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Related Experiment Video

Updated: Jul 26, 2025

Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics

Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics

Published on: February 18, 2009

Simultaneous profiling of host expression and microbial abundance by spatial metatranscriptome sequencing.

Lin Lyu¹, Xue Li¹, Ru Feng¹

¹Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Genome Research

|June 13, 2023

Summary

We created a spatial metatranscriptome (SMT) pipeline to analyze microbial and host gene expression in tissues. This method reveals spatial host-microbe interactions, advancing spatial biology and microbiome research.

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Last Updated: Jul 26, 2025

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Area of Science:

Microbiome research
Spatial biology
Transcriptomics

Background:

Spatial transcriptomic (ST) data typically focuses on host gene expression.
Understanding the spatial distribution of microbes within host tissues is crucial for studying host-microbe interactions.
Current methods lack the ability to simultaneously analyze host and microbial spatial information.

Purpose of the Study:

To develop and validate a computational pipeline for extracting and analyzing microbial sequences from ST data.
To enable simultaneous analysis of host gene expression and microbial abundance in a spatial context.
To investigate host-microbe interactions at various spatial scales within tissues.

Main Methods:

Developed the spatial metatranscriptome (SMT) analysis pipeline to extract microbial sequences and assign taxonomic labels from ST data.
Generated a spatial microbial abundance matrix alongside the host expression matrix.
Applied the SMT pipeline to human and murine intestinal sections and validated results with alternative assays.

Main Results:

Successfully generated spatial microbial abundance matrices, enabling simultaneous analysis of host expression and microbial distribution.
Validated the accuracy of spatial microbial abundance data using independent experimental methods.
Identified novel biological insights into host-microbe interactions across different spatial scales.
Optimized experimental conditions to enhance microbial capture while maintaining host expression quality.

Conclusions:

The spatial metatranscriptome (SMT) pipeline is a feasible method for analyzing microbial and host spatial data.
SMT analysis opens new avenues for investigating host-microbe interactions in situ.
This work provides a foundation for further optimization and application of spatial microbiome analysis in biological research.