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Spatial host–microbiome sequencing reveals niches in the mouse gut
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; New York Genome Center, New York, NY, USA.ORCID iD: 0000-0003-3545-5489
Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts, General Hospital, Harvard Medical School, Boston, MA, USA.
Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; Genentech, South San Francisco, CA, USA.
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2024 (English)In: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 42, no 9, p. 1394-1403Article in journal (Refereed) Published
Abstract [en]

Mucosal and barrier tissues, such as the gut, lung or skin, are composed 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 understanding homeostasis and disease. Here we present spatial host–microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host–bacteria interactions. SHM-seq should enhance the study of native host–microbe interactions in health and disease.

Place, publisher, year, edition, pages
Springer Nature , 2024. Vol. 42, no 9, p. 1394-1403
National Category
Microbiology in the medical area
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URN: urn:nbn:se:kth:diva-350005DOI: 10.1038/s41587-023-01988-1ISI: 001104879700002PubMedID: 37985876Scopus ID: 2-s2.0-85177061640OAI: oai:DiVA.org:kth-350005DiVA, id: diva2:1882371
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QC 20240705

Available from: 2024-07-05 Created: 2024-07-05 Last updated: 2025-02-11Bibliographically approved

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Lötstedt, Britta

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