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← IBD single-cell transcriptomics

Single-cell RNA sequencing of healthy mouse colon and mouse colon with acute or chronic colitis induced by DSS

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Dataset overview

Participants 10
Samples 10
Reuse readiness 5.9/10 evidence-backed score

Integrative analysis of single-cell RNA-seq and gut microbiome metabarcoding data elucidates macrophage dysfunction in mice with DSS-induced ulcerative colitis.

Hong D, Kim HK, Yang W, Yoon C, Kim M, Yang CS, Yoon S. · Communications biology · 2024

Abstract

Ulcerative colitis (UC) is a significant inflammatory bowel disease caused by an abnormal immune response to gut microbes. However, there are still gaps in our understanding of how immune and metabolic changes specifically contribute to this disease. Our research aims to address this gap by examining mouse colons after inducing ulcerative colitis-like symptoms. Employing single-cell RNA-seq and 16 s rRNA amplicon sequencing to analyze distinct cell clusters and microbiomes in the mouse colon at different time points after induction with dextran sodium sulfate. We observe a significant reduction in epithelial populations during acute colitis, indicating tissue damage, with a partial recovery observed in chronic inflammation. Analyses of cell-cell interactions demonstrate shifts in networking patterns among different cell types during disease progression. Notably, macrophage phenotypes exhibit diversity, with a pronounced polarization towards the pro-inflammatory M1 phenotype in chronic conditions, suggesting the role of macrophage heterogeneity in disease severity. Increased expression of Nampt and NOX2 complex subunits in chronic UC macrophages contributes to the inflammatory processes. The chronic UC microbiome exhibits reduced taxonomic diversity compared to healthy conditions and acute UC. The study also highlights the role of T cell differentiation in the context of dysbiosis and its implications in colitis progression, emphasizing the need for targeted interventions to modulate the inflammatory response and immune balance in colitis.

doi:10.1038/s42003-024-06409-w ↗ PMID 38879692 ↗ PMC11180211 ↗

Study facts

Organism
Mus musculus
Platform
10x Genomics Chromium
Age group
—
Disease groups
—
Anatomical sites
colon

Data availability

  • Raw counts

File types CSVMTXTSV

Strengths & limitations for reuse

Strengths

  • Raw counts are advertised
  • Participant counts are documented

Limitations

  • Not documented: processed matrices are advertised
  • Not documented: cell metadata are advertised
Extraction evidence & provenance

Each extracted field is shown with the source excerpt and location used to resolve it.

Assay

FieldValueEvidence
assay.assay_type single-cell gene expression
Expression profiling by high throughput sequencing

Section —, offset —
assay.library_chemistry Chromium Next GEM Single Cell 3p RNA library v3.1
We used Chromium Next GEM Single Cell 3p RNA library v3.1.

Section Single-cell RNA sequencing and preprocessing, offset —
assay.platform 10x Genomics Chromium
using Chromium (10X Genomics)

Section —, offset —
assay.reference_genome mm10
Assembly: mm10

Section —, offset —
assay.sequencing_type scrna_seq
we prepared mice with DSS-induced acute and chronic colitis to perform single-cell RNA-sequencing

Section Introduction, offset —

Cohort

FieldValueEvidence
cohort.disease_activity_metadata_available True
Colitis scores were determined based on clinical parameters such as weight loss, stool consistency, and bleeding

Section Disruption of the epithelial barrier in DSS-induced colitis, offset —
cohort.total_participants 10
We conducted single-cell RNA sequencing (scRNA-Seq) using Chromium (10X Genomics) on samples from 3 healthy mouse colons, 3 mouse colons with acute colitis induced by DSS, and 4 mouse colons with chronic colitis induced by DSS.

Section —, offset —
cohort.treatment_exposure_documented True from source
GEO sample characteristics document treatment/therapy

Section GEO family SOFT, offset —

Data_Assets

FieldValueEvidence
data_assets.open_access True
"license": "cc by"

Section —, offset —
data_assets.raw_counts True
obtain the standard count matrix

Section —, offset —

Processing

FieldValueEvidence
processing.cell_type_annotation_method HiCAT marker-based cell-type annotation
cell-types were annotated using HiCAT, a marker-based cell-type annotation tool

Section Cell type annotation, offset —
processing.normalization_method Normalize counts to 10^4 per cell and log1p transformation
the count matrix was normalized to sum up to 10 4 for each cell and then underwent log transformation using the log1p function

Section Preprocessing, offset —
processing.quality_control_reported True
We discarded the cells of which the number of genes expressed were higher than 6000 or the percentage of mitochondrial gene (Hugo symbols starting with MT-) expression were higher than 15%.

Section Cell filtering, offset —

Specimens

FieldValueEvidence
specimens.anatomical_sites ['colon']
"source_name": "colon"

Section —, offset —
specimens.inflamed_status_available True
"disease state": "Acute Colitis"

Section —, offset —
specimens.number_of_samples 10
"n_samples": 10

Section —, offset —