Presented by Roxana Hickey (Postdoctoral Scholar, University of Oregon) at the 16th International Symposium on Microbial Ecology (#ISME16) in Montreal, Quebec, Canada on August 21-26, 2016

1. BACKGROUND
METHODS
RESULTS
SUMMARY & NEXT STEPS
@ROXANA_HICKEY
CLOUDS IN A CROWD: DECIPHERING INDIVIDUAL
CONTRIBUTIONS TO THE HUMAN MICROBIAL CLOUD
ROXANA HICKEY, JAMES MEADOW, ASHLEY BATEMAN, CLARISSE BETANCOURT-ROMAN & JESSICA GREEN1 1 1 1,22
[1] BIOLOGY & THE BUILT ENVIRONMENT CENTER, UNIVERSITY OF OREGON, EUGENE, OR; [2] PHYLAGEN, SAN FRANCISCO, CA
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Air filters
(a)
NMDS 1
NMDS2
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Settling dishes
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NMDS 1
NMDS2
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unoccupied
Subject 1
Subject 2
Subject 3
(b)
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(d)
(e)
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0.04
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0.06
CanberraSimilarity
self other self other self other
All
Samples
Air
Filters
Settling
Dishes
Figure 1. Occupied and
unoccupied rooms are
significantly different in
bioaerosols collected on air filters
(a, b) and settled dust (c, d)
during a 4-hour sampling period.
Occupant microbial clouds were
more similar to other samples
from the same person than to
other occupants, regardless of
sampling method. (Previously
published in Meadow et al. PeerJ
2015; DOI: 10.7717/peerj.1258)
The human microbiome is highly individualized, with each person
harboring a unique assortment of microbes at various sites
throughout the body. This individuality appears to extend to the
microbial cloud of bacteria-laden particles emitted from a person
while inhabiting a room, even after a relatively short period of
occupancy (Meadow et al. PeerJ 2015). The ability to identify
individuals from bioaerosols has potential forensic applications
and, more fundamentally, can help us understand the dispersal of
human-associated microbes in the built environment. We
performed an experiment at the crowd-scale to characterize the
composition and spatial organization of individuals’ contributions
to the microbial cloud under variable occupancy and ventilation.
a b
c
Figure 2. Experimental setup in the Energy Studies in Buildings Laboratory in Portland, OR. (a) The
Climate Chamber is an experimental room with radiant heating panels and customizable ventilation
system. (b) L: three occupants sat in chairs spaced equidistantly across the room while bioaerosols
were collected onto 0.2µm filters for 90 min. Each occupant sat for a solo run as well as a group run.
R: air samples were collected in the supply and exhaust air ducts. (c) Schematic diagrams from the
top (L) and side (R) of the chamber. Photographs and diagrams courtesy of ESBL.
S01
S02
S03
S01
S02
S03
Solo run: 90 min @ 1 ACH
Group run: 90 min @ 1 ACH
Skin: pre/post run, nylon swabs
Air: c. 24.5 L/min, 0.2µm filters
Sample Collection
3 x 3 = 9 subjects, 3 groups
Figure 4. Heatmap of top 100 OTUs across a subset of skin and air samples.
A subset of samples from Group B (subjects S04, S05, S06; both solo and
group runs) are shown here and organized by sample type. OTU read counts
were normalized using variance stabilizing transformation and are labeled
according to the genus assigned by the RDP Classifier.
skin pre supply chamber (box) exhaust skin post
Figure 3.Principal coordinates analysis of skin and airborne microbial communities in three
groups of occupants. (a) PCoA (Bray-Curtis distance) on 69 skin and 203 air microbial
communities (supply, chamber and exhaust) from 9 solo-occupant runs and 3 group-
occupant runs. OTU read counts were normalized using variance stabilizing transformation
in the DESeq2 package (Love et al. Genome Biology 2014). (b) PCoA (Bray-Curtis) on only
skin samples. (c) PCoA (Bray-Curtis) on only air samples. (d) PCoA from part (a) split into
three co-occupant groups. Each panel features a subset of samples from co-occupants on
the same ordination space.
a b c
d
Reported results are preliminary and analyses are actively ongoing.
Next steps will utilize approaches such as oligotyping (Murat Eren,
merenlab.org) and metagenomic codes (Franzosa et al. PNAS
2015; http://huttenhower.sph.harvard.edu/idability) to trace
bacterial strains sourced from individuals' skin microbiome to
bioaerosols captured in the chamber and exhaust air.
Additionally, we have data to determine whether higher
ventilation rates under realistic indoor conditions (3, 10 and 20 air
changes per hour) dilutes the microbial cloud signal such that
bioaerosolized bacteria cannot be detected or distinguished on an
individual basis.
Sample & Data
Processing
Extract DNA (MoBio PL/PS)
Amplify 16S rRNA V4
(515f / 806r)
Generate sequences
(NextSeq PE 150)
Quality filter, cluster OTUs,
classify (Flash, USEARCH, RDP)
Analyze data
(phyloseq, DESeq2, vegan)
Preliminary observations reveal clear community dissimilarity
between skin and air, but no obvious patterns emerge for supply
vs. occupied air or solo vs. group runs. Additional analyses are
needed and will rely on finer-resolution techniques and statistical
approaches to assess whether individuals can be distinguished
by specific components of the microbial cloud.