intensity in
wild type extract.
of three 2 µL replicates for each extract. Chromatographic
separation was carried out using an RSLC system (Dionex)
with a 100 x 2.1 mm Kinetex C18 (2.6µm, 100A) column
(Phenomenex), at a flow rate of 0.5 mL/min, with Solvent A:
Water + 0.1% HCOOH and Solvent B: Acetonitrile
+ 0.1% HCOOH. The following LC gradient program was
used: 5% B (0.5 min constant), linear increase from 5% B
to 45% B (over 9 min), linear increase to 95% B
(over 2 min), constant at 95% B (for 2 min).
MS detection was performed using a maXis impact UHRQq-TOF mass spectrometer. The instrument was operated
in ESI positive mode acquiring MS full scan and autoMS/MS
data at 2.5 Hz acquisition speed. In addition, Pseudo-MS³
spectra of flavonoid aglycons were acquired by applying
60 eV voltage between the two ion funnels in the entrance
region of the MS, thereby generating In-Source CID (ISCID)
fragments, which were subsequently isolated in the
analytical quadrupole and fragmented in the collision cell
(see Fig. 4).
ProfileAnalysis 2.1 was used for statistical data analysis
based on features extracted by the FindMolecularFeatures
(FMF) algorithm, which can combine all ions belonging
to the same compound (isotopes, charge states, adducts
and common neutral losses). Further data evaluation was
performed using DataAnalysis 4.1 software. Molecular
formula determination was carried out by combined
evaluation of mass accuracy, isotopic patterns, adduct
and fragment information using SmartFormula3D. The
FragmentExplorer enabled the correlation of compound
structures with MS/MS fragment information. MS/MS
spectra were stored and queried in the LibraryEditor.
Results
Data pre-processing and statistical analysis
Applying the FindMolecularFeatures (FMF) peak finder is
an important step in data pre-processing before statistical
analysis of metabolomics datasets. This FMF algorithm
combines ions belonging to one compound, such as
common adducts (e.g. +Na, +K, +NH4), isotopes and
charge states. Features were extracted using this strategy
from high-resolution full scan MS data acquired from each
of the three Col-0 (wild type), tt4 and tt7 extract replicates.
A Principal Component Analysis (PCA) and Hierarchical
Clustering Analysis (HCA) were calculated based on the
extracted features. The PCA scores plot shown in Figure 1A
reveals a clear separation of the three sample types, as was
also observed in the dendrogram of the HCA (not shown).
Several loadings indicated compounds mainly responsible
for the observed sample differentiation. A compound
with m/z = 611 constituted a major difference between
Col-0 and mutant A. thaliana plants. Plotting extracted ion
chromatograms (EICs) for this compound within all samples
using ProfileAnalysis software confirmed the higher
abundance of this metabolite in wild type plants (Fig. 1B).
Compound Identification: SmartFormula3D and
FragmentExplorer
The SmartFormula tool integrated into ProfileAnalysis
software was used to elucidate the identity of the target
compound with m/z = 611. The molecular formula was
calculated based on accurate mass and isotopic pattern
information. Three possible formulas were generated in a
1 ppm mass accuracy window (Fig. 2). The mSigma value
is a measure for the quality of the fit between measured
and theoretical isotopic pattern, with a low mSigma value
indicating a good match. The candidate with the lowest
mSigma value corresponded to C27H 31O16 . A query of this
elemental composition in public databases (Chemspider,
Metlin, KEGG*) using the CompoundCrawler tool was
triggered from the ProfileAnalysis software and returned
Molecular Formula Generation by SmartFormula
Figure 2: Molecular formula generated by SmartFormula based on accurate mass and isotopic
pattern information returned C27H 31O16 as the hit with the lowest mSigma value (isotopic fit).
A CompoundCrawler database search suggested quercetin-3-O-rhamnoside-7-O-glucoside as
a possible structure.
Identification of quercetin-3-O-rhamnoside-7-O-glucoside
MS/MS
FragmentExplorer
MS
MS/MS
SmartFormula3D
Figure 3: Identification of quercetin-3-O-rhamnoside-7-O-glucoside was enabled by high-resolution accurate mass maXis impact MS/MS
data combined with SmartFormula3D results and FragmentExplorer structural assignment.
quercetin-3-O-rhamnoside-7-O-glucoside as a possible
structure for the target compound.
For further elucidation of the compound structure, an MS/
MS spectrum was acquired using the maXis impact. The
metabolite with m/z = 611 showed a neutral loss of C 6 H10 O 5
and C12H20 O 9, which is in accordance with a successive loss
of a hexose (e.g. glucose) and deoxyhexose (e.g. rhamnose)
unit (Fig. 3). Accurate mass and isotopic pattern matching
by SmartFormula3D provided C15 H11O 7 as the elemental
composition for fragment mass m/z = 303, indicating that
quercetin is the aglycon of this flavonoid glycoconjugate.
This approach was also able to limit the possible elemental
compositions for the parent ion to a unique hit, C27H 31O16 .
Pseudo-MS 3 spectra can identify flavonoid aglycon
structure
A Pseudo-MS³ experiment was performed to confirm
quercetin as the aglycon of the flavonoid glycoconjugate.
The maXis impact allows a voltage difference to be applied
between the two ion funnels in the entrance region of the
MS system (see Fig. 4). This can lead to a collision-induced
dissociation (CID) of compounds to generate fragment ions
“in source”, and therefore this type of fragmentation is
termed In-Source CID (ISCID). The fragmentation
mechanism here is similar to the CID fragmentation that
takes place in the collision cell of the MS and therefore
creates the same fragment ions. By applying ISCID
fragmentation, the aglycon fragment (m/z = 303) was
generated in the ion funnel region and subsequently
isolated in the analytical quadrupole before fragmentation
in the collision cell. This Pseudo-MS³ spectrum (“pseudo”
because the precursor m/z = 611 was not isolated before
ISCID fragmentation) was matched against a library MS²
spectrum of a quercetin standard measured on a maXis
impact.
Very highly matching values (Fit, R-Fit, Purity) confirmed
quercetin as the aglycon structure (see Fig. 5 A+B). Based
on this strategy, the compound could be tentatively
identified as quercetin-3-O-rhamnoside-7-O-glucoside, a
flavonoid that was shown to be absent in A. thaliana tt4 and
tt7 mutants in previous studies [4, 5, 6].
In addition to comparing the Pseudo-MS³ spectrum
of quercetin with the standard measured on the same
instrument type, the spectrum was queried against an
MS/MS library containing spectra from 33 purified
flavonoids measured on a micrOTOF-Q instrument. This
micrOTOF-Q data was generated in a different laboratory
under conditions not matching the parameters used in this
study [2]. As shown in Figure 5C, quercetin was returned
as hit in this library with a good match of fragment ions and
intensities. This observation demonstrates the possibility
of sharing MS/MS data acquired on different Q-TOF
instruments between different labs for quick identification
of known compounds.
Conclusion
The maXis impact provides a unique combination of mass
accuracy, resolution, dynamic range, sensitivity and
MS/MS performance, making this instrument the tool of
choice for analyzing highly complex samples.
These technical features – especially when combined with
powerful, dedicated software – enable straightforward
determination of characteristic compounds by statistical
analysis (ProfileAnalysis) and highly accurate and precise
structural confirmation and elucidation by SmartFormula3D
and the FragmentExplorer.
High resolution, mass accuracy and isotopic fidelity – even
for low mass fragment ions – form the basis for safely
and quickly assigning molecular formulas to precursor and
fragment ions using SmartFormula3D.
Based on well-known ChemDraw™ technology,
FragmentExplorer provides an interactive link between
SmartFormula3D results, mass spectra and molecular
structures. Designed to enable faster evaluation of
MS/MS data, the FragmentExplorer enables adding
fragment and precursor structures as annotations, making
reporting and publication of interpreted data an easy task.
Dedicated MS/MS interpretation incorporating structural
assignments allows establishment of well-characterized
MS/MS libraries that can be used to quickly identify
compounds in other metabolite extracts. Of special
interest in this study was the fact that MS/MS libraries
shared between laboratories using different hardware and
acquisition parameters proved useful in the identification of
a flavonoid aglycon.
Acquisition of MS3 product ions
impact
Glycosylated Flavonoid
Dual-stage reflector
In-source CID
in ion funnel region
Aglycon
Isolation of aglycon
in Q and fragmentation
in collision cell
Pseudo-MS 3 of aglycon
ESI source
Fragmented aglycon
Patented Dual
Ion Funnel
Quadrupole
High transmission
CID cell
Figure 4: Acquisition of MS 3 product ions by combining In-Source CID (ISCID) with Q isolation and fragmentation within the
collision cell (Pseudo-MS 3 ). Schema shows quercetin-3-O-rhamnoside-7-O-glucoside fragmentation.
Library query of Pseudo-MS3 spectrum
A
B
C
A: Pseudo-MS 3 query spectrum; B: Quercetin standard measured on a maXis impact;
C: Quercetin standard measured on a micrOTOF-Q
Figure 5: Library query of measured pseudo-MS 3 spectrum confirms the aglycon structure to be quercetin: Matching the query spectrum
(A) with a library spectrum from a quercetin standard measured on maXis Impact (B) gives very high purity, fit and reversed fit values.
(C) MS/MS spectrum of quercetin from an MS/MS library measured on a micrOTOF-Q (provided by Anna Staszków and Maciej Stobiecki,
Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poland [2]) shows the same fragmentation pattern observed in the maXis
impact spectra.
References
and Applications. CRC Press Taylor & Francis Group,
Boca Raton, FL, 2006
[2] Stobiecki, M. et al.: LC-MSMS Profiling of Flavonoid Conjugates in Mexican Wild Lupine, Lupinus reflexus, Journal of Natural Products (2010) 73:1254-1260
[3] Shirley B.W. et al.: Analysis of Arabidopsis mutants deficient in flavonoid biosynthesis. The Plant Journal (1995) 8:659-671
[4] Stracke R. et al.: Differential regulation of closely related R2R3-MYB transcription factors controls flavonol glycosides-dependent flavonol glycoside accumulation in Arabidopsis thaliana plants reveals MYB11-, MYB12- and MYB111- independent flavonol glycoside accumulation.
New Phytol (2010) 188:985-1000
[6] Böttcher C. et al.: Metabolome Analysis of Biosynthetic Mutants Reveals a Diversity of Metabolic Changes and Allows Identification of a Large Number of New Compounds in Arabidopsis. Plant Physiology (2008) 147:2107-2120
Acknowledgments
accumulation in different parts of the Arabidopsis thaliana Special thanks to Dr. Ralf Stracke (Department of Biology,
seedling. The Plant Journal (2007) 50, 660-677
Genome Research, Bielefeld University, Bielefeld, Germany)
for providing the A.thaliana metabolite extracts as well as to
Dr. Anna Staszków and Prof. Maciej Stobiecki
(Institute of Bioorganic Chemistry, Polish Academy of Sciences,
*
Poland) for providing the MS/MS library of flavonoid standards
measured on a micrOTOF-Q.
For research use only. Not for use in diagnostic procedures.
Bruker Daltonik GmbH
Bruker Daltonics Inc.
Bremen · Germany
Phone +49 (0)421-2205-0
Fax +49 (0)421-2205-103
sales@bdal.de
Billerica, MA · USA
Phone +1 (978) 663-3660
Fax +1 (978) 667-5993
ms-sales@bdal.com
www.bruker.com/maxis
Fremont, CA · USA
Phone +1 (510) 683-4300
Fax +1 (510) 490-6586
ms-sales@bdal.com
to change specifications without notice. © Bruker Daltonics 05-2012, ET-30, #700921
[[1]Anderson, O.M. et al.: Flavonoids: Chemistry, Biochemistry Bruker Daltonics is continually improving its products and reserves the right
[5] Stracke R. et al.: Analysis of production of flavonol