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ARTICLE OPEN
Experimental parameters defining ultra-low biomass
bioaerosol analysis
Irvan Luhung
1,3
, Akira Uchida
1,3
, Serene B. Y. Lim
1
, Nicolas E. Gaultier
1
, Carmon Kee
1
, Kenny J. X. Lau
1
, Elena S. Gusareva
1
,
Cassie E. Heinle
1
, Anthony Wong
1
, Balakrishnan N. V. Premkrishnan
1
, Rikky W. Purbojati
1
, Enzo Acerbi
1
, Hie Lim Kim
1
,
Ana C. M. Junqueira
1,2
, Sharon Longford
1
, Sachin R. Lohar
1
, Zhei Hwee Yap
1
, Deepa Panicker
1
, Yanqing Koh
1
, Kavita K. Kushwaha
1
,
Poh Nee Ang
1
, Alexander Putra
1
, Daniela I. Drautz-Moses
1
and Stephan C. Schuster
1
✉
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.
npj Biofilms and Microbiomes (2021) 7:37 ; https://doi.org/10.1038/s41522-021-00209-4
INTRODUCTION
Great naturalists of centuries-past have catalogued planetary
ecosystems at the macroscopic level, primarily for terrestrial and
aquatic environments, where organisms were most accessible
1,2
.
Microscopic life was subsequently given the same attention, again
initially focusing on terrestrial and aquatic systems
3,4
. Microbial
inhabitants of the third ecosystem of planetary scale, the
atmosphere, proved much more difficult 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
5–7
. As a consequence,
atmospheric research first described the physicochemical nature
of the atmosphere, thereby generating a comprehensive under-
standing of inanimate components of the troposphere and
stratosphere
8
. 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
9,10
. The following progression in
the field involved the identification of airborne organisms via
cultivation and microscopy
11,12
. This provided a foundation for
understanding the composition of airborne microbial organisms
via nucleic acid taxonomic identification. 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 amplification
13–18
.
The nascent field of bioaerosol studies was further progressed
by employing metagenomics, which enabled direct nucleic acid
analysis without the biases associated with gene amplification.
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
19–21
.
Advances in temporal and taxonomic resolution only became
possible with the onset of new technologies involving high
volumetric flow rate air samplers coupled with metagenomic data
generated by next-generation sequencing platforms that had low
biomass requirements
22
. 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.
RESULTS
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 fluctuations, as shown in marine environments
23
as well as
1
Singapore Centre for Environmental Life Sciences Engineering (SCELSE), Nanyang Technological University, Singapore, Singapore.
2
Present address: Departamento de Genética,
Instituto de Biologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-590, Brazil.
3
These authors contributed equally: Irvan Luhung, Akira Uchida.
✉email: scschuster@ntu.edu.sg
www.nature.com/npjbiofilms
Published in partnership with Nanyang Technological University
1234567890():,;
atmospheric environments where higher bioaerosol concentrations
are typically observed at night
22
(Fig. 1c) or during haze events
24
.
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 pipeline’sefficacy 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.
Amassment
The ultimate success of sequencing and PCR-based analyses rests
on sufficient quantities of nucleic acids being amassed, which for
air sampling is a trade-off between sampling flow rate and
sampling duration. While this study uses a filter-based sampler,
other types of air samplers, such as liquid impingers serve a similar
function and produce comparable results (Supplementary
Fig. 8)
25
. For our purpose, ideal air samplers should be portable,
battery-powered and have an acceptable noise emission (~50 dB).
The air sampling flow 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 profile of the sample. Using 300 L/min flow 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 first and second 15-min intervals (5:00–5:15 am
and 5:15 to 5:30 am) were individually analysed and compared
to a 30-min sample (5:00–5:30 am) taken in parallel. This process
was undertaken for 15, 30 and 60-min intervals with a final
sampling duration up to 180 min. Quantitative analysis showed
consistently increasing DNA yields as a function of sampling
duration (Fig. 2a–c). No notable loss of DNA yield was observed
within the tested range of duration (15 min–3 h). Within this
range, combining two successive time segments resulted in
similar DNA quantities as a single time segment of the combined
duration, as quantified 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 profiles 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 filter
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 flow
rate and the total volume of air sampled. With the sampling
duration set at 2 h, airflow was varied between 100, 200 and 300 L/
min, resulting in total air volumes of 12, 24 and 36 m
3
,
respectively. The DNA yield and copy number of marker genes
(16S and 18S rRNAs) increased as a function of air volume sampled
(Fig. 2d–f). However, DNA concentration normalised per air
volume diminished by up to 20% when the flow rate was
increased from 100 to 300 L/min (Supplementary Fig. 2a). The
diminishing return of amassment is likely due to decreasing
particle retention efficiency at higher flow rates for extended
periods of time
26
. For the purpose of this study, optimal sampling
efficiency is forfeited in favour of higher flow rates (300 L/min)
because the total amount of biomass collected per unit of time
still out-performs the decrease in amassment efficiency. This
enables measurements with higher time resolution within a day
for environmental time-series studies. The biological significance
of this was demonstrated by the discovery of diel dynamics of
outdoor airborne microbial communities
22
.
Amassment Storage Nucleic Acid Analysisd
OceanSoil Air
15
10
5
0
-5
DNA yield (Log10)
(ng/mass equivalent)
a
OceanSoil Air
5
0
-5
-10
Log10 (unit of sample
to yield 5 ng DNA)
b
Extraction
0
50
100
150
200
250
DNA yield (ng)
11 15 19 23
37
c
PBS buffer
+0.1% Triton X-100
Air samples
Anodiscs
0.02 µm DNA extraction
DNA sequencing
Air
sampler
(filter)
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, 25th–75th 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.
2
npj Biofilms and Microbiomes (2021) 37 Published in partnership with Nanyang Technological University
1234567890():,;
Further analysis demonstrated that flowratedoesnotimpactthe
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 significantly different
for samples collected with different flow rates.
Storage
Analysis of the storage component in this pipeline evaluated the
integrity (biomass quality and composition) of air filter 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 significant 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 filter processing. However, RT
samples were significantly different from the fresh and freezer
regimes in regard to DNA quantities (20–30% loss) (Fig. 2g–i). 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 filter substrate
is impeded within the course of several days, thus enabling sample
collection for field surveys without the need for refrigeration
27
.
Filter processing and DNA extraction
As library construction for DNA sequencing requires the removal
of particle/biomass from the air filter substrate (referred to as filter
processing), the protocol was optimised for efficient biomass
retrieval. Importantly, the ultra-low biomass nature of the sample
renders filter processing the most limiting, and hence, the most
critical step across the entire pipeline for maximising yield.
In general, filter samples can be processed in one of two ways,
either direct DNA extraction on the filter, or by first removing the
biomass from the filter prior to DNA extraction. Direct DNA
extraction was deemed inefficient as the filter absorbs most of the
lysis buffer, which consequently inhibits cell lysis. In contrast, first
removing the biomass by washing the filter in a buffer (PBS) and
then concentrating on a thinner membrane with smaller mesh-
size (0.2 µm PES or Anodisc membrane)
28
, resulted in significantly
higher DNA recovery (Fig. 4b).
To further improve biomass recovery, additional steps such as
water-bath sonication (RT, 1 min)
29,30
and the use of detergent
(Triton-X 100) during filter wash were tested. For comparison of
samples processed with and without sonication, no significant
difference in either quantitative or metagenomic analyses was
found (Fig. 2j–l and Supplementary Fig. 4). In contrast, adding
detergent during the filter wash significantly improved DNA yield
(Fig. 2m–o). The hydrophobic nature of the air sampling filter
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 significant 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. 2m–o). The metagenomic analysis
confirmed this finding (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 significant difference between
the three concentrations for quantitative analyses (Fig. 2m–o).
However, metagenomic analysis identified notable differences in
microbiome composition (BrayCurtis p< 0.05, Supplementary Fig.
16S rRNA CN(x105) 18S rRNA CN(x108)
0
2
3
1
0
50
100
150
200
DNA yield(ng)
0
2
4
6
100 200 300
Flowrate
(L/min)
0
50
100
150
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1
2
3
Fsh Frz RT
0
1
2
3
0
50
100
150
200
0
1
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4
0
1
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4
Incubation
at 55˚C
1h 2h ON
0
40
80
120
0
2.5
2.0
1.5
1.0
0.5
0
2
4
6
0
0.01
0.1
0.5
Triton X-100
(%, v/v)
FungiBacteria
*
*
*
*
*
45
a
b
c
d
e
f
g
h
i
j
k
l
0
2
4
6
0
100
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300
0
3
6
9
Sonication
+-
m
n
o
0
20
40
60
80
100
15 30 60 120
0
0.8
1.6
2.4
0
50
100
150
Sampling duration
(min)
Amassment Storage NA Analysis
WGS
ITS
16S
Fungi
Bacteria
Extraction
p
q
r
s
Storage
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). a–cAssessment of air
sampling duration from 15 min to 3 h. d–fAssessment of air sampling flow rate from 100 L/min to 300 L/min. g–iThe integrity of sampled
biomass when processed fresh (Fsh), stored in freezer for 5 d (Frz) or stored at room temperature for 5 d (RT). j–lImpact of sonication on DNA
yield. (m–o) Impact of detergent addition at different concentrations (0.01–0.5% v/v) during filter sample wash. p–rImpact of extended pre-
incubation (1 h to overnight) at 55 °C during DNA extraction. The centre line, bound of box and whiskers represent median, 25th–75th
percentile and min-to-max values, respectively. sWhole-genome shotgun (WGS) and amplicon (ITS/16S) sequencing approaches. * denotes
statistical significance (p< 0.05) tested with Mann–Whitney tests.
I. Luhung et al.
3
Published in partnership with Nanyang Technological University npj Biofilms and Microbiomes (2021) 37
5a–b) driven by an increase in bacterial taxa. Increasing Triton X-
100 beyond 0.1% concentration, yielded no significant further
gains (Fig. 4d). Hence, Triton X-100 at 0.1% was deemed sufficient
for wetting the filter and releasing attached bioaerosol particles
into the buffer medium. Despite the 0.1% concentration of Triton
X-100 being above the critical micelle concentration
31
, Triton X-
100 did not trigger unwanted premature lysis of microbial cells, as
there were no significant 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 filter processing, the recovered biomass was filtered
through a 0.02 µm pore-sized Anodisc membrane (Whatmann,
USA) mounted on a vacuum manifold (Fig. 1d), with the Anodisc
directly fitting into the DNA extraction kit bead tube. DNA
extraction used the standard protocol of the extraction kit with
slight modification to improve lysis
26
. 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. 2p–r, 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 0.1–7.1 ng DNA/m
3
of air sampled). These can subsequently be analysed, not only by
amplification-based techniques (16S/ITS), but also via direct DNA
Amassment
5:00
am
5:30 6:00 7:00 8:00
150
200
DNA yield (ng)
15 30 60120 180
Sampling duration
(min)
0
50
100
a
b
Pestalotiopsis fici
Ktedonobacter racemifer
050010001500
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
d
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
c
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. first 15-min yield (orange) +second 15-min yield (light blue) compared to first 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 first and second 15-min samples. d
Comparison of relative abundances of the selected species, the first and second 15-min samples were averaged and compared to the
taxonomic composition of first 30-min sample. The bars represent mean values and the error bars were standard deviation among the
replicates.
I. Luhung et al.
4
npj Biofilms and Microbiomes (2021) 37 Published in partnership with Nanyang Technological University
sequencing (shotgun), resulting in either gene-based or metage-
nomic profiles 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
13–18
.
This was due to the assumption that the low amount of amassable
biomass from air was insufficient for shotgun metagenomics
32
.
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 (0.5–10 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
0.5–10 ng, no significant change of species-level composition was
observed. The species-level metagenomic profile 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
22
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 profiles of samples within the
same replicate group were highly consistent, with an average
similarity of 91% (87–95%, SIMPER analysis). The taxonomic profiles,
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 0.5–10 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 (36–90%), within the four climatic zones
(temperate, dessert, sub-arctic and tropical) (Fig. 5c).
Contamination control: The negative controls consisted of filter
blanks (clean, unused filter) mounted on the air samplers for 1 min
without airflow, 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 filter 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 final step, extracted genomic DNA from the pipeline was
analysed by both metagenomic and 16S/ITS amplicon sequencing,
resulting in sets of distinct taxonomic profiles based on their
respective databases (Fig. 6a). For fungi, results from both
sequencing analysis methods concur with the observed trends
for the specific 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 (4–46 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 profile 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 amplification for air samples from a
diverse range of environmental conditions. Numerous studies
have reported similar challenges
13,16
. 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
0
20
40
60
80
DNA yield (ng)
+-
Filter
processing
bc d
0
10
20
30
40
50
Bacteria
0
0.01
0.1
0.5
Fungi
0
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120
Triton X-100 (%)
Species richness
a
Storage
Fsh Frz RT
-6 -4 -2
-2
-1
0
1
2
3
24
0
PCO 2 (9%)
PCO 1 (66%)
PCO 2 (4%)
PCO 1 (93%)
-6
-3
0
3
6
10 20
-10 0
0% 0.01% 0.1% 0.5%
Triton X-100
Extraction
0
1
2
3
4
0
5
10
15
1h 2h ON
Incubation at 55˚C
PERMDISP
FungiBacteria
e
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 filter wash step. cTotal identified
species for fungi (orange) and bacteria (blue) for samples processed with different concentration of detergent (0–0.5% 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 (0–0.5% 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,
25th–75th percentile and min-to-max values, respectively.
I. Luhung et al.
5
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
33
, while
the amplicon reads were aligned to the 16S SILVA database for
bacteria
34
and ITS UNITE
35
database for fungi using blastn
36
. The
resulting taxonomic classifications from the two analysis
approaches show significant 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
72–78% 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 profiles 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 benefits of 16S- and ITS-based taxonomy to
investigate understudied ultra-low biomass environments, while
simultaneously enabling taxonomic and functional analyses
37
.
DISCUSSION
The here-presented air sampling and analysis pipeline enable
qualitative and quantitative assessment of microbial diversity in an
ultra-low biomass ecosystem. The 5–7 log difference in biomass
concentration of air samples, compared to seawater or soil,
requires sufficiently 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
~8–170-fold higher than reported in recent studies
26,28,38
(Sup-
plementary Table 1). This large improvement allows for shorter
sampling time (≥15 min), while still enabling WGS metagenomic
analysis with species-level taxonomic classification. 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
a
Temperate
(Germany)
Dessert
(Israel)
Arctic
(Russia)
Tropical
(Singapore)
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 (%):
Climate:
-10 to -15 27 to 3335 to 3912 to 15
36 to 38 70 to 9078 to 8080 to 83
c
-60 -40 -20 0 20 40
-40
-20
0
20
40
PCO1 (59.7%)
PCO2 (23.7%)
Night
Day
9am
% ass. reads
11am
% ass. reads
1pm
% ass. reads
3pm
% ass. reads
1am
% ass. reads
3am
% ass. reads
11pm
% ass. reads
9pm
% ass. reads
5pm
% ass. reads
7pm
% ass. reads
5am
% ass. reads
7am
% ass. reads
Basidiomycota
Ascomycota
Fungi
Proteobacteria
Actinobacteria
aCyanobacteri
Firmicutes
Bacteroidetes
Bacteria
Viridiplantae
Other phyla
Others
PCO:
Bar composition:
Avg. SIMPER (replicates): 91.3%
b
Nucleic Acid Analysis
Fig. 5 Whole genome shotgun (WGS) sequencing of air samples.
aComparison of taxonomic profile at species level for the same air
sample that was subjected to WGS sequencing with different DNA
input amounts (10–0.5 ng). bReproducibility of samples collected at
the same time and location (triplicates) illustrated in principal
coordinate analysis (Bray–Curtis) 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.
6
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 filter-based devices during remote fieldwork 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
methods
39
, this approach is not recommended during sampling
campaigns, as it would require additional handling of the air filter
Nucleic Acid Analysis
Viridiplantae
Others
Basidiomycota
Ascomycota
Firmicutes
Cyanobacteria
Actinobacteria
Proteobacteria
Day1 Night1 Day2 Night2
Extracted genomic DNA
Day1 Night1 Day2 Night2
PCR (ITS1F - ITS2R)
Amplicon Sequencing
0
20
40
60
80
100
% 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
1
9114
15 15 15 15
18224 16 275
430346
25 25 25 25
Legends:
a
16S
Acinetobacter
Pseudomonas
Sphingomonas
Belnapia
Chondromyces
Rickettsia
Bartonella
Paracoccus
Methylobacterium
Blautia
Clostridium
Faecalibacterium
Lactobacillus
Staphylococcus
Megamonas
Enterococcus
Lachnoclostridium
Jeotgalicoccus
Eubacterium
Bacillus
Streptococcus
Deinococcus
Alistipes
Bacteroides
Chryseobacterium
Kocuria
Corynebacterium
Brachybacterium
Streptomyces
Microbacterium
Mycobacterium
Dietzia
Nocardiopsis
Brevibacterium
Kibdelosporangium
Arthrobacter
Collinsella
Bifidobacterium
Methanobrevibacter
Prevotella
WGS
Pseudomonas
Acinetobacter
Rickettsiella
Sphingomonas
Craurococcus
Paracoccus
Shinella
Fusobacterium
Turicibacter
Erysipelatoclostridium
Subdomigranulum
Faecalibacterium
Romboutsia
Peptococcus
Oribacterium
Lachnoclostridium
Fusicatenibacter
Blautia
Clostridium
Streptococcus
Lactobacillus
Enterococcus
Aerococcus
Staphylococcus
Macrococcus
Jeotgalicoccus
Bacillus
Deinococcus
Ktedonobacter
Alistipes
Parabacteroides
Bacteroides
Streptomyces
Rothia
Micrococcus
Kocuria
Brachybacterium
Brevibacterium
Dietzia
Corynebacterium
16S
WGS
Schizophyllum
Dichomitus
Rhizotonia
Trametes
Fomitiporia
Phlebiopsis
Phanerochaete
Schizopora
Moniliophthora
Ganoderma
Tulasnella
Gelatoporia
Agaricus
Punctularia
Fibroporia
Heterobasidion
Botryobasidium
Cylindrobasidium
Pleurotus
Phlebia
Stereum
Serendipita
Coprinopsis
Tilletiaria
Laccaria
Termitomyces
Eutypa
Talaromyces
Fusarium
Pestalotiopsis
Verruconis
Diaporthe
Aspergillus
Cyphellophora
Colletotrichum
Valsa
Pseudocercospora
Saitoella
Penicillium
Auricularia
WGS
ITS
WGS
Resinicium
Pseudolagarobasidium
Schizophyllum
Peniophora
Ceriporia
Phellinus
Earliella
Lentinus
Auricularia
Grammothele
Porostereum
Ganoderma
Trichaptum
Scopuloides
Phlebia
Perenniporia
Heterochaete
Rigidoporus
Sidera
Fomitopsis
Peniophorella
Hyphodermella
Phanerochaete
Hyphodontia
Phlebiopsis
Favolus
Trechispora
Trametes
Thanatephorus
Exidia
Gymnopus
Antrodia
Hymenochaete
Pilatoporus
Megasporoporia
Hyphoderma
Marasmius
Sistotrema
Peroneutypa
Helvella
ITS
Bacteria Fungi
Genus
Proteobacteria
Fusobacteria
Firmicutes
Deinococcus
Chloroflexi
Basidiomycota
Ascomycota
Chlamydiae
Bacterioidetes
Actinobacteria
Archaea
Phylum
Reference
Reference Reference Reference
b
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
Species
Bacteria Fungi
Reference
Reference Reference Reference
16S
WGS
16S
WGS
ITS
WGS
ITS
WGS
c
Fig. 6 Comparison of taxonomic profiles between WGS and amplicon sequencing pipelines. a Taxonomic profile of WGS, ITS amplicon and
16S amplicon pipeline at phylum level of four independently collected air samples (two days and two nights). bPresence–absence
comparison of the top 40 most abundant genus for bacteria (WGS vs 16S) and fungi (WGS vs ITS). cPresence–absence comparison of the top
40 most abundant species for bacteria (WGS vs 16S) and fungi ( WGS vs ITS).
I. Luhung et al.
7
Published in partnership with Nanyang Technological University npj Biofilms and Microbiomes (2021) 37
samples in the field. 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 filter, but rather
first be removed owing to the adherence of the low quantities of
nucleic acids to the large surface area of the filter 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, significantly 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
40
.
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 identification 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 sufficient 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 efficacy of the polymerase chain reaction. Never-
theless, specific 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
significant agreement. To harness the advantages from both
sequencing technologies, it is beneficial 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 filter medium (0.5−>10 µm, <50% efficiency 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 profile particle
size range, recent studies have demonstrated that the most
relevant airborne bacteria and fungi fall within the size range of
the filter medium
13,15
.
In summary, the above-described ultra-low biomass analysis
pipeline provides detailed insights into the factors that influence
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 findings are potentially also applicable to other low-
biomass environments, such as dust and surfaces.
METHODS
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-flow
rate, filter-based air sampler (SASS3100, Research International, USA) was
used to collect total suspended particles (TSP) with no size cut-off. The
filter medium was SASS Bioaerosol electret filter (6 cm diameter, expected
50% efficiency 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 floor 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 filters were returned to their original filter pouches
and transported to the laboratory for direct processing or storage at
−20 °C. Information on exact sampling time, flow 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
sequencing
All filter samples were subsequently processed for DNA extraction,
quantitation, qPCR, metagenomic sequencing and computational analysis
as described in our previous study
22
. In brief, the filter samples were first
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 filter (Whatman, UK) using a vacuum
manifold (DHI, Denmark). DNA was then extracted from the Anodisc with
the DNeasy PowerWater kit (Qiagen, USA) following the manufacturer’s
standard protocol with modifications to increase DNA yield
26
.
Final DNA solution was subjected for fluorometer quantification, 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)
41
and fungal (18S rRNA gene)
42
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
Biosciences’Accel-NGS 2S Plus DNA Library kit following the standard
protocol. All libraries were subsequently dual-barcoded with Swift
Biosciences’2S Dual Indexing kit. PCR amplification 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 (8–15
cycles). Upon pooling at equal volumes, libraries were sequenced on
Illumina HiSeq2500 Rapid runs at a final concentration of 10–11 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 first trimmed from adapter
sequences, low quality bases (<20 score) and short reads (<30 bp) using
Cutadapt (v.1.8.1)
43
. The processed reads were then aligned against the
NCBI’s NR database (v.25-02-2016) using RAPSearch2 (v.2.15)
44
. Results
from the RAPSearch2 alignment were finally converted to read-match
archive (rma) to be visualised with MEGAN5 software
33
.
Experimental parameters optimisation
Important parameters for sampling, extraction and sequencing were tested
and optimised based on absolute (fluorometer 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.
8
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 flow
rate. Sampling duration experiment: With a fixed air flow 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. first and second 15 min samples
were compared to the matching 30 min sample. Air flow rate experiment:
With a fixed duration (2 h), three groups of air samplers (n=4) were run at
the same time with varying flow rate at 100, 200 and 300 L/min. The
experiments were assessed based on the impact of sampling duration and
airflow variations on DNA quantity and microbial composition.
Table 2. List of primers and probes applied in the study.
Name Sequence Notes
16S 341F 5′-CCTACGGGDGGCWGCA-3′Bacteria qPCR
16S 805R 5′-GGACTACHVGGGTMTCTAATC-3′Bacteria qPCR
Taqman probe 6FAM-5′-CAGCAGCCGCGGTA-3′-BBQ Bacteria qPCR probe
FungiQuant-F 5′-GGRAAACTCACCAGGTCCAG-3′Fungi qPCR
FungiQuant-R 5′-GSWCTATCCCCAKCACGA-3′Fungi qPCR
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 -3′Amplicon for bacteria
16S 805R Illumina 5′-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGG ACT ACH VGG GTW TCT AAT -3′Amplicon for bacteria
ITS1F Illumina 5′- TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CTT GGT CAT TTA GAG GAA GTA A -3′Amplicon for fungi
ITS2R Illumina 5′- GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGC TGC GTT CTT CAT CGA TGC -3′Amplicon for fungi
Table 1. Details of sampling activities.
Sample set Sampling date Sampling time, duration and
flow rate
Temperature (°C) RH (%) Rain Sample size No. of
samples
Singapore
1 29-Nov-17 01:00–03:00 (2 h, 100 L/min, 200 L/
min, 300 L/min)
24.8–25.3 98–100 No 4 12
2 15-Dec-17 05:05–08:05 (15 min, 30 min, 1 h, 2 h,
3 h, 300 L/min)
24.7–25.7 99–100 No 3 27
3 29-Nov-17 06:15–08:15 (2 h, 300 L/min) 24.6–25.4 99–100 No 4 12
4 23-Feb-17 17:00–17:00 (2 h, 300 L/min) 24.0–34.0 63–100 Yes 3 36
24-Feb-17
5 8-May-16 17:00–17:00 (2 h, 300 L/min) 28.0–33.0 59–89 No 3 36
9-May-16
6 29-Nov-17 03:50–05:50 (2 h, 300 L/min) 24.5–24.8 99–100 Yes 3 12
7 28-Nov-17 20:40–22:40 (2 h, 300 L/min) 24.9–25.3 98–99 No 3 12
8 24-Nov-17 05:00–07:00 (2 h, 300 L/min) 23.9–24.5 99–100 No 4 12
9 22-Nov-17 23:00–01:00 (2 h, 300 L/min) 26.0–26.5 97–99 No 3 3
10 23-Nov-17 11:00–13:00 (2 h, 300 L/min) 29.0–30.0 77–80 No 2 2
11 27-Nov-17 23:00–01:00 (2 h, 300 L/min) 24.5–25.5 93–96 No 3 3
12 28-Nov-17 11:00–13:00 (2 h, 300 L/min) 28.5–29.5 75–77 No 2 2
13 29-Aug-16 13:00–15:00 (2 h, 300 L/min) 31.0–32.0 70–80 No 1 1
14 30-Aug-16 13:00–15:00 (2 h, 300 L/min) 31.0–32.0 70–80 No 1 1
15 21-Sep-15 13:30–15:30 (2 h, 300 L/min) 31.0–32.0 63–70 Yes 1 1
Germany
16 30-Jul-17 12:00–14:00 (2 h, 300 L/min) 12.0–15.0 80–83 No 2 2
Israel
17 4-Jul-17 08:30–10:30 (2 h, 300 L/min) 35.0–39.0 36–38 No 2 2
Russia
18 2-Dec-17 09:00–11:00 (2 h, 300 L/min) −10.0–−15.0 78–80 No 1 1
19 3-Dec-17 15:00–17:00 (2 h, 300 L/min) −10.0–−15.0 78–80 No 1 1
Total no. of samples
(including blanks):
183
I. Luhung et al.
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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 profiles.
Parameters optimised for filter 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 filter 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 (14–16 h). These durations were selected to enable the
completion of the entire extraction process (filter washing and DNA
extraction) within a standard working day (~8 h).
All the above experiments were assessed based on DNA quantity and
microbial profiles of the resulting analysis.
The DNA sequencing result was evaluated for the DNA input amount,
reproducibility, robustness and taxonomic classification 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 final 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 profiles 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 flow 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
flow 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 first stage PCR was
performed with the extracted genomic DNA as a template and the
ITS1F-ITS2R
45
primers for fungi and 16S 341F-805R
46
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,
amplification cycles with 95 °C for 30 s, 65 °C for 30 s, 72 °C for 30 s, and a
final extension at 72 °C for 5 min. The fungal samples were amplified with
15 cycles and the bacteria samples were amplified with 25 cycles. The PCR
products were then purified 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 Illumina’s“16S Metagenomic Sequencing Library
Preparation”application 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 first stage PCR (Amplicon PCR) were used as template for the
indexing PCR and samples were amplified 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 Promega’s 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 quantification 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 first trimmed from adapter sequences,
low-quality bases and short reads using Cutadapt (v.1.8.1)
43
. After trimming,
the R1 and R2 reads were first 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
36
(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 Mann–Whitney 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, significant 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 Bray–Curtis 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 presence–absence 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 filter blank samples were collected and analysed. Filter blank samples
were collected by attaching a clean, unused filter 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.
DATA AVAILABILITY
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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ACKNOWLEDGEMENTS
The work was supported by Singapore Ministry of Education Academic Research
Fund Tier 3 grant (grant MOE2013-T3-1-013).
AUTHOR CONTRIBUTIONS
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.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41522-021-00209-4.
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