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\begin{document}

\title{Exact mass GC-MS analysis: protocol, database, advantages and
application to plant metabolic profiling}



\author[1]{Cyril Abadie}%
\author[1]{Julie Lalande}%
\author[1]{Guillaume Tcherkez}%
\affil[1]{Institut de Recherche en Horticulture et Semences}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Plant metabolomics has been used widely in plant physiology, in
particular to analyse metabolic responses to environmental parameters.
Derivatization (via trimethylsilylation-methoximation) followed by GC-MS
metabolic profiling is a major technique to quantify low molecular
weight, common metabolites of primary carbon, sulphur and nitrogen
metabolism. There are now excellent opportunities for new generation
analyses, using high resolution, exact mass GC-MS spectrometers that are
progressively becoming relatively cheap. However, exact mass GC-MS
analyses for routine metabolic profiling are not common, there is no
dedicated available database. Also, exact mass GC-MS is usually
dedicated to structural resolution of targeted secondary metabolites.
Here, we present a curated database for exact mass metabolic profiling
(made of 336 analytes, 1,064 characteristic exact mass fragments)
focused on molecules of primary metabolism. We show advantages of exact
mass analyses, in particular to resolve isotopic patterns, localise
S-containing metabolites, and avoid identification errors when analytes
have common nominal mass peaks in their spectrum. We provide a practical
example using leaves of different \emph{Arabidopsis} ecotypes and show
how exact mass GC-MS analysis can be applied to plant samples and
identify metabolic profiles.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Plant metabolome analysis is now of crucial importance to describe
responses to environmental conditions and thus understand associated
metabolic mechanisms. Standardised metabolomics protocols have been
proposed to characterise crop metabolism (Zheng, Johnson, Mandal, \&
Wishart, 2021). The term ``metabolomics'' is used to refer to techniques
exploited to investigate ``small'' biological molecules (metabolites),
i.e., extractible molecules with limited molecular weight, usually less
than 500 atomic mass units (a.m.u.) (Roessner \& Bowne, 2009). Two
mainstream mass spectrometry techniques can be used: gas chromatography
coupled to mass spectrometry (GC-MS) and liquid chromatography coupled
to mass spectrometry (LC-MS) (Allwood et al., 2011; Perez de Souza,
Alseekh, Naake, \& Fernie, 2019). The output of metabolomics is a
dataset of metabolic features (m/z metabolite peaks with retention time
in LC-MS; analytes resulting from metabolite derivatisation in GC-MS)
that can be used for statistics and detect metabolic changes between
samples.

GC-MS metabolomics (also sometimes referred to as metabolic profiling)
has been used extensively in plants under many conditions, species, or
genetic backgrounds: a simple search with a literature database with
query keywords `plant', `metabolomics' and `gc-ms' returns 43,700
entries. When restricted to Arabidopsis, it returns 13,500 entries. It
shows the massive utilisation of GC-MS for plant physiology and
molecular biology. In particular, this technique is very useful to have,
in a single sample analysis, a relative quantitation of most important
metabolites of plant primary metabolism, such as amino acids, small
soluble sugars, polyamines, or organic acids. It has thus been used to
describe the response of C and N primary metabolism to major
environmental cues, for example, herbivores (Jansen et al., 2009),
CO\textsubscript{2} mole fraction (H\selectlanguage{ngerman}ögy, Keck, Niehaus, Franzaring, \&
Fangmeier, 2010; Misra \& Chen, 2015), drought (Bowne et al., 2012;
Sanchez, Schwabe, Erban, Udvardi, \& Kopka, 2012), nutrient conditions
(Cui, Abadie, Carroll, Lamade, \& Tcherkez, 2019; Cui, Davanture,
Lamade, Zivy, \& Tcherkez, 2021; Cui, Davanture, Zivy, Lamade, \&
Tcherkez, 2019), or abiotic stress combinations (Ghatak, Chaturvedi, \&
Weckwerth, 2018; Nakabayashi \& Saito, 2015; Shulaev, Cortes, Miller, \&
Mittler, 2008).

To date, the vast majority of GC-MS analyses for metabolic profiling
utilise nominal mass acquisition (i.e. at a.m.u. resolution).
Accordingly, databases associated with GC-MS metabolomics such as the
Golm Metabolomics Database (GMD) provide spectral data at a.m.u.
resolution, and in a recent review of metabolomics resources, only
nominal mass databases are discussed for GC-MS analyses (Vinaixa et al.,
2016). In other words, to our knowledge, there is no directly
accessible, high resolution (exact mass, i.e. at 0.0001 a.m.u.
resolution or lower) and comprehensive GC-MS resource for plant
metabolomics. This lack of curated, accessible and available resource
for GC-MS analyses has three origins: (\emph{i} ) The availability of
(affordable) exact mass GC-MS instruments is relatively recent, since
the implementation of the orbitrap technology took place in the 2000s
(Makarov, 2000; Makarov, Denisov, \& Lange, 2009; Peterson, McAlister,
Quarmby, Griep-Raming, \& Coon, 2010) and the description of standard
practices for high resolution GC-MS analyses has been proposed in 2021
only (Misra, 2021); (\emph{ii} ) Many ordinary applications of GC-MS
metabolomics profiling do not require exact mass resolution since they
are targeted on common, well-known compounds; and (\emph{iii} ) Whenever
high resolution is required, LC-MS can be used. The use of high
resolution in LC-MS may be important using full scan analyses, because
there is a limited number of fragments (mainly parental ion and adducts)
and therefore, identification essentially relies on both exact mass and
isotopic pattern (De Vos et al., 2007; Kind \& Fiehn, 2006). By
contrast, in GC-MS analyses, the fragmentation pattern along with the
retention index are used to identify analytes, with generally good
accuracy. Several tools have been recently proposed to automatically
annotate ions or fragments in mass spectra, in particular from LC-MS
spectral data, for example in (Doerfler et al., 2014; Gaquerel, Kuhl, \&
Neumann, 2013; Matsuda et al., 2011; Qiu, Fine, Wherritt, Lei, \&
Sumner, 2016).

However, there are circumstances where high mass resolution may be
desirable with GC-MS, since (\emph{i} ) several compounds with the same
retention time could generate fragments with the same nominal mass and
(\emph{ii} ) it could be useful to distinguish isotopic species
(isotopologues) using their mass difference (for example, there is a
mass excess of +1.003355 Da with \textsuperscript{13}C while it is
+1.006277 Da with \textsuperscript{2}H), and (\emph{iii} ) one may
desire to perform untargeted GC-MS analyses with broad chemical
coverage. Here, we describe an exact mass GC-MS method for high
resolution routine plant metabolic profiling and provide the associated
curated database, checked with authentic standards. This allows us to
address aspects (\emph{i} ) and (\emph{ii} ) directly. We also provide
the list of current compounds having similar nominal-mass fragments and
similar retention time but can be distinguished easily with exact mass,
avoiding quantification errors. We also take advantage of sulphur
isotopes at natural abundance to allow the identification of
S-containing fragments in datasets. Finally, we applied our protocol and
the database using Arabidopsis leaves to show how it can be applied to
real samples, allowing facile differentiation of genetic accessions.

\textbf{Material and methods}

\emph{Chemicals}

Chemicals were from Sigma Aldrich (Merck; with reference numbers between
parentheses): Mass Spectrometry Metabolite Library (MSMLS, IROA
Technologies), 2-Oxoadipic acid (75447), 3- Iodo-L-Tyrosine (I8250),
5-Aminolevulinic acid (08339), \selectlanguage{greek}α-\selectlanguage{english}Tocopherol (T3251), \selectlanguage{greek}β-\selectlanguage{english}Gentiobiose
(G3000), Betain (W422312), Chlorogenic acid (C3878), Choline (C1879),
Citraconic acid (C82604), D-2-Aminobutyric acid (116122), D-3
phosphoglyceric acid (P8877), D-Erythronic acid (75025), D-Erythronic
acid \selectlanguage{greek}γ-\selectlanguage{english}lactone (374385), D-Galacturonic acid (48280), D-Glucuronic acid
(G5269), Diaminopimelate (33240), D-Melibiose (92413), D-Pinitol
(441252) , D-Quinic acid (138622), D-Threitol (377619), D-Xylonic acid
\selectlanguage{greek}γ-\selectlanguage{english}lactone (89339), Dulcitol (D0256), Galactinol (79544), Hydrocinnamic
acid (135232), L-\selectlanguage{greek}α-\selectlanguage{english}Glycerophosphocholine (G5291), Itaconic acid
(I29204), L-carnitine (C0283), L-Cystathionine (C7505), Levoglucosan
(06724), L-Fucose (F2252), L-Pyroglutamic acid (83160), Nicotinic acid
(N4126), Norleucine (N6877), O-Acetyl-Serine (CDS020792), Phosphocholin
(P0378), Sigmasterol (S2424), Sinapinic Acid (D7927), Mystiric acid
(M3128), Tryptamine (193747), Urea (U5378), Xanthosine (CDS020790).

\emph{Plant material}

The height \emph{Arabidopsis thaliana} accessions were obtained from
INRA Versailles Genomic Resource Centre VNAT collection: An-1 (96AV,
Belgium), Bl-1 (42 AV, Italy), Col-0 (186AV, Poland), Cvi-0 (166AV, Cape
Verde), Ge-0 (101AV, Switzerland), Mt-0 (94AV, Libya), Oy-0 (224AV,
Norway) and Shahdara (Tadjikistan). Plants were grown in a growth
chamber under short photoperiod (8h light/16h dark), PPFD = 150 \selectlanguage{greek}µ\selectlanguage{english}mol
m\textsuperscript{-2} s\textsuperscript{-1}, temperature 22\selectlanguage{ngerman}°C/18°C and
relative humidity 60\%. Plants were watered every 3 days and once a week
with Plant-Prod (15-10-30, N-P-K, Fertil). Fifty-five (55) days after
sowing, two fully developed leaves were sampled on each rosette and
instantly quenched in liquid nitrogen. Frozen samples were then
frozen-dried, dry material was grinded and 5 mg of dry powder used for
metabolites extraction by adding 400 \selectlanguage{greek}µ\selectlanguage{english}L of methanol/water (90/10) with
adonitol (55 \selectlanguage{greek}µ\selectlanguage{english}M) as an internal standard.

\emph{GC-MS analyses}

A detailed, step-by-step protocol is provided as a Supplementary
Material. This section summarizes how exact mass GC-MS analyses and data
extraction with TraceFinder\selectlanguage{ngerman}® were performed. GC-MS analyses were carried
out using a GC-MS-Orbitrap Q Exactive (Thermo Scientific, Courtaboeuf,
France). Fifty five (55) \selectlanguage{greek}µ\selectlanguage{english}L of each standard solution (50~\selectlanguage{greek}µ\selectlanguage{english}M), or 4 \selectlanguage{greek}µ\selectlanguage{english}L
from the plant leaf extract, were poured into a vial (with insert) and
spin-dried at 39\selectlanguage{ngerman}°C. Samples were derivatized (automatically with a
preparative robot) with 20 \selectlanguage{greek}µ\selectlanguage{english}L methoxyamine (20 mg
mL\textsuperscript{-1}in pyridine; 90 min at 37\selectlanguage{ngerman}°C) and 30 \selectlanguage{greek}µ\selectlanguage{english}L
N-methyl-N(trimethylsilyl)-trifluoroacetamide (MSTFA) for 30 min at
37\selectlanguage{ngerman}°C. Prior to injection, 5 \selectlanguage{greek}µ\selectlanguage{english}L of alkane mix (14 alkanes from
C\textsubscript{9} to C\textsubscript{36}, 3 \selectlanguage{greek}µ\selectlanguage{english}g \selectlanguage{greek}µ\selectlanguage{english}L\textsuperscript{-1},
Connecticut n-Hydrocarbon Mix, Supelco) were added in each sample to
compute the retention index. Analyses were performed by injecting 1 \selectlanguage{greek}µ\selectlanguage{english}L
in splitless mode at 230\selectlanguage{ngerman}°C (injector temperature) in a TG-5 SILMS column
(30 m x 0.25 mm x 0.25 \selectlanguage{greek}µ\selectlanguage{english}m; Thermo Scientific) set in a Trace 1300 Series
GC (Thermo Scientific). Helium was used as gas carrier with a constant
flow of 1 mL min\textsuperscript{-1}. After one minute at initial GC
oven temperature (70\selectlanguage{ngerman}°C), temperature was raised to 325°C at 15°C
min\textsuperscript{-1} and finally kept at 325°C for 4 min. MS analyses
were operated in positive polarity in full MS scan mode with the
following source settings: mass scan range 50-750 m/z, resolution
60,000, AGC target 1E6, MS transfer line 300°C and filament delay 4.12
min. Ionisation by electron impact (70 eV) was performed at 250°C ion
source temperature. Amino acids were identified automatically using
TraceFinder® (Thermo Scientific) using retention time, major
characteristic fragment (m/z ion) and a confirmation fragment, with a
maximum tolerance of 0.00007 Da via targeted screening.

\textbf{Results}

\emph{Overview of GC-MS analysis}

As an example, the chromatogram associated with an
\emph{Arabidopsis}leaf sample derivatised with methoxamine and\emph{N}
-methyl-\emph{N} -(trimethylsilyl)trifluoroacetamide (MSTFA) in pyridine
is shown in Fig. 1, where the signal shown in panel (a) is total ion
current (TIC). Abundant compounds form peaks that can be easily seen on
the chromatogram, including some amino acids (e.g. proline), organic
acids (fumarate) and sugars (e.g. sucrose), as trimethlysilylated
(--Si(CH\textsubscript{3})\textsubscript{3}, abbreviated TMS)
derivatives. The TIC signal does not directly reflect the quantity (in
moles) in the sample since analytes do not have all the same response
coefficient in the mass spectrometer. The internal standard used for
semi-quantitation (i.e. relative quantification) is adonitol (ribitol),
the derivative of which (ribitol 5TMS) elutes at 10.68 min. As expected,
there is some overlapping between analytes (i.e. several analytes have
very close retention time and co-elute). This is visible, for example,
at 15.05 (panel (b) of Fig. 1) where many m/z features related to sugars
are visible together with 6-phosphogluconate specific m/z features (for
example at 387.14254 Da). Interestingly, a relatively close mass
(387.32912 Da) can be found at a similar retention time (15.25 min) from
arachidic acid 1TMS, but such a mass difference is in practice too large
(nearly 0.18 Da) to allow confusion with exact mass analysis.

\emph{Mass resolution and isotopic pattern}

Mass resolution across the m/z window (50-750 Da) was not found to be
constant, varying from less than 0.0001 Da at low mass (\textless{} 100
Da) to 0.0017 Da at higher mass (\textgreater{} 350 Da), i.e. within \selectlanguage{english}[?]1
and 5 ppm. In principle, this should allow to solve the natural isotopic
pattern and distinguish \textsuperscript{13}C, \textsuperscript{15}N,
and \textsuperscript{33}S isotopologues (\emph{m} +1)
and\textsuperscript{13}C\textsubscript{2} and \textsuperscript{34}S
isotopologue (\emph{m} +2). However, this depends on whether analytes of
interest are silylated or not, because Si has two isotopes
(\textsuperscript{29}Si and \textsuperscript{30}Si), with mass excess
values (with respect to the monoisotopic species) very close to that
of\textsuperscript{33}S and \textsuperscript{34}S (sulphur-containing
species are addressed further below). This is illustrated in Fig. 2,
using a non-silylated compounds (naturally volatile and thus not
requiring derivatisation), isopropyl-4-methylthiazole (IMT). In fact,
using the example of the major fragment (molecule minus
CH\textsubscript{3}) (Fig. 2a),
\textsuperscript{15}N,\textsuperscript{33}S, \textsuperscript{2}H and
\textsuperscript{13}C isotopologues are resolved (Fig. 2b,c). With
methionine 2TMS (Fig. 2d), the presence of Si does not allow full
resolution of the isotopic pattern. This is shown using the \emph{m} +2
isotopologues (Fig. 2e) where some isotopic species are found to be
undistinguishable (\textsuperscript{34}S, \textsuperscript{30}Si).
Despite this limitation, the expected isotopologue abundance (predicted
from isotope abundance and elemental composition) is very close to
observed abundance (Fig. 2f). A difference between observed and
predicted abundance is visible for the deuterated isotopologue of IMT
(red circle), likely due to the very small natural abundance of
deuterium (0.015\%) and thus the larger imprecision in quantitation.
Also, it should be noted that\textsuperscript{13}C isotopologues are
well-separated (resolved) from other isotopologues, allowing facile
monitoring of \textsuperscript{13}C molecules during plant labelling
experiments, for example.

\emph{Differentiation of metabolites with exact mass}

Exact mass allows one to distinguish m/z features with the same nominal
mass but with very slight differences (well below 0.1 Da), reflecting
differences in elemental composition. This is particularly useful when
retention time of analytes at the origin of similar m/z features are
very close. We took advantage of GC-MS exact mass analyses of authentic
standards used to construct the database (available as a Supplementary
Files), to tackle this problem. Amongst most common plant metabolites,
many analytes were found to have both similar retention time and
identical nominal mass m/z signals, and they are listed in Table 1. This
problem can potentially lead to identification mistakes with nominal
mass analyses especially when mass spectra are rather similar. Two
examples are further illustrated in Fig. 3. Ferulic acid 2TMS and
tyramine 2TMS have the same retention time (12.09 min) and share the
same nominal mass ion 338 a.m.u. However, there is a clear difference in
exact mass (Fig 3a,b). Also, their spectra share little similarity (Fig.
3a). Malic acid 3TMS and threitol 4TMS have a retention time difference
of less than 4 s and share a feature at a nominal mass of 189. In
addition, their mass spectra are partly similar, in particular for
features \textless{} 200 a.m.u (Fig. 3c). However, the exact mass of
features at 189 a.m.u. is readily distinguishable (189.11309 and
189.076741 Da) (Fig. 3d).

\emph{Arginine derivatives}

In terms of biochemical analysis, one of the most problematic
metabolites of plant primary N metabolism is arginine, because it can be
converted to several by-products upon derivatisation. It is particularly
so during silylation, since the guanidium group of arginine may be
cleaved to form ornithine, which in turn can lead to a cyclic form,
ornithine lactam. In addition, one N atom of arginine can be lost and
form citrulline. Taken as a whole, arginine derivatisation generates
ornithine lactam 2TMS, ornithine 3TMS, arginine 3TMS and citrulline 3TMS
(Fig. 4a). Arginine 3TMS is the minor derivative and has a retention
time extremely close to citrulline 3TMS, and therefore it has often been
overlooked. Here, arginine 3TMS is visible on the TIC, can also be
identified with certainty with exact mass and thus cannot be confused
with citrulline 3TMS. Although small, features typical of arginine 3TMS
and citrulline 3TMS can be found at 187.108676 Da (guanidium group 2TMS)
and 171.08278 Da (decarboxylated citrulline), respectively (Fig. 4b-e).

\emph{Identification of S-containing analytes}

The resolution of the isotopic pattern can be exploited to gain
information on uncommon (or less common) elements contained by analytes.
Nitrogen \textsuperscript{15}N is not sufficiently abundant (0.36\%
only) and can be overlapped by \textsuperscript{29}Si (4.7\%)
and\textsuperscript{33}S (0.76\%) isotopes. Therefore, the use of the
isotopic pattern to detect specific elements in analytes is only
suitable for sulphur, provided (\emph{i} ) the signal coming
from\textsuperscript{34}S is high enough (\textsuperscript{34}S
abundance is 4.2\%), and (\emph{ii} ) it can be somehow distinguished
from\textsuperscript{30}Si. The mass excess of the \textsuperscript{34}S
isotopologue is +1.995796 while that of \textsuperscript{30}Si is
+1.996844, meaning a difference of 0.001048 Da (i.e., 1.048\selectlanguage{english}%0). If mass
resolution during analysis is sufficient, mass excess at nearly exactly
+1.995796 can be found. A similar analysis with high resolution LC-MS
(without the issue of Si isotopes) has been undertaken previously
(Nakabayashi \& Saito, 2017). In Fig. 5, automatic searching of mass
excess of +1.995796 is shown, with the difference (in \selectlanguage{english}%0) as a function
of feature m/z. Using a mixture of authentic standards that includes
methionine and cysteine (Fig. 5a), only 9 (out of more than 2,000)
features were found to fall in the mass excess +1.995796+-0.00005 (red
shaded area). From these, manual checking allows confirmation of 3 of
them, another one (labelled as \textbf{4} in Fig. 5a) being
ambiguous:\textbf{1} and \textbf{2} are S-containing fragments of
cysteine 2TMS (61.011195 and 131.035071 Da at 5.61 min), while
\textbf{3} is a fragment of methionine 2TMS (176.092921 at 9.12 min).
\textbf{4} appears at the retention time of methionine 2TMS but in the
spectrum, the peak is too wide to ascertain that it is not another
fragment with\textsuperscript{30}Si (i.e., other fragments without S
with nearly the same exact mass). Using a plant sample (Arabidopsis leaf
extract, Fig. 5b), about 10 features fall in the +-0.05\selectlanguage{english}%0 window, of which
two can be confirmed further: \textbf{1} is the same as in Fig. 5a
(S-containing fragment of cysteine 2TMS) and \textbf{5} is
C\textsubscript{3}H\textsubscript{6}S (74.018421 Da) and comes from
methionine 2TMS, at 9.12 min. The appearance of this feature is further
illustrated in Fig. 5c, where it is found to be very close to the
isotopic pattern of another methionine 2TMS fragment
(C\textsubscript{2}H\textsubscript{6}OSi). Of course, looking at the
mass excess (due to \textsuperscript{34}S) with a high precision in
Figs. 5a and 5b is only possible if resolution is high enough to have a
better probability to have an acquired m/z datapoint exactly at, or very
close to +1.995796. This is illustrated in Fig. 5d-f, where the observed
signal (grey) is likely to show a mass signal coming
from\textsuperscript{34}S at medium and high resolution. This can
optimally be achieved for relatively small features (m/z \textless{} 150
a.m.u.) for which resolution is larger.

\emph{Application to plant samples}

The compound database used here contains 1,064 target fragments that
have all been confirmed with standards, representing 336 compounds with
target identification exact mass features, and confirming exact mass
features (see Supplementary File 1). Using Arabidopsis as a plant model,
we found 234 compounds in leaves (266 analytes, some of them generating
several derivatives). We assessed the usefulness and repeatability of
exact mass analysis by conducting GC-MS analyses on several Arabidopsis
ecotypes (BL1, AN1, Col0, Cvi0, Oy0, Ge0, Mt0 and Sha). Results are
shown in Fig. 6 as a heatmap of analytes that are significantly
different between ecotypes. Hierarchical clustering shows easy
discrimination of samples (without any clustering error between
ecotypes), suggesting good repeatability. Many metabolites were found to
be different between ecotypes, and they are here subdivided into 7
groups (Fig. 6a). For example, Cvi0 was found to be enriched in various
amino acid derivatives, while Shahdara was enriched in sugars.
Repeatability (both technical and biological) was assessed by
calculating standard errors, expressed in \% of mean value, for each
genotype. For the vast majority of metabolites, there was a very good
reproducibility, with a median value of SE of 9.7\% only across all
genotypes (Fig. 6b). Some metabolites were associated with a high
variability, larger than 40\%. They were all metabolites present in very
low amount, and/or having very poor ionisation, and/or encountering
degradation during derivatisation, such that their relative signal was
extremely small, 10\textsuperscript{-5} or less (Fig. 6c).

\textbf{Discussion}

\emph{Exact mass GC-MS database for routine analysis}

The present curated database contains 336 compounds, 234 of them being
identified and quantified in Arabidopsis leaves. This might appear
relatively small compared with the total estimated number of small
metabolites (several thousands) in plants. However, this compares well
with most targeted routine GC-MS analyses for metabolic profiling, which
yield a list of about 80-100 metabolites in the vast majority of cases
(for example, there are 162 metabolites in (Cui, Davanture, et al.,
2019), and 178 in (Cui et al., 2021) found in leaves). Of course, the
ability of instruments and softwares to extract a proper dataset from
raw data using the database depends on the quality of analyses. In
effect, despite the considerable dynamic range of modern instruments
(here, 6 orders of magnitude in peak height), precise quantification can
only be carried out when analytes are not too concentrated (inadequate
peak shape do not allow peak extraction by softwares like Tracefinder(r))
(Kaufmann \& Walker, 2017). This can be challenging when some
metabolites are present in high amounts (e.g. sucrose or proline) while
others are present in trace amounts or generate a weak signal (e.g.
salicylamide) (Fig. 6). It should be noted that data extraction from raw
data can also be processed via untargeted peak searching, providing a
much more powerful way to appreciate the diversity of molecules present
in extracts (Perez de Souza et al., 2019). However, this has two
drawbacks: (\emph{i} ) processing time is very long (at least 20 times
slower with Tracefinder(r)), and (\emph{ii} ) many peaks would appear as
unidentified, with only the m/z value and retention time (and thus
post-hoc identification is required using exact mass and potentially,
co-occurring fragments). Therefore, for routine analyses, it is probably
more convenient to rely on targeted analyses with the database we
propose here.

\emph{Advantages of exact mass analysis}

Nominal mass GC-MS analyses rely on two main criteria: retention index,
and mass spectrum (Garcia \& Barbas, 2011). A well-recognised difficulty
with mass spectrum analysis and comparisons in biological samples is
co-elution of analytes, leading to mixed mass spectra. To address this
problem, deconvolution algorithms can be used (e.g. in ChromaTOF(r), or
the deconvolution plugin in TraceFinder(r)) (Du \& Zeisel, 2013; H. Lu,
Liang, Dunn, Shen, \& Kell, 2008) or alternatively, data extraction can
focus on individual target m/z, e.g., in MetabolomeExpress (Carroll,
Badger, \& Harvey Millar, 2010). However, there remains a risk that
co-elution leads to erroneous identification and quantitation when
common m/z nominal mass features are present. Here, we have covered this
aspect by searching systematically co-eluting nominal masses m/z (Table
1, Fig. 3). Rather common plant metabolites are concerned by this
problem (e.g., malate, serine, etc.). Exact mass offers an efficient way
to avoid it since exact mass values of targeted m/z features are clearly
different. In addition, the isotopic pattern reflects elemental
composition (Fig. 2) and can be used as a post-hoc verification.
Erroneous identification is therefore much less probable. Here, using
Arabidopsis samples, there was only one case of misidentification:
iodo-tyrosine 3TMS has been identified by TraceFinder(r) at 16.12 min
(Fig. 6c). This molecule is very unlikely since enzymes of iodo-tyrosine
metabolism (thyroperoxidase and iodo-tyrosine deiodinase) are absent in
plants (Phatarphekar, Buss, \& Rokita, 2014; Taurog, 1999). Our database
available in Supplementary material has been curated accordingly.
Coincidentally, several unusual sugar-containing molecules with similar
retention time can generate fragments (m/z features) with the same exact
mass. For example, rutinose 6TMS 1Meox elutes just before sucrose 8TMS
(16.17 min here) and can form a target fragment at 218.1033 like
iodo-tyrosine (Fig. SX). The observed signal is very low
(10\textsuperscript{-6} that of sucrose 8TMS), suggesting it is a minor
compound. Further work is needed (for example using
MS\textsuperscript{2} on a purified fraction where this compound is more
concentrated and thus generates well-visible MS2 fragments) to determine
what compound generates a signal similar to iodo-tyrosine in Arabidopsis
samples.

Another advantage of exact mass analyses is the resolution of
isotopologues. This is of particular interest to
identify\textsuperscript{13}C-molecules upon double-labelling
(typically\textsuperscript{13}C-\textsuperscript{15}N,
or\textsuperscript{13}C-\textsuperscript{33}S) since the signal
associated with \textsuperscript{13}C mass excess (+1.003355 Da) is
readily visible. This allows direct quantitation of
\textsuperscript{13}C isotopologues unlike in nominal mass analyses
where all \emph{m} +1 isotopologues are under the same peak. In a recent
study, the systematic, non-targeted analysis of \textsuperscript{13}C
isotopologues (+1.003355 Da, +2.006710 Da, +3.010065 Da, etc.) has been
used with high resolution LC-MS to look at variations in leaf
metabolites with CO\textsubscript{2} and O\textsubscript{2} gaseous
conditions (Abadie, Lalande, Limami, \& Tcherkez, 2021).
For\textsuperscript{34}S-isotopologues, there is an interference
with\textsuperscript{30}Si (silicium being carried by trimethylsilyl
groups). Despite this limitation, it is worth noting that whenever mass
resolution is sufficiently high (in particular for low m/z values), it
is possible to identify S-containing fragments (Fig. 5). Resolution is
less of an issue upon labelling since \textsuperscript{34}S would
prevail over \textsuperscript{30}Si and thus the peak apex would be
closer to the mass excess of \textsuperscript{34}S (+1.995796 Da).

\emph{Application to plant samples}

Here, we took advantage of Arabidopsis samples to show how exact mass
GC-MS analysis can be performed routinely on plant samples, with a
rather good outcome, i.e. identification and quantification of more than
200 analytes. Another attempt to carry out high-resolution GC-MS for
primary metabolites and pesticides analysis in Arabidopsis can be found
in, e.g., (Peterson et al., 2010). In terms of methodology and
instrumentation, high resolution exact mass GC-MS is similar to nominal
mass GC-MS analysis in that it requires standardised derivatisation
(optimally with a robotic facility) ensuring that all samples are
treated evenly (including the same time lag between the end of
derivatisation and injection), and quality controls to assess
reproducibility and quantitativity (for a specific discussion about
automation of derivatisation for GC-MS, see (Zarate et al., 2016)). Of
course, a specific feature of exact mass analysis is that it requires
checking mass accuracy (W. Lu et al., 2017), and here this was performed
on a day-to-day basis with calibration gas FC43 containing
perfluorotributylamine PFTBA (from ThermoFisher Scientific). Also, it is
extremely useful to have specific samples to appreciate mass accuracy,
for example with a compound that does not require derivatisation like
IMT (Fig. 2a-b). This allows one to check not only mass accuracy but
also precision of mass excess values associated with all relevant
isotopes (C, N, S isotopes; Fig. 2c).

It should be noted that exact mass GC-MS, however, does not solve the
issue of multiple derivatives for some specific metabolites. In
particular, amino acids can form by-products upon silylation. So is the
case of glutamic acid and glutamine, that can both yield pyroglutamate
(for a recent discussion on this issue, see (Miyagawa \& Bamba, 2019)).
It is also the case of arginine, which is known to yield several
products (Molnar-Perl \& Katona, 2000), and here it formed four products
(Fig. 4). High resolution analysis nevertheless provides a method to
have a better picture of derivatives and here, we identified 4 main
products of arginine derivatisation: ornithine lactam 2TMS, ornithine
3TMS, arginine 3TMS and citrulline 3TMS. Since these different compounds
do not have the same response coefficient in the mass spectrometer (i.e.
distinct observed signal response curve to concentration), arginine
cannot be quantified very precisely by this method.

Basically, GC-MS analysis provides semi-quantitative information on
plant samples. A good method to have information on effective quantity
(i.e., absolute quantity) of analytes is GC-C-IRMS (GC coupled to
combustion and isotope ratio mass spectrometry). In effect, the same
sample (same derivatisation) can be injected and the GC-C-IRMS converts
quantitatively each peak to CO\textsubscript{2} and
N\textsubscript{2}and thus can provide direct information on the amount
of carbon (via mass 44 monitoring) or nitrogen (via mass 28 monitoring)
in each peak of interest. This method has been used recently (using
alfalfa seed protein extracts) and shown to provide satisfactory
estimates of absolute amino acid contents (Domergue, Lalande, Abadie, \&
Tcherkez, 2022). More classical method can also be used, such as
external calibration curves, or deuterated standards (internal
references). The use of deuterated standards has three drawbacks: First,
it complicates mass spectra, with some probability to coincide with m/z
features of interest. An example with plant samples is cysteine 3TMS
where the target peak
(C\textsubscript{8}H\textsubscript{22}NSSi\textsubscript{2}) at 220.1009
Da might coincide with a deuterated fragment
({[}\textsuperscript{2}H{]}-C\textsubscript{7}H\textsubscript{21}NOSi\textsubscript{3},
220.0995 Da). Second, having all deuterated standards is tedious and
expensive. Third, deuteration can cause an isotope effect in either
chromatography or ionisation efficiency so that the GC-MS signal differs
between deuterated and the protiated forms (Alzweiri, Khanfar, \&
Al-Hiari, 2015; Caban \& Stepnowski, 2020; Matucha, Jockisch, Verner, \&
Anders, 1991; Ripszam, Grabic, \& Haglund, 2013).

\emph{Perspectives}

Our results show that high resolution exact mass GC-MS can be used in
plants, as an efficient way to carry out metabolic profiling. This
technique shows several advantages over classical, nominal mass GC-MS,
such as identification less prone to errors thanks to exact mass m/z
features. The database we propose here to perform targeted analyses
covers more than 200 metabolites of different chemical families. This
database is evolutive, in that it will be enriched over the coming years
and updated versions kept accessible via the journal website. We
recognise that plants contain a lot of sugars, with many isomers. Some
of them have identical retention times, and thus cannot be resolved with
exact mass since m/z features have the same elemental composition. To
solve this problem, specific devices like ion mobility may be required
(Morrison \& Clowers, 2018; Mu, Schulz, \& Ferro, 2018; Przybylski \&
Bonnet, 2021). In terms of isotopic analysis via exact mass GC-MS, Si
isotopes represent a limitation to distinguish isotopologues. We showed
that high mass precision allows reasonable access
to\textsuperscript{34}S isotopologues, but less so
for\textsuperscript{15}N or \textsuperscript{33}S that are much less
abundant. When isotopic enrichments are modest, GC-C-IRMS analysis is
the best alternative, but this technology is presently not able to
perform \textsuperscript{34}S analysis. Therefore, exact mass LC-MS
seems to be better suited to isotopic analysis of sulphur-containing
compounds.

\textbf{Acknowledgements}

Authors acknowledge the support of the \emph{Region Pays de la Loire}and
\emph{Angers Loire Metropole} via the grant Isoseed, awarded to G.T.
Authors also thank ThermoScientific for helping us setting up the
robotic facility and modifying programs to fix the sample preparation
sequence.

\textbf{Conflict of interest}

The authors declare no conflict of interest.

\textbf{Supplementary material provided in this paper}

\textbf{Protocol S1.} Protocol for exact-mass GC-MS analysis. (PDF)

\textbf{Supplementary Fig. S1} . Alternative compounds generating same
exact mass fragments as iodotyrosine.

\textbf{Supplementary File 1} . Database is source format.

\textbf{Supplementary File 2.} List of compounds and fragments contained
in the database, and compounds found in Arabidopsis samples (Excel
format).

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\textbf{Table 1. Common analytes that share both similar nominal mass
signals and retention time but can be distinguished using exact mass} .
For each example, the elemental composition of the fragment and its
exact mass is shown. When the observed mass of interest belongs to the
isotopic pattern, it is indicated with the isotope in front
(\textsuperscript{13}C). See also Fig. 4 for a detailed analysis of two
examples, with fragment chemical structure. This table shows couple of
analytes with a difference in retention time of less than 0.4 min. (*)
Note that m/z ions at \selectlanguage{english}[?]226.092 Da can also come from a fragment of
allantoin 2TMS
(C\textsubscript{4}H\textsubscript{18}N\textsubscript{4}O\textsubscript{3}Si\textsubscript{2},
226.09119 Da). All analytes and fragments in this table have been
checked using authentic standards.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{RT window (min)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Nominal mass (a.m.u.)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Analyte}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Fragment and exact mass (Da)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Analyte}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Fragment and exact mass (Da)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
4.40-4.42\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{160}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Pentanoic acid 1TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textsuperscript{13}C-C\textsubscript{8}H\textsubscript{19}OSi
160.12380\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Glyoxylic acid 1TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{5}H\textsubscript{10}NO\textsubscript{3}Si
160.04299\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
8.00-8.32\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{174}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{O}-acetylserine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{16}NO\textsubscript{2}Si
174.09503\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\selectlanguage{greek}β-\selectlanguage{english}alanine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{20}NSi\textsubscript{2}
174.11343\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
8.80-8.86\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{189}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Malic acid 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{21}OSi\textsubscript{2}
189.11309\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Threitol 4TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{17}O\textsubscript{2}Si\textsubscript{2}
189.07671\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
5.35-5.55\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{190}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
2-hydroxybutyric acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{18}O\textsubscript{2}Si\textsubscript{2}
190.08453\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Alanine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{20}NOSi\textsubscript{2}
190.10834\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
13.17-13.20\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{191}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Caffeic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{11}O\textsubscript{2}Si
191.05283\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Myoinositol 6TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{19}O\textsubscript{2}Si\textsubscript{2}
191.09236\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
9.85-10.0\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{192}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Anthranilic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{14}NOSi 192.08446\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Phenylalanine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{11}H\textsubscript{18}Nsi 192.12085\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12.21-12.24\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{203}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Glucosamine 6TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{21}NOSi\textsubscript{2}
203.11617\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Tryptophan 4TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textsuperscript{13}C-C\textsubscript{12}H\textsubscript{16}NSi
203.10855\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
7.70-7.92\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{204}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Serine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{22}NOSi\textsubscript{2}
204.12399\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{N}-formylglycine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{7}H\textsubscript{18}NO\textsubscript{2}Si\textsubscript{2}
204.08760\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
9.4-9.64\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{205}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Cinnamic acid 1TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{11}H\textsubscript{13}O\textsubscript{2}Si
205.06848\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Glutathione 4TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{13}N\textsubscript{2}OSi
205.07971\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
6.91-7.13\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{218}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Hydroxypyruvic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{9}H\textsubscript{22}O\textsubscript{2}Si\textsubscript{2}
218.11583\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Isoleucine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{20}NO\textsubscript{2}Si\textsubscript{2}
218.10326\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
7.94-8.00\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{218}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Threonine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{9}H\textsubscript{24}NOSi\textsubscript{2}
218.13964\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{O}-acetylserine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{20}NO\textsubscript{2}Si\textsubscript{2}
218.10326\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
9.37-9.39\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{220}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Threonic acid 4TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{20}O\textsubscript{3}Si\textsubscript{2}
220.09510\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Cysteine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{8}H\textsubscript{22}NSSi\textsubscript{2}
220.10115\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
11.82-11.92\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{226}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Methionine sulfoximine 2TMS* 2MeOx\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{5}H\textsubscript{18}N\textsubscript{4}O\textsubscript{2}SSi
226.09197\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Histamine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{22}N\textsubscript{2}Si\textsubscript{2}
226.13215\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
9.13-9.16\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{230}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Pyroglutamic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{9}H\textsubscript{20}NO\textsubscript{2}Si\textsubscript{2}
230.10326\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Hydroxyproline 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{24}NOSi\textsubscript{2}
230.13964\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
8.34-8.70\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{247}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Erythronolactone 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{9}H\textsubscript{19}O\textsubscript{4}Si\textsubscript{2}
247.08219\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Citramalic acid 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{23}O\textsubscript{3}Si\textsubscript{2}
247.11857\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
10.31-10.42\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{261}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{N}-acetylmethionine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{23}NO\textsubscript{3}Si\textsubscript{2}
261.12165\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{N}-methylglutamic acid 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textsuperscript{13}C-C\textsubscript{11}H\textsubscript{26}NO\textsubscript{2}Si\textsubscript{2}
261.15356\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
11.78-12.09\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{264}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Adenine 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{10}H\textsubscript{18}N\textsubscript{5}Si\textsubscript{2}
264.11007\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Tyramine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{13}H\textsubscript{22}NOSi\textsubscript{2}
264.12399\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12.66-12.80\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{267}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Urocanic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{11}H\textsubscript{19}N\textsubscript{2}O\textsubscript{2}Si\textsubscript{2}
267.09851\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Octopamine 4TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{13}H\textsubscript{23}O\textsubscript{2}Si\textsubscript{2}
267.12366\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
11.93-12.22\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{280}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Pyridoxine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{13}H\textsubscript{22}NO\textsubscript{2}Si\textsubscript{2}
280.11891\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Tyrosine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{14}H\textsubscript{26}NOSi\textsubscript{2}
280.15529\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12.09-12.26\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{308}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Ferulic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{14}H\textsubscript{20}O\textsubscript{4}Si\textsubscript{2}
308.09001\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Coumaric acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{15}H\textsubscript{24}O\textsubscript{3}Si\textsubscript{2}
308.12640\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
11.94-12.09\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{308}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
4-hydroxyphenyl lactic acid 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{15}H\textsubscript{24}O\textsubscript{3}Si\textsubscript{2}
308.12640\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Ferulic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{14}H\textsubscript{20}O\textsubscript{4}Si\textsubscript{2}
308.09001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12.13-12.38\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{319}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Mannitol 6TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{13}H\textsubscript{31}O\textsubscript{3}Si\textsubscript{3}
319.15810\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
3-indoleacetic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{16}H\textsubscript{25}NO\textsubscript{2}Si\textsubscript{2}
319.14238\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12.09 (same RT)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textbf{338}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Ferulic acid 2TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{16}H\textsubscript{26}O\textsubscript{4}Si\textsubscript{2}
338.13696\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Tyramine 3TMS\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
C\textsubscript{16}H\textsubscript{32}NOSi\textsubscript{3}
338.17917\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/491574/articles/574533-exact-mass-gc-ms-analysis-protocol-database-advantages-and-application-to-plant-metabolic-profiling}

\textbf{Fig. 1. Overview of a GC-MS chromatogram from an Arabidopsis
leaf extract} (here, ecotype Cvi-0). (a) total ion current chromatogram,
showing annotation of typical peaks (in red). The observed mass spectrum
at 15.05 min is shown in (b), with characteristic ions of
6-phosphogluconate 3TMS annotated in red. Note the contribution of ion
m/z = 387.14254 Da (fragment
C\textsubscript{13}H\textsubscript{32}O\textsubscript{7}PSi\textsubscript{2}\textsuperscript{+}).
Note that a close, but easily distinguishable mass (387.32912 Da,
fragment
C\textsubscript{22}H\textsubscript{47}O\textsubscript{3}Si\textsuperscript{+})
can be observed at 15.25 min, from arachidic acid 1TMS; nominal mass
analysis would have generated the same apparent mass at 387 (or 388) Da.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/491574/articles/574533-exact-mass-gc-ms-analysis-protocol-database-advantages-and-application-to-plant-metabolic-profiling}

\textbf{Fig. 2. Isotopic pattern in two N- and S-containing molecules,
isopropyl-4-methylthiazole (IMT) and methionine 2TMS} . (a) overall mass
spectrum of IMT, showing the prevalence of the demethylated fragment at
126.03770 Da. (b) magnification of the mass spectrum showing the
isotopic pattern of the major fragment of IMT and (c) detailed view
of\emph{m} +1 isotopologues showing the resolution
of\textsuperscript{15}N, \textsuperscript{33}S, \textsuperscript{13}C
and\textsuperscript{2}H isotopologues. (d) overall spectrum of
methionine 2TMS with two major fragments (128.08955 Da and 176.09290 Da)
and (e) magnification showing \emph{m} +2 isotopologues. (f)
relationship between expected and observed relative abundance of
isotopologues in IMT using two fragments (blue, green) and methionine
2TMS (pink), plotted using a log scale. Note the underestimation of
deuterium (\textsuperscript{2}H) content (red circle). The black line
stands for the 1:1 line. In (a) and (d), fragmentations are shown with
chemical formulas to illustrate how the fragments of interest are
formed. Note IMT does not require derivatisation while methionine is
silylated (two TMS groups). In (e), note that there is a common peak for
\textsuperscript{34}S (+1.995796 Da) and \textsuperscript{30}Si
(+1.996844 Da) and also\textsuperscript{33}S (+0.999387 Da) and
\textsuperscript{29}Si (+0.999568 Da).

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/491574/articles/574533-exact-mass-gc-ms-analysis-protocol-database-advantages-and-application-to-plant-metabolic-profiling}

\textbf{Fig. 3. Illustration of some common nominal mass m/z ions
between analytes with the same retention time:} ferulic acid 2TMS and
tyramine 2TMS, both at \emph{c} . 12.09 min (a,b) and malic acid 3TMS
and threitol 4TMS, both at \emph{c} . 8.85 min (c,d). Comparisons of
overall spectra (a,c) with nearly common masses distinguishable with
exact mass (insets, in red), and origin of fragments (b,d). In (c), ions
''other'' refer to
C\textsubscript{7}H\textsubscript{18}O\textsubscript{2}Si\textsubscript{2}\textsuperscript{+}(190.083983
Da) and its protonated (+1.007825 Da) species (191.091808 Da). Note that
tyramine yields a much higher signal (order of magnitude
10\textsuperscript{9}) then ferulic acid (10\textsuperscript{7}) despite
the use of identical concentration in samples (500 ng
mL\textsuperscript{-1}). \emph{m} +1 and \emph{m} +2 refer to +1 a.m.u.
(mostly \textsuperscript{13}C, and \textsuperscript{29}Si) isotopologues
and +2 a.m.u. (mostly
\textsuperscript{13}C\textsubscript{2},\textsuperscript{30}Si,
and\textsuperscript{13}C-\textsuperscript{29}Si) isotopologues,
respectively.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/491574/articles/574533-exact-mass-gc-ms-analysis-protocol-database-advantages-and-application-to-plant-metabolic-profiling}

\textbf{Fig. 4. Mass spectrum of arginine derivatives} . (a)
chromatogram showing the four products of arginine derivatisation:
ornithine lactam 2TMS, ornithine 3TMS, arginine 3TMS and citrulline
3TMS. The mass spectrum of arginine 3TMS and citrulline 3TMS are shown
in panels (b) and (c). Typical fragments are illustrated in (d) and (e),
respectively. Note that arginine 3TMS and citrulline 3TMS have very
close retention times (11.37 and 11.41 min) and thus might appear merged
in the same peak under low resolutive conditions.

\textbf{Hosted file}

\verb`image5.emf`  available at \url{https://authorea.com/users/491574/articles/574533-exact-mass-gc-ms-analysis-protocol-database-advantages-and-application-to-plant-metabolic-profiling}

\textbf{Fig. 5. Identification of S-containing fragments using exact
mass data} : appearance of \emph{m} +2 isotopologues at +1.995796 with a
precision of 0.05%0 in a standard mixture containing methionine and
cysteine (a) and an extract from Arabidopsis leaves (b). Ions that
effectively contain S are circled in red. Other ions do not contain S
despite their mass difference value (w.r.t. the isotopologue at
+1.995796) close to zero (see main text for calculations). In (a) and
(b), red frames illustrate the chemical structure of identified
S-containing fragments. (c) detailed observed isotopic pattern (\emph{m}
+2 isotopologues) of the fragment ion with a monoisotopic mass of 74.018
Da, showing the \textsuperscript{34}S isotopologue (observed at
+1.99549) of structure \textbf{2} in (b), as well
as\textsuperscript{30}Si and \textsuperscript{29}S+\textsuperscript{13}C
isotopologues of C\textsubscript{2}H\textsubscript{6}OSi (74.018791 Da),
the monoisotopic form of which also contributes to the observed signal
at [?]74.018 Da. The signal at +2.00738 likely corresponds to another
fragment rather than the
\textsuperscript{13}C\textsubscript{2}isotopologue considering the
relatively high signal. Other signals (na) are unrelated to the ion of
interest. (d-f) simulation of the peak of\emph{m} +2
\textsuperscript{34}S and \textsuperscript{30}Si isotopologues using the
arbitrary example of a monoisotopic mass at 156.04700 Da containing one
Si and one S, and three levels of mass resolution. The observed signal
(sum) is in grey, while the separate contribution of
\textsuperscript{34}S and \textsuperscript{30}Si is in blue and orange,
respectively. The sampled (i.e. measured by the mass spec) mass that is
closest to \textsuperscript{34}S is circled in red. It shows that there
is no sampled mass at exactly +1.995796 (\textsuperscript{34}S mass
excess w.r.t. monoisotopic) except at high resolution, and that
\textsuperscript{34}S can be easily confused with\textsuperscript{30}Si
due to their very small mass difference (0.001048 Da).

\textbf{Hosted file}

\verb`image6.emf`  available at \url{https://authorea.com/users/491574/articles/574533-exact-mass-gc-ms-analysis-protocol-database-advantages-and-application-to-plant-metabolic-profiling}

\textbf{Fig. 6. Overview of exact GC-MS metabolic profiling of
Arabidopsis genotypes} . (a) heatmap with hierarchical clustering
showing groups of metabolites that are significantly different
(\emph{P}\textless{} 0.0002, one-way ANOVA) between genotypes. Numbers
refers to groups named on top left. (b) Standard error between 6
biological replicates, in \% of average value, in each genotype. The red
dotted line stands for the median value (9.7\%). The dotted frame
comprises metabolites that are also framed and named in panel (c). Data
are shown as whisker plots with median, 25\% and 75\% quartiles, and
interquartile range. (c) Relationship between average standard error (in
\% of overall mean) and relative signal (relative to sucrose set at 1)
obtained on the GC-MS instrument (quantifying target mass). Metabolites
that appear highly variable are framed (and are the same as metabolites
framed in panel (b)). Note that metabolite 14 is labelled
''3-iodotyrosine'' because it is identified with high certainty by
Tracefinder\selectlanguage{ngerman}® using the database although it is not expected in plants
(see main text).

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