Spatial host-microbiome sequencing reveals niches in the mouse gut

Area of Science:

• Microbiology
• Genomics
• Bioinformatics
• Immunology

Background:

• Mucosal and barrier tissues (gut, lung, skin) host complex cell-microbe networks essential for health.
• Understanding host-microbiome symbiosis and its role in homeostasis and disease requires high-resolution characterization.
• Existing methods often lack the ability to simultaneously capture spatial, cellular, and microbial information.

Purpose of the Study:

• To develop a novel, all-sequencing-based method for high-resolution spatial mapping of host-microbiome interactions.
• To characterize the spatial organization of host cells and microbial communities within native tissue environments.
• To investigate how cellular composition and microbial geography define distinct spatial niches and influence host-bacteria interactions.

Main Methods:

• Spatial host-microbiome sequencing (SHM-seq): An innovative technique modifying spatially barcoded glass surfaces.
• Simultaneous capture of host polyadenylated RNAs and bacterial 16S rRNA hypervariable regions directly from tissue.
• Application to the mouse gut model system, coupled with deep learning for data mapping and niche identification.

Main Results:

• SHM-seq successfully captured tissue histology, host gene expression, and microbial composition with spatial resolution.
• Identification of distinct spatial niches within the mouse gut, defined by specific cellular populations and microbial geography.
• Demonstration that gut cell subpopulations exhibit unique gene expression programs correlating with regional commensal bacteria, impacting host-bacteria interactions.

Conclusions:

• SHM-seq provides an unprecedented tool for dissecting the spatial architecture of host-microbiome interactions.
• The study reveals a direct link between host cell gene expression, microenvironment, and resident microbial communities.
• This approach holds significant potential for advancing research into host-microbe dynamics in both health and disease states.