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Headspace analyses using multi-capillary column-ion mobility spectrometry allow rapid pathogen differentiation in hospital-acquired pneumonia relevant bacteria


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Background Hospital-acquired pneumonia (HAP) is a common problem in intensive care medicine and the patient outcome depends on the fast beginning of adequate antibiotic therapy. Until today pathogen identification is performed using conventional microbiological methods with turnaround times of at least 24 h for the first results. It was the aim of this study to investigate the potential of headspace analyses detecting bacterial species-specific patterns of volatile organic compounds (VOCs) for the rapid differentiation of HAP-relevant bacteria. Methods Eleven HAP-relevant bacteria (Acinetobacter baumanii, Acinetobacter pittii, Citrobacter freundii, Enterobacter cloacae, Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae, Pseudomonas aeruginosa, Proteus mirabilis, Staphylococcus aureus, Serratia marcescens) were each grown for 6 hours in Lysogeny Broth and the headspace over the grown cultures was investigated using multi-capillary column-ion mobility spectrometry (MCC-IMS) to detect differences in the VOC composition between the bacteria in the panel. Peak areas with changing signal intensities were statistically analysed, including significance testing using one-way ANOVA or Kruskal-Wallis test (p < 0.05). Results 30 VOC signals (23 in the positive ion mode and 7 in the negative ion mode of the MCC-IMS) showed statistically significant differences in at least one of the investigated bacteria. The VOC patterns of the bacteria within the HAP panel differed substantially and allowed species differentiation. Conclusions MCC-IMS headspace analyses allow differentiation of bacteria within HAP-relevant panel after 6 h of incubation in a complex fluid growth medium. The method has the potential to be developed towards a feasible point-of-care diagnostic tool for pathogen differentiation on HAP.
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R E S E A R C H A R T I C L E Open Access
Headspace analyses using multi-capillary
column-ion mobility spectrometry allow
rapid pathogen differentiation in hospital-
acquired pneumonia relevant bacteria
Nils Kunze-Szikszay
, Maximilian Euler
, Martin Kuhns
, Melanie Thieß
, Uwe Groß
, Michael Quintel
Thorsten Perl
Background: Hospital-acquired pneumonia (HAP) is a common problem in intensive care medicine and the patient
outcome depends on the fast beginning of adequate antibiotic therapy. Until today pathogen identification is
performed using conventional microbiological methods with turnaround times of at least 24 h for the first results. It
was the aim of this study to investigate the potential of headspace analyses detecting bacterial species-specific
patterns of volatile organic compounds (VOCs) for the rapid differentiation of HAP-relevant bacteria.
Methods: Eleven HAP-relevant bacteria (Acinetobacter baumanii, Acinetobacter pittii,Citrobacter freundii,Enterobacter
cloacae,Escherichia coli,Klebsiella oxytoca,Klebsiella pneumoniae,Pseudomonas aeruginosa,Proteus mirabilis,
Staphylococcus aureus,Serratia marcescens) were each grown for 6 hours in Lysogeny Broth and the headspace over
the grown cultures was investigated using multi-capillary column-ion mobility spectrometry (MCC-IMS) to detect
differences in the VOC composition between the bacteria in the panel. Peak areas with changing signal intensities
were statistically analysed, including significance testing using one-way ANOVA or Kruskal-Wallis test (p< 0.05).
Results: 30 VOC signals (23 in the positive ion mode and 7 in the negative ion mode of the MCC-IMS) showed
statistically significant differences in at least one of the investigated bacteria. The VOC patterns of the bacteria
within the HAP panel differed substantially and allowed species differentiation.
Conclusions: MCC-IMS headspace analyses allow differentiation of bacteria within HAP-relevant panel after 6 h of
incubation in a complex fluid growth medium. The method has the potential to be developed towards a feasible
point-of-care diagnostic tool for pathogen differentiation on HAP.
Keywords: Pneumonia, Microbiological techniques, Volatile organic compound, Metabolite, Ion mobility
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* Correspondence:
Department of Anesthesiology, University Medical Center Göttingen,
Robert-Koch-Straße 40, 37075 Göttingen, Germany
Full list of author information is available at the end of the article
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69
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About one-quarter of all infections in intensive care medi-
cine are episodes of hospital-acquired pneumonia (HAP)
[1]. Behind central line-associated bloodstream infections,
HAP is the second common infectious condition in the in-
tensive care unit (ICU) [2]. Mechanical ventilation is the
most important risk factor for HAP. Up to 27% of all pa-
tients ventilated > 48 h suffer from HAP. ICU and hospital
stays are prolonged in these patients and mortality rates
aremarkedlyincreased[3]. Eventually, HAP leads to sep-
sis and septic shock with mortality rates up to 50% [4].
Rapid and adequate antibiotic therapy contributes to im-
proved outcomes in these patients [5,6].
The judgement of adequacy of antibiotic therapy
thereby still depends on the results of conventional
microbiological diagnostics with mean turnaround
times of 71 h from sampling to the final results [7].
During that time, both patients and intensive care
practitioners are forced to rely on the adequacy of
the initially chosen calculated broad-spectrum anti-
biotic therapy, which is based on the patients individ-
ual risk profile and the knowledge of local microbial
characteristics and probability for antibiotic resis-
tances [8]. Adaption of antibiotic therapy is based on
the results of pathogen identification and antibiotic
resistance testing. Thereby, both escalation and down-
grading of antibiotic therapy should be carried out as
early as possible to ensure effectiveness on the etio-
logical pathogen and to avoid induction of antimicro-
bial drug-resistances.
Besides proteomic (e.g. MALDI-TOF-MS) and gen-
omic (e.g. multiplex PCR) methods the detection of
microbial volatile organic compounds (VOC) was re-
cently introduced as a possible strategy for pathogen
identification [9,10]. Multi-capillary column-ion mo-
bility spectrometry (MCC-IMS) is a highly sensitive
analytical method enabling the detection of volatile
substances down to a range of picograms per liter
(parts per trillion). MCC-IMS allows the user to in-
vestigate complex and humid gas samples, such as
headspace samples of bacterial cultures or even
breathing air [11]. Recent studies did show the feasi-
bility of MCC-IMS analyses to detect microbial VOC
and to differentiate between species of human patho-
genic bacteria and fungi [1214].
Based on the results of our previous studies we as-
sumed that differentiation of a panel of HAP-relevant
bacteria might be possible already after 6 h of incuba-
tion. We conducted an experimental study to test this
hypothesis on a panel of 11 HAP-relevant bacteria.
Successful differentiation of such a panel after 6 h
would confirm the applications eligibility as an in-
novative diagnostic approach in the setting of inten-
sive care medicine.
Growth medium and volatile background
All bacteria were grown in Lysogeny Broth fluid medium
(LB, Carl Roth GmbH + Co. KG, Karlsruhe, Germany).
A pH of 7.2 was adjusted at a temperature of 37 °C using
standard Tris-HCl buffer. Aliquots of 100 ml of the
medium were transferred into 250-ml culture flasks
(Schott Duran, Mainz, Germany) and autoclaved for 25
min at a temperature of 121 °C.
To investigate the volatile background six measure-
ments of the cleaned and autoclaved culture flasks and six
measurements of the sterile LB medium were performed
using both ion modes of the MCC-IMS. For this, sterile
LB samples were agitated constantly at a temperature of
37 °C for at least 1 h before measurements.
Compounds that were detected in the measurements
of sterile LB medium and/or the sole culture flasks were
considered to be part of the volatile background. These
compounds were neglected if they did not show any ob-
viously visible changes. In case of doubt these VOCs
were analysed. Newly occurring signals as well as signals
with increasing or decreasing intensity were considered
to be useful discriminators and were therefore analysed
for the present study.
HAP panel
For the HAP-specific pathogen panel, one culture of the
following 11 bacterial strains was used: Acinetobacter
baumanii (DSM 24110, German Collection of Microor-
ganisms and Cell Cultures, DSMZ, Braunschweig,
Germany), Acinetobacter pittii (DSM 103739), Citrobac-
ter freundii (DSM 24120), Enterobacter cloacae (DSM
30054), Escherichia coli (DSM 1103), Klebsiella oxytoca
(DSM 24121), Klebsiella pneumoniae (DSM 2026),
Pseudomonas aeruginosa (DSM 1117), Proteus mirabilis
(DSM 4479), Staphylococcus aureus (DSM 13661), Serra-
tia marcescens (DSM 50904). All strains were stored at
80 °C as glycerol stocks (LB w/v 55% glycerol).
For headspace analyses, overnight cultures were inocu-
lated from glycerol stocks. From this culture, 100 μl were
transferred into a 250 ml Schott flask containing 100 ml
of LB fluid medium. Cultures were left constantly mov-
ing at a temperature of 37 °C for 6 h before headspace
analyses were performed. Afterwards, each culture was
grown in addition for 72 h on Columbia sheep agar
plates and verified by MALDI-TOF-MS analyses using
the Bruker BioTyper 3.0 system (Bruker Daltonics,
Bremen, Germany). A single culture was grown for each
headspace measurement. A flow chart of the experimen-
tal setup can be found as supplemental figure.
Headspace sampling
Headspace analyses were performed after 6 h of incuba-
tion at 37 °C. For each species, six measurements were
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 2 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
performed using the positive ion mode of the MCC-IMS
and six measurements were performed using the nega-
tive ion mode.
For the headspace analyses, each culture flask was at-
tached to the MCC-IMS using a two-way (in- and out-
let) screw cap. A constant flow of 100 ml/min of
synthetic air was applied via the gas inlet of the screw
cap and the gas outlet was attached to the sample inlet
of the MCC-IMS device. The MCC-IMSs sampling loop
was flushed with the gas sample for 30 s at a constant
flow of 100 ml/min. After the sampling was completed,
the culture flasks were disconnected, and incubation was
continued until the next measurement could be started.
In order to keep the sample loop clean, at least two
humid air measurements were performed between the
headspace analyses. The next measurement was per-
formed after these measurements confirmed cleanliness
of the system. Otherwise, more humid air measurements
were performed.
Multi-capillary column-ion mobility spectrometer
The specifications of the MCC-IMS device used for this
study are summarised in Table 1. The principles of gas
sampling analyses by MCC-IMS using a multi-capillary
column (MCC) for pre-separation has been described in
detail before [15]. The authors of this present study did
also describe the working principle of this MCC-IMS de-
vice previously [13].
In the drift region of the IMS, ions move through an
external electrical field that can be charged positively or
negatively. In this study, headspace samples were investi-
gated in both the positive and the negative ion mode.
MCC-IMS data analyses
Analyses of the detected MCC-IMS signals were per-
formed using the VisualNow software (Version 1.2, B&S
Analytik GmbH, Dortmund, Germany). The software vi-
sualizes MCC-IMS data as a three-dimensional matrix in
which the x-axis indicates the inverse ion mobility in
volt seconds per square centimetre (Vs/cm
) and the y-
axis indicates the MCC retention time in seconds (s). In
the resulting topographic plot, the matrix values indicate
the signal intensity (SI) in volts (V). In order to
normalize all MCC-IMS data, alignment was performed
according to Vautz et al. 2009 and Perl et al. 2010 [16,
17]. MCC-IMS signals were identified by comparing the
detected signals with a database, including the reference
data for 125 substances. For each substance, reference
measurements were performed prior to this study as it
has been described before [12]. The IUPAC names of
the substances were used in this study. Signals we were
unable to identify were named according to their pos-
ition in the 2D-topographic plot as P_x_y, whereas x
is representing the inverse ion mobility × 1000 and yis
representing the MCC retention time (e.g. P_685_20 =
0.685 Vs/cm
; 20 s).
Determination of the VOC patterns of the HAP panel
In order to identify all VOC signals that were potential
discriminators between the bacteria of the HAP panel,
all obviously visible changing peak regions were statisti-
cally analysed using Excel (Microsoft Excel Professional
Plus 2010, Microsoft Co., Redmond, USA) and Prism 7
(GraphPad Software Inc., San Diego, USA). In cases of
doubt if a peak region changed it was statistically
analysed. Each signal was investigated for normal distri-
bution using the Shapiro-Wilks test of normality. Signifi-
cance testing was performed using an ordinary one-way
ANOVA for normally distributed parameters and the
Kruskal-Wallis test for non-normally distributed param-
eters. Thereby, the median signal intensity of each com-
pound in the headspace above sterile LB growth
medium was compared with the median signal intensity
over each bacterial culture of the panel. P-values < 0.05
were considered to statistically significant.
After 6 h of incubation in LB, we detected changing
VOC signals in the headspace of all bacterial species of
the HAP panel. Significant changes in signal intensity in
at least one of the bacterial species of the HAP panel
were observed in 30 VOC signals (23 in the positive ion
mode, 7 in the negative ion mode). Table 2lists these
VOC signals and Fig. 1shows their positions in a 2D
topographic MCC-IMS plot. Table 3shows the median
(min-max) signal intensities of all VOC signals for each
species and summarizes the results of the statistical
Table 1 Specifications of the MCC-IMS used for the study
MCC-IMS device BioScout, B&S Analytik GmbH, Dortmund,
Preseparation Multi-capillary column OV-5 (length 22 cm),
Multichrom Ltd., Novosibirsk, Russia
MCC temperature 40 °C
Sample loop Stainless steel, volume 10 ml
Ionization source ß-radiation,
Ni (550 MBq)
Electric field strength 330 V/cm
Shutter opening time 300 μs
Drift and carrier gas Synthetic air, Air Liquide AG, Düsseldorf,
Drift gas flow 100 ml/min
Carrier gas flow 150 ml/min
Sampling time 30 s
Sample flow 100 ml/min
Temperature ambient
Pressure ambient
Tubing PTFE, Bohlender GmbH, Grünsfeld, Germany
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 3 of 9
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analyses. Figure 2shows the peak regions of all VOC
signals for all bacteria species.
Signals that were detected in the headspace of sterile
LB growth medium and that did not change obviously
visible were considered to be part of the volatile back-
ground and therefore neglected. Figure 3shows MCC-
IMS plots of the headspace over sterile LB medium
(positive and negative ion mode) with the positions of
the VOC signals that were considered to differentiate
the HAP panel. The remaining VOC signals and plot
areas did not change during the experiments and were
considered to be part of the volatile background.
With only four and five significantly changing VOC
signals, A. pittii and S. aureus showed the least differ-
ences in the headspace analyses. In contrast, S. marces-
cens and P. mirabilis with 16 and 15 showed the highest
numbers of significantly changing VOC signals. E. clo-
acae, K. oxytoca, K. pneumoniae, P. mirabilis and S.
marcescens showed similar changes. Their VOC patterns
were partly overlapping but still differed from each other
in more than one VOC signal. They were therefore still
considered differentiable among one another.
HAP is a significant cause of morbidity and mortality in
intensive care medicine. Treatment outcome is positively
affected by the fast onset of adequate antibiotic therapy
[1,6]. Besides genomic techniques (e.g. multiplex PCR)
and proteomic applications (e.g. MALDI-TOF-MS), the
metabolomic approach by using VOCs of microbial ori-
gin has been proposed for bacterial species differenti-
ation [9]. MCC-IMS technology using multi-capillary
columns for pre-separation of complex and humid
gas samples has been used in several studies focusing
on the detection of VOCs of microbial origin. Its abil-
ity for differentiating several human pathogenic mi-
crobes was described in both in vitro and in vivo
experiments [12,14,18].
Although pre-analytical culturing and incubation is
mandatory in respiratory samples, MALDI-TOF-MS is
often used in medical microbiological laboratories result-
ing in a significant shortening of result turnaround times
[19]. However, the assessment of microbial samples in
MALDI-TOF-MS is complex and specially trained
personnel is needed. Due to investment costs, its dimen-
sions and the complexity of its handling MALDI-TOF-
MS is not suitable for point-of-care use in an intensive
care unit or even in small or remote hospitals.
In contrast, MCC-IMS technology is rather inexpen-
sive, robust and has the potential for significant
miniaturization. Given a suitable application and intui-
tive user interface it could serve as a point-of-care diag-
nostic tool operated by the local staff of e.g. an intensive
care unit. At the current state, MCC-IMS does not pro-
vide information on antimicrobial resistance. However,
fast and reliable pathogen identification could result in a
significant advantage in guiding the initial antibiotic
The results of this study show that MCC-IMS can
differentiate between 11 bacterial species in a HAP-
relevant panel of bacteria. Therefore, MCC-IMS technol-
ogy has the potential to be developed towards becoming
a valuable diagnostic tool. Several questions need to be
Table 2 VOC signals that changed statistically significant in at
least one bacterial species of the HAP panel
VOC No. Substance name (or position) 1/K
] RT [s]
Positive ion mode
1 Ethanol 0.509 3.7
2 P_745_4 0.745 4.3
3 P_810_5 0.81 4.7
4 P_612_6 0.612 6.3
5 P_678_7 0.678 6.7
6 P_720_16 0.72 16
7 P_508_17 0.508 16.7
8 P_685_20 0.685 19.9
9 P_603_25 0.603 25
10 P_669_25 0.669 25.2
11 P_726_25 0.726 25.2
12 P_756_25 0.756 25.2
13 2-Phenylacetaldehyd 0.616 30.4
14 P_648_36 0.648 36
15 P_580_42 0.58 41.8
16 Octan-1-ol (monomer) 0.722 44.3
17 Octan-1-ol (dimer) 0.929 44.3
18 Nonanal 0.732 53.5
19 P_748_54 0.748 53.5
20 P_634_57 0.634 56.6
21 P_755_106 0.755 106
22 P_763_127 0.763 127
23 Decan-1-ol 0.788 256.6
Negative ion mode
24 P_528_6 0.528 6.3
25 P_632_7 0.632 6.8
26 P_631_10 0.631 10.3
27 P_587_28 0.587 28.4
28 P_613_29 0.613 29
29 P_621_42 0.621 42.3
30 Indole 0.609 239
Unknown substances were named according to their position in the 2D-
topographic plot as P_x_y, whereas xis representing the inverse ion
mobility × 1000 and yis representing the MCC retention time (e.g.
P_685_20 = 0.685 Vs/cm
; 20 s).
Drift time in Ion mobility spectrometry [Vs/cm
], RT Retention time [s]
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 4 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
investigated in this way. For instance, in this present
study we neither investigated the changes in VOC com-
position with more than one pathogen present nor did
we validate our results on real-life clinical samples.
However, together with other recently published studies,
our results underline the potential of the method as an
innovative diagnostic microbiological tool [20].
Multiplex PCR devices are commercially available
for point-of-care microbial diagnostics. In the context
of HAP these systems can lead to significantly re-
duced result turnaround times. However, technical
performance and sensitivity need to improve to justify
the routine use of these expensive sample-in,
answer-outcartridge systems [7,21].
The panel used in our study represents the most rele-
vant part of etiological pathogens in bacterial HAP [22].
Previous studies confirmed the ability of MCC-IMS
headspace analyses to differentiate a large bacterial panel
after 24 h of growth on agar plates [12]. Further investi-
gations focused on the metabolites emitted during the
incubation of fast-growing E. coli and the slow-growing
P. aeruginosa in the complex fluid medium LB [13].
Based on these previous results we hypothesized that 6 h
incubation time might be suitable for both slow- and
fast-growing bacterial species. This assumption was con-
firmed by the results of this present study. Further inves-
tigations might focus on the earliest possible time point
for their differentiation.
Assuming that the composition of bacterial VOC
patterns depends on the nutrient medium used for
growing bacteria it is not surprising that the results
of this present study partly differ from findings of
previous studies [23]. Jünger et al. 2012 used
Columbia sheep blood agar plates for their study and
incubation lasted 24 h before the headspace over the
bacteria cultures was analysed. Only a few substances
that occurred in their study were reproduced with LB
as growth medium. Ethanol, indole and P_631_10
were found in both studies and showed a consistent
association with the VOC patterns of the bacteria in
the panel. Ethanol can be found in different amounts
in the headspace of almost every bacterial culture. It
therefore does not seem to be particularly helpful for
pathogen differentiation. Quantification of the actual
ethanol amount and statistical significance testing
may however add relevant information to the ques-
tion. Indole correlated in both studies with the
growth of E. coli and K. oxytoca, a VOC commonly
reported for both bacteria [24]. Until now it remains
unknown which substance stands behind P_631_10.
However, the substance occurred in both studies over
growing cultures of E. cloacae and P. mirabilis.Inthe
present study, it additionally occurred in the head-
space of S. aureus. Although only three VOCs were
reproducible, we still consider the results to be con-
gruent. Despite substantial differences in the compos-
ition of the nutrient media and the methodological
approaches in both studies, the results confirm the
potential of MCC-IMS headspace analyses for the dif-
ferentiation of clinically relevant bacteria.
Fig. 1 The positions of all VOC signals analysed in this study (positive and negative ion mode) in a 2D topographic plot with the x-axis indicating
inverse ion mobility (1/K
]) and the y-axis indicating the MCC retention time (RT [s])
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 5 of 9
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Our present study did not show any inconsistent re-
sults compared to previous studies using MCC-IMS for
microbial differentiation [1214]. It confirms that even
in a panel of 11 HAP relevant bacteria differentiation
can be achieved after only 6 h of growth in a complex
nutrient medium.
There are some methodological limitations to our
present study. Although a major part of HAP-relevant
bacteria was investigated, there are obviously more
than these 11 species that can cause HAP. Future in-
vestigations should therefore include further patho-
gens causative for HAP. These investigations should
lay a special focus on bacteria that rely on special
growth requirements under in-vitro conditions (e. g.
H. influenzae and S. pneumoniae). Furthermore, we
did not investigate the headspace over cultures with
more than one bacterial species. Tracheal and bron-
chial colonization is common in intensive care medi-
cine and is one mechanism leading to presence of
more than one pathogen in the e. g. a specimen of
tracheal aspirate. Future studies should address this
problem by investigating the most common combina-
The results of this study, and potential future studies
addressing the problems mentioned above, should be
tested in a clinical study investigating the feasibility of
Table 3 Median signal intensities of all VOC signals for each species
Signal intensities (SI) are given in volts. All values are median (min-max) values of the signal intensity (SI). Statistically significant changes in SI (compared to LB)
are highlighted in gray with arrows indicating an increasing ( ) or decreasing ( ) trend in SI. Single arrows mark statistical significance with p-values < 0.05 and
double arrows mark p-values < 0.001
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 6 of 9
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Fig. 2 Images of all VOCs analysed in this study comparing the respective peak regions in all bacterial species of the HAP panel
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 7 of 9
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headspace analyses for early pathogen differentiation in
MCC-IMS headspace analyses enabled differentiation of the
11 HAP relevant bacteria after not more than 6 h of growth
in LB fluid medium. The limitations of our study need to be
addressed in future investigations, but in principle, the
method has the potential to be developed towards a feasible
point-of-care diagnostic tool in intensive care medicine.
Supplementary Information
The online version contains supplementary material available at https://doi.
Additional file 1.
The work of Maximilian Euler is funded by the Deutsche
Forschungsgemeinschaft (DFG, German Research Foundation) 413501650.
The support is gratefully acknowledged.
Fig. 3 The positions of all 30 VOCs analysed in this study in a representative topographic plot of MCC-IMS measurements of the headspace over
sterile LB medium in (a) the positive and (b) the negative ion mode of the MCC-IMS device. The remaining VOC signals and plot areas did not
change during the experiments and were considered to be part of the volatile background
Kunze-Szikszay et al. BMC Microbiology (2021) 21:69 Page 8 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
NKS, MT and TP performed the MCC-IMS experiments and performed the
data analyses. MK performed the MALDI-TOF-MS analyses. NK, MQ, ME and
TP co-wrote the paper. All authors discussed and commented in the manu-
script. All authors read and approved the final manuscript.
Authors information
Not applicable.
This work was part of the cooperation project KF 2111207AK0 and was
founded by the Central Innovation Programme SME (ZIM). The financial
support of the German Federal Ministry of Economics and Technology is
gratefully acknowledged. Open Access funding enabled and organized by
Projekt DEAL.
Availability of data and materials
All original data are available at the corresponding author.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
All authors declare no competing interests.
Author details
Department of Anesthesiology, University Medical Center Göttingen,
Robert-Koch-Straße 40, 37075 Göttingen, Germany.
Institute for Medical
Microbiology, University of Göttingen, Kreuzbergring 57, 37075 Göttingen,
Institute of Plant Science and Microbiology, Molecular Plant
Genetics, University of Hamburg, Ohnhornstraße 18, 22609 Hamburg,
Department of General, Visceral and Pediatric Surgery, University
Medical Center Göttingen, Robert-Koch-Straße 40, 37075 Göttingen,
Received: 6 October 2020 Accepted: 12 January 2021
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... Some VOCs can be seen as common metabolites, produced by many different bacteria or bacterial groups, while other VOCs are specific to certain genera or even species [10]. Due to the unique fingerprint of VOCs in bacterial species, the differentiation between species is possible and has been studied using different detection methods, such as an electric nose, ion mobility spectrometry, and gas chromatography/mass spectrometry (GC/MS) [12][13][14][15][16]. Nevertheless, these techniques come with some challenges, such as the inability to identify and quantify VOCs (electric nose), the large, lab-based instruments (GC-MS), and high costs (GC-MS and ion mobility spectrometry) [17,18]. ...
Full-text available
Background: Bacteria produce volatile organic compounds (VOCs) during growth, which can be detected by colorimetric sensor arrays (CSAs). The SpecifAST® system (Specific Diagnostics) employs this technique to enable antibiotic susceptibility testing (AST) directly from blood cultures without prior subculture of isolates. The aim of this study was to compare the SpecifAST® AST results and analysis time to the VITEK®2 (bioMérieux) system. Methods: In a 12-month single site prospective study, remnants of clinical positive monomicrobial blood cultures were combined with a series of antibiotic concentrations. Volatile emission was monitored at 37 °C via CSAs. Minimal Inhibitory Concentrations (MICs) of seven antimicrobial agents for Enterobacterales, Staphylococcus, and Enterococcus spp. were compared to VITEK®2 AST results. MICs were interpreted according to EUCAST clinical breakpoints. Performance was assessed by calculating agreement and discrepancy rates. Results: In total, 96 positive blood cultures containing Enterobacterales, Staphylococcus, and Enterococcus spp. were tested (269 bug-drug combinations). The categorical agreement of the SpecifAST® system compared to the VITEK®2 system was 100% and 91% for Gram-negatives and Gram-positives, respectively. Errors among Gram-positives were from coagulase-negative staphylococci. Overall results were available in 3.1 h (±0.9 h) after growth detection without the need for subculture steps. Conclusion: The AST results based on VOC detection are promising and warrant further evaluation in studies with a larger sample of bacterial species and antimicrobials.
Full-text available
Background: O’Neill’s recent Review on Antimicrobial Resistance expressed the view that by 2020 high-income countries should make it mandatory to support antimicrobial prescribing with rapid diagnostic evidence whenever possible. Methods: Routine microbiology diagnosis of 95 respiratory specimens from patients with severe infection were compared with those generated by the Unyvero P55 test, which detects 20 pathogens and 19 antimicrobial resistance markers. Supplementary molecular testing for antimicrobial resistance genes, comprehensive culture methodology and 16S rRNA sequencing were performed. Results: Unyvero P55 produced 85 valid results, 67% of which were concordant with those from the routine laboratory. Unyvero P55 identified more potential pathogens per specimen than routine culture (1.34 vs. 0.47 per specimen). Independent verification using 16S rRNA sequencing and culture (n = 10) corroborated 58% of additional detections compared to routine microbiology. Overall the average sensitivity for organism detection by Unyvero P55 was 88.8% and specificity was 94.9%. While Unyvero P55 detected more antimicrobial resistance markers than routine culture, some instances of phenotypic resistance were missed. Conclusions: The Unyvero P55 is a rapid pathogen detection test for lower respiratory specimens, which identifies a larger number of pathogens than routine microbiology. The clinical significance of these additional organisms is yet to be determined. Further studies are required to determine the effect of the test in practise on antimicrobial prescribing and patient outcomes.
Full-text available
It is important to realize that guidelines cannot always account for individual variation among patients. They are not intended to supplant physician judgment with respect to particular patients or special clinical situations. IDSA considers adherence to these guidelines to be voluntary, with the ultimate determination regarding their application to be made by the physician in the light of each patient's individual circumstances. These guidelines are intended for use by healthcare professionals who care for patients at risk for hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP), including specialists in infectious diseases, pulmonary diseases, critical care, and surgeons, anesthesiologists, hospitalists, and any clinicians and healthcare providers caring for hospitalized patients with nosocomial pneumonia. The panel's recommendations for the diagnosis and treatment of HAP and VAP are based upon evidence derived from topic-specific systematic literature reviews.
Full-text available
The early beginning of an adequate antibiotic therapy is crucial in hospital-acquired pneumonia (HAP), but depends on the results of conventional microbiological diagnostics (cMD). It was the aim of this study to evaluate the performance and turnaround times of a new point-of-care multiplex polymerase chain reaction (mPCR) system for rapid identification of pathogens and antibiotic resistance markers. We assessed the applicability of the system under real-life conditions in critical ill patients with HAP. We enrolled forty critical ill patients with clinical signs for HAP into an observational study. Two samples of respiratory secretions were collected during one course of aspiration and cMD and mPCR testing (Unyvero, Curetis AG, Holzgerlingen, Germany) were performed immediately. The mPCR device was operated as a point-of-care system at the intensive care unit. We compared turnaround times, results of pathogen identification and results of antibiotic resistance testing of both methods. Mean turnaround times (min-max) were 6.5 h (4.7-18.3 h) for multiplex PCR and 71 h (37.2-217.8 h) for conventional microbiology (final cMD results, incomplete results neglected). 60 % (n = 24) of the mPCR tests were completely valid. Complete test failure occurred in 10 % (n = 4) and partial test failure occurred in 30 % (n = 12). We found concordant results in 45 % (n = 18) and non-concordant results in 45 % (n = 18) of all patients. 55 % (n = 16) of the results were concordant in patients with a clinical pulmonary infection score (CPIS) > 5 (n = 29). Concordant results included three cases of multidrug resistant bacteria. MPCR frequently detected antibiotic resistance markers that were not found by cMD. Unyvero allowed point-of-care microbial testing with short turnaround times. The performance of the system was poor. However, an improved system with a more reliable performance and an extended microbial panel could be a useful addition to cMD in intensive care medicine. NCT01858974 (registered 16 May 2013).
Full-text available
Headspace analyses over microbial cultures using multi-capillary column-ion mobility spectrometry (MCC-IMS) could lead to a faster, safe and cost-effective method for the identification of pathogens. Recent studies have shown that MCC-IMS allows identification of bacteria and fungi, but no information is available from when on during their growth a differentiation between bacteria is possible. Therefore, we analysed the headspace over human pathogenic reference strains of Escherichia coli and Pseudomonas aeruginosa at four time points during their growth in a complex fluid medium. In order to validate our findings and to answer the question if the results of one bacterial strain can be transferred to other strains of the same species, we also analysed the headspace over cultures from isolates of random clinical origin. We detected 19 different volatile organic compounds (VOCs) that appeared or changed their signal intensity during bacterial growth. These included six VOCs exclusively changing over E. coli cultures and seven exclusively changing over P. aeruginosa cultures. Most changes occurred in the late logarithmic or static growth phases. We did not find differences in timing or trends in signal intensity between VOC patterns of different strains of one species. Our results show that differentiation of human pathogenic bacteria by headspace analyses using MCC-IMS technology is best possible during the late phases of bacterial growth. Our findings also show that VOC patterns of a bacterial strain can be transferred to other strains of the same species.
Full-text available
We demonstrated previously that genetic inactivation of tryptophanase is responsible for a dramatic decrease in biofilm formation in the laboratory strain Escherichia coli S17-1. In the present study, we tested whether the biochemical inhibition of tryptophanase, with the competitive inhibitor oxindolyl-L-alanine, could affect polystyrene colonization by E. coli and other indole-producing bacteria. Oxindolyl-L-alanine inhibits, in a dose-dependent manner, indole production and biofilm formation by strain S17-1 grown in Luria-Bertani (LB) medium. Supplementation with indole at physiologically relevant concentrations restores biofilm formation by strain S17-1 in the presence of oxindolyl-L-alanine and by mutant strain E. coli 3714 (S17-1 tnaA::Tn5) in LB medium. Oxindolyl-L-alanine also inhibits the adherence of S17-1 cells to polystyrene for a 3-h incubation time, but mutant strain 3714 cells are unaffected. At 0.5 mg/mL, oxindolyl-L-alanine exhibits inhibitory activity against biofilm formation in LB medium and in synthetic urine for several clinical isolates of E. coli, Klebsiella oxytoca, Citrobacter koseri, Providencia stuartii, and Morganella morganii but has no affect on indole-negative Klebsiella pneumoniae strains. In conclusion, these data suggest that indole, produced by the action of tryptophanase, is involved in polystyrene colonization by several indole-producing bacterial species. Indole may act as a signalling molecule to regulate the expression of adhesion and biofilm-promoting factors.
Matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has been successfully applied in recent years for first-line identification of pathogens in clinical microbiology because it is simple to use, rapid, and accurate and has economic benefits in hospital management. The range of clinical applications of MALDI-TOF MS for bacterial isolates is increasing constantly, from species identification to the two most promising applications in the near future: detection of antimicrobial resistance and strain typing for epidemiological studies. The aim of this review is to outline the contribution of previous MALDI-TOF MS studies in relation to detection of antimicrobial resistance and to discuss potential future challenges in this field. Three main approaches are ready (or almost ready) for clinical use, including the detection of antibiotic modifications due to the enzymatic activity of bacteria, the detection of antimicrobial resistance by analysis of the peak patterns of bacteria or mass peak profiles, and the detection of resistance by semiquantification of bacterial growth in the presence of a given antibiotic. This review provides an expert guide for MALDI-TOF MS users to new approaches in the field of antimicrobial resistance detection, especially possible applications as a routine diagnostic tool in microbiology laboratories.
Objectives: The detection of microbial volatile organic compounds or host response markers in the exhaled gas could give an earlier diagnosis of ventilator-associated pneumonia. Gas chromatography-ion mobility spectrometry enables noninvasive, rapid, and sensitive analysis of exhaled gas. Using a rabbit model of ventilator-associated pneumonia we determined if gas chromatography-ion mobility spectrometry is able to detect 1) ventilator-associated pneumonia specific changes and 2) bacterial species-specific changes in the exhaled gas. Design: Experimental in vivo study. Setting: University research laboratory. Subjects: Female New Zealand White rabbits. Interventions: Animals were anesthetized and mechanically ventilated. To induce changes in the composition of exhaled gas we induced ventilator-associated pneumonia via endobronchial instillation of either Escherichia coli group (n = 11) or Pseudomonas aeruginosa group (n = 11) after 2 hours of mechanical ventilation. In a control group (n = 11) we instilled sterile lysogeny broth endobronchially. Measurements and main results: Gas chromatography-ion mobility spectrometry gas analysis, CT scans of the lungs, and blood samples were obtained at four measurement points during the 10 hours of mechanical ventilation. The volatile organic compound patterns in the exhaled gas were compared and correlated with ventilator-associated pneumonia severity. Sixty-seven peak areas showed changes in signal intensity in the serial gas analyses. The signal intensity changes in 10 peak regions differed between the groups. Five peak areas (P_648_36, indole, P_714_278, P_700_549, and P_727_557) showed statistically significant changes of signal intensity. Conclusions: This is the first in vivo study that shows the potential of gas chromatography-ion mobility spectrometry for early detection of ventilator-associated pneumonia specific volatile organic compounds and species differentiation by noninvasive analyses of exhaled gas.
Diagnostic strategies currently used for pneumonia are time-consuming, lack accuracy and suffer from large inter-observer variability. Exhaled breath contains thousands of volatile organic compounds (VOCs), which include products of host and pathogen metabolism. In this systematic review we investigated the use of so-called 'breathomics' for diagnosing pneumonia. A Medline search yielded 18 manuscripts reporting on animal and human studies using organic and inorganic molecules in exhaled breath, that all could be used to answer whether analysis of VOC profiles could potentially improve the diagnostic process of pneumonia. Papers were categorised based on their specific aims; the exclusion of pneumonia; the detection of specific respiratory pathogens; and whether targeted or untargeted VOC analysis was used. Ten studies reported on the association between VOCs and presence of pneumonia. Eight studies demonstrated a difference in exhaled VOCs between pneumonia and controls; in the individual studies this discrimination was based on unique sets of VOCs. Eight studies reported on the accuracy of a breath test for a specific respiratory pathogen: five of these concerned preclinical studies in animals. All studies were valued as having a high risk of bias, except for one study that used an external validation cohort. The findings in the identified studies are promising. However, as yet no breath test has been shown to have sufficient diagnostic accuracy for pneumonia. We are in need of studies that further translate the knowledge from discovery studies to clinical practice.
Bacteria are the main cause of many human diseases. Typical bacterial identification methods, for example culture-based, serological and genetic methods, are time-consuming, delaying the potential for an early and accurate diagnosis and the appropriate subsequent treatment. Nevertheless, there is a stringent need for in situ tests that are rapid, noninvasive and sensitive, which will greatly facilitate timely treatment of the patients. This review article presents volatile organic metabolites emitted from various micro-organism strains responsible for common bacterial infections in humans. Additionally, the manuscript shows the application of different analytical techniques for fast bacterial identification. Details of these techniques are given, which focuses on their advantages and drawbacks in using for volatile organic components analysis.
The rapid diagnosis of respiratory infections has always been an important goal for medical professionals, because rapid and accurate diagnosis leads to proper and timely treatment, and consequently, reduces the costs of incorrect and long-term treatments, and antibiotic resistance. The present study was conducted with the aim of detecting volatile organic compounds (VOCs) in three bacteria: Pseudomonas aeruginosa, Acinetobacter baumannii, Klebsiella pneumoniae. Headspace of the studied bacteria, after separately culturing in two types of liquid medium in three different time-periods, was extracted by solid phase microextraction and analysed by gas chromatography mass spectrometry The analysis results of the VOCs produced by the studied bacteria indicate that some VOCs are common and some are unique in each bacterium. 1-penten-3-ol, levomenthol, and 2-octyl-1-ol for P. aeruginosa, cyclohexene, 4-ethenyl, and cisDihydro-α-terpinyl acetate for A. baumannii and 1,3-butadiene, butyraldehyde, longifolene, octyl acetate, tridecanol, dodecenal, (E)-2-hexyl ester, butanoic acid and 5,5-dodecadinyl-1,12-diol for K.pneumoniae were identified as unique VOCs for each bacterium. Finally, it can be said that an accurate and rapid bacterial detection method can be achieved by using a tool that can detect bacterial VOCs. However, more studies are needed to design a tool for which all aspects have been assessed, so that it can give us a more complete pattern for the use of these compounds as biomarkers.