Spatial Host-Microbiome sequencing (SHM-seq)
Lötstedt B et al (2022) Spatial host-microbiome sequencing
Mucosal and barrier tissues such as the gut, lung or skin, are comprised of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for our understanding of homeostasis and disease. Spatial transcriptomics has emerged as a key technology to positionally profile RNAs at high resolution in tissues. Here, we present spatial host-microbiome sequencing, an all-sequencing based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from tissues on spatially barcoded glass surfaces. We apply our approach to the mouse gut as a model system, use a novel deep learning approach for data mapping and detect spatial niches impacted by microbial biogeography. Spatial host-microbiome sequencing should enhance study of native host-microbe interactions in health and disease.
SHM-seq workflow
Illustration kindly made by Ania Hupalowska.
Data availability
The raw and processed sequencing and image files needed to recreate all the results in this study have been made avaiable at Broad's Single Cell Portal.
Data pre-processing
Initial host sequncing data processing was performed with ST Pipeline (v.1.7.6) and initial bacterial sequencing data processing was perfromed using the taxonomy assigment pipeline.
For using our spatial spots alignments and reporting tool, please go to our SpoTteR repository.
Spatial expression estimates using Splotch
For generating spatial gene expression estimates and spatial differential expression analysis, we advise you to follow instruction at: https://github.com/tare/Splotch and cite Äijö T, Maniatis S & Vickovic S et al: Splotch: Robust estimation of aligned spatial temporal gene expression data, doi: https://doi.org/10.1101/757096. In order to ease use, we have made the complete Splotch workflow available trough Broad's Firecloud platform.
To use Ilastik for spatial analysis of bacterial fluorescence, please check pre-processing code and to implement it with gene expression estimates, please check post-processing.
Deep learning model used in the taxonomy assignment pipeline
For deep learning model architecture, training and evaluation, please see DL model