Spatial metagenomic characterization of microbial biogeography in the gut

By Ravi U. Sheth, Mingqiang Li, Weiqian Jiang, P. Sims, K. Leong, Harris H. Wang. 2019

144 citations recorded by the source corpus.

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’.

Subfields: Ecology, Food Science, Molecular Biology

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In Molecular Biology

Citations in the corpus

openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (2019, background): “…transcriptomics (Macosko et al. 2015; Cao et al. 2019; Tasic et al. 2018), human genetics (Hirata et al. 2019), metagenomic assembly (Beaulaurier et al. 2018), spatial organization of microbial communities (Sheth, Li, Jiang, Sims, Leong, and Wang 2019), and metabolomics (Tkachev et al. 2019).”