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Spatial metagenomic characterization of microbial biogeography in the gut

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Spatial structuring is important for the maintenance of natural ecological systems1,2. Many microbial communities, including the gut microbiome, display intricate spatial organization3–9. Mapping the biogeography of bacteria can shed light on interactions that underlie community functions10–12, but existing methods cannot accommodate the hundreds of species that are found in natural microbiomes13–17. Here we describe metagenomic plot sampling by sequencing (MaPS-seq), a culture-independent method to characterize the spatial organization of a microbiome at micrometer-scale resolution. Intact microbiome samples are immobilized in a gel matrix and cryofractured into particles. Neighboring microbial taxa in the particles are then identified by droplet-based encapsulation, barcoded 16S rRNA amplification and deep sequencing. Analysis of three regions of the mouse intestine revealed heterogeneous microbial distributions with positive and negative co-associations between specific taxa. We identified robust associations between Bacteroidales taxa in all gut compartments and showed that phylogenetically clustered local regions of bacteria were associated with a dietary perturbation. Spatial metagenomics could be used to study microbial biogeography in complex habitats. The spatial distribution of bacterial species in the mouse gut is measured by microscopic ‘plot sampling’.
Spatial organization of the microbiota in the mouse distal colon a, MaPS-seq profiling of distal colon clusters with a median diameter of ~30 µm. Raw RA data from MaPS-seq are displayed as a heat map; columns represent individual clusters (n = 1,406) and rows represent abundant and prevalent OTUs (RA > 2% in >10% of all clusters; 24 of 246 detected OTUs) aggregated from two datasets from technical replicates of the same sample. Shading denotes the RA of individual OTUs in each cluster (linear scale); OTUs are sorted by decreasing prevalence (proportion of clusters in which each OTU has RA > 2%), and clusters are clustered by Euclidean distance. The prevalence of each OTU across clusters is displayed to the right as a bar plot, and each bar is colored according to the OTUs assigned taxonomy at the family level (legend in d). b, Correlation between OTU RA measurements obtained by standard bulk 16S sequencing and the prevalence of OTUs in each cluster from the same sample (RA > 2% across n = 1,406 clusters). ND, not detected (that is, RA ≤ 2% in all clusters); 219 OTUs with > 0.01% RA as measured by bulk 16S sequencing are displayed. r indicates Pearson’s correlation. c, Histogram of the number of OTUs per cluster (OTUs with RA > 2%) shown for homogenized fecal clusters serving as a mixed control (red; n = 162) and distal colon clusters (gray; n = 1,406) of the same size. Dashed lines indicate the median value for each group. d, For each abundant and prevalent OTU pair (OTUi,j; n = 24 OTUs), spatial associations were calculated across n = 1,406 clusters. Shading indicates the log2-transformed odds ratio, and x indicates a statistically significant association (Fisher’s exact test, two-sided, P < 0.05; FDR = 0.05). The colored boxes on the vertical and horizontal axes represent OTU taxonomy at the family level.
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Survey of spatial organization in the mouse gastrointestinal tract a, Top, absolute abundance within gut intestinal regions calculated from spike-in sequencing (arbitrary units (AU), normalized to the maximum value) and the number of OTUs (that is, alpha diversity, the number of OTUs with RA > 0.1%). Bottom, absolute abundance of abundant OTUs (>1% of the maximum OTU absolute abundance in any sample) is shown as a heat map (log10 scale); OTUs are clustered by Bray–Curtis dissimilarity. SI1–SI6, small intestine sections 1–6 (SI6 corresponds to the ileum); Cec, cecum; Co1, proximal colon; Co2, distal colon. b, Histogram of the number of OTUs per cluster (OTUs with RA > 2%). The number of clusters aggregated from two technical replicates is indicated (SI6: ~20 μm, n = 386; Cec: ~20 μm, n = 405; Co2: ~20 μm, n = 259; Co2: ~7 μm, n = 529), and the dashed line indicates median value. c, t-SNE visualization of 1,050 ~20-μm clusters from SI6, Cec and Co2 sites generated utilizing the Bray–Curtis dissimilarity of OTU RAs (subsampled to 314 reads across all clusters; the number of clusters is indicated). On the left, each cluster is colored by site of origin; on the right, each cluster is colored by the RA of the six most abundant families within each cluster (linear scale). d, Pairwise spatial associations for abundant and prevalent OTUs visualized as a circular graph; the number of clusters utilized was subsampled to the lowest number across the samples (n = 259 clusters). Nodes correspond to OTUs, with sizing proportional to the prevalence of OTUs across clusters and color representing OTU taxonomy at the family level; dotted edges denote all possible associations and shaded edges denote statistically significant associations (Fisher’s exact test, two-sided, P < 0.05; FDR = 0.05).
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Letters
https://doi.org/10.1038/s41587-019-0183-2
1Department of Systems Biology, Columbia University Medical Center, New York, NY, USA. 2Integrated Program in Cellular, Molecular, and Biomedical
Studies, Columbia University, New York, NY, USA. 3Department of Biomedical Engineering, Columbia University, New York, NY, USA. 4Sulzberger Columbia
Genome Center, Columbia University Medical Center, New York, NY, USA. 5Department of Biochemistry and Molecular Biophysics, Columbia University
Medical Center, New York, NY, USA. 6Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA.
*e-mail: hw2429@cumc.columbia.edu
Spatial structuring is important for the maintenance of natural
ecological systems1,2. Many microbial communities, including
the gut microbiome, display intricate spatial organization39.
Mapping the biogeography of bacteria can shed light on
interactions that underlie community functions1012, but exist-
ing methods cannot accommodate the hundreds of species
that are found in natural microbiomes1317. Here we describe
metagenomic plot sampling by sequencing (MaPS-seq), a cul-
ture-independent method to characterize the spatial organi-
zation of a microbiome at micrometer-scale resolution. Intact
microbiome samples are immobilized in a gel matrix and
cryofractured into particles. Neighboring microbial taxa in
the particles are then identified by droplet-based encapsula-
tion, barcoded 16S rRNA amplification and deep sequencing.
Analysis of three regions of the mouse intestine revealed het-
erogeneous microbial distributions with positive and negative
co-associations between specific taxa. We identified robust
associations between Bacteroidales taxa in all gut compart-
ments and showed that phylogenetically clustered local
regions of bacteria were associated with a dietary perturba-
tion. Spatial metagenomics could be used to study microbial
biogeography in complex habitats.
The local spatial organization of the gut microbiome influences
various properties including colonization10,1719, metabolism11,
host–microbe and intermicrobial interactions20, and community
stability1,21,22. However, current microbiome profiling approaches
such as metagenomic sequencing require homogenization of input
material, which means that underlying spatial information is lost.
While imaging techniques can reveal spatial information, they rely
on hybridization with short DNA probes of limited spectral diver-
sity, yielding data with low taxonomic resolution, and often require
extensive empirical optimization15,23. Bacteria are densely packed in
communities, limiting identification and analysis of individual cells
using visual methods8. Although imaging approaches can simulta-
neously profile simple synthetic communities composed of a small
number of cultivable species16,17 (for example, six in ref. 17), they are
challenging to scale to complex and diverse natural microbiomes.
Therefore, an unbiased method for high-taxonomic-resolution
and micrometer-scale dissection of natural microbial biogeography
is needed to better study the role of the gut microbiome in health
and disease.
In macroecology, plot sampling is used to study the spatial
organization of large ecosystems, which are otherwise impractical
to fully characterize. By surveying many smaller plots from a
larger region, one can tractably delineate local distributions of spe-
cies and statistically infer fundamental properties of global com-
munity organization and function. Inspired by this approach, we
developed MaPS-seq, a multiplexed sequencing technique that
analyzes microbial cells in their native geographical context to sta-
tistically reconstruct the local spatial organization of the microbi-
ome (Fig. 1a).
To perform MaPS-seq, an input sample is first physically fixed
and the microbiota is immobilized via perfusion and insitu polym-
erization of an acrylamide polymer matrix, which also contains a
covalently linked reverse 16S ribosomal RNA (rRNA) amplification
primer. The embedded sample is then fractured via cryo-bead beat-
ing, subjected to cell lysis and passed through nylon mesh filters for
size selection to yield cell clusters or particles of desired and tunable
physical sizes (by utilizing different mesh filter sizes). The result-
ing clusters contain genomic DNA immobilized in the original
arrangement, preserving local spatial information. Next, a micro-
fluidic device is used to co-encapsulate these clusters with gel beads,
each containing uniquely barcoded forward 16S rRNA amplifica-
tion primers. Primers are photocleaved from the beads and clusters,
genomic DNA is released from the clusters by triggered degradation
of the polymer matrix within droplets and PCR amplification of the
16S V4 region is performed. Droplets are then broken apart, and the
resulting library is subjected to deep sequencing. Sequencing reads
are filtered and grouped by their unique barcodes, which yield the
identity and relative abundance (RA) of bacterial operational taxo-
nomic units (OTUs) within individual cell clusters of defined size
(Methods; Supplementary Figs. 1–4).
To rigorously test the feasibility of this spatial metagenom-
ics approach, we first generated separate cluster communities
from either homogenized mouse fecal bacteria or Escherichia coli
(Methods; Supplementary Fig. 5) and profiled them with MaPS-
seq. The resulting data revealed that the majority of the detected
barcodes mapped uniquely to their respective initial communities
with minimal mixing (Fig. 1b; 4.3% mixed) and negligible con-
tamination introduced during sample processing (<0.2% of reads).
In addition, the average abundance of taxa across individual fecal
clusters obtained by enzymatic lysis and droplet PCR displayed
good correlation with measurements from standard mechanical
cell lysis and bulk 16S PCR (Fig. 1c; Pearson’s correlation r = 0.76).
A replicate community-mixing experiment with new particles of a
smaller size confirmed the technical performance of the approach
Spatial metagenomic characterization of microbial
biogeography in the gut
Ravi U. Sheth 1,2, Mingqiang Li 3, Weiqian Jiang3, Peter A. Sims1,4,5, Kam W. Leong1,3 and
Harris H. Wang 1,6*
NATURE BIOTECHNOLOGY | VOL 37 | AUGUST 2019 | 877–883 | www.nature.com/naturebiotechnology 877
Content courtesy of Springer Nature, terms of use apply. Rights reserved
... Sheth et al. developed a metagenomic plot sampling by a sequencing (MaPS-Seq) method to analyze the spatial organization of gut microbiota [217] (Figure 5). As a proof of concept, mouse small and large intestine samples were dissected and fixed by in situ crosslinking of a perfused acrylamide polymer precursor. ...
... As a proof of concept, mouse small and large intestine samples were dissected and fixed by in situ crosslinking of a perfused acrylamide polymer precursor. The polymer precursor solution was also embedded with 16 rRNAs as an amplification primer commonly used in metagenomics studies [217]. After embedding, the tissue samples were then fractured without disrupting the spatial arrangement of the tissue. ...
... After embedding, the tissue samples were then fractured without disrupting the spatial arrangement of the tissue. The fractured particles could then be captured by a microfluidic device followed by the release of genomic DNA for qRT-PCR analysis of taxa of microbiota information coupled with spatial barcodes [217]. This unbiased and accurate spatial metagenomics approach provides highresolution (~20 μm) analysis of the gut microbiome and provides definitive proof of the high spatial heterogeneity of gut microbiota. ...
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... This finding is similar to previous investigations by Jahanian et al. [55] and Śliżewska et al. [56] who found that dietary mycotoxins increased the total amount of negative bacteria and reduced Lactobacillus sp. and Bifidobacterium sp. in poultry when these findings were compared to the control group. The changes that occurred in the gut microbiota population correspond to the impact of mycotoxins on the gut microbiota [54,57]. The composition of the microbiome varies depending on which part of the gut is being studied. ...
... The composition of the microbiome varies depending on which part of the gut is being studied. The effects of mycotoxins on the gut microbiota are difficult to characterize, and the findings may differ depending on the study's experimental design [57]. Because of their fungal origin, the antimicrobial properties of mycotoxins were hypothesized as soon as these compounds were purified. ...
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... [11][12][13][14][15][16][17] Differences in the localization of microbes, referred to as microbial biogeography, have been implicated in IBD but are incompletely understood, especially in the context of innate immunity. 12,[18][19][20][21] The biogeography of microbes in the colon has both crosssectional and longitudinal features. Cross sectional biogeography includes the spatial separation of microbes from the intestinal epithelium. ...
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... A high level of genetic diversity has been unveiled by the growing number of comprehensive metagenome-assembled genomes (MAGs), particularly from human gut microbiomes [3,4]. The progress in metagenomics has shed new light on the study of spatial distribution and dynamics of complex microbial communities from the human gut [5][6][7]. ...
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... In summary, we show that StrainPanDA is able to provide accurate profiling of strain composition and gene content from metagenomic data. We envision that the application of StrainPanDA to the rapidly increasing metagenomic data sets, especially in the context of spatiotemporal characterization of microbiomes [64][65][66][67], will help elucidate novel associations between molecular functions and microbial/host phenotypes as well as microbial ecology at the infraspecies level. ...
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