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While yeast is one of the most studied organisms, its intricate biology remains to be fully mapped and understood. This is especially the case when it comes to capture rapid, in vivo fluctuations of metabolite levels. Secondary electrospray ionization-high resolution mass spectrometry SESI-HRMS is introduced here as a sensitive and noninvasive analytical technique for online monitoring of microbial metabolic activity. The power of this technique is exemplarily shown for baker’s yeast fermentation, for which the time-resolved abundance of about 300 metabolites is demonstrated. The results suggest that a large number of metabolites produced by yeast from glucose neither are reported in the literature nor are their biochemical origins deciphered. With the technique demonstrated here, researchers interested in distant disciplines such as yeast physiology and food quality will gain new insights into the biochemical capability of this simple eukaryote.
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SCientifiC RepoRTS | 7: 14236 | DOI:10.1038/s41598-017-14554-y
Comprehensive Real-Time Analysis
of the Yeast Volatilome
Alberto Tejero Rioseras
1,2,3, Diego Garcia Gomez1,3, Birgitta E. Ebert
4, Lars M. Blank4,
Alfredo J. Ibáñez1,5 & Pablo M-L Sinues
While yeast is one of the most studied organisms, its intricate biology remains to be fully mapped and
understood. This is especially the case when it comes to capture rapid, in vivo uctuations of metabolite
levels. Secondary electrospray ionization-high resolution mass spectrometry SESI-HRMS is introduced
here as a sensitive and noninvasive analytical technique for online monitoring of microbial metabolic
activity. The power of this technique is exemplarily shown for baker’s yeast fermentation, for which the
time-resolved abundance of about 300 metabolites is demonstrated. The results suggest that a large
number of metabolites produced by yeast from glucose neither are reported in the literature nor are
their biochemical origins deciphered. With the technique demonstrated here, researchers interested in
distant disciplines such as yeast physiology and food quality will gain new insights into the biochemical
capability of this simple eukaryote.
Metabolites are small molecules that provide a direct readout of organisms’ phenotypes and cellular activity1.
During metabolic processes2, a subset of the metabolites, the so-called volatile organic compounds (VOCs) are
released to the ambient at normal pressure and temperature. VOCs are organic compounds typically C5-C203 with
molecular weights up to 500 Da4, boiling point up to 250 °C5 and high vapor pressure. Many living organisms
including humans, animals, plants, and even microorganisms, produce large varieties of VOCs6. e sum of all
VOCs produced by an organism has been termed the volatilome79. While some VOCs have dedicated pathways,
many are intermediates of anabolic pathways or are synthesized by moonlighting activities of enzymes10, hence
reect promiscuous enzymatic activities in the underlying metabolic network11,12. Although generally produced
in small concentrations (parts-per-million to parts-per-trillion range)13, VOCs reect the metabolic state of a
cell and, thus, can be used to understand many biological processes such as, e.g., oxidative stress1416. VOCs are
also at the core of the avor and fragrance manufacturing, an industry with a focus on plant-derived VOCs.
Volatile metabolites are also of importance in the food industry as taste and smell is informing the consumer
on the quality of a product. A volatile metabolite, industrially produced in large quantities, is alcohol (i.e., eth-
anol), synthesized by yeast from glucose during fermentation. Interestingly, since ethanol is neutral-avored,
the taste of fermented drinks originates from minor compounds, such as higher alcohols, aldehydes, esters, and
acids. Most of these metabolites are volatile and intensely avored. Indeed, monitoring VOCs (i.e., diacetyl,
2,3-pentanedione) abundance in beer is industrial standard, since it has a strong eect on the product quality17.
e economic impact of monitoring such compounds is thus vast18.
Gas chromatography-mass spectrometry (GC-MS)-based methods have been the workhorse to analyze VOC
proles. e main limitation of such methods is that it requires sample manipulation, resulting in laborious pro-
cedures19,20. A more recently deployed approach is ion mobility spectrometry, which provides near real-time anal-
ysis, but its poorresolution compared to mass spectrometry compromises metabolite coverage and compound
identication capabilities2124. Real-time mass spectrometric methods used for on-line quantication of a handful
of VOCs include proton transfer reaction (PTR)13 and selected ion ow tube (SIFT)25. A recent mass spectromet-
ric method based on direct ow injection, has shown to monitor metabolite dynamics in the 15–30 second range
and was used to gain insights into cellular responses to environmental changes26.
1Department of Chemistry and Applied Biosciences, ETH Zurich, 8093, Zurich, Switzerland. 2SEADM S.L., C\ José
Lázaro Galdiano, 1, Madrid, 28036, Spain. 3Department of Analytical Chemistry, University of Cordoba, Cordoba,
Spain. 4Institute of Applied Microbiology – iAMB, Aachen Biology and Biotechnology – ABBt, RWTH Aachen
University, Worringerweg 1, Aachen, 52074, Germany. 5Instituto de Ciencias Ómicas y Biotecnología Aplicada
- Pontificia Universidad Católica del Perú (ICOBA-PUCP), Lima, Peru. 6University Children’s Hospital Basel,
Department of Biomedical Engineering, University of Basel, Basel, Switzerland. Correspondence and requests for
materials should be addressed to A.J.I. (email: or P.M.-L.S. (email:
Received: 30 May 2017
Accepted: 12 October 2017
Published: xx xx xxxx
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In line with such eorts to develop instrumentation capable of monitoring time-resolved metabolic informa-
tion, we show here how secondary electrospray ionization (SESI)2739 coupled to high resolution mass spectrom-
etry (HRMS) captures on-line an unprecedented wealth of volatile analytes emitted in vivo by growing baker’s
yeast (Saccharomyces cerevisiae).
Results and Discussion
Volatiles emitted by wild type Saccharomyces cerevisiae during growth in13C6-glucose.
Initially, to conrm that SESI-HRMS is indeed suitable to track growth-related metabolites in real-time, we grew
the yeast in glucose minimal medium and monitored well-known end products40 like ethanol or acetic acid. As
expected, we observed the production of ethanol despite full aeration because baker’s yeast is Crabtree positive.
Figure1a shows such an example, in which ethanol production kicked o shortly aer the start of the growth
experiment. e concentration increased exponentially to then level o, presumably concomitant with glucose
depletion. Eventually, ethanol started to decrease again. Such a diauxic growth is expected for yeast fermentation
on glucose. e depletion kinetic, however, seems too fast to be solely explained by metabolization. Evaporation
of this volatile metabolite might have been enforced by the vigorous aeration (25 gas volume ow per unit of liq-
uid volume per minute; vvm) of the bioreactor. When ethanol was almost completely depleted, we spiked glucose
into the medium (~27 h; see Methods section for experimental details). e cells immediately re-started glucose
consumption as evidenced by the instantaneous production of ethanol and acetic acid.
One of the advantages of SESI coupled to high-resolution mass spectrometry (HRMS) is that it can detect
a wide range of analytes, including species of low vapor pressure at concentrations in the low parts-per-trillion
range41. Indeed, along with the well-known volatile metabolites ethanol and acetic acid, numerous other volatile
metabolites were detected during yeast growth. us, we replicated the experiment independently; however, we
administered glucose labelled with 13C in all six carbon atoms. 13C label incorporation into metabolites syn-
thesized from glucose allowed dierentiating analytes of biological origin from environmental contaminants
or media components. Figure1b shows the resulting time proles of ethanol, acetic acid, and yeast biomass
estimated from image grayscale levels in a time-lapse video. e observed correlation between increase of etha-
nol and production of biomass conrmed that the analytical setup allowed capturing biological events soundly.
13C-label incorporation conrmed that 263 signals observed in the previous experiment originated from the
yeast’s metabolism. Inaddition, weconducted a negative control experiment whereby a bioreactor was run under
Figure 1. Real-time monitoring of volatile metabolites during yeast growth; (a) raw time proles of ethanol
(detected as the dimer) and acetic acid during growth on 12C6-glucose and re-injection of glucose aer ~27 h;
(b) normalized raw time proles of ethanol (detected as the dimer), acetic acid and the image signal captured
by a time-lapse camera during growth on 13C6-glucose; c) heatmap showing 263 time-dependent signals during
yeast growth. All the signals in the heatmap were labeled with 13C. For reference, the ethanol signal is shown on
the top.
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the very same conditions but without inoculation with yeast (monitored for ~17 hours). As expected, neither
ethanol nor acetic acid were produced (FigureS1).
e time-resolved dataset of the yeast fermentation enabled a comprehensive overview of the volatilome
dynamics (Fig.1c). The heatmap is ordered by a cluster analysis, easing the visualization of the different
time-proles. Note that these metabolites were captured simultaneously and in real-time without any sample
manipulation. TableS1 lists the 263 signals detected in both experiments (i.e., labeled and non-labeled glucose)
as they appear in the heatmap of Fig.1c. Interestingly, homologous series of compounds tend to cluster together,
suggesting similar kinetics for closely related metabolites. For example, one cluster comprised the homologous
series C6H10, C7H12, C8H14 and C9H16. is bouquet of volatile metabolites remains to be annotated, but the
chemical formula suggests that these compounds could well be alkadienes. Several olenic compounds have
been reported for diverse organisms4246. 1,3-octadiene (C8H14), for example, is produced by several fungi and
predicted to be a degradation product of linoleic acid, but the responsible enzymes and encoding genes are yet
to be identied47. None of the potential alkadienes detected here were previously identied in the headspace of
yeast fermentations.
Volatiles emitted by wild type and mutants of Saccharomyces cerevisiae during growth in
13C1-glucose. To demonstrate that the detected series of hydrocarbons were produced by the baker’s yeast and
not by a biological contamination of the bioreactor, we repeated the measurements. Specically, we performed
a comparison of two baker’s yeast mutants (i) devoid of the oxidative pentose phosphate pathway (zwf1), which
is associated with oxidative stress response or (ii) limited in glycolysis (p1); and compared them with a wild
type (WT) strain. To discriminate in vivo pathway usage, we fed WT, zwf1, and p1 with 13C1-glucose. Figure2a
shows a zoom of the m/z range 200–215 of a typical mass spectrum acquired during such measurements for WT
during the stationary phase. It shows two distinct bell-shaped isotopic distributions. e rst one assigned to a
series of Cx(13C)yH25O2 with x ranging 11 to 7 and y 1 to 5 (i.e., molecular formula C12H25O2). Similarly, another
more abundant distribution of C15H25 (13C ranging from 2 to 9) was clearly observed. Apart from high resolution
(~ 30,000) enabling the discrimination of complete 13C isotopic envelopes, MS/MS capability is just another
advantage of our proposed technique over other common on-line technologies used to monitor volatiles in indus-
trial processes48. us, further insights in this respect were gained by identifying some of the detected com-
pounds. For example, the two compounds in Fig.2a were assigned to ethyl decanoate and farnesene based on
the MS/MS spectrum (Figs2b and S2). Ethyl esters have fruity and oral avor and constitute important aroma
compounds in wine or beer49. It is hypothesized that fatty acid ethyl esters are formed to prevent accumulation of
medium chain fatty acid under anaerobic conditions during which fatty acid synthesis is inhibited and these fatty
acids are prematurely released from the fatty acid synthase50. e sesquiterpene farnesene is synthesized from
farnesyl diphosphate, an intermediate of the mevalonate pathway.
Figure2c shows an example of some representative time proles for a set of metabolites present in the zwf1
mutant but absent in WT and p1. Notably, this set of metabolites is a homologous series of odd-numbered
carbons ranging from C7 to C15. is observation was replicated in an independent experiment (FigureS3). We
hypothesize that these compounds are methyl ketones, which can be derived from β-oxidation of fatty acids, but
their identity remains to be fully elucidated51. Likewise, some analytes were characteristic for p1 (see example
in FigureS4).
Figure2d (top) shows the time traces for ethanol. We found that the WT produced approximately twice as
much ethanol as the mutants under the same experimental conditions. e same results were obtained in a rep-
licate experiment (FigureS3). is is consistent with previous work, where similar, reduced glucose uptake and
growth rate and as a consequence, a low ethanol formation rate was found for the zwf1 mutant52. As for
Fig.1c, we subjected the time traces for all detected signals of the three strains to a cluster analysis and visualized
them in heatmaps. Figure2d (bottom) shows the results, providing an overview of the dierences in volatile
proles between the strains. While the general picture seems relatively similar for WT and p1, the zwf1 mutant
showed marked dierences for some compounds. For example, at the bottom of the heatmap one can observe
the cluster of molecules from Fig.2b. TableS2 lists all the peaks detected in this experiment. Overall, this initial
assessment illustrates the potential of SESI-HRMS to provide a comprehensive overview of VOC production by
baker’s yeast in real-time.
Although the p1 cells showed a dierent metabolic prole than WT and zwf1 cells, our study showed an
unexpected result. e p1 strain was able to undergo glycolysis (apparent from lower than expected loss of 13C)
aer an extended time of lag-phase growth. It is not entirely clear to us, how the p1 strain was able to accomplish
this. However, S. cerevisiae’s phosphofructokinase is a heterooctameric enzyme with an alpha subunit encoded
by PFK1 and a beta subunit encoded by PFK2. us, a single gene deletion mutant decient in either of these
genesmight retain phosphofructokinase activity, which may explain the observed labeling pattern53,54.
Another common approach to visualize multivariate data is principal component analysis (PCA). e score
plot of the mass spectra during the exponential phase (Fig.3a) clearly shows a separation between the three
strains, conrming the existence of a distinct volatile prole for each strain. Please note that we have excluded
ethanol and acetic acid from the analysis in order to show that even without these major end-products, the less
abundant volatile compounds suggest a very distinct prole. As expected, a similar result was obtained when we
included ethanol and acetic acid in the PCA matrix (FigureS5). To understand which metabolites contributed
mostly to the separation shown in the score plot, Fig.3b displays the loadings for the rst PC, which separate WT
from the mutants. Some other highly discriminating molecules were identied via MS/MS and by comparison
with standards (FigureS2). e esters ethyl octanoate and ethyl decanoate were major contributors, along with
farnesene and octanoic acid. TablesS3 and S4 list the major contributions for PC1 and PC2, respectively. When
ethanol was included in the analysis, it was by far the major contributor to the separation shown in the score plot
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Some of the identified compounds are not only relevant biologically, but also industrially. For example,
ethyl octanoate is a yeast-based food product with sour apple aroma55. Figure3c shows the time traces for ethyl
octanoate for the three strains investigated. It clearly shows that the levels produced by WT were about four times
as much as that of the mutants, reinforcing the potential to monitor industrial processes. Interestingly, ethyl hex-
anoate showed a very similar trend (this and additional relevant examples are shown in FigureS6). Fatty acids
such as hexanoic, octanoic, and decanoic acids are just some other examples of relevant metabolites for the food
and cosmetic industry. To demonstrate that these products can also be conveniently monitored, we switched the
polarity to negative ion mode. SESI-HRMS spectra in negative ion mode are dominated by deprotonated fatty
acids56,57. erefore, isomeric interferences such as esters could be excluded. Figure3d shows the time traces of a
complete series of 11 fatty acids detected in WT aer injecting 13C1-glucose. Acetic, octanoic, and decanoic acids
were the major products. e complete list of all 43 compounds detected in this experiment is given in supple-
mentary TableS5.
us, in total, 8 compounds were identied with a high degree of condence by real-time MS/MS, although
isomeric structures cannot be excluded. 11 fatty acids were also conrmed in negative ion mode with a high
degree of condence based on prior studies56,57. Additional hints of potential compounds were sought by que-
rying publicly available databases. Of the 263 peaks associated to glucose metabolism in positive ion mode, 111
formulae matched at least one compound present in the yeast metabolome database (TableS1)58.
By injecting 13C1-glucose, we also aimed to conrm the validity of our approach to capture well-known biolog-
ical processes. For example, while the contribution of the oxidative pentose phosphate pathway is rather small in
baker’s yeast grown on glucose, this little contribution should be detectable59. is is indeed suggested in Fig.3c,
Figure 2. Growth of WT and mutants on 13C1-glucose led to complex mass spectra that revealed a unique
in vivo metabolic response; (a) Centroided mass spectrum of the region m/z 200–215 during the stationary
phase of WT yeast growth upon injection of 13C1-glucose into the system. Two isotopic envelopes for C12H25O2
and C15H25 were clearly resolved. e incorporation of 13C into ethyl decanoate and farnesene reects the
metabolism of the yeast; (b) Fragmentation (SESI MS/MS) spectrum produced using m/z 201 as the precursor
ion from a yeast sample (top) and ethyl decanoate standard (bottom); (c) Set of odd-numbered carbon
molecules built up during growth of the zwf1 mutant but absent in WT and p1; (d) Heatmap for the 636
signals detected for the three strains. For reference, ethanol proles are shown on the top.
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Figure 3. Distinct volatile metabolic proles and production kinetics for yeast WT and mutants; (a) PCA
score plot of average spectra in the exponential phase for WT and mutants suggests a clear distinction based
on the volatile metabolic prole; (b) the corresponding loading plot for score 1 shows that esters, acids, and
sesquiterpenes are major contributors to the separation between WT and mutants. Identied compounds are
highlighted in red. Note that ethanol and acetic acid were excluded from the PCA analysis; (c) Kinetic proles
of the food-relevant metabolite ethyl octanoate for WT, p1 and zwf1 illustrates the potential to monitor
industrial processes; (d) series of fatty acids detected in negative ion mode during WT growth in 13C1-glucose;
(e) isotopic distribution for farnesene obtained during the stationary phase. As expected, it shows a greater
accumulation of 13C for zwf1; (f) Time proles of 13C/12C ratios for ethanol dimer (i.e., m/z 95/93; black) and
acetic acid (red) for the three strains investigated. Note the dierent kinetic proles. e average spectra during
the stationary phase for the ethanol dimer is shown in the insets.
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which shows the normalized isotopic distributions for farnesene for the three strains. e maximum isotope
accumulation for all three strains was found at 13C4, but clearly, the relative abundance of C5 to C9 is greater in
the culture of the zwf1 knockout strain as no 13CO2 is released during breakdown of glucose to acetyl-CoA. e
same trend was observed for acetic acid, ethanol, ethyl octanoate, ethyl decanoate, and octanoic acid. All these
observations were reproduced in a replicate experiment (FigureS7).
As metabolic systems are highly plastic, the ability to monitor metabolite dynamics is crucial for under-
standing cellular processes. Here, our time-resolved data was pivotal for understanding the metabolic path-
ways involved in the metabolism of glucose that are favored by each yeast strain. Figure3f shows the 13C/12C
ratio for ethanol and acetic acid. Note that, in the case of ethanol, we monitored the dimer, which lead to a
triplet-like isotopic distribution (i.e., m/z 93 for C2H6O-C2H6O; m/z 94 for C(13C)H6O-C2H6O and m/z 95 for
C(13C)H6O-C(13C)H6O; inset in Fig.3f). Both, the kinetic and isotopic ratios were dierent for the three strains.
Mutant zwf1 incorporated 13C into ethanol steeply to reach a plateau of 13C/12C ratio of ~1.2 aer 5 h. e label
incorporation in ethanol formed by the WT was similar but reached lower nal fractional labeling. In contrast,
no labeled ethanol was observed for p1 in the rst 4 h aer the 13C tracer injection and did not reach a steady
labeling within the 25 h of the experiment. is is explained by a slowed-down glucose metabolism and the
delayed ethanol formation in this mutant. Interestingly, the 13C in acetic acid built up immediately upon glucose
injection in all strains. Other compounds showed dierent kinetics (FigureS8). e time resolution capability
allowed to exquisitely capture ne details of kinetic proles, which is crucial to adjust industrial parameters to
tune in real-time the desired volatile prole.
In conclusion, we deployed a sensitive and selective, yet, real-time mass spectrometric technique to investigate
the production of volatile metabolites during yeast growth. e technique gently tracks biological processes at
the metabolic level in vivo with a time resolution of less than one minute. us, we benchmarked the technique
by observing well-known processes such as production of ethanol from glucose. However, despite being one of
the most widely studied organisms, the rich volatile proles (~300 metabolites combining positive and nega-
tive mode) of S. cerevisiae detected in these analyses, including non-reported analytes suggests that much work
remains to be accomplished to fully map the metabolism of yeast. Such comprehensive metabolic coverage may
also have potential to tune industrial processes where yeast fermentation is involved.
Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS). SESI-
HRMS experiments were carried out using a commercial ion source (SEADM S.L.)60 plugged onto a ermo-
Fisher LTQ Orbitrap mass spectrometer. e SESI solvent was 0.1% formic acid in water infused at ~100 nL/
min through a 20 μm ID silica capillary. e electrospray voltage was set to 5.4 kV in positive mode (i.e., focusing
electrode 2.59 kV and impact electrode 1.6 kV) and to 5 kV in negative mode. e sweep gas used for cleaning the
electrospray region was set to 2 a.u. All other internal Orbitrap parameters were optimized during calibration.
Orbitrap scan parameters: scan type: FTMS full MS [50–500 Da]; source fragmentation: o; resolution: 30.000;
polarity: positive and negative; Typical mass accuracies were within 2 ppm by using common chemical noise
encountered in SESI-HRMS background as lock masses (i.e. m/z 149.0233, 279.1591 and 445.1200)61,62. AGC
target: 30.000; Maximum injection time: 150 ms; micro scans: 30, which led to acquire a prole mass spectrum
every ~49 seconds to prevent the generation of intractable large les during several hours of volatiles monitoring.
e system was properly calibrated in the respective polarities prior to the analysis. MS/MS fragmentation spectra
for both, yeast and standards, were obtained under identical conditions: Same collision energies; 5 microscans;
Isolation width: 1 Da; Activation Q: 0.25 and Activation time: 30 ms.
Yeast cell cultures. e bioreactor consisted of an autoclaved 100 mL three-neck ask lled with 20 mL of
medium, stirred at 800 rpm with a Teon magnet and uniformly heated at 40 °C in a water bath. e metabolites
produced during yeast growth were dragged downstream towards the SESI source by a continuous ow of com-
pressed air at 0.5 L/min. e compressed air was ltered and humidied upstream the bioreactor. e bioreactor
was connected to the ion source through a stainless-steel tube (OD 6 mm) heated at 130 °C to minimize metabo-
lite adsorption onto the tube walls.
All the described experiments in this study were performed with Saccharomyces cerevisiae, prototrophic,
YSBN.6 (wild-type) strain. Cells were grown in minimal dened medium: BD (DIFCO) yeast nitrogen base
(#233520); and 2% glucose (as only carbon-source). For each experiment, pre-cultures were inoculated from SD
plates and grown at 30 °C while shaking with 300 rpm for 8 to 10 hours in 1 mL pre-culture tubes. en, the cells
were inoculated (starting optical density; OD ~0.1) in 500 mL Erlenmeyer asks with 50 mL of growth medium.
Cultivation was performed at 30 °C with stirrer bar at 300 rpm until an OD of 1.2. e YSBN.6 strains zwf1Δ
(Δzwf::Kan) and p1Δ (ΔPFK1::Kan), were grown in a similar minimal dened medium with the addition of
0.02% (v/v) of kanamycin and under the same time, temperature, and mixing conditions. To start the experiment,
1 mL of inoculum with an OD (between 0.8 to 1.0) was injected into the bioreactor for obtaining a starting OD
of 0.1. When the ethanol signal decayed, 0.8 mL of 50% glucose was injected to prolong the exponential growth
During the experiment, a time-lapse camera took a picture of the bioreactor every minute. Using ImageJ, an
open platform for scientic image analysis63, yeast concentrations of relative values were obtained by averaging
grayscale level in a region of interest of the image in a video. Absolute OD values were also obtained at the begin-
ning and at the end of each experiment with a ermo electron corporation GENESYS 10 UV spectrophotometer.
Figure4 shows a schematic of the experimental set-up (photograph in FigureS9).
Data analysis. e raw mass spectra were transformed into mzXML format via MSConvert (Proteowizard)64
and imported to MATLAB (R2016b). Each sample file was interpolated linearly (106 points in the range
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50–500 Da). ese interpolated prole mass spectra were then centroided (intensity threshold of 50 a.u.), result-
ing in ~4,400 mass spectral features (number of peaks dependent of course on the experiment performed).
Subsequently, we computed the time traces for each of these peaks by adding the signal intensities ± 2, 5 mDa
around the peaks. e resulting time traces (i.e., signal intensity for each m/z value as a function of time) were
subjected to an agglomerative hierarchical cluster tree (Ward method; Euclidean distance; Figs1c and 2d). To
ease visualization, the time traces were smoothed (moving mean; span = 25). e 263 features nally considered
(TableS1) were retained as they raised upon the 12C-glucose and 13C-glucose injection but did not experience
any temporal change in a blank experiment were glucose was spiked in sterile fermentation medium. e signal
to noise ratios were determined as the ratio of the variances of the signals of the experiment with yeast and the
negative control where we spiked glucose into sterile medium (FigureS1).
e generation of molecular formulae from accurate mass was performed assuming protonated ions in the
positive ion mode and deprotonated ions in negative mode. e following rules65 were taken into account: Masses
up to 500 Da; considered elements were restricted to C, H, N and O; restricted maximum number of each of these
elements, bounded H/C ratio and heteroatom/C ratio and limited number of double-bond equivalent.
Principal component analysis (PCA) was also used for dimensionality reduction and visualization of the mass
spectra (Fig.3a,b). e matrix subjected for PCA consisted of 30 rows (i.e., ten time points during the exponential
growth for each of the three strains investigated) and 636 variables (i.e., peaks listed in TableS2). e data was
mean-centered prior PCA. No further transformation of the matrix was applied.
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We are thankful to Prof. Renato Zenobi for hosting the ACID project at ETH Zurich. We gratefully acknowledge
Dr. Juan Zhang (Novartis AG) for the donation of the LTQ Orbitrap instrument used in this study and Martin
omas Gaugg (ETH) for his support with some of the Matlab scripts. e research leading to these results has
received funding from the European Community’s Seventh Framework Programme (FP7–2013-IAPP) within
the project “Analytical Chemistry Instrumentation Development” (609691). Lars M. Blank and Birgitta E. Ebert
acknowledge the German Federal Ministry for Education and Research (BMBF) for funding (Az. 031A301A).
is work is dedicated to Prof. John B. Fenn (1917–2010) on the occasion of the centennial celebration of his
Author Contributions
D.G.G., A.J.I. and P.M.L.S. designed the research; A.T.R., D.G.G., A.J.I. and P.M.L.S. performed the experiments;
P.M.L.S. analyzed the data; all authors interpreted the data; A.T.R., B.E.E., L.M.B., A.J.I. and P.M.L.S. wrote the
manuscript; all authors contributed to editing the manuscript.
Additional Information
Supplementary information accompanies this paper at
Competing Interests: Alberto Tejero Rioseras and Pablo Martinez-Lozano Sinues work in the framework of
a project (Analytical Chemistry Instrumentation Development, FP7-2013-IAPP, 609691), one of whose main
objectives is to develop the commercial ion source used in this study.
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... These substances are small molecular mass compounds. Their low boiling points and highvapour pressures (Ebert, Halbfeld, Blank, & L., 2016;Tejero-Rioseras et al., 2017) allow non-invasive monitoring, with easier sampling and minimum sample handling when compared with other metabolites (Singh et al., 2018;Sinha, Khot, Schroeder, & Si, 2017). Thus, many previous works found in literature have been focused on the study of VOCs from Iberian products (Arroyo-Manzanares et al., 2018;Martín-Gómez, Arroyo-Manzanares, Rodríguez-Estévez, & Arce, 2019;Ramírez & Cava, 2007;Sánchez del Pulgar, García, Reina, & Carrapiso, 2013;Timón, Ventanas, Carrapiso, Jurado, & García, 2001). ...
Cured Iberian ham is a worldwide known product due to its high quality. Nowadays, there is a lack of official analytical methods to differentiate geographical origin (Protected Designation of Origin (PDO)), the curing plant where hams are processed, as well as the commercial categories in each industry. In this work, volatile organic compounds (VOCs) extracted from 998 Iberian hams were analyzed by Gas Chromatography coupled to Ion Mobility Spectrometry (GC-IMS), using the subsequent information to design discriminant models. High classification percentages were obtained for the three objectives of the study: 98,5% for geographical origin, 93,5% and 100% for curing plant discrimination, and an average rate of 84,5% for commercial category discrimination in the seven curing plants subject to study. Differences obtained in discriminant models are probably due to the complexity of Iberian ham manufacturing process. In this sense, the results obtained in the present study suggest slight differences between geographical areas and industries evaluated, even covered by the same PDO. Also, those differences may be related to the existing variability in terms of breed purity and feeding regime of Iberian pigs, which are two of the main determining factors of ham aroma.
... In this work, we addressed these open questions using SESI-HRMS, which features limits of detection for VOC features as low as part-per-trillion (Martínez-Lozano et al., 2009) and operates in real time. Additionally, SESI-HRMS allows for the simultaneous detection of hundreds of VOCs from microorganisms because of the high resolution of the mass analyzer (Tejero Rioseras et al., 2017). To do so, we conducted quantitative measurements of two different S. aureus and S. pneumoniae strains. ...
Full-text available
Early detection of pathogenic bacteria is needed for rapid diagnostics allowing adequate and timely treatment of infections. In this study, we show that Secondary Electro-Spray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) can be used as a diagnostic tool for rapid detection of bacterial infections as a supportive system for current state of the art diagnostics. Volatile Organic Compounds (VOCs) produced by growing S. aureus or S. pneumoniae cultures on blood agar plates were detected within minutes and allowed for the distinction of these two bacteria on a species and even strain level within hours. Furthermore, we obtained a fingerprint of clinical patient samples within minutes of measurement and predominantly observed a separation of samples containing living bacteria compared to samples with no bacterial growth. Further development of this technique may reduce the time required for microbiological diagnosis and should help to improve patient’s tailored treatment.
... Although with its nominal mass of 46 Da, it is slightly too small to be measured by the SESI-Orbitrap MS-system, with its detection range of 50-500 m/z. The ethanol dimer has an [M + H] + of 93.0910 m/z and is therefore detectable as a so-called proton-bound dimer 26,29 . With this exact mass, the only possible molecular formula within the 3 ppm uncertainty interval of the machine is C 4 H 12 O 2 . ...
Full-text available
Volatile organic compounds play an essential role in every domain of life, with diverse functions. In this study, we use novel secondary electrospray ionisation high-resolution Orbitrap mass spectrometry (SESI-Orbitrap MS) to monitor the complete yeast volatilome every 2.3 s. Over 200 metabolites were identified during growth in shake flasks and bioreactor cultivations, all with their unique intensity profile. Special attention was paid to ethanol as biotech largest product and to acetaldehyde as an example of a low-abundance but highly-volatile metabolite. While HPLC and Orbitrap measurements show a high agreement for ethanol, acetaldehyde could be measured five hours earlier in the SESI-Orbitrap MS. Volatilome shifts are visible, e.g. after glucose depletion, fatty acids are converted to ethyl esters in a detoxification mechanism after stopped fatty acid biosynthesis. This work showcases the SESI-Orbitrap MS system for tracking microbial physiology without the need for sampling and for time-resolved discoveries during metabolic transitions.
... This makes it ideal for real-time observation of metabolic activity inliving systems by monitoring the incorporation of isotopes upon introduction of labeled substrates (eg, metabolism of glucose by yeast). [10] Such information reflects changes in the intrinsic metabolome and helps for a better understanding of underlying metabolic processes, drug pharmacokinetics and disease-related alterations. [11,12] In this study we used an untargeted approach to investigate the capability of SESI-HRMS as a complementary technique to dynamically monitor metabolic 2 H-incorporation in vivo in a non-invasive and realtime manner. ...
Full-text available
Objective:. The aim of this work was to explore the feasibility of in vivo and non-invasive monitoring of deuterium/hydrogen (2H/1H) exchange at the metabolic level upon exposure to heavy water (2H2O). Methods:. The healthy female mice were randomly assigned to two groups after day 0 when both mice received standard drinking water. The treated mouse was fed with 2H2O (80%, v/v) and the control mouse fed with standard drinking water (H2O) over next 13 days. Real-time mass spectrometric analysis of volatile metabolism emitted through breathing and the skin was performed on days 1, 2, 3, 10, 12, and 13. Animal experiment was approved by the Laboratory Animal Ethics Committee of Jinan University (approval No. 20161117163322) on October 29, 2021. Results:. We observed a replacement of 1Hby2H in 52 mass spectral features (60 2H/1 H isotopologue pairs) for the mouse fed with 2H2O, but not for the control mouse. These included pyruvic acid and lactic acid, lysine and methyl-lysine as well as short-chain fatty acids comprising acetic acid, propionic acid, butyric acid and valeric acid. Conclusion:. Secondary electrospray ionization-high resolution mass spectrometry allows monitoring in vivo 2H-incorporation of metabolites in a non-invasive and real-time setup and opens new opportunities to use 2H tracing to extend current metabolic studies, especially those with a focus on anaerobic glycolysis, lysine methylation and gut microbiome via monitoring of short-chain fatty acids.
... La DI-ESI-MS a été utilisée pour profiler les métabolites de S. cerevisiae dans le but de discriminer un certain nombre de mutants, notamment avec l'aide d'analyses multivariées pour comparer les résultats des différentes analyses [150,151]. Il existe également une technique en temps réel d'analyse de métabolites volatils injectant des molécules du mélange en phase gazeuse vers une source d'ionisation par électrospray secondaire (SESI) [152]. Ces travaux réalisés avec un analyseur de type Orbitrap ont résulté dans la détection d'approximativement 300 molécules formant un profil métabolique riche de la levure. ...
Cette thèse s’intitule « Identification et cartographie des métabolites secondaires de nouvelles souches de levures tropicales ». L’objectif principal est le développement d’une méthodologie de déréplication pour l’exploration des métabolites secondaires extraits de cultures de champignons unicellulaires. La méthode développée fait intervenir la chromatographie liquide à ultra-haute performance couplée à la spectrométrie de masse avec l’outil des réseaux moléculaires en complémentarité de la spectrométrie de masse à ultra-haute résolution. La méthodologie permet d’annoter les métabolites détectés par le procédé analytique, c’est-à-dire formuler des hypothèses sur l’identité et la structure de nombreuses molécules produites par les nouvelles espèces de levures sur lesquelles elle est appliquée. De plus, des cartographies et profils métaboliques sont dressés grâce aux diagrammes et réseaux moléculaires construits. L’application de la méthodologie à de nouvelles souches de levures a permis l’annotation de plusieurs centaines de métabolites, parmi lesquels une large proportion d’alcaloïdes mais aussi des acides aminés, des terpénoïdes et des stérols.
... The volatilome constitutes the set of volatile organic compounds (VOCs) produced by an organism (Amann et al., 2014;Filipiak et al., 2016). These are low-mass molecular substances characterised by low boiling points and high vapour pressures (Ebert et al., 2016;Tejero Rioseras et al., 2017), which can therefore be monitored by non-invasive methods and easier samplings compared to other metabolites (Sinha et al., 2017;Singh et al., 2018). On the other hand, faeces are a complex biological material, interesting for their easy and accessible collection as well as the information they provide about physiology (Rodríguez-Hernández et al., 2020). ...
Nowadays, people are increasingly interested in the food they consume. Authenticity and natural origin are among the most valued issues of food products by society. Although various national and international laws have been created for the regulation of labelling and trade of food, unfortunately, they are often not effective in avoiding food product fraud. The Iberian pig and the cured products obtained with this breed have a great international reputation due to their high quality and added value. However, the authentication of these pigs feeding regime is sometimes difficult. Therefore, the objective of this study was to use faecal volatilome information to differentiate the different feeding regimes which determine the final commercial category of Iberian products. Individual faeces samples were sampled on 10 farms from 133 Iberian pigs to evaluate their volatilome through gas chromatography (GC) coupled to ion mobility spectrometry (IMS). The intensity of GC-IMS plot features were extracted and chemometric tools were employed to develop two different models: one, focused on the discrimination between acorn-fed (completely natural diet grazed) and feed-fed samples, and another one for commercial category classification. Both models were carried out in duplicate, using spectral fingerprint information and a different approach studying specific markers. Good classification rates were obtained in both models: 92,3% and 96,3% were the rates obtained in acorn-fed vs feed-fed model with fingerprint and specific markers information, respectively; and the same classification success was also achieved with both approaches in the second model, focused on commercial category classification. The misclassified samples in both modes, which belonged to acorn-fed pigs, may be related to the diet heterogeneity of these animals and the differences in natural resources foraged. The results of the present study highlight GC-IMS as an useful tool to carry out an in vivo authentication of Iberian pig feeding regime and subsequent commercial category, as well as to avoid labelling fraud. Further studies including a larger number of samples are needed in order to obtain more complex models to classify more different samples.
... Remarkably, the Pd-NPs-modified SPE/GO electrode did not show any sensitivity at the used conditions (read-out mode and pH) up to the level of 100 mM neither towards other peroxides tested in this study (see Experiment) nor hydro-peroxides (tert-butyl-hydroperoxide (TBHP)), carboxylic acids (acetic acid, formic acid), alcohols (MeOH, EtOH, BuOH), ketones (acetone, acetylacetone), amides (formamide, dimethylformamide), glucose and aldehydes (formaldehyde, acetaldehyde, crotonaldehyde, furaldehyde) formed during life cycle [29,[38][39][40][41][42] of yeasts or initially present (for example, glucose) in a cultivation media, see also GC-MS results (see ESI, Fig. S1). A little sensitivity at 0.4 V starting from the level of 10 mM was observed on Pd-NPs to ethylamine, pyrrole and ammonia solution prepared in HC medium. ...
This study describes the development of a one-pot electrochemical miniaturized system for simultaneous cultivation and monitoring of the oxidative status of living cells. This system consisted of screen-printed electrodes modified by electroplated Pd-NPs as an electrocatalyst (i) and living yeast cells (Saccharomyces cerevisiae) (ii) immobilized on the cytocompatible alginate layer (iii). Briefly, during the course of electrochemical investigations a novel electroactive compound methylhydrazine derivative as a secondary metabolite and result of microbial activity was found in yeast cells and used as a signaling molecule for their biochemical profiling. Under the optimized experimental conditions the signal corresponding to the found electroactive secondary metabolite formed in medium of living cells was measured without sample collecting, transport, storage or pre-treatment steps (i.e. extraction, pre-concentration, chemical derivatization or labeling). The electrochemical dependencies, which were derived by a miniaturized electroanalytical system, were fully validated in a conventional three-electrode system under inert atmosphere (Ar) and in the presence of oxygen (air, O2). It is believed that the proposed one-pot nanoreactors serving simultaneously as nanofermenters and amperometric detectors for the quantification of secondary metabolites formed in medium of living cells can significantly enhance the understanding of ongoing fermentation processes in the future and our knowledge on the biochemistry of yeasts.
Allicin is the main flavour component of crushed raw garlic. This plant defence molecule has strong antibiotic properties. While measurements in the liquid phase using LC-MS are established, accessing reactive organosulfur compounds in the gas phase is still a challenge due to heat-degradation in the gas chromatograph. Using a gentle secondary electrospray ionisation coupled Orbitrap mass spectrometry procedure (SESI-Orbitrap MS), we measured gas phase concentrations of allicin evaporating from a pure solution. Despite the mild conditions, two quantitatively major allicin-derived breakdown products were found. The SESI-Orbitrap MS technique was used to follow the known chemistry of alliin, isoallin and methiin conversion in garlic, onion and ramsons. Allicin and its metabolites were also measured over two hours in human breath after garlic consumption. These results demonstrate the utility of SESI-Orbitrap MS for analysis of sulfur-containing volatiles from plants in the genus Allium and potentially for capturing volatilomes of foodstuffs in general.
Satay as Indonesian food is made from several pieces of meat stabbed with a bamboo stick and grilled. Satay has a unique aroma because of the diversity in total organic volatile contents. Differences in volatile compounds are also influenced by species breed and processing method. Volatilomics is a method used to ensure the authenticity of meat products through the detection, characterization, and quantification of all volatile metabolites in biological systems. This study aims to evaluate volatile profiles in beef, chicken, pork satay, and their mixtures. The volatile components of satay were extracted using the solid-phase micro extraction (SPME) method and analyzed using gas chromatography-mass spectrometry (GC-MS) instrument. Data were analyzed by multivariate principles component analysis (PCA). Beef, chicken, and pork satays were identified as having 104,134, and 112 volatile compounds, respectively. Nonanal was a volatile compound with the highest intensity in beef satay samples, benzaldehyde in chicken satay, and cyclohexanol in pork satay. Volatile components of satay with different types of meat showed good separation using the PCA model. Beef, chicken, and pork satays are grouped separately. Mixed-meat satay containing pork was grouped next to the pork satay. Volatilomic analysis identified a hexanal compound was potential to be used as a marker to distinguish between pork and other meat satays in halal authentication process. Keywords: beef, chicken, halal, pork, volatilomics
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YMDB or the Yeast Metabolome Database ( is a comprehensive database containing extensive information on the genome and metabolome of Saccharomyces cerevisiae Initially released in 2012, the YMDB has gone through a significant expansion and a number of improvements over the past 4 years. This manuscript describes the most recent version of YMDB (YMDB 2.0). More specifically, it provides an updated description of the database that was previously described in the 2012 NAR Database Issue and it details many of the additions and improvements made to the YMDB over that time. Some of the most important changes include a 7-fold increase in the number of compounds in the database (from 2007 to 16 042), a 430-fold increase in the number of metabolic and signaling pathway diagrams (from 66 to 28 734), a 16-fold increase in the number of compounds linked to pathways (from 742 to 12 733), a 17-fold increase in the numbers of compounds with nuclear magnetic resonance or MS spectra (from 783 to 13 173) and an increase in both the number of data fields and the number of links to external databases. In addition to these database expansions, a number of improvements to YMDB's web interface and its data visualization tools have been made. These additions and improvements should greatly improve the ease, the speed and the quantity of data that can be extracted, searched or viewed within YMDB. Overall, we believe these improvements should not only improve the understanding of the metabolism of S. cerevisiae, but also allow more in-depth exploration of its extensive metabolic networks, signaling pathways and biochemistry.
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Background: Volatile organic compounds (VOCs) are small molecular mass substances, which exhibit high-vapor pressures, low boiling points, and lipophilic character. VOCs are produced by all organisms including eukaryotic microbes like yeast, which volatile metabolites are for centuries exploited for examples as flavors in bread, beer, and wine. Notably, while the applications of VOCs are many, the knowledge on their biochemical synthesis is still limited. Objective: We review her the current information of yeast volatile metabolites and techniques to further explore the VOC landscape made possible by improvements of the analytical possibilities, regarding sampling frequency, identification, and quantification and the development to computationally interpret (high-throughput) data. Especially possibilities for online and even real-time analysis should trigger new experimental approaches that elucidate the biochemistry as well as the regulation of VOC synthesis. Baker’s yeast is here the organism of choice as the genetic inventory can be linked to VOC formation and with this in hand improved applications can be envisaged. The physical, chemical or biological properties make many VOCs interesting targets for different industrial sectors while their natural function as semiochemicals or in defense mechanisms can be exploited to engineer synthetic microbial communities or to develop new antibiotics. Conclusion: VOCs produced by microbes including yeast are a chemical diverse group of compounds with highly different applications. The new analytical techniques briefly summarized here will enable the use of VOCs in even broader applications including human health monitoring and bioprocess control. We envisage a bright future for VOC research and for the resulting applications.
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During evolution, cells acquired the ability to sense and adapt to varying environmental conditions, particularly in terms of fuel supply. Adaptation to fuel availability is crucial for major cell decisions and requires metabolic alterations and differential gene expression that are often epigenetically driven. A new mechanistic link between metabolic flux and regulation of gene expression is through moonlighting of metabolic enzymes in the nucleus. This facilitates delivery of membrane-impermeable or unstable metabolites to the nucleus, including key substrates for epigenetic mechanisms such as acetyl-CoA which is used in histone acetylation. This metabolism-epigenetics axis facilitates adaptation to a changing environment in normal (e.g., development, stem cell differentiation) and disease states (e.g., cancer), providing a potential novel therapeutic target.
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Aspergillus flavus produces dangerous secondary metabolites known as aflatoxins, which are toxic and carcinogenic, and their contamination of agricultural products results in health issues and economic hardships in the U.S. and around the world. Early identification of aflatoxigenic isolates of A. flavus is the key in the management of these fungi. An emerging detection method for specific fungi identification involves the analysis of microbial volatile organic compounds (MVOCs) released by the fungi. Complicating this approach is the understanding that many factors influence metabolic production, including growth parameters, such as growth media, temperature, spore counts and oxidation stress. In addition, analytical and data analysis methods can also influence the results. Several growth and analysis methods were evaluated and optimized in order to better understand the effect of the methods on fungi MVOC signatures. The results indicate that carboxen/polydimethylsiloxane (CAR/PDMS) has the best extraction efficiency for the MVOCs emitted by A. flavus. Both chemical defined agar (CDA) and chemical defined liquid (CDL) are suitable growth media for MVOC emission studies. The highest MVOC production was found at 30 ˝ C. Log transformation was considered one of the best data pretreatment methods when analyzing MVOC data and resulted in the best principal component analysis (PCA) clustering in the experiments with different growth media. This study aims to elucidate fungal volatile organic compounds (VOCs) differences due to variations in growth parameters as a first step in the development of an analytical method for the monitoring of aflatoxigenic A. flavus contamination in crop storage facilities.
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In light of the increasing attention towards "green" solutions to improve food quality, the use of aromatic-enhancing microorganisms offers the advantage to be a natural and sustainable solution that did not negatively influence the list of ingredients. In this study, we characterize, for the first time, volatile organic compounds (VOCs) associated with aromatic bakery yeasts. Three commercial bakery starter cultures, respectively formulated with three Saccharomyces cerevisiae strains, isolated from white wine, red wine, and beer, were monitored by a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS), a direct injection analytical technique for detecting volatile organic compounds with high sensitivity (VOCs). Two ethanol-related peaks (m/z 65.059 and 75.080) described qualitative differences in fermentative performances. The release of compounds associated to the peaks at m/z 89.059, m/z 103.075, and m/z 117.093, tentatively identified as acetoin and esters, are coherent with claimed flavor properties of the investigated strains. We propose these mass peaks and their related fragments as biomarkers to optimize the aromatic performances of commercial preparations and for the rapid massive screening of yeast collections.
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Online breath analysis is an attractive approach to track exhaled compounds without sample preparation. Current commercially available real-time breath analysis platforms require the purchase of a full mass spectrometer. Here we present an ion source compatible with virtually any preexisting atmospheric pressure ionization mass spectrometer that allows real-time analysis of breath. We illustrate the capabilities of such technological development by upgrading an orbitrap mass spectrometer. As a result, we detected compounds in exhaled breath between 70 and 900 Da, with a mass accuracy of typically <1 ppm; resolutions between m /Δ m 22 000 and 70 000 and fragmentation capabilities. The setup was tested in a pilot study, comparing the breath of smokers ( n = 9) and non-smokers ( n = 10). Exogenous compounds associated to smoking, as well as endogenous metabolites suggesting increased oxidative stress in smokers, were detected and in some cases identified unambiguously. Most of these compounds correlated significantly with smoking frequency and allowed accurate discrimination of smokers and non-smokers.
Human breath contains thousands of volatile organic compounds(VOCs), which would be potentially helpful for studying disease diagnosis and environmental exposure. However, owing to limitations in current analytical methods, only the compounds with molecular weights <300 have been widely reported in exhaled human breath. In this study, a high performance homemade secondary nanoelectrospray ionization source coupled with ultrahigh resolution mass spectrometer(nanoSESI-UHRMS) was set up and applied to investigating the presence of compounds with relatively high molecular weight(RHMW)(300-500) in healthy human breath. The results indicated that 29 RHMW compounds have been detected in real time by nanoSESI-UHRMS. These organic compounds mostly consist of C, H, N and O atoms, and the average of ring-double bond equivalent(RDB) values is (4.5±3.1), implying they are most likely to be ketones, aldehydes and fatty acids. These unsaturated compounds are characterized with high ionization efficiency in SESI, and thus readily detected by nanoSESI-UHRMS.
Rationale: Direct mass spectrometry (MS)-based methods make it possible to monitor the molecular compositions of hundreds of volatile organic compounds (VOCs) in exhaled human breath in real time. Mass resolution and mass accuracy play important roles for direct MS analysis, especially for the low-concentration isobaric compounds in non-target research. Methods: Direct detection of VOCs in exhaled breath of four healthy subjects (3 males and 1 female aged between 25 to 35 years old) has been performed by using secondary nano-electrospray ionization mass spectrometry (Sec-nanoESI-UHRMS) at resolutions (R) of 15,000, 30,000, 60,000 and 120,000. Results: For some low-intensity isobaric ions, they could be distinguished only when R ≥ 60,000, e.g., signals at m/z 96.9591 (sulfate/sulfuric acid), m/z 96.9687 (phosphate/phosphoric acid) and m/z 96.9756 ([C4 H2 O7 S]- ), m/z 234.1161 ([C10 H20 O3 NS]+ ) and m/z 234.1338 ([C10 H20 O5 N]+ ), m/z 119.0686 (isotope of indole) and m/z 119.0705 (an interfering signal), respectively. At R 120,000, the mass errors were obtained from a set of reference ions, and the values were ≤0.6 mDa for ions detected in positive detection mode and in the range of -1.0-1.1 mDa for the negative mode. These mass errors were used to exclusively identify unknown compounds detected in the breath samples. By utilizing the present setup, besides the normal VOCs reported previously, we detected non-volatile species (sulfate/sulfuric acid, silicate/silicic acid, phosphate/phosphoric acid and nitrate/nitric acid), dichlorobenzene and an ammonium adduct ([(C2 H6 SiO)6 + NH4 ]+ ), which were ascribed to exhaled particles, indoor air pollution and an endogenous source, respectively. Conclusions: For direct breath analysis, high mass resolution of ≥60,000 and mass errors of 1.0 mDa (absolute value) covering the mass range of interests (e.g., m/z 50-500) are necessary for the exploration and accurate identification of low-intensity unknown isobaric compounds in non-target research. Copyright © 2017 John Wiley & Sons, Ltd.
We have deployed an efficient secondary electrospray ionization source coupled to an Orbitrap mass analyzer (SESI-MS) to investigate the emissions of a Begonia Semperflorens. We document how hundreds of species can be tracked with an unparalleled time resolution of 2 min during day-night cycles. To further illustrate the capabilities of this system for volatile organic compounds (VOCs) analysis, we subjected the plant to mechanical damage and monitored its response. As a result, ~ 1,200 of VOCs were monitored displaying different kinetics. To validate the soundness of our in vivo measurements, we fully characterized some key compounds via MS/MS and confirmed their expected behavior based on prior gas chromatography-mass spectrometry (GC-MS) studies. For example, β-caryophyllene, which is directly related to photosynthesis, was found to show a periodic day-night pattern with highest concentrations during the day. We conclude that the capability of SESI-MS to capture highly dynamic VOC emissions and wide analyte coverage makes it an attractive tool to complement GC-MS in plant studies.