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Mondragon‐Palomino et al. p. 1
3D imaging for the quantification of spatial patterns
in microbiota of the intestinal mucosa
Octavio Mondragón-Palomino
1,β
, Roberta Poceviciute
1
, Antti Lignell
1,2,α
, Jessica A. Griffiths
2
, Heli Takko
1
,
and Rustem F. Ismagilov
1,2
*
1
Division of Chemistry and Chemic
al Engineering, California Inst
itute of Technology
2
Division of Biology and Biological Engineering, California Institute of Technology
1200 E. California Blvd., Pasadena, CA, Uni
ted States of America
* Correspondence to:
rustem.admin@caltech.edu
β
Current address: Laboratory of Par
asitic Diseases, National Ins
titute of Allergy and Infectious Diseases, B
ethesda, MD, United
States of America
α
Current address: Department of Chem
istry, University of Helsin
ki, University of H
elsinki, Finland
Abstract
Improving our understanding of host-microbe relationships in th
e gut requires the ability to both visualize and
quantify the spatial organization of microbial communities in their native orientation with the host tissue. We
developed a systematic procedure to quantify the 3D spatial str
ucture of the native mucosal microbiota in any
part of the intestines with taxonomic and high spatial resoluti
on. We performed a 3D biogeographical analysis of
the microbiota of mouse cecal crypts at different stages of antibiotic exposure. By tracking eubacteria and four
dominant bacterial taxa, we found that the colonization of cryp
ts by native bacteria is a dynamic and spatially
organized process. Ciprofloxacin treatment drastically reduced
bacterial loads and eliminated Muribaculaceae
(or all Bacteroidetes entirely) even 10 days after recovery whe
n overall bacterial loads returned to pre-antibiotic
levels. Our 3D quantitative imaging approach revealed that the
bacterial colonization of crypts is organized in a
spatial pattern that consists of clusters of adjacent colonized
crypts that are surrounded by unoccupied crypts,
and that this spatial pattern was resistant to the elimination of Muribaculaceae or of all Bacteroidetes by
ciprofloxacin. Our approach also revealed that the composition
of cecal crypt communities is diverse and that
bacterial taxa are distributed differently within crypts, with Lactobacilli laying closer to the lumen than
Bacteroidetes, Ruminococcaceae, and Lachnospiraceae. Finally, we found that crypts communities with similar
taxonomic composition were physically closer to each other than
communities that were taxonomically different.
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Key words:
microbiota, quantitative biogeography, tissue clearing
Significance Statement
Many human diseases are causally linked to the gut microbiota,
yet the field still lacks mechanistic understanding
of the underlying complex interactions because existing tools cannot simultaneously quantify microbial
communities and their intact native context. In this work, we p
rovide a new approach to tissue clearing and
preservation that enables visualization, in 3D and at scales ranging from centimeters to micrometers, of the
complete biogeography of the host-microbiota interface. We comb
ine this new tool with sequencing and
multiplexed labelling of the microbiota to provide the field wi
th a platform on which to discover patterns in the
spatial distribution of microbes. We validated this platform by
quantifying the distribution of bacteria in the cecal
mucosa at different stages of antibiotic exposure. This approach will enable researchers to formulate and test
new hypotheses about host-microbe and microbe-microbe interacti
ons.
Introduction
The composition of resident microbial communities is driven by nutrient availability
1–3
, the physical environment
4,5
, host-microbiota interactions
6,7
, and interactions within the microbiota
8,9
. The sum of all these forces may
shape the spatial arrangement of intestinal microbes and, in tu
rn, the spatial structure of the microbiota could
influence how host-microbe and microbe-microbe interactions occ
ur
10
. The synergy between the micro-
geography of intestinal bacterial consortia and the interactions of microbes with their environment or other
microbes has been studied
in vitro
using synthetic communities and computational simulations
11–15
. In the
context of the gastrointestinal system, studying the connection between the native spatial structure of the
microbiota and its function naturally calls for three-dimension
al (3D) imaging strategies that enable the
simultaneous visualization of bacterial communities and host st
ructures at multiple scales
16,17
. However, existing
3D imaging approaches remain hindered by the opacity of intesti
nal tissues and their contents as well as their
impermeability to labeling probes. Methods have been developed to obtain cross-sectional slices from paraffin-
or plastic-embedded intestinal tissues
18–20
. Thin sections eliminate the optical and diffusion barriers th
at thick
tissues present to imaging and molecular staining, but fragment
host tissues and microbial assemblies. The
advent of tissue-clearing technologies has enabled the imaging
of cellular structures in thick tissues such as the
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Mondragon‐Palomino et al. p. 3
brain
21,22
. However, the full potential of tissue-clearing techniques has yet to be realized to quantify the
composition and organization of the host-microbiota interface w
ith spatial resolution.
Sequencing of bacterial 16S rRNA genes has been effective at su
rveying the composition of the bacterial
microbiota in different compartments along and across the gastr
ointestinal tract (GIT). Indeed, sequencing has
revealed that the mucosal microbiota is distinct and spatially heterogeneous, and bioinformatics tools have
enabled the inference of bacterial networks of interaction
23,24,33,25–32
. However, sequencing alone cannot be used
to reconstruct the spatial distribution of bacteria relative to the host with high spatial resolution. Therefore,
microscopic imaging of thin sections of intestinal tissue is the
de facto
approach to study the fine spatial structure
of the microbiota and the host
2,18,19,34
. Thin-section imaging (TSI) is ordinarily coupled with fluorescence
in situ
hybridization (FISH), immunohistochemistry, and other labeling methods that link the molecular identity of
bacteria and host elements to their location. For example, TSI has been used to study the spontaneous
segregation of
Escherichia coli
and mucolytic bacteria in the colonic mucus layer
35
, by measuring the distance
of different bacterial taxa from the epithelial surface
19
, such as during inflammation
36
. In notable recent examples
of the quantitative application of TSI, semi-automated computational image analysis was used to measure the
thickness of the colonic mucus layer and the proximity of bacteria to the host as a function of diet
18
, and highly
multiplexed FISH was used to investigate the microscopic spatia
l structure of microbiota in the distal colon
20
.
Although TSI is valuable to investigate the biogeography of the intestines and the microbiota, it is unable
to completely capture the spatial structure of bacterial communities in the gut. The first limitation of TSI is that it
sets two-dimensional bounds on the spatial exploration of a het
erogeneous, 3D system. TSI sections are typically
5–10 μm thick, whereas topographic epithelial features and mucosal microbial communities can be 1–4 orders
of magnitude larger. Mucosal biofilms can be hundreds of micron
s long
37
, and bacterial colonies in the colonic
crypts have a heterogeneous taxonomic composition with a 3D spa
tial structure that cannot be charted unless
the entire crypt (diameter 50 μm) is imaged
29,38
.
Quantitative descriptions of the 3D spatial structure of native bacterial biofilms with taxonomic resolution
are challenging to develop because of the natural opacity of th
e intestinal tissue and contents, and the complex
composition of the microbiota, in which potentially hundreds of
bacterial species coexist. Moreover, a quantitative
description of a diverse and spatially heterogeneous system req
uires abundant data that can only be obtained
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Mondragon‐Palomino et al. p. 4
through unrestricted optical access to samples. Tissue-clearing
techniques have been developed for some
tissues and organs (including
brain, heart, kidney, lung, stomach, and sputum)
22,39–42
. However, the direct
application of tissue-clearing techniques typically results in
the loss of the delicate mucus layer and associated
bacterial communities
43
.
Here, we developed an advanced tissue-clearing technique that preserved the spatial structure of the
mucosal microbiota and the host tissue, including the delicate
mucus layer. We combined this method with
sequencing of 16S rRNA genes, amplified
in situ
labeling of rRNA, spectral imaging, and statistical analyses.
This method is capable of revealing patterns in the composition of the microbiota with taxonomic and spatial
resolution. We use this methodology to test the effects of anti
biotic on the bacterial colonization of the intestinal
mucosa. By applying this imaging method, we were able to quantify patterns in the spatial structure of the
mucosal microbiota of the cecum at multiple scales and at diffe
rent stages of antibiotic exposure
Results
Sample preparation, staining, and imaging
To achieve unrestricted optical access to the mucosa, we develo
ped a tissue-clarification method that exposes
the intestinal mucosa in a fully laid out display (Fig. 1). Mou
nting tissue samples flat enabled us to image any
point of the mucosa using a standard confocal microscope, and c
learing the tissue increased the depth of
imaging with refractive-index-matching long-working-distance ob
jectives (Supplementary Materials and
Methods). However, to achieve optical transparency of exposed i
ntestinal tissues we had to solve multiple
experimental challenges. Clearing techniques that do not create
a hydrogel matrix do not protect and preserve
the delicate materials (mucus, biofilms) on the mucosa,
39
and CLARITY and PACT techniques involve multiple
mechanically stressful sample-preparation steps to transform the cellular matrix of tissue into an acrylamide gel
21,22,40
. Moreover, application of CLARITY or PACT to whole-mount tissues would irreversibly deform them and
destroy the patterns of bacterial colonization on the mucosa.
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p
Figure 1. Sample preparation and imaging for 3D mapping of the
mucosal microbiota’s spatial structure.
(
a
) The workflow of the
method has five key steps in whic
h a section of intestinal tiss
ue is prepared for whole-mount confocal imaging of the mucosal
microbiota.
(
b
) A sample of preserved murine cecal tissue before and after 4
d of lipid removal. The dimensions and shape of the sample are
not
visibly altered by clearing. Scale bar: 1 cm. (
c
) Tiled image of a typical int
estinal tissue sample after the m
ethod. The image of the cecum
was obtained by stitching multiple fields of view acquired with a 5X objective that is not flat-field corrected. Bacteria were
stained by
hybridization chain reaction (HCR) with a eubacterial detection
probe, and host nuclei were stained with DAPI. (
d
) 3D rendering of the
confocal imaging of the area enc
losed in the dashed white squar
e in (c) shows clearly the location of bacteria with respect to
each other
and the host.
To maintain the spatial integrity of bacteria and mucus during
whole-mount sample preparation, we
developed a method that addresses separately the preservation o
f the materials on the tissue surface from the
preservation of the rest of the sample, and that minimizes the duration of steps that can dislodge mucus and
biofilms. The overall workflow of our method (Fig. 1a), which w
e developed in a murine model, was as follows:
After careful dissection and removal of intestinal contents, tissues were fixed in paraformaldehyde for 1 h to
prevent biochemical decay. Next, we created a capillary layer o
f acrylamide mix between the exposed mucosa
and the glass bottom of a shallow chamber. Upon heating, the ac
rylamide mix polymerized into a surface gel
layer with a thickness on the order of 100
m. Once the mucosal surface of the sample was protected, the
remainder of the tissue was embedded and gelled. Finally, the uncovered surface of the sample (the muscle
side) was glued to a rigid, flat, plastic substrate to keep the sample flat (Fig. 1b). In this configuration, samples
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could be passively cleared, stained, and imaged without damagin
g the mucosal surface. A detailed description
of the workflow is available here (Materials and Methods).
To locate bacteria
in situ
, we fluorescently labelled bacterial 16S rRNA transcripts thro
ugh hybridization
chain reaction (HCR
44,45
) (Materials and Methods). Standard FISH probes are labelled with up to two
fluorophores, which produce a fluorescent emission that is suff
iciently intense to image bacteria on thin sections.
However, bacteria in the mammalian gut can be found in thick bi
ofilms, epithelial crypts, or across the epithelial
barrier, all of which obscure visibility. Therefore, we used HC
R for labelling because it increases the intensity of
fluorescence by at least one order of magnitude compared with F
ISH probes
44
.
The method presented here enables the mapping of bacteria on th
e mucosa at multiple length scales.
To reveal patterns of colonization over spatial scales on the o
rder of centimeters, tissue samples were imaged
in a laser-scanning confocal microscope at low magnification (5
X), and the images were tiled (Fig. 1c). To image
the detailed spatial structure of bacterial biofilms with micro
meter resolution (Fig. 1d and Supplementary Video
1), we mounted samples in a refractive-index-matching solution (n = 1.46) and used a 20X CLARITY objective
with a collar for the compensation of spherical aberrations (Materials and Methods).
Sensitivity and specificity of bacterial staining
Sensitive and specific identification of mucosal bacteria through fluorescence imaging was accomplished by
optimizing HCR tagging and controlling for off-target effects (
Materials and Methods, Supplementary Materials
and Methods and Fig. S1-S4 and S8). Fluorescent tagging through
HCR was achieved by making the bacterial
cell wall permeable to DNA probes and HCR hairpins. However, th
e acrylamide gel sheet that we created to
protect the mucosal surface of samples formed a barrier for the
diffusion of lysozyme (Fig. 2a) that digests the
bacterial peptidoglycan. Poor permeabilization of bacteria limi
ts the sensitivity of imaging to bacteria closer to
the mucosal surface and impedes the detection of bacteria deep in the tissue samples. To determine the correct
concentration of lysozyme for optimal permeabilization of the cell wall, we created acrylamide gel slabs and
embedded them with Gram-positive (
Clostridium scindens
) and Gram-negative (
Bacteroides fragilis
) bacteria.
The purpose of these gels was to mimic the geometry and composi
tion of the acrylamide layer on tissue samples.
The gel slabs were obtained by using the same procedure as in t
he preservation and clearing of tissues, had
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similar dimensions to tissue samples, and were exposed to lysozyme on one side only (Supplementary Materials
and Methods and Fig. S1-S2). The duration of the treatment with lysozyme was kept constant at 6 h, and we
varied the concentration of lysozyme in the range 1–5 mg/mL to
determine the optimal concentration for bacterial
permeabilization. Bacteria were tagged with an HCR probe that i
ncluded a eubacterial detection sequence
(eub338), and we imaged from the surface of the gels to a depth
of 600 μm (Fig. 2b). We measured the intensity
of HCR tagging of bacteria, which were identified with the blue
-fluorescent DNA intercalated dye DAPI (4′,6-
diamidino-2-phenylindole). The sensitivity of our method was de
fined as the proportion of bacteria down to 600
μm with a fluorescent signal-to-background ratio
20 (Fig. 2c). At a lysozyme concentration of 5 mg/mL,
sensitivity was 94% and it dropped to ~50% for 1 mg/mL.
Nonspecific detection and amplification
are potential sources of background signal in HCR. Control expe
riments
showed that in the absence of a target (GF + eub338) or a detecting probe (SPF + non338), there was no
amplification, whereas when both the target and the probe were present (SPF + eub338), there was amplification
(Fig. 2d-f) (Supplementary Materials and Methods). Plotting the
intensity values showed that in situ HCR tagging
of bacteria produced a signal that is 8.5-9 times as strong as
the background in 90% of bacteria (Fig. 2g).
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Figure 2. Sensitivity and specificity of fluorescence imaging o
f bacteria embedded in acrylamide gels using dual embedding.
(
a
) Maximum intensity projection of a digital cross-section (152
μm) of intestinal tissue. The th
ickness of the protective acryl
amide gel
layer is revealed by blue-fluorescent
beads on its surface. The layer of gel is a diffusive barrier f
or lysozyme during HCR staining of
bacteria. (
b
) Maximum intensity projections of digital cross-sections (50 μm) of gel slabs seeded with bacteria. The effect of lysozyme
concentration on the sensitivit
y of HCR staining is illustrated
. At a suboptimal concentration of lysozyme (1 mg/mL) only bact
eria near the
surface of the gel can be detect
ed, whereas a concentration of
lysozyme of 5 mg/mL enables the detection of bacteria throughou
t the
gel. (
c
) Experimental cumulative distri
butions of HCR staining of bact
eria embedded in gel slabs that were treated with different lysozyme
concentrations. At a lysozyme c
oncentration of 5 mg/mL, approximately 94% of bacteria within 600 μm of the surface have a HCR
signal-
to-background signal ratio
20 (vertical dashed line). (
d-f
) Maximal intensity projections of representative luminal views
of proximal colon
tissue used to test the specificity of HCR staining of bacteria
in situ
. (d) HCR with a eubacterial detection sequence (eub338) in ger
m-
free (GF) tissue, (e) HCR with a
nonspecific control probe (non338) on tissue of with a microbi
ota (specific-pathogen-free, SP
F), and (f)
HCR with a eubacterial detection sequence (eub338) on tissue wi
th a microbiota. Scale bars: 100 μm. (
g
) Experimental cumulative
distribution of the HCR signal-to-background signal-ratio from
controls for
in situ
HCR staining of bacteria in panels (d-f). Three fields
(n=3) of view from each sample (
d-f) were acquired. The average
intensity of the background signal was calculated from the con
trols with
no target and a nonspecific probe
. In (f) bacteria were segment
ed with an intensity filter to
obtain their average HCR fluores
cence.
General 3D spatial organization of bacteria in the ileum, cecum and proximal colon
To evaluate our 3D imaging methods, we imaged bacteria, mucus a
nd the host epithelium in disparate sections
of the GIT with different biological functions, mucosal topogra
phies, and amounts of mucosal materials
46,47
.
Proximal colon
At low magnification (5X), we observed the crests and valleys o
f the epithelial folds and that most of the mucosa
was covered by food particles and mucus (Supplementary Fig. S5a
). At higher magnification (20X), our method
enabled the exploration of the 3D organization of the host-microbiota interface in the proximal colon (Fig. 3a).
3D imaging can be analyzed through digital cross-sections with arbitrary orientation and thickness. Examining
digital cross-sections, we found that bacteria were mixed with
mucus threads and granules in a layer that had
an average thickness of 125 μm (Fig. 3b and Supplementary Video
2). We also found that bacteria were
separated from the epithelium by a single layer of mucus with a
n average thickness of 22 μm. 3D imaging
provides the ability to examine tissues in their totality through computational 3D rendering. Thus, we were we
able to scan the tissue and find rare but conspicuous locations where bacteria had penetrated the mucus layer
or crossed it and reached a crypt and the subepithelial space (
Fig. 3c-d).
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Figure 3. Spatial structure of the host-microbiota interface of
the murine proximal colon and distal ileum after being process
ed
with the method presented here (Fig. 1).
(
a
) 3D rendering of confocal imagi
ng (20X) of the crest of a fold
in the proximal colon. The
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epithelium (blue) is covered by a
mix of mucus (green) and bact
eria (orange). (
b-d
) Maximum intensity projection of the digital cross-
section (7 μm) depicted in panel (a). Mucus and bacteria are organized in well-defined layers.
Two layers of mucus separate mo
st of
bacteria from the mucosa and from the luminal contents (removed
from this area of the sample). The thin layer of mucus that separates
the epithelium from the majority of the microbiota in the lumen
can be crossed by bacteria in h
ealthy tissue. Scale bars: 100
μm. (
e
)
Zoom-in view from panel (d). Examples of bacteria inside and across the thin mucus layer
that lines the epithelium. (
f
) Maximum intensity
projection of a digital cross-se
ction (7 μm) from the same samp
le as in panel (a). In
side the oval is anot
her example of bacte
ria crossing
the thin mucus layer and the epithelium. (
g
) 3D rendering of confocal imagi
ng (20X) of villi of the small
intestine covere
d with mucus and
bacteria. (
h-i
) Maximum intensity projections of the digital cross-section (16 μm) depicted in panel (g). Bacteria accumulate on mucus
around the top of villi. A
ll scale bars: 100 μm.
Ileum
At low magnification (5X), imaging revealed that bacteria were
not uniformly distributed throughout villi and were
mostly found as part of large agglomerations of food particles
and mucus that adhere to the epithelium
(Supplementary Fig. S5b). At higher magnification (20X), 3D ima
ging showed that bacteria were contained by
mucus to a layer near the top of villi (Fig. 3e-3f).
Cecum
The epithelial layer of the murine cecum is organized as a regu
lar array of recessed mucus-secreting glands
known as crypts
48
. At low magnification (5X), imaging showed that bacteria in the cecal mucosa formed colonies
that were associated with one or multiple crypts (Fig. 4a). However, the colonization of crypts was not
homogeneous across the tissue. Colonized crypts were spatially
clustered and surrounded by crypts with few or
no bacteria. In contrast, mucus was somewhat evenly distributed
across crypts. 3D imaging at higher
magnification (20X) confirmed that not all crypts were occupied by bacterial colonies, but that all crypts secreted
mucus (Fig. 4b).
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Figure 4. Multiscale imaging show
s that the cecal mucosa is col
onized in clusters.
(
a
) Tiled image of luminal imaging of a tissue
sample from the cecum. The image was obtained by stitching multiple fields of view acquired at 5X magnification. Bacteria were stained
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by HCR with a eubacterial detection probe (orange), the DNA of
host cells was stained with DAPI (blue), and the host mucus was
stained
with WGA lectin (green). The epithelium of the cecum was lined
with crypts, some of which were isolated and some of which were
connected to other crypts by c
revices. The colonization of the
mucosal crypts was discontinuous. Clusters of colonized crypts
were
separated by areas with fewer bac
teria. The spatial distribution of mucus was more uniform. Scale bar: 1 mm. (
b
) 3D rendering of confocal
imaging (20X) of the cecal mucosa
enclosed in the square area i
n panel (a). (
c
) Maximum intensity projection
of the digital cross-section
(70 μm) is indicated by a dashed line in (b). Bacteria that col
onize the cecum occupy the crypts
and the mucus these glands secrete. All
crypts produce mucus, but not all
crypts are colonized by bacte
ria. Scale bar: 75 μm.
Quantification of the composition and spatial structure of the microbiota of crypts
As shown in our 3D imaging of the mucosa (Figs. 3–4), bacteria occupy habitats with different geometries along
the mouse GIT. In the proximal colon, bacteria accumulated in a layer that ran parallel to the epithelium, whereas
in the cecum, bacteria were split into colonies that were assoc
iated with crypts. The microbiota of the cecal
mucosa and of intestinal crypts is diverse
29
. However, the spatial structure of these communities remains
unexplored.
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