Evidence map›Paper›PMID 37985876›Full record

ArticleNature biotechnology2024

Spatial host-microbiome sequencing reveals niches in the mouse gut.

Britta Lötstedt, Martin Stražar, Ramnik Xavier, Aviv Regev, Sanja Vickovic

Abstract read
In one paragraph

Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

92 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Letter to the Editor: Deciphering macrophage heterogeneity and optimizing probioticsWorld journal of gastrointestinal pharmacology and therapeutics · 2026
    Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Review
  18. Review
  19. Review
  20. Review

32 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Britta LötstedtKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Martin StražarBroad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-3064-1055
Ramnik XavierBroad Institute of MIT and Harvard, Cambridge, MA, USA.
Aviv RegevKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. aviv.regev.sc@gmail.com.ORCID http://orcid.org/0000-0003-3293-3158
Sanja VickovicKlarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. svickovic@nygenome.org.ORCID http://orcid.org/0000-0003-0985-9885

Funding

Pilot & Feasibility ProgramP30DK043351 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI RAYMOND T CHUNG · 1991 to 2026
$35.3M
Center for Integrated Cellular Analysis - Valeria A. Sanchez EstradaRM1HG011014 · NHGRI · NEW YORK GENOME CENTER · PI LANDAU, DAN, SATIJA, RAHUL · 2020 to 2025
$22.1M
NHGRI NIH HHS RM1 HG011014NIDDK NIH HHS P30 DK043351U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) HG011014-01
6 · The paper itself

Abstract

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.

Indexed as

Gastrointestinal MicrobiomeRNA, Ribosomal, 16SAnimalsBacteriaHost Microbial InteractionsMiceMice, Inbred C57BLSymbiosisRNA, Ribosomal, 16S

Identifiers

PMID37985876
PMCPMC11392810

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.