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Experimental parameters defining ultra-low biomass bioaerosol analysis

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Abstract and Figures

Investigation of the microbial ecology of terrestrial, aquatic and atmospheric ecosystems requires specific sampling and analytical technologies, owing to vastly different biomass densities typically encountered. In particular, the ultra-low biomass nature of air presents an inherent analytical challenge that is confounded by temporal fluctuations in community structure. Our ultra-low biomass pipeline advances the field of bioaerosol research by significantly reducing sampling times from days/weeks/months to minutes/hours, while maintaining the ability to perform species-level identification through direct metagenomic sequencing. The study further addresses all experimental factors contributing to analysis outcome, such as amassment, storage and extraction, as well as factors that impact on nucleic acid analysis. Quantity and quality of nucleic acid extracts from each optimisation step are evaluated using fluorometry, qPCR and sequencing. Both metagenomics and marker gene amplification-based (16S and ITS) sequencing are assessed with regard to their taxonomic resolution and inter-comparability. The pipeline is robust across a wide range of climatic settings, ranging from arctic to desert to tropical environments. Ultimately, the pipeline can be adapted to environmental settings, such as dust and surfaces, which also require ultra-low biomass analytics.
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Experimental parameters dening ultra-low biomass
bioaerosol analysis
Irvan Luhung
, Akira Uchida
, Serene B. Y. Lim
, Nicolas E. Gaultier
, Carmon Kee
, Kenny J. X. Lau
, Elena S. Gusareva
Cassie E. Heinle
, Anthony Wong
, Balakrishnan N. V. Premkrishnan
, Rikky W. Purbojati
, Enzo Acerbi
, Hie Lim Kim
Ana C. M. Junqueira
, Sharon Longford
, Sachin R. Lohar
, Zhei Hwee Yap
, Deepa Panicker
, Yanqing Koh
, Kavita K. Kushwaha
Poh Nee Ang
, Alexander Putra
, Daniela I. Drautz-Moses
and Stephan C. Schuster
Investigation of the microbial ecology of terrestrial, aquatic and atmospheric ecosystems requires specic sampling and analytical
technologies, owing to vastly different biomass densities typically encountered. In particular, the ultra-low biomass nature of air
presents an inherent analytical challenge that is confounded by temporal uctuations in community structure. Our ultra-low
biomass pipeline advances the eld of bioaerosol research by signicantly reducing sampling times from days/weeks/months to
minutes/hours, while maintaining the ability to perform species-level identication through direct metagenomic sequencing. The
study further addresses all experimental factors contributing to analysis outcome, such as amassment, storage and extraction, as
well as factors that impact on nucleic acid analysis. Quantity and quality of nucleic acid extracts from each optimisation step are
evaluated using uorometry, qPCR and sequencing. Both metagenomics and marker gene amplication-based (16S and ITS)
sequencing are assessed with regard to their taxonomic resolution and inter-comparability. The pipeline is robust across a wide
range of climatic settings, ranging from arctic to desert to tropical environments. Ultimately, the pipeline can be adapted to
environmental settings, such as dust and surfaces, which also require ultra-low biomass analytics.
npj Biofilms and Microbiomes (2021) 7:37 ;
Great naturalists of centuries-past have catalogued planetary
ecosystems at the macroscopic level, primarily for terrestrial and
aquatic environments, where organisms were most accessible
Microscopic life was subsequently given the same attention, again
initially focusing on terrestrial and aquatic systems
. Microbial
inhabitants of the third ecosystem of planetary scale, the
atmosphere, proved much more difcult to assess due to
technological challenges in regard to accessibility. These chal-
lenges are largely associated with the low-density gaseous state
and resulting ultra-low biomass of air
. As a consequence,
atmospheric research rst described the physicochemical nature
of the atmosphere, thereby generating a comprehensive under-
standing of inanimate components of the troposphere and
. The origin of these components of air is typically
categorised as either inorganic gases or volatile organic com-
pounds (VOCs), the latter of which serve as proxies for the
biological activity of organisms
. The following progression in
the eld involved the identication of airborne organisms via
cultivation and microscopy
. This provided a foundation for
understanding the composition of airborne microbial organisms
via nucleic acid taxonomic identication. A large increase of the
taxonomic resolution was subsequently achieved by the use of ITS
and 16S rRNA gene markers. The ultra-low biomass nature of air
posed major technical obstacles to using these molecular
techniques, with inherent requirements such as long sampling
duration and high amounts of gene marker amplication
The nascent eld of bioaerosol studies was further progressed
by employing metagenomics, which enabled direct nucleic acid
analysis without the biases associated with gene amplication.
However, to overcome issues associated with limited biomass,
long sampling duration times (days to weeks) were unavoidable,
which in turn impeded the temporal resolution and the number of
required samples analysed
Advances in temporal and taxonomic resolution only became
possible with the onset of new technologies involving high
volumetric ow rate air samplers coupled with metagenomic data
generated by next-generation sequencing platforms that had low
biomass requirements
. This approach, which analyses the accessible
spectrum of airborne community DNA, therefore enables assessment
of the functional complement of airborne microorganisms.
Here, we detail optimisation of multiple stages of an ultra-low
biomass analysis pipeline for air samples, which can also be tailored
to studies of similarly ultra-low biomass environments such as dust
and surfaces. The versatility and robustness of the presented
pipeline enable analysis of a wide range of environmental settings,
both indoor and outdoor, encompassing a wide scope of climatic
settings including tropical, temperate, desert and arctic regions.
Environmental samples: soil, water, air
Ecosystems and habitats are highly variable and complex, and hence
a universal approach is not always applicable. Using DNA
concentration as a proxy, terrestrial, aquatic and atmospheric
ecosystems can harbour up to a six-log difference in microbial
biomass (Fig. 1a). This results in vastly different sampling require-
ments and volumes for molecular analysis (Fig. 1b). In addition,
biomass concentrations might follow cyclic processes resulting in
density uctuations, as shown in marine environments
as well as
Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore, Singapore.
Present address: Departamento de Genética,
Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-590, Brazil.
These authors contributed equally: Irvan Luhung, Akira Uchida.
Published in partnership with Nanyang Technological University
atmospheric environments where higher bioaerosol concentrations
are typically observed at night
(Fig. 1c) or during haze events
To address the challenges in analysing a wide range of biomass
concentrations at different spatial and temporal settings, we
developed a robust ultra-low biomass pipeline, comprising the
four-stages of amassment, storage, extraction and nucleic acid
analysis. Parameters that impact upon the pipelinesefcacy were
investigated, with the aim of enabling customisation (Fig. 1d). The
summarised results are displayed in Fig. 2. The subsequent
sections detail each investigated parameter individually.
The ultimate success of sequencing and PCR-based analyses rests
on sufcient quantities of nucleic acids being amassed, which for
air sampling is a trade-off between sampling ow rate and
sampling duration. While this study uses a lter-based sampler,
other types of air samplers, such as liquid impingers serve a similar
function and produce comparable results (Supplementary
Fig. 8)
. For our purpose, ideal air samplers should be portable,
battery-powered and have an acceptable noise emission (~50 dB).
The air sampling ow rate and duration were optimised to
improve the temporal resolution of each sample from days, weeks
or months to hours or even minutes, while still maintaining
maximal taxonomic resolution. This was achieved by evaluating
how these two factors directly impact the DNA quantity and
metagenomic prole of the sample. Using 300 L/min ow rate, the
minimal required sampling duration was investigated using
different time-based sampling regimes (Fig. 3a). Sampling
duration was segmented into sequentially doubling time intervals.
For example, the rst and second 15-min intervals (5:005:15 am
and 5:15 to 5:30 am) were individually analysed and compared
to a 30-min sample (5:005:30 am) taken in parallel. This process
was undertaken for 15, 30 and 60-min intervals with a nal
sampling duration up to 180 min. Quantitative analysis showed
consistently increasing DNA yields as a function of sampling
duration (Fig. 2ac). No notable loss of DNA yield was observed
within the tested range of duration (15 min3 h). Within this
range, combining two successive time segments resulted in
similar DNA quantities as a single time segment of the combined
duration, as quantied using Qubit and qPCR (Fig. 3b, Supple-
mentary Fig. 1). Within the three investigated intervals (three
duration groups each for Qubit, bacterial and fungal qPCR), the
differences averaged 25%, with a median of 18%. Importantly, the
microbial taxonomic proles from comparable time intervals were
not affected. This is demonstrated by the shift in relative
abundances of taxa, such as Kocuria palustris, and Leifsonia xyli,
between the two subsequent 15-min samples (Fig. 3c). Averaging
these species compositions from the two subsequent 15-min lter
samples resulted in abundances that mirror that of the 30 min
time interval sample collected in parallel (Fig. 3d). This was
consistent across all sampling duration regimes with three
replicates each (BrayCurtis and Jaccard p> 0.05).
The second experiment examined the impact of the air ow
rate and the total volume of air sampled. With the sampling
duration set at 2 h, airow was varied between 100, 200 and 300 L/
min, resulting in total air volumes of 12, 24 and 36 m
respectively. The DNA yield and copy number of marker genes
(16S and 18S rRNAs) increased as a function of air volume sampled
(Fig. 2df). However, DNA concentration normalised per air
volume diminished by up to 20% when the ow rate was
increased from 100 to 300 L/min (Supplementary Fig. 2a). The
diminishing return of amassment is likely due to decreasing
particle retention efciency at higher ow rates for extended
periods of time
. For the purpose of this study, optimal sampling
efciency is forfeited in favour of higher ow rates (300 L/min)
because the total amount of biomass collected per unit of time
still out-performs the decrease in amassment efciency. This
enables measurements with higher time resolution within a day
for environmental time-series studies. The biological signicance
of this was demonstrated by the discovery of diel dynamics of
outdoor airborne microbial communities
Amassment Storage Nucleic Acid Analysisd
OceanSoil Air
DNA yield (Log10)
(ng/mass equivalent)
OceanSoil Air
Log10 (unit of sample
to yield 5 ng DNA)
DNA yield (ng)
11 15 19 23
PBS buffer
+0.1% Triton X-100
Air samples
0.02 µm DNA extraction
DNA sequencing
Fig. 1 Challenges in air microbiome analysis. a Total DNA yield (ng/mass equivalent) for soil, ocean water and air sample collected from the
same proximity and processed with the same method. bestimated sample volume required to yield 5 ng of DNA. For box plots, the centre
line, bound of box and whiskers represent median, 25th75th percentile and min-to-max values, respectively. cFluctuation of airborne
biomass (ng) at different times of the day. The red dots and error bars are mean and standard deviation among the replicates. dDeveloped
sampling and analysis pipeline for metagenomic analysis of ultra-low biomass environmental samples.
I. Luhung et al.
npj Biofilms and Microbiomes (2021) 37 Published in partnership with Nanyang Technological University
Further analysis demonstrated that owratedoesnotimpactthe
qualitative and quantitative assessment of metagenomic data
(Supplementary Fig. 2b). The community structure (BrayCurtis, p>
0.05) and richness (Jaccard, p> 0.05) were not signicantly different
for samples collected with different ow rates.
Analysis of the storage component in this pipeline evaluated the
integrity (biomass quality and composition) of air lter samples
stored under different conditions. The three conditions investigated
were (i) instant processing (Fsh), (ii) 5-day storage at 20 °C (Frz), and
(iii) 5-day storage at room temperature (RT, average 23 °C, RH 65%).
No signicant differences were observed between fresh and
freezer samples in terms of both absolute (Qubit, qPCR) and relative
(metagenomic) abundances. This suggests temporary freezer storage
is a viable alternative to immediate lter processing. However, RT
samples were signicantly different from the fresh and freezer
regimes in regard to DNA quantities (2030% loss) (Fig. 2gi). Also, a
minor decrease of relative abundance of certain taxa was observed
(BrayCurtis, p<0.05) (Fig. 4a); however, there was no loss in the
number of species detected (Jaccard, p> 0.05) (Supplementary Fig.
3). This outcome implies that microbial growth on the lter substrate
is impeded within the course of several days, thus enabling sample
collection for eld surveys without the need for refrigeration
Filter processing and DNA extraction
As library construction for DNA sequencing requires the removal
of particle/biomass from the air lter substrate (referred to as lter
processing), the protocol was optimised for efcient biomass
retrieval. Importantly, the ultra-low biomass nature of the sample
renders lter processing the most limiting, and hence, the most
critical step across the entire pipeline for maximising yield.
In general, lter samples can be processed in one of two ways,
either direct DNA extraction on the lter, or by rst removing the
biomass from the lter prior to DNA extraction. Direct DNA
extraction was deemed inefcient as the lter absorbs most of the
lysis buffer, which consequently inhibits cell lysis. In contrast, rst
removing the biomass by washing the lter in a buffer (PBS) and
then concentrating on a thinner membrane with smaller mesh-
size (0.2 µm PES or Anodisc membrane)
, resulted in signicantly
higher DNA recovery (Fig. 4b).
To further improve biomass recovery, additional steps such as
water-bath sonication (RT, 1 min)
and the use of detergent
(Triton-X 100) during lter wash were tested. For comparison of
samples processed with and without sonication, no signicant
difference in either quantitative or metagenomic analyses was
found (Fig. 2jl and Supplementary Fig. 4). In contrast, adding
detergent during the lter wash signicantly improved DNA yield
(Fig. 2mo). The hydrophobic nature of the air sampling lter
impeded wetting by the wash buffer. Hence, particles were not
effectively suspended in the wash buffer when mechanically
agitated. The addition of non-ionic detergent, Triton X-100, at
varying concentrations (%v/v) (PBS-T) to the initial PBS buffer was
effective in overcoming this challenge.
The detergent wash resulted in signicant differences in
absolute and relative abundance analyses, especially in the
instance of bacteria. DNA yield, as well as copy number of
bacterial 16S and fungal 18S rRNA genes, increased 2.4, 8.6 and
2.0-fold, respectively (Fig. 2mo). The metagenomic analysis
conrmed this nding (BrayCurtis and Jaccard, p< 0.05, Fig. 4c,
Supplementary Fig. 5). The number of detected bacterial taxa
increased eight-fold compared to a 1.3-fold increase in fungal
taxa. Expectedly, PBS-T treated samples also showed greater
taxonomic diversity (Fig. 4c).
Varying concentrations of Triton X-100 (0.01, 0.1 and 0.5% (v/v))
in PBS were investigated, with no signicant difference between
the three concentrations for quantitative analyses (Fig. 2mo).
However, metagenomic analysis identied notable differences in
microbiome composition (BrayCurtis p< 0.05, Supplementary Fig.
16S rRNA CN(x105) 18S rRNA CN(x108)
DNA yield(ng)
100 200 300
Fsh Frz RT
at 55˚C
1h 2h ON
Triton X-100
(%, v/v)
15 30 60 120
Sampling duration
Amassment Storage NA Analysis
n = 3 n = 3 n = 3n = 4 n = 4 n = 4
Fig. 2 Summary of quantitative analysis with DNA yield, 18S copy number (CN) and 16S copy number (CN). acAssessment of air
sampling duration from 15 min to 3 h. dfAssessment of air sampling ow rate from 100 L/min to 300 L/min. giThe integrity of sampled
biomass when processed fresh (Fsh), stored in freezer for 5 d (Frz) or stored at room temperature for 5 d (RT). jlImpact of sonication on DNA
yield. (mo) Impact of detergent addition at different concentrations (0.010.5% v/v) during lter sample wash. prImpact of extended pre-
incubation (1 h to overnight) at 55 °C during DNA extraction. The centre line, bound of box and whiskers represent median, 25th75th
percentile and min-to-max values, respectively. sWhole-genome shotgun (WGS) and amplicon (ITS/16S) sequencing approaches. * denotes
statistical signicance (p< 0.05) tested with MannWhitney tests.
I. Luhung et al.
Published in partnership with Nanyang Technological University npj Biofilms and Microbiomes (2021) 37
5ab) driven by an increase in bacterial taxa. Increasing Triton X-
100 beyond 0.1% concentration, yielded no signicant further
gains (Fig. 4d). Hence, Triton X-100 at 0.1% was deemed sufcient
for wetting the lter and releasing attached bioaerosol particles
into the buffer medium. Despite the 0.1% concentration of Triton
X-100 being above the critical micelle concentration
, Triton X-
100 did not trigger unwanted premature lysis of microbial cells, as
there were no signicant differences in DNA yield between the
three concentrations. If premature lysis occurred, extracellular
DNA would not have been retained on the subsequent Anodisc
membrane, resulting in lower DNA recovery.
Following lter processing, the recovered biomass was ltered
through a 0.02 µm pore-sized Anodisc membrane (Whatmann,
USA) mounted on a vacuum manifold (Fig. 1d), with the Anodisc
directly tting into the DNA extraction kit bead tube. DNA
extraction used the standard protocol of the extraction kit with
slight modication to improve lysis
. In this regard, the addition
of overnight pre-incubation of the samples at 55 °C is recom-
mended as it improves evenness among the samples, especially
for the representation of fungal taxa, as shown by the quantitative
and PERMDISP analysis (Fig. 2pr, Fig. 4e).
Nucleic acid analysis of ultra low biomass samples
The outcome of the above sample processing pipeline results in
double-stranded DNA samples (in the range of 0.17.1 ng DNA/m
of air sampled). These can subsequently be analysed, not only by
amplication-based techniques (16S/ITS), but also via direct DNA
5:30 6:00 7:00 8:00
DNA yield (ng)
15 30 60120 180
Sampling duration
Pestalotiopsis fici
Ktedonobacter racemifer
Number of reads
0 500 1000 1500 2000
Number of reads
1st 15 min
2nd 15 min
Kocuria palustris
Coprinopsis cinerea
Tulasnella calospora
Leifsonia xyli
Moniliophthora roreri
Ceriporiopsis subvermispora
Eutypa lata
Punctularia strigosozonata
Zea mays
Brachybacterium muris
Staphylococcus cohnii
Subdoligranulum variabile
Corynebacterium xerosis
Micrococcus luteus
Kocuria marina
Agaricus bisporus
Fibroporia radiculosa
Cyphellophora europaea
30 min
avg. of 15-min samples
Dichomitus squalens
Fomitiporia mediterranea
Schizophyllum commune
Phlebiopsis gigantea
Trametes cinnabarina
Auricularia delicata
Phanerochaete carnosa
Schizopora paradoxa
Trametes versicolor
Rhizoctonia solani
Kocuria palustris
Coprinopsis cinerea
Tulasnella calospora
Leifsonia xyli
Moniliophthora roreri
Ceriporiopsis subvermispora
Eutypa lata
Punctularia strigosozonata
Zea mays
Brachybacterium muris
Staphylococcus cohnii
Subdoligranulum variabile
Corynebacterium xerosis
Micrococcus luteus
Kocuria marina
Agaricus bisporus
Fibroporia radiculosa
Cyphellophora europaea
Pestalotiopsis fici
Ktedonobacter racemifer
5:00 5:005:15 5:30 6:00
15 min 30 min 1 hr 2 hrs 3 hrsSampling time
Start time 5:00 5:00 5:00
Fig. 3 Sampling duration assessment. a Illustration of different time-based sampling regimes. bComparison of DNA yield (ng) between the
corresponding sampling regimes, e.g. rst 15-min yield (orange) +second 15-min yield (light blue) compared to rst 30-min yield (orange).
The bars represent mean values and the error bars were standard deviation among the replicates. cTaxonomic compositions of the top
30 species, the highlighted portion focuses on species which shifted in abundance between the rst and second 15-min samples. d
Comparison of relative abundances of the selected species, the rst and second 15-min samples were averaged and compared to the
taxonomic composition of rst 30-min sample. The bars represent mean values and the error bars were standard deviation among the
I. Luhung et al.
npj Biofilms and Microbiomes (2021) 37 Published in partnership with Nanyang Technological University
sequencing (shotgun), resulting in either gene-based or metage-
nomic proles of airborne environmental communities. Both
approaches, 16S/ITS amplicon and whole-genome shotgun meta-
genomic (WGS), produce sequence data that may be compared
against publicly available data archives. For the remainder of this
manuscript, the advantages and disadvantages of both techniques
will be discussed in relation to ultra-low biomass analysis.
Amplicon-based sequencing approaches have been the
method of choice in the majority of past bioaerosol studies
This was due to the assumption that the low amount of amassable
biomass from air was insufcient for shotgun metagenomics
Our study shows that the above-described ultra-low biomass air
sampling and processing pipeline is capable of robustly producing
metagenomic datasets, as demonstrated i) for a range of DNA
input amounts, ii) by the reproducibility among replicates, iii) by
the robustness of air samples analysis from various climatic
conditions and iv) contamination control.
Required input amount: from the same DNA sample, a range of
DNA input amounts for shotgun metagenomic sequencing and
analysis (0.510 ng) were tested. Using our pipeline, taxonomic
representation for each DNA input condition was visualised at the
species level using bubble charts (Fig. 5a). For the tested range of
0.510 ng, no signicant change of species-level composition was
observed. The species-level metagenomic prole for each sample
was consistent even when the PCR cycles required during DNA
library construction were increased from 6 to 15 (Fig. 5a).
Reproducibility: An experimental time series of outdoor air in a
tropical setting
was used to assess sample-to-sample variability.
Over 24 h, air samples were collected at 2-h time intervals (12 time
points) in triplicate. The metagenomic proles of samples within the
same replicate group were highly consistent, with an average
similarity of 91% (8795%, SIMPER analysis). The taxonomic proles,
however, were distinct between sampling time points (Fig. 5b). The
higher variability observed for day-time samples can be attributed
to increased atmospheric turbulence due to convection, while a
narrower range was observed during night-time hours.
Robustness: The above-tested range of 0.510 ng of DNA
templates, with their respective PCR cycles (15 to 6 cycles), was
suitable for a global air microbiome survey that involved a wide
range of environmental conditions. The pipeline presented here
robustly produced metagenomic datasets from air samples
collected in locations with a diverse range of temperature (10
to 39 °C) and humidity (3690%), within the four climatic zones
(temperate, dessert, sub-arctic and tropical) (Fig. 5c).
Contamination control: The negative controls consisted of lter
blanks (clean, unused lter) mounted on the air samplers for 1 min
without airow, which were then transported and processed in an
identical manner to air samples. The DNA yield from negative
controls was not detectable (Supplementary Fig. 7a). The number
of reads generated by Illumina sequencing were on average 1000-
fold less for the negative controls compared to the air samples
(Supplementary Fig. 7b), with taxonomic analysis indicating
human contamination as the most likely source (Supplementary
Fig. 7c). The number of reads from our air samples which could be
mapped back to the lter blanks were very low and they were
removed by our statistical analysis threshold (<0.05% of assigned
reads). It can be deemed that despite the ultra-low biomass nature
of our analytical pipeline, contamination is not a concern
(Supplementary Discussion 7).
In a nal step, extracted genomic DNA from the pipeline was
analysed by both metagenomic and 16S/ITS amplicon sequencing,
resulting in sets of distinct taxonomic proles based on their
respective databases (Fig. 6a). For fungi, results from both
sequencing analysis methods concur with the observed trends
for the specic abundances of microbial taxa during day/night at
higher taxonomic resolution, e.g., Ascomycota being prevalent
during day-time and Basidiomycota during night-time. The 16S
amplicon analysis, however, was less robust as three out of four
samples resulted in no detectable PCR product, even with higher
DNA input (446 ng) and additional PCR cycles (Fig. 6a). This was
caused by low amounts of 16S rDNA gene template in tropical air
samples (Supplementary Fig. 9). The only successfully analysed
16S sample resulted in a similar taxonomic prole to that of the
WGS pipeline at the phylum level, with Firmicutes dominating
over Actinobacteria and Proteobacteria.
The above analysis highlights biases in the success rate of
fungal ITS and/or bacterial 16S amplication for air samples from a
diverse range of environmental conditions. Numerous studies
have reported similar challenges
. In contrast, regardless of
potential inhibitor content and/or taxonomic composition of the
air samples, the WGS pipeline consistently captured the biological
diversity of airborne microbial communities in various climatic
DNA yield (ng)
bc d
Triton X-100 (%)
Species richness
Fsh Frz RT
-6 -4 -2
PCO 2 (9%)
PCO 1 (66%)
PCO 2 (4%)
PCO 1 (93%)
10 20
-10 0
0% 0.01% 0.1% 0.5%
Triton X-100
1h 2h ON
Incubation at 55˚C
n = 3 n = 4
Fig. 4 Storage and biomass extraction. a Principal coordinate analysis (Bray-Curtis) on genus level for samples processed fresh (Fsh), stored
in freezer (Frz) and room temperature (RT). bComparison of DNA yield (ng) with (+) and without () the lter wash step. cTotal identied
species for fungi (orange) and bacteria (blue) for samples processed with different concentration of detergent (00.5% v/v) during the wash
step. The bars represent mean values and the error bars were standard deviation among the replicates. dPrincipal coordinate analysis (Bray-
Curtis) on genus level for samples processed with different concentration of detergent (00.5% v/v) during the wash step. ePERMDISP analysis
for samples processed with extended incubation at 55 °C prior to cell lysis. The centre line, bound of box and whiskers represent median,
25th75th percentile and min-to-max values, respectively.
I. Luhung et al.
Published in partnership with Nanyang Technological University npj Biofilms and Microbiomes (2021) 37
conditions (Figs. 5c, 6a). Moreover, unlike the single gene
amplicon approach, the WGS pipeline directly compared DNA
read abundances from a diverse set of taxa (bacteria, fungi, plants
and others) at a single quantitative scale.
In contrast to phylum level analysis, WGS and amplicon
analytical pipelines are substantially less congruent at the genus
or species level, due to the respective database sizes. Our
metagenomic reads were aligned to the non-redundant (nr)
database and assigned to taxa using the MEGAN software
, while
the amplicon reads were aligned to the 16S SILVA database for
database for fungi using blastn
. The
resulting taxonomic classications from the two analysis
approaches show signicant agreement at higher taxonomic
levels (e.g., up to phylum level). At genus and species levels,
taxonomic concordance is diminished, as shown for the top 40
most abundant taxa for both analysis types (Fig. 6b, c). In this
regard, the metagenomic and 16S amplicon approach agree in
7278% of instances on the genus level. However, only one out of
40 taxa (2.5%) was in agreement on the species level. This
concordance is even less for fungal taxonomy. On the genus level,
19 out of 40 taxa (47.5%) were in agreement when the WGS was
used as a reference. When the ITS was chosen as a reference,
seven out of 40 (17.5%) assignments were in agreement. As
observed for bacteria, only 1 out of 40 taxonomic assignments
was shared on a species level. In general, the amplicon databases
possess a much larger representation of fungal and bacterial taxa.
The higher overlap for bacteria was likely due to higher
representation of bacterial genomes in the nr sequence database
due to increasing accessibility for generating genome-wide data
for small microbial genomes. In contrast, the accessibility does not
extend to genome sizes exceeding 100 MB for some fungal
organisms. In this regard, the sequencing, assembly and annota-
tion of fungal genomes are still challenging.
rDNA sequences generated by both sequencing methods concur
when analysed for marker gene content. In this regard, the
metagenomic datasets analysed in this study contain about 1%
rDNA genes (ITS and 16S), which can be aligned to 16S SILVA and
ITS UNITE databases. The metagenomics rDNA read analysis and the
amplicon sequencing results produced highly overlapping taxo-
nomic proles for the top 40 most abundant taxa for fungi and the
top 10 most abundant taxa for bacteria (Supplementary Fig. 6). With
metagenomic sequencing becoming more accessible, it is therefore
possible to combine the benets of 16S- and ITS-based taxonomy to
investigate understudied ultra-low biomass environments, while
simultaneously enabling taxonomic and functional analyses
The here-presented air sampling and analysis pipeline enable
qualitative and quantitative assessment of microbial diversity in an
ultra-low biomass ecosystem. The 57 log difference in biomass
concentration of air samples, compared to seawater or soil,
requires sufciently large volumes of air to be sampled. Based on
our optimisation results, we propose default sampling parameters
of 300 L/min for 2 h. This enables DNA accumulation rate which is
~8170-fold higher than reported in recent studies
plementary Table 1). This large improvement allows for shorter
sampling time (15 min), while still enabling WGS metagenomic
analysis with species-level taxonomic classication. Such high
temporal and taxonomic resolution are crucial for ecological
studies of air microbiomes, which rapidly respond to diel
dynamics or sudden environmental changes. It should be noted
that factors such as time of sampling within a day, sampling
duration and climatic settings of the sampling location impact the
10 5 2 0.5DNA input (ng)
Anncaliia algerae
Musca domestica
Harpegnathos saltator
Ceratitis rosa
Aedes aegypti
Culex quinquefasciatus
Gemmatimonadetes bacterium KBS708
Drosophila melanogaster
Hydra vulgaris
Dichomitus squalens
Candidatus Sulcia muelleri
Cryptolestes ferrugineus
Actinomycetospora chiangmaiensis
Fomitopsis pinicola
Ctenolepisma lineata
Camponotus floridanus
Sporisorium reilianum
Fomitiporia mediterranea
Pyrinomonas methylaliphatogenes
Anopheles gambiae
Anopheles darlingi
Anopheles sinensis
Acidobacteriaceae bacterium KBS 96
Calothrix sp. PCC 7103
Tribolium castaneum
Cystobacter fuscus
Sorghum bicolor
uncultured bacterium
Ceratitis capitata
Acinetobacter baumannii
Baudoinia compniacensis
Aureobasidium melanogenum
Silanimonas lenta
Gloeocapsa sp. PCC 7428
Oryza sativa
Trametes cinnabarina
Bombyx mori
Gemmata obscuriglobus
Singulisphaera acidiphila
Cotesia sesamiae bracovirus
PCR Cycle 6 8 12 15
Saccharopolyspora rectivirgula
Micrococcus luteus
Cutibacterium acnes
Geodermatophilus obscurus
Actinomycetospora chiangmaiensis
Sphingomonas astaxanthinifaciens
Streptococcus pneumoniae
Thermoactinomyces vulgaris
Gemmatirosa kalamazoonesis
Acinetobacter baumannii
Paracoccus sanguinis
Roseomonas gilardii
Kocuria polaris
Klebsiella pneumoniae
Nocardioides sp. CF8
Nevskia ramosa
Rubellimicrobium mesophilum
Singulisphaera acidiphila
Skermanella stibiiresistens
Thermoactinomyces sp. CDF
Dichomitus squalens
Aspergillus ruber
Phlebiopsis gigantea
Fomitiporia mediterranea
Trametes cinnabarina
Eutypa lata
Aspergillus nidulans
Phanerochaete carnosa
Baudoinia panamericana
Botrytis cinerea
Wallemia mellicola
Trametes versicolor
Schizophyllum commune
Penicillium steckii
Verruconis gallopava
Pestalotiopsis fici
Vitis vinifera
Morus notabilis
Oryza sativa
Eucalyptus grandis
Bacteria Fungi Plants
Temperature (C):
RH (%):
-10 to -15 27 to 3335 to 3912 to 15
36 to 38 70 to 9078 to 8080 to 83
-60 -40 -20 0 20 40
PCO1 (59.7%)
PCO2 (23.7%)
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
% ass. reads
Other phyla
Bar composition:
Avg. SIMPER (replicates): 91.3%
Nucleic Acid Analysis
Fig. 5 Whole genome shotgun (WGS) sequencing of air samples.
aComparison of taxonomic prole at species level for the same air
sample that was subjected to WGS sequencing with different DNA
input amounts (100.5 ng). bReproducibility of samples collected at
the same time and location (triplicates) illustrated in principal
coordinate analysis (BrayCurtis) at species level. The bars show the
microbial community composition of the triplicates in % of assigned
reads. cRobustness of air sampling and processing pipeline tested
at locations with temperate, dessert, sub-arctic and tropical climates.
I. Luhung et al.
npj Biofilms and Microbiomes (2021) 37 Published in partnership with Nanyang Technological University
analysis outcome, and therefore comparability between the above
studies. Our proposed method, however, has also been evaluated
for its robustness across a wide range of environmental settings
(arctic, desert, temperate and tropical climatic zones) (Fig. 5c).
Further, our results indicate that biomass amassed from air
samples using lter-based devices during remote eldwork may
be stored at room temperature for extended periods of time with
tolerable loss of extractable DNA (20% in 5 d) and without
compromising microbial community structure. While these effects
could potentially be counter-acted by nucleic acid stabilisation
, this approach is not recommended during sampling
campaigns, as it would require additional handling of the air lter
Nucleic Acid Analysis
Day1 Night1 Day2 Night2
Extracted genomic DNA
Day1 Night1 Day2 Night2
Amplicon Sequencing
% Assigned reads (phylum)
Day1 Night1 Day2 Night2
WGS Sequencing
Day1 Night1 Day2 Night2
PCR (16S 338F - 806R)
Amplicon Sequencing
No Amplification
No Amplification
No Amplification
DNA Yield(ng)
36 m³ of air
DNA Input(ng)
PCR Cycle
18224 16 275
55 55
88 88
18224 16 275
15 15 15 15
18224 16 275
25 25 25 25
Bacteria Fungi
Reference Reference Reference
C. apiculatus
C. crocatus
C. fuscus
S. variabile
F. prausnitzii
L. aviarius
L. crispatus
L. ingluviei
L. reuteri
L. salivarius
L. vaginalis
E. cecorum
S. arlettae
S. aureus
S. cohnii
Phascolarctobacterium sp.
Turicibacter sp.
Firmicutes bacterium
Firmicutes bacterium
S. saprophyticus
J. marinus
B. thuringiensis
B. cereus
B. desmolans
T. carboxidivorans
K. racemifer
C. psittaci
Alistipes sp.
B. salanitronis
M. luteus
K. rhizophila
K. marina
K. palustris
L. xyli
B. muris
B. linens
C. falsenii
C. stationis
T. massiliensis
Eubacterium sp.
Acinetobacter sp.
Acinetobacter sp.
Acinetobacter sp.
A. soli
Acinetobacter sp.
Rickettsiella sp.
C. mobilis
S. fusca
F. cylindroides
Ruminococcaceae sp.
Anaerostipes sp.
S. gallolyticus
L. agilis
L. aviarius
L. pontis
Lactobacillus sp.
Lactobacillus sp.
Staphylococcus sp.
Staphylococcus sp.
Staphylococcus sp.
A. persicus
B. anthracis
rumen bacterium
Enterococcus sp.
Ktedonobacter sp.
Deinococcus sp.
B. barnesiae
B. caecigallinarum
R. amarae
R. endophytica
Micrococcus sp.
M. yunnanensis
Kocuria sp.
Kocuria sp.
Corynebacterium sp.
Corynebacterium sp.
C. glutamicum
C. nuruki
C. nasicanis
M. woesei
S. commune
D. squalens
R. solani
F. mediterranea
P. gigantea
A. delicata
T. cinnabarina
P. carnosa
S. paradoxa
T. versicolor
M. roreri
T. calospora
C. subvermispora
A. bisporus
P. strigosozonata
F. radiculosa
H. irregulare
B. botryosum
C. torrendii
G. sinense
P. ostreatus
S. hirsutum
P. radiata
P. indica
C. cinerea
T. anomala
L. bicolor
Termitomyces sp.
E. lata
P. fici
V. gallopava
D. amepelina
C. europaea
V. mali
P. fijiensis
S. complicata
R. emersonii
P. nodorum
M. oryzae
A. stygium
S. commune
G. incarnatum
P. maipoensis
T. subsphaerospora
P. lycii
P. pyricola
G. lineata
G. fuligo
E. scabrosa
P. spadiceum
P. flavidoalba
P. sordida
C. alachuana
C. lacerata
B. adusta
L. sajor-caju
G. australe
T. hinnuleus
R. ulmarius
M. giganteus
M. bannaensis
F. pinicola
A. lalashana
S. lowei
R. saccharicola
R monticola
R. friabile
P. noxius
S. hydnoides
P. chrysocreas
P. acanthocystis
P. praetermissa
H. rhizomorpha
H. cineracea
H. mutatum
T. cucumeris
H. delicata
A. nigricans
H. rivularis
E. citricola
Bacteria Fungi
Reference Reference Reference
Fig. 6 Comparison of taxonomic proles between WGS and amplicon sequencing pipelines. a Taxonomic prole of WGS, ITS amplicon and
16S amplicon pipeline at phylum level of four independently collected air samples (two days and two nights). bPresenceabsence
comparison of the top 40 most abundant genus for bacteria (WGS vs 16S) and fungi (WGS vs ITS). cPresenceabsence comparison of the top
40 most abundant species for bacteria (WGS vs 16S) and fungi ( WGS vs ITS).
I. Luhung et al.
Published in partnership with Nanyang Technological University npj Biofilms and Microbiomes (2021) 37
samples in the eld. This could result in contamination and
complicate transport due to the introduction of liquid materials
(e.g., commercial air travel). The advantage of dry storage and
transport also does not extend to other types of air samplers, such
as liquid impingers.
For nucleic acid extraction, it could be shown that the amassed
biomass should not be extracted directly on the lter, but rather
rst be removed owing to the adherence of the low quantities of
nucleic acids to the large surface area of the lter membrane.
Therefore, extraction and wash buffer conditions should be
optimised to enable the extraction of sub-nanomolar concentra-
tions of DNA/RNA. This optimisation includes the use of detergent
and extended incubation times. In particular, the addition of non-
ionic detergents, such as Triton X-100, signicantly increases the
recovered biomass, while extended incubation times improve the
evenness of the large sets of samples. This observation is also
highly relevant in the context of sampling potentially infectious
biological materials, such as airborne retroviruses, which can
concurrently be inactivated with Triton X-100
Both metagenomic and amplicon sequencing methods can be
applied to air samples (Fig. 2s). The metagenomic approach is
advantageous with regards to enabling simultaneous functional
and taxonomic analysis and has the advantage that bacteria and
fungi can be analysed within the same quantitative scale. Further,
the rapid expansion of the public WGS databases continues to
enable species-level taxonomic identication at an increasing rate.
In contrast, the content of amplicon sequencing databases (ITS or
16S) are likely to grow at a slower rate, given the increased
accessibility of WGS.
While our study demonstrated that the extracted DNA from the
ultra-low biomass pipeline was sufcient for WGS and ITS
amplicon analyses, 16S amplicons did not perform equally well
for tropical air samples (Fig. 6a). This may be due to the fact that
the DNA library construction for WGS is less sensitive to inhibitors
and the relative ratio of bacterial vs. fungal DNA. Both factors
impact on the efcacy of the polymerase chain reaction. Never-
theless, specic gene marker/amplicon analysis can be advanta-
geous for studies that target well characterised, less diverse
microbial communities.
Finally, due to database biases, both methods appear to
converge on the phylum level, but to a lesser degree at the
genus level. On the species level both methods do not produce
signicant agreement. To harness the advantages from both
sequencing technologies, it is benecial to combine both
approaches by also analysing the rDNA sequences from the
metagenomic data (Supplementary Fig. 6). The results from this
combined analysis enable data interpretation from a single data
source (metagenomic data), to inform both WGS and marker
genes analysis pipelines.
The here-presented methodology is limited by the size range of
the chosen lter medium (0.5>10 µm, <50% efciency for
particles <0.5 µm). As this study aims to reduce required sampling
times, total suspended (biological) particles (TSP) need to be
collected and analysed. While this study does not prole particle
size range, recent studies have demonstrated that the most
relevant airborne bacteria and fungi fall within the size range of
the lter medium
In summary, the above-described ultra-low biomass analysis
pipeline provides detailed insights into the factors that inuence
analysis outcomes for low-biomass microbial environments. High
volumetric air sampling techniques in combination with applied
nucleic acid analysis, results in high temporal and taxonomic
resolution of inherent airborne microbial communities. The
presented ndings are potentially also applicable to other low-
biomass environments, such as dust and surfaces.
Air sampling
Air samples for optimisation purposes were collected in Singapore at a
roof-top balcony of a university building (N1.346247, E103.679467). As the
study focuses on improving the time resolution of the analysis, a high-ow
rate, lter-based air sampler (SASS3100, Research International, USA) was
used to collect total suspended particles (TSP) with no size cut-off. The
lter medium was SASS Bioaerosol electret lter (6 cm diameter, expected
50% efciency for 0.5 µm particle size, Research International, USA). For
sample collection, air samplers were attached upright on a tripod 1.5 m
above the concrete oor of the balcony.
In addition to Singapore, samples from different climatic settings were
collected in a consistent manner from sites in Germany, Russia and Israel to
test the robustness of the proposed pipeline. These international locations
showed contrasting settings for temperature (T) and relative humidity (RH).
After sampling, the lters were returned to their original lter pouches
and transported to the laboratory for direct processing or storage at
20 °C. Information on exact sampling time, ow rate, duration and the
environmental parameter measurements of all sampling activities used in
this study can be found in Table 1.
Temperature and relative humidity
Temperature (T) and relative humidity (RH) at the sampling site were
measured using HOBO Temp/RH 2.5% Data Logger (Onset, USA).
Filter processing, DNA extraction, quantitation and
All lter samples were subsequently processed for DNA extraction,
quantitation, qPCR, metagenomic sequencing and computational analysis
as described in our previous study
. In brief, the lter samples were rst
washed 3 times using 2 mL of phosphate-buffered saline (pH 7.2) with
0.1% (v/v) Triton X-100 assisted with water-bath sonication at room
temperature for 1 min. After washing, the suspension liquid was
concentrated onto a 0.02 µm Anodisc lter (Whatman, UK) using a vacuum
manifold (DHI, Denmark). DNA was then extracted from the Anodisc with
the DNeasy PowerWater kit (Qiagen, USA) following the manufacturers
standard protocol with modications to increase DNA yield
Final DNA solution was subjected for uorometer quantication, qPCR
and shotgun metagenomic sequencing. Fluorometer quantitation was
measured with Qubit 2.0 (Invitrogen, USA) using the High Sensitivity
double stranded DNA (HS dsDNA) kit. Taqman qPCR assays with universal
bacterial (16S rRNA gene)
and fungal (18S rRNA gene)
primer set and
probes were used to quantify the copy numbers of bacteria and fungi,
respectively. The complete list of primers can be found in Table 2.
For direct metagenomic sequencing, libraries were prepared using Swift
BiosciencesAccel-NGS 2S Plus DNA Library kit following the standard
protocol. All libraries were subsequently dual-barcoded with Swift
Biosciences2S Dual Indexing kit. PCR amplication selectively enriches
for library fragments that have adapters ligated on both ends. The number
of cycles were adjusted based on the starting amount of DNA (815
cycles). Upon pooling at equal volumes, libraries were sequenced on
Illumina HiSeq2500 Rapid runs at a nal concentration of 1011 pM and a
read-length of 251 bp paired-end (Illumina V2 Rapid sequencing reagents).
Each ultra-low biomass sample was sequenced to a depth of at least two
million paired-reads.
Raw reads from the sequencer were rst trimmed from adapter
sequences, low quality bases (<20 score) and short reads (<30 bp) using
Cutadapt (v.1.8.1)
. The processed reads were then aligned against the
NCBIs NR database (v.25-02-2016) using RAPSearch2 (v.2.15)
. Results
from the RAPSearch2 alignment were nally converted to read-match
archive (rma) to be visualised with MEGAN5 software
Experimental parameters optimisation
Important parameters for sampling, extraction and sequencing were tested
and optimised based on absolute (uorometer and qPCR) and relative
abundance assessment (DNA sequencing). Importantly, it should be noted,
that only samples collected at an identical time and location may be
compared. Therefore, it is mandatory as an experimental setup to deploy
multiple air samplers for each set of the parameter optimisation
experiments. This is due to the high volatility of biomass concentration
and composition of air, particularly when sampling at different time points
throughout day and night. The replicability and robustness of this study
I. Luhung et al.
npj Biofilms and Microbiomes (2021) 37 Published in partnership with Nanyang Technological University
was, therefore, enabled through simultaneous deployment of up to 12 air
samplers at any given time (n=12).
Comparison to other types of environmental samples: The ultra-low
concentration of airborne biomass was investigated relative to other types
of environmental samples. To negate possible differences due to sampling
location and/or processing method, soil (1 gram per sample extraction),
water (1 mL per sample extraction) and air samples (300 L/min, 2 h
sampling duration) were collected within the same proximity (in Singapore)
and were subsequently processed with identical protocol. Only DNA yield
(ng/unit mass or volume of the samples) was assessed for this experiment.
The amassment parameters are sampling duration and sampling ow
rate. Sampling duration experiment: With a xed air ow rate (300 L/min,
n=3), sampling duration was varied at 15, 30, 60, 120 and 180min. Further,
multiple shorter duration samples were also compared to longer duration
samples with matching time segments, i.e. rst and second 15 min samples
were compared to the matching 30 min sample. Air ow rate experiment:
With a xed duration (2 h), three groups of air samplers (n=4) were run at
the same time with varying ow rate at 100, 200 and 300 L/min. The
experiments were assessed based on the impact of sampling duration and
airow variations on DNA quantity and microbial composition.
Table 2. List of primers and probes applied in the study.
Name Sequence Notes
Taqman probe 6FAM-5-CAGCAGCCGCGGTA-3-BBQ Bacteria qPCR probe
FungiQuant-PrbLNA 6FAM-5-TGGTGCATGGCCGTT-3-BBQ Fungi qPCR probe
16S 341F Illumina 5-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN BGC ASC AG -3Amplicon for bacteria
Table 1. Details of sampling activities.
Sample set Sampling date Sampling time, duration and
ow rate
Temperature (°C) RH (%) Rain Sample size No. of
1 29-Nov-17 01:0003:00 (2 h, 100 L/min, 200 L/
min, 300 L/min)
24.825.3 98100 No 4 12
2 15-Dec-17 05:0508:05 (15 min, 30 min, 1 h, 2 h,
3 h, 300 L/min)
24.725.7 99100 No 3 27
3 29-Nov-17 06:1508:15 (2 h, 300 L/min) 24.625.4 99100 No 4 12
4 23-Feb-17 17:0017:00 (2 h, 300 L/min) 24.034.0 63100 Yes 3 36
5 8-May-16 17:0017:00 (2 h, 300 L/min) 28.033.0 5989 No 3 36
6 29-Nov-17 03:5005:50 (2 h, 300 L/min) 24.524.8 99100 Yes 3 12
7 28-Nov-17 20:4022:40 (2 h, 300 L/min) 24.925.3 9899 No 3 12
8 24-Nov-17 05:0007:00 (2 h, 300 L/min) 23.924.5 99100 No 4 12
9 22-Nov-17 23:0001:00 (2 h, 300 L/min) 26.026.5 9799 No 3 3
10 23-Nov-17 11:0013:00 (2 h, 300 L/min) 29.030.0 7780 No 2 2
11 27-Nov-17 23:0001:00 (2 h, 300 L/min) 24.525.5 9396 No 3 3
12 28-Nov-17 11:0013:00 (2 h, 300 L/min) 28.529.5 7577 No 2 2
13 29-Aug-16 13:0015:00 (2 h, 300 L/min) 31.032.0 7080 No 1 1
14 30-Aug-16 13:0015:00 (2 h, 300 L/min) 31.032.0 7080 No 1 1
15 21-Sep-15 13:3015:30 (2 h, 300 L/min) 31.032.0 6370 Yes 1 1
16 30-Jul-17 12:0014:00 (2 h, 300 L/min) 12.015.0 8083 No 2 2
17 4-Jul-17 08:3010:30 (2 h, 300 L/min) 35.039.0 3638 No 2 2
18 2-Dec-17 09:0011:00 (2 h, 300 L/min) 10.015.0 7880 No 1 1
19 3-Dec-17 15:0017:00 (2 h, 300 L/min) 10.015.0 7880 No 1 1
Total no. of samples
(including blanks):
I. Luhung et al.
Published in partnership with Nanyang Technological University npj Biofilms and Microbiomes (2021) 37
Sample storage experiment. Three sets of air samples collected simulta-
neously (300 L/min, 2 h, n=4) were subjected to the following storage
regimes; direct processing (fresh), 20 °C storage for 5 days (freezer) and
room temperature storage for 5 days (RT) and compared for both DNA
quantity and microbial proles.
Parameters optimised for lter processing and DNA extraction were the
use of sonication, detergent and impact of pre-incubation. Sonication
experiment: Two sets of air samples collected at the same time (300 L/min,
2h, n=3) were subjected to lter washing with the room temperature
water-bath sonication step included and excluded. Detergent experiment:
Four sets of air samples collected at the same time (300 L/min, 2 h, n=3)
were washed with buffer containing four different concentrations of non-
ionic detergent Triton-X 100 (%v/v): No detergent (0%), 0.01, 0.1 and 0.5%.
Pre-incubation experiment: Three sets of air samples collected at the same
time (300 L/min, 2 h, n=4) were subjected to three different durations of
pre-incubation in 55 °C water bath prior to proceeding with the
subsequent lysis step of the DNA extraction. The durations were 1 h, 2 h
and overnight (1416 h). These durations were selected to enable the
completion of the entire extraction process (lter washing and DNA
extraction) within a standard working day (~8 h).
All the above experiments were assessed based on DNA quantity and
microbial proles of the resulting analysis.
The DNA sequencing result was evaluated for the DNA input amount,
reproducibility, robustness and taxonomic classication difference
between metagenomics and amplicon. DNA input experiment: From a
given extracted DNA sample, four different DNA input amounts for direct
metagenomic sequencing were tested: 10 ng, 5 ng, 2ng and 0.5 ng. The
number of PCR cycles during library construction were adjusted based on
the DNA amount. The nal result was assessed based on the taxonomic
composition of the sequencing analysis. Reproducibility between replicates:
A set of time series samples was analysed to investigate the similarity of
the metagenomic proles between the replicates. The time-series data
contains twelve sets of time points with three replicates each. Each set was
collected with 300 L/min ow rate and 2-hour sampling duration, spanning
across 24 h.Robustness across a range of climatic settings: Air samples
collected from locations with different climates (highly variables T and RH)
were analysed regarding the success rate of DNA sequencing library
construction due to varying amounts and quality of DNA input. 300 L/min
ow rate and 2 h sampling duration were used to collect samples in
Germany (temperate), Israel (dessert) and Russia (sub-arctic). Comparison of
shotgun metagenomic and amplicon marker gene sequencing: The two
sequencing approaches were evaluated using taxonomic assignments
from identical sets of extracted air samples. DNA samples were split for
shotgun metagenomic, 16S bacterial amplicon and ITS fungal amplicon
sequencing. The sequencing and analysis methods for the bacterial and
fungal amplicon sequencing are detailed in the following section.
PCR-based amplicon sequencing and analysis
A subset of our ultra-low biomass samples were also subjected to amplicon
sequencing for direct comparison with the shotgun metagenomic
sequencing approach. For these samples, the rst stage PCR was
performed with the extracted genomic DNA as a template and the
primers for fungi and 16S 341F-805R
primers for bacteria.
Details of these primer sequences can be found in Table 2. KAPA HiFi
HotStart master mix was used with a total reaction volume of 25 µL. For
DNA input amount, 3 µL and 10 µL of DNA templates were used for fungi
and bacteria, respectively. The cycling condition was 95 °C for 3 min,
amplication cycles with 95 °C for 30 s, 65 °C for 30 s, 72 °C for 30 s, and a
nal extension at 72 °C for 5 min. The fungal samples were amplied with
15 cycles and the bacteria samples were amplied with 25 cycles. The PCR
products were then puried with AMPure XP beads (Beckman Coulter)
before performing the second stage PCR.
The second stage PCR (Indexing PCR) was performed according to the
recommendations in Illuminas16S Metagenomic Sequencing Library
Preparationapplication note. This step uses a limited cycle PCR to
complete the Illumina sequencing adapters and add dual-index barcodes
to the amplicon target. Five microliters of the intermediate PCR product
from the rst stage PCR (Amplicon PCR) were used as template for the
indexing PCR and samples were amplied with eight PCR cycles. Nextera
XT v2 indices were used for dual-index barcoding to allow pooling of the
amplicon targets for sequencing.
Finished amplicon libraries were quantitated using Promegas QuantiFluor
dsDNA assay and the average library size was determined on an Agilent
Tapestation 4200. Library concentrations were then normalised to 4 nM and
validated by qPCR on a QuantStudio-3 real-time PCR system (Applied
Biosystems), using the Kapa library quantication kit for Illumina platforms
(Kapa Biosystems). The libraries were then pooled at equimolar concentrations
and sequenced on the Illumina MiSeq platform with 20% PhiX spike-in and at
a read-length of 300 bp paired-end (MiSeq V3 reagents).
After sequencing, raw reads were rst trimmed from adapter sequences,
low-quality bases and short reads using Cutadapt (v.1.8.1)
. After trimming,
the R1 and R2 reads were rst paired with minimum overlap of 10 bp and
subsequently aligned against UNITE ITS database (v.7.1) for the ITS sequences
and SILVA 16S database (release 132) for the 16S sequences using command
line blastn
(version 2.2.28 +). Results from blastn alignments were also
converted to read-match archive (rma) format for visualisation with the
MEGAN5 software to facilitate direct comparison with the metagenomic
sequencing analysis. The default LCA parameters were used.
Statistical analysis
For quantitative analysis from Qubit 2.0 Fluorometer and qPCR, all
statistical tests were conducted with MannWhitney test. As mentioned
previously, we acknowledge the limitations of these tests due to the
relatively low number of observations (n=3orn=4) for each set of
samples. Due to the volatile nature of air sample, only samples collected at
the same time and location can be directly compared. Thus, the number of
replications was limited by the number of samplers which could be
deployed at a given time (n=12).
For metagenomic analysis, signicant differences between groups of
samples were mainly determined by ANOSIM test based on distance
matrices between the samples compared. Distance matrices were created
through PRIMER7 software based on taxa (genus level, cut-off at 0.05% of
total assigned reads) read counts of each sample generated by MEGAN5.
The distance matrix calculated based on BrayCurtis algorithm was used to
evaluate proportional difference (community structure) of the microbial
communities between samples, while the distance matrix calculated based
on Jaccard algorithm was used to determine presenceabsence difference
(community membership/richness) of different taxa detected in the
compared group of samples. For reproducibility assessment among
replicates, environmental time series data were used in which air samples
with two-hourly time resolution were collected in 24 h with three replicates
each. Similarity percentage (SIMPER) analysis was conducted with
PRIMER7 software with the samples grouped based on the replicates.
Blank sample collection and analysis
Five lter blank samples were collected and analysed. Filter blank samples
were collected by attaching a clean, unused lter onto the air sampler at
the sampling location and collecting them after 1 min without running the
sampler. They were subjected to the same extraction methods and
metagenomic analysis pipeline as the actual air samples.
Reporting summary
Further information on research design is available in the Nature Research
Reporting Summary linked to this article.
All raw unprocessed reads have been submitted to NCBI under the bio-project
accession number PRJNA638794.
Received: 6 November 2020; Accepted: 19 March 2021;
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The work was supported by Singapore Ministry of Education Academic Research
Fund Tier 3 grant (grant MOE2013-T3-1-013).
I.L., A.U., S.C.S.: Designed the study, conducted experiments, analysed the data, wrote
the manuscript. S.B.Y.L.: Conducted experiments, analysed the data. D.I.D.-M., S.L.:
Analysed the data, wrote the manuscript. N.E.G., C.K., K.L.: Conducted experiments. B.
N.V.P., R.W.P., E.G., E.A., C.E.H., A.W., H.L.K., A.C.M.J.: Analysed the data. S.R.L., Y.Z.H. D.
P., K.Y., K.K.K., A.P.N., A.P.: Conducted experiments. I.L. and A.U. contributed equally to
this manuscript.
The authors declare no competing interests.
Supplementary information The online version contains supplementary material
available at
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... This method was previously applied to bioaerosol samples, resulting in a successful microbial community analysis 141 . The extraction yields of nucleic acids and the microbial community analyses of frozen and instantly extracted (directly after sampling) ultra-low biomass aerosol samples showed no significant differences between the two methods in a previous study 142 . However, storing the samples at −20 C°before the extraction may lead to the loss of nucleic acids due to cell lysis, potentially leading to some bias in the community analysis depending on the cell type. ...
... A total of 129 samples representing rRNA and rRNA-gene of three air mass origins in 6 particle-size classes were analyzed, as follows: 24 and 10 samples of northwest; 30 and 30 of southwest; 17 and 18 of the east (rRNA and rRNA-gene, respectively). A significant limitation in bioaerosol studies is the ultra-low biomass content of air samples 142 . Previous studies at the same location showed that aerosol samples, especially those with low PM 10 mass, contain very low amounts of genomic DNA (i.e., 16 S rRNA-gene copies) [3][4][5] . ...
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Processes influencing the transport of airborne bacterial communities in the atmosphere are poorly understood. Here, we report comprehensive and quantitative evidence of the key factors influencing the transport of airborne bacterial communities by dust plumes in the Eastern Mediterranean. We extracted DNA and RNA from size-resolved aerosols sampled from air masses of different origins, followed by qPCR and high-throughput amplicon sequencing of 16 S ribosomal RNA gene and transcripts. We find that airborne bacterial community composition varied with air mass origin and particle size. Bacterial abundance, alpha diversity and species richness were higher in terrestrially influenced air masses than in marine-influenced air masses and higher in the coarse particle fraction (3.0 to 10.0 µm) than in the fine fraction (0.49 to 1.5 µm). This suggests that airborne bacteria mainly were associated with dust particles or transported as cell aggregates. High abundances of rRNA from human, animal and plant pathogen taxa indicate potential ecological impacts of atmospheric bacterial transport.
... The collected PM 10 filters were packaged in the sterilized aluminum foil and zip bags and were immediately transferred to the lab in the ice box. The pretreatment of filter samples followed the previously published protocol with modifications [26]. Half of the A4-size filter was sonicated with sterilized phosphate-buffered saline (PBS, pH = 7.2) added 0.1% (v/v) Triton X-100. ...
Municipal solid waste treatment (MSWT) system emits a cocktail of microorganisms that jeopardize environmental and public health. However, the dynamics and risks of airborne microbiota associated with MSWT are poorly understood. Here, we analyzed the bacterial community of inhalable air particulates (PM10, n = 71) and the potentially exposed on-site workers’ throat swabs (n = 30) along with waste treatment chain in Shanghai, the largest city of China. Overall, the airborne bacteria varied largely in composition and abundance during the treatment (P < 0.05), especially in winter. Compared to the air conditions, MSWT-sources that contributed to 15 ∼ 70% of airborne bacteria more heavily influenced the PM10-laden bacterial communities (PLS-SEM, β = 0.40, P < 0.05). Moreover, our year-span analysis found PM10 as an important media spreading pathogens (104 ∼ 108 copies/day) into on-site workers. The machine-learning identified Lactobacillus and Streptococcus as pharynx-niched featured biomarker in summer and Rhodococcus and Capnocytophaga in winter (RandomForest, ntree = 500, mtry = 10, cross = 10, OOB = 0%), which closely related to their airborne counterparts (Procrustes test, P < 0.05), suggesting that MSWT a dynamic hotspot of airborne bacteria with the pronounced inhalable risks to the neighboring communities.
... The filter membranes were washed three times by vortexing in phosphate buffer saline (PBS) pH 7.2 with 0.01% Triton X-100 for 30 s prior to water-bath sonication at room temperature for 1 min following the protocol by Luhung et al. (2021). The vortex-sonication process was repeated twice. ...
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Analyzing and monitoring air microbiomes in the subway environment has a great impact on public health, especially in urban cities. In this study, temporal distribution profile of air mycobiome at Bangkok's Sukhumvit subway station (MRT-SKV) was explored over 12 samplings during December 2021–November 2022 by the environmental DNA-metabarcoding approach. The top 5 most abundant fungal genera identified were Yarrowia, Cladosporium, Hortaea, Cutaneotrichosporon, and Leptobacillum. Among the 496 genera found, 24 genera could be considered “core mycobiome” of the MRT-SKV air, as they were consistently found in ≥85% of the samples. Many members of the core mycobiome constituted human commensal genera, but several of them can elicit allergic reactions in humans and thus pose a potential health risk. Cladosporium was the most abundant allergenic fungi found in the MRT-SKV, which is in contrast to Malassezia, Ustilago, or Aspergillus being the most abundant allergenic fungi in subway environments in other countries. The fungal compositions in the subway environment differed temporally, and the abundances of allergenic fungi were the highest in samples obtained during the first and seventh samplings (done in December 2021–June 2022). These compositional variations were most likely influenced by air quality variables, including particulate matter (PM), temperature, and relative humidity, which showed seasonal variations. Finally, certain fungal genera were shown to co-occur together in co-occurrence network. Many of the allergenic fungal genera formed networks more prominently in rush hour than in other traffic hours. The co-occurrence network suggested associations among certain fungal genera, such as the association between Malassezia–Nigrospora, Alternaria–Aspergillus, and Aspergillus–Cutaneotrichosporon in the MRT-SKV bioaerosols. The implied associations between these fungi are worth further investigation since they may point toward their impact on human health (as they are opportunistic fungi found in human). Our results thus facilitate understanding the ecological and health impacts of fungal components of the subway air environment.
... However, low flowrate samplers (e.g., BioSampler®) provide a more accurate estimate of the virus concentration in the air. A similar finding was also reported by Luhung et al. 40 , where they investigated the effect of increasing the bioaerosol sampler flow rate (100 lpm to 300 lpm) on the bioaerosol recovery and concluded that high-flow air sampling maximized the time resolution and improved virus capture rate, especially at ultra-low bioaerosol concentrations. However, high-flow sampling is susceptible to inaccurate estimation of bioaerosol concentration per unit air volume. ...
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Real-time surveillance of airborne SARS-CoV-2 virus is a technological gap that has eluded the scientific community since the beginning of the COVID-19 pandemic. Offline air sampling techniques for SARS-CoV-2 detection suffer from longer turnaround times and require skilled labor. Here, we present a proof-of-concept pathogen Air Quality (pAQ) monitor for real-time (5 min time resolution) direct detection of SARS-CoV-2 aerosols. The system synergistically integrates a high flow (~1000 lpm) wet cyclone air sampler and a nanobody-based ultrasensitive micro-immunoelectrode biosensor. The wet cyclone showed comparable or better virus sampling performance than commercially available samplers. Laboratory experiments demonstrate a device sensitivity of 77–83% and a limit of detection of 7-35 viral RNA copies/m³ of air. Our pAQ monitor is suited for point-of-need surveillance of SARS-CoV-2 variants in indoor environments and can be adapted for multiplexed detection of other respiratory pathogens of interest. Widespread adoption of such technology could assist public health officials with implementing rapid disease control measures.
... It has been reported that bioaerosols may account for up to 30% of the aerosol mass in urban and rural air 3 , and the concentration of microbial cells varies from ~ 10 2 to ~ 10 6 cells m ˗3 of air in the atmosphere 8 . Thus, collected bioaerosol samples typically have a low biomass in indoor environments (e.g., residential homes, offices, or hospitals) and outdoor environments (urban or rural air), with the exception of certain specialized environments, such as farm or rainforest environments 3,13, 14 . This makes it challenging to analyze the target matter despite the increased attention on bioaerosols in some specialized environments showing ultra-low biomass, for example, operating rooms and intensive care units in hospitals, clean rooms in industrial or pharmaceutical manufacturing sites, or the international space station searching for life on other planets. ...
Unlabelled: Bioaerosols play essential roles in the atmospheric environment and can affect human health. With a few exceptions (e.g., farm or rainforest environments), bioaerosol samples from wide-ranging environments typically have a low biomass, including bioaerosols from indoor environments (e.g., residential homes, offices, or hospitals), outdoor environments (e.g., urban or rural air). Some specialized environments (e.g., clean rooms, the Earth's upper atmosphere, or the international space station) have an ultra-low-biomass. This review discusses the primary sources of bioaerosols and influencing factors, the recent advances in air sampling techniques and the new generation sequencing (NGS) methods used for the characterization of low-biomass bioaerosol communities, and challenges in terms of the bias introduced by different air samplers when samples are subjected to NGS analysis with a focus on ultra-low biomass. High-volume filter-based or liquid-based air samplers compatible with NGS analysis are required to improve the bioaerosol detection limits for microorganisms. A thorough understanding of the performance and outcomes of bioaerosol sampling using NGS methods and a robust protocol for aerosol sample treatment for NGS analysis are needed. Advances in NGS techniques and bioinformatic tools will contribute toward the precise high-throughput identification of the taxonomic profiles of bioaerosol communities and the determination of their functional and ecological attributes in the atmospheric environment. In particular, long-read amplicon sequencing, viability PCR, and meta-transcriptomics are promising techniques for discriminating and detecting pathogenic microorganisms that may be active and infectious in bioaerosols and, therefore, pose a threat to human health. Supplementary information: The online version contains supplementary material available at 10.1007/s41745-023-00380-x.
... These are specific challenges in microbiology due to the wide range of biomasses which need to be analysed. This problem was tackled in [40], where they developed a four-stage ultra-low biomass pipeline: amassment, storage, extraction and nucleic acid analysis. They use several decontamination procedures, including a negative control, where they processed a sample collected with zero airflow into their detector for 1 minute. ...
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The purpose of this paper is to re-open from a practical perspective the question of the extent in altitude of the Earth's biosphere. We make a number of different suggestions for how searches for biological material could be conducted in the mesosphere and lower thermosphere, colloquially referred to as the ignoreosphere due to its lack of investigation in the meteorological community compared to other regions. Relatively recent technological advances such as CubeSats in Very Low Earth Orbit or more standard approaches such as the rocket borne MAGIC meteoric smoke particle sampler, are shown as potentially viable for sampling biological material in the ignoreosphere. The issue of contamination is discussed and a potential solution to the problem is proposed by the means of a new detector design which filters for particles based on their size and relative-velocity to the detector.
... These are specific challenges in microbiology due to the wide range of biomasses which need to be analysed. This problem was tackled in [40], where they developed a four-stage ultra-low biomass pipeline: amassment, storage, extraction and nucleic acid analysis. They use several decontamination procedures, including a negative control, where they processed a sample collected with zero airflow into their detector for 1 minute. ...
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The purpose of this article is to reopen from a practical perspective the question of the extent in altitude of Earth's biosphere. We make a number of different suggestions for how searches for biological material could be conducted in the mesosphere and lower thermosphere, colloquially referred to as the “ignore-osphere” because it has been generally ignored in the meteorological community compared to other regions. Relatively recent technological advances such as CubeSats in very low Earth orbit or more standard approaches such as the rocket-borne MAGIC meteoric smoke particle sampler are shown as potentially viable for sampling biological material in the ignore-osphere. The issue of contamination is discussed, and a potential solution to the problem is proposed by means of a new detector design that filters for particles based on their size and relative velocity to the detector.
... At any location, the biomass of microorganisms in the ABL is extremely low, with values ranging from 10 1 to 10 8 cells/m 3 for bacteria and from undetectable to 10 5 cells/m 3 for fungi , whilst hotspots of microbial abundance and diversity occur in clouds (Amato et al., 2007), and particulate plumes from desert dust (Maki et al., 2017) and wildfires (Kobziar et al., 2022). Recent advances in methodology for the study of ultra-low biomass microbiomes (Eisenhofer et al., 2019;Luhung et al., 2021;Šantl-Temkiv et al., 2020), have led to enhanced understanding of microbial diversity of the ABL at several locations. A number of recent studies have assessed spatial and temporal changes in ABL microbial communities and concluded that variation reflected passage of air mass trajectories above land or ocean with different uses Caliz et al., 2019;Els et al., 2019;Lang-Yona et al., 2022;Tignat-Perrier et al., 2019). ...
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The dispersion of microorganisms through the atmosphere is a continual and essential process that underpins biogeography and ecosystem development and function. Despite the ubiquity of atmospheric microorganisms globally, specific knowledge of the determinants of atmospheric microbial diversity at any given location remains unresolved. Here we describe bacterial diversity in the atmospheric boundary layer and underlying soil at twelve globally distributed locations encompassing all major biomes, and characterise the contribution of local and distant soils to the observed atmospheric community. Across biomes the diversity of bacteria in the atmosphere was negatively correlated with mean annual precipitation but positively correlated to mean annual temperature. We identified distinct non-randomly assembled atmosphere and soil communities from each location, and some broad trends persisted across biomes including the enrichment of desiccation and UV tolerant taxa in the atmospheric community. Source tracking revealed that local soils were more influential than distant soil sources in determining observed diversity in the atmosphere, with more emissive semi-arid and arid biomes contributing most to signatures from distant soil. Our findings highlight complexities in the atmospheric microbiota that are relevant to understanding regional and global ecosystem connectivity.
... Aerobiological sampling methods involve impaction, impingement, membrane filtration ( Figure 3) (2019), who evaluated airborne recovery efficiencies using filtration and liquid impingement and found filtration using polycarbonate filters to give the best recovery, with impingement recommended for shorter duration of sampling. Luhung et al (2021) investigated DNA extraction efficiencies from filters and found that direct extraction (by placing sampling filter into extraction kit) underestimated total cell numbers. Instead, they suggested a two-stage approach: first removing biomass from the filter, then re-filtering onto a smaller, thinner membrane. ...
Many characteristics of atmospheric air are measured in Svalbard, including levels of chemical pollution, dark dust connected to soot, and living organisms, but most of these studies happen in Ny-Ålesund. Air monitoring was initiated as early as the 1970s, and multiple atmospheric components have been added to the monitoring over time (especially since 2010; in the early 2000s a few parameters measured at Hornsund joined the regular programme). New types of contaminants are being discovered and measured in Svalbard. Methods for detecting simpler substances and particles have been established for a long time, while certain complex chemicals and small living organisms are more difficult to capture and study. Laboratory and field equipment upgrades help improve understanding of the Svalbard environment. In this chapter, we find that collecting information on many characteristics of the air at the same time helps solve long-standing scientific questions in Svalbard, such as the origins of pollution in the Arctic air and the future of the Arctic atmosphere in a changing world. This is especially important since the Arctic is changing fast, both due to global warming and to the shift in local people’s activity from mining to services, e.g. tourism.
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The scientific community has responded to the COVID-19 pandemic by rapidly undertaking research to find effective strategies to reduce the burden of this disease. Encouragingly, researchers from a diverse array of fields are collectively working towards this goal. Research with infectious SARS-CoV-2 is undertaken in high containment laboratories, however, it is often desirable to work with samples at lower containment levels. To facilitate the transfer of infectious samples from high containment laboratories, we have tested methods commonly used to inactivate virus and prepare the sample for additional experiments. Incubation at 80°C, a range of detergents, Trizol reagents and UV energies were successful at inactivating a high titre of SARS-CoV-2. Methanol and paraformaldehyde incubation of infected cells also inactivated the virus. These protocols can provide a framework for in house inactivation of SARS-CoV-2 in other laboratories, ensuring the safe use of samples in lower containment levels.
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Indoor microbial diversity and composition are suggested to affect the prevalence and severity of asthma by previous home microbiome studies, but no microbiome-health association study has been conducted in a school environment, especially in tropical countries. In this study, we collected floor dust and environmental characteristics from 21 classrooms, and health data related to asthma symptoms from 309 students, in junior high schools in Johor Bahru, Malaysia. The bacterial and fungal composition was characterized by sequencing 16s rRNA gene and internal transcribed spacer (ITS) region, and the absolute microbial concentration was quantified by qPCR. In total, 326 bacterial and 255 fungal genera were characterized. Five bacterial (Sphingobium, Rhodomicrobium, Shimwellia, Solirubrobacter, Pleurocapsa) and two fungal (Torulaspora and Leptosphaeriaceae) taxa were protective for asthma severity. Two bacterial taxa, Izhakiella and Robinsoniella, were positively associated with asthma severity. Several protective bacterial taxa including Rhodomicrobium, Shimwellia and Sphingobium have been reported as protective microbes in previous studies, whereas other taxa were first time reported. Environmental characteristics, such as age of building, size of textile curtain per room volume, occurrence of cockroaches, concentration of house dust mite allergens transferred from homes by the occupants, were involved in shaping the overall microbial community but not asthma-associated taxa; whereas visible dampness and mold, which did not change the overall microbial community for floor dust, was negatively associated with the concentration of protective bacteria Rhodomicrobium (β = -2.86, p = 0.021) of asthma. The result indicates complex interactions between microbes, environmental characteristics and asthma symptoms. Overall, this is the first indoor microbiome study to characterize the asthma-associated microbes and their environmental determinant in the tropical area, promoting the understanding of microbial exposure and respiratory health in this region.
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The atmosphere is vastly underexplored as a habitable ecosystem for microbial organisms. In this study, we investigated 795 time-resolved metagenomes from tropical air, generating 2.27 terabases of data. Despite only 9 to 17% of the generated sequence data currently being assignable to taxa, the air harbored a microbial diversity that rivals the complexity of other planetary ecosystems. The airborne microbial organisms followed a clear diel cycle, possibly driven by environmental factors. Interday taxonomic diversity exceeded day-to-day and month-to-month variation. Environmental time series revealed the existence of a large core of microbial taxa that remained invariable over 13 mo, thereby underlining the long-term robustness of the airborne community structure. Unlike terrestrial or aquatic environments, where prokaryotes are prevalent, the tropical airborne biomass was dominated by DNA from eukaryotic phyla. Specific fungal and bacterial species were strongly correlated with temperature, humidity, and CO 2 concentration, making them suitable biomarkers for studying the bioaerosol dynamics of the atmosphere.
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The interplay between microbes and atmospheric physical and chemical conditions is an open field of research that can only be fully addressed using multidisciplinary approaches. The lack of coordinated efforts to gather data at representative temporal and spatial scales limits aerobiology to help understand large scale patterns of global microbial biodiversity and its causal relationships with the environmental context. This paper presents the sampling strategy and analytical protocols developed in order to integrate different fields of research such as microbiology, –omics biology, atmospheric chemistry, physics and meteorology to characterize atmospheric microbial life. These include control of chemical and microbial contaminations from sampling to analysis and identification of experimental procedures for characterizing airborne microbial biodiversity and its functioning from the atmospheric samples collected at remote sites from low cell density environments. We used high-volume sampling strategy to address both chemical and microbial composition of the atmosphere, because it can help overcome low aerosol and microbial cell concentrations. To account for contaminations, exposed and unexposed control filters were processed along with the samples. We present a method that allows for the extraction of chemical and biological data from the same quartz filters. We tested different sampling times, extraction kits and methods to optimize DNA yield from filters. Based on our results, we recommend supplementary sterilization steps to reduce filter contamination induced by handling and transport. These include manipulation under laminar flow hoods and UV sterilization. In terms of DNA extraction, we recommend a vortex step and a heating step to reduce binding to the quartz fibers of the filters. These steps have led to a 10-fold increase in DNA yield, allowing for downstream omics analysis of air samples. Based on our results, our method can be integrated into pre-existing long-term monitoring field protocols for the atmosphere both in terms of atmospheric chemistry and biology. We recommend using standardized air volumes and to develop standard operating protocols for field users to better control the operational quality.
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Dispersal of airborne microorganisms is an important ecological process, resulting in the distribution of bacteria to all habitats on Earth. Investigation of this process is limited by the ability to collect uncontaminated high-altitude microbial samples for use with next-generation sequencing approaches. Here, we describe the design of a Remote Airborne Microbial Passive sampling system. Troubleshooting experiments demonstrate that the samplers collect adequate DNA for bacterial 16S rRNA (ribosomal RNA) amplicon–based Mi-Seq sequencing at 2 and 150 m from the ground. When samplers are closed, they retain only a low number of sequences, and may be used as a negative control. We also demonstrate that the optimal amount of collection dishes to include in the sampler is 8, and that freezing collection dishes at −80°C is an alternative to immediate DNA extraction. Samplers may be used to address a variety of ecological and human health–related questions.
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Introduction Ventilation system filters process recirculated indoor air along with outdoor air. This function inspires the idea of using the filter as an indoor bioaerosol sampler. While promising, there remains a need to investigate several factors that could limit the accuracy of such a sampling approach. Among the important factors are the dynamics of microbial assemblages on filter surfaces over time and the differential influence of outdoor versus recirculated indoor air. Methods This study collected ventilation system filter samples from an air handling unit on a regular schedule over a 21-week period and analyzed the accumulation patterns of biological particles on the filter both quantitatively (using fluorometry and qPCR) and in terms of microbial diversity (using 16S rDNA and ITS sequencing). Results The quantitative result showed that total and bacterial DNA accumulated monotonically, rising to 41 ng/cm² for total DNA and to 2.8 ng/cm² for bacterial DNA over the 21-week period. The accumulation rate of bacterial DNA correlated with indoor occupancy level. Fungal DNA first rose to 4.0 ng/cm² before showing a dip to 1.4 ng/cm² between weeks 6 and 10. The dip indicated a possible artifact of this sampling approach for quantitative analysis as DNA may not be conserved on the filter over the months-long service period. The sequencing results indicate major contributions from outdoor air for fungi and from recirculated indoor air for bacteria. Despite the quantitative changes, the community structure of the microbial assemblages was stable throughout the 21-week sampling period, highlighting the robustness of this sampling method for microbial profiling. Conclusion This study supports the use of ventilation system filters as indoor bioaerosol samplers, but with caveats: 1) an outdoor reference is required to properly understand the contribution of outdoor bioaerosols; and 2) there is a need to better understand the persistence and durability of the targeted organisms on ventilation system filters.
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During the last decades, research on the function of volatile organic compounds focused primarily on the interactions between plants and insects. However, microorganisms can also release a plethora of volatiles and it appears that microbial volatile organic compounds (mVOCs) can play an important role in intra- and inter-kingdom interactions. So far, most studies are focused on aboveground volatile-mediated interactions and much less information is available about the function of volatiles belowground. This minireview summarizes the current knowledge on the biological functions of mVOCs with the focus on mVOCs-mediated interactions belowground. We pinpointed mVOCs involved in microbe-microbe and microbe–plant interactions, and highlighted the ecological importance of microbial terpenes as a largely underexplored group of mVOCs. We indicated challenges in studying belowground mVOCs-mediated interactions and opportunities for further studies and practical applications.
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Summary. The air we breathe contains microscopic biological particles such as viruses, bacteria, fungi and pollen, some of them with relevant clinic importance. These organisms and/or their propagules have been traditionally studied by different disciplines and diverse methodologies like culture and microscopy. These techniques require time, expertise and also have some important biases. As a consequence, our knowledge on the total diversity and the relationships between the different biological entities present in the air is far from being complete. Currently, metagenomics and next-generation sequencing (NGS) may resolve this shortage of information and have been recently applied to metropolitan areas. Although the procedures and methods are not totally standardized yet, the first studies from urban air samples confirm the previous results obtained by culture and microscopy regarding abundance and variation of these biological particles. However, DNA-sequence analyses call into question some preceding ideas and also provide new interesting insights into diversity and their spatial distribution inside the cities. Here, we review the procedures, results and perspectives of the recent works that apply NGS to study the main biological particles present in the air of urban environments.
Knowledge of the factors controlling the diverse chemical emissions of common environmental bacteria and fungi is crucial because they are important signal molecules for these microbes that also could influence humans. We show here not only a high diversity of mVOCs but that their abundance can differ greatly in different environmental contexts. Microbial volatiles exhibit dynamic changes across microbial growth phases, resulting in variance of composition and emission rate of species-specific and generic mVOCs. In vitro experiments documented emissions of a wide range of mVOCs (> 400 different chemicals) at high time resolution from diverse microbial species grown under different controlled conditions on nutrient media, or residential structural materials (N = 54, Ncontrol=23). Emissions of mVOCs varied not only between microbial taxa at a given condition but also as a function of life stage and substrate type. We quantify emission factors for total and specific mVOCs normalized for respiration rates to account for the microbial activity during their stationary phase. Our VOC measurements of different microbial taxa indicate that a variety of factors beyond temperature and water activity, such as substrate type, microbial symbiosis, growth phase, and lifecycle affect the magnitude and composition of mVOC emission.