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← IBD single-cell transcriptomics

Longitudinal single cell RNA-seq of the inflamed colon

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Dataset overview

Participants 18
Samples None
Reuse readiness 6.9/10 evidence-backed score

Longitudinal Single-Cell Transcriptomics Reveals a Role for Serpina3n-Mediated Resolution of Inflammation in a Mouse Colitis Model.

Ho YT, Shimbo T, Wijaya E, Kitayama T, Takaki S, Ikegami K, Miyashita K, Ouchi Y, et al. · Cellular and molecular gastroenterology and hepatology · 2021

Abstract

<h4>Background & aims</h4>Proper resolution of inflammation is essential to maintaining homeostasis, which is important as a dysregulated inflammatory response has adverse consequences, even being regarded as a hallmark of cancer. However, our picture of dynamic changes during inflammation remains far from comprehensive.<h4>Methods</h4>Here we used single-cell transcriptomics to elucidate changes in distinct cell types and their interactions in a mouse model of chemically induced colitis.<h4>Results</h4>Our analysis highlights the stromal cell population of the colon functions as a hub with dynamically changing roles over time. Importantly, we found that Serpina3n, a serine protease inhibitor, is specifically expressed in stromal cell clusters as inflammation resolves, interacting with a potential target, elastase. Indeed, genetic ablation of the Serpina3n gene delays resolution of induced inflammation. Furthermore, systemic Serpina3n administration promoted the resolution of inflammation, ameliorating colitis symptoms.<h4>Conclusions</h4>This study provides a comprehensive, single-cell understanding of cell-cell interactions during colorectal inflammation and reveals a potential therapeutic target that leverages inflammation resolution.

doi:10.1016/j.jcmgh.2021.04.004 ↗ PMID 33862275 ↗ PMC8258998 ↗

Study facts

Organism
Mus musculus
Platform
Illumina NextSeq500
Age group
—
Disease groups
—
Anatomical sites
proximal colon, distal colon, rectum

Data availability

  • Raw counts
  • Processed matrix

File types TSV

Strengths & limitations for reuse

Strengths

  • Raw reads are advertised
  • Raw counts are advertised
  • Processed matrices are advertised
  • Cell metadata are advertised
  • Participant counts are documented
Extraction evidence & provenance

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

Assay

FieldValueEvidence
assay.assay_type FACS-based Smart-seq2
RNA-seq library was constructed using modified Smart-seq2 method.

Section series metadata, offset —
assay.library_chemistry modified Smart-seq2
RNA-seq library was constructed using modified Smart-seq2 method.

Section samples, offset —
assay.platform Illumina NextSeq500
The sequencing libraries were sequenced on the NextSeq500 platform.

Section Single-Cell RNA-seq, offset —
assay.reference_genome mm10
The mouse reference genome (mm10) was obtained from ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63472/suppl/GSE63472_mm10_reference_metadata.tar.gz.

Section Methods—Bioinformatics—Preprocessing: from FASTQ to gene expression table, offset —
assay.sequencing_type scrna_seq
Longitudinal single cell RNA-seq of the inflamed colon

Section series_title, offset —

Cohort

FieldValueEvidence
cohort.disease_activity_metadata_available True
Body weight was recorded every day.

Section Single Cell RNA-Sequencing Analysis on a Mouse Colitis Model, offset —
cohort.study_design longitudinal
Longitudinal single cell RNA-seq of the inflamed colon

Section series_title, offset —
cohort.total_participants 18
We profiled 14,624 cells in total from 18 mice at 6 different time points.

Section Single Cell RNA-Sequencing Analysis on a Mouse Colitis Model, offset —
cohort.treatment_exposure_documented True from source
GEO sample characteristics document treatment/therapy

Section GEO family SOFT, offset —

Data_Assets

FieldValueEvidence
data_assets.cell_metadata True
GSE148794_serpin.metadata.tsv.gz

Section repository files, offset —
data_assets.open_access True
Public on Sep 03 2021

Section series, offset —
data_assets.processed_matrix True
count_table.tsv contains digital gene expression for every sample.

Section —, offset —
data_assets.raw_counts True
GSE148794_serpin.count_table.tsv.gz

Section repository files, offset —
data_assets.raw_reads True
SRA: https://www.ncbi.nlm.nih.gov/sra?term=SRP256617

Section —, offset —

Processing

FieldValueEvidence
processing.cell_type_annotation_method SingleR and previously described cell-specific markers
We annotated cell types using SingleR 12 and previously described cell-specific markers

Section Results; Single Cell RNA-Sequencing Analysis on a Mouse Colitis Model, offset —
processing.normalization_method Monocle3 gene expression normalization
The Monocle3 workflow includes the following steps: gene expression normalization, cell clustering, UMAP visualization, marker gene identification, pseudotime analysis, and differential gene expression analysis.

Section Methods; Bioinformatics; Downstream analysis overview, offset —
processing.quality_control_reported True
Low-quality cells given by the Drop-seq_tool/DigitalExpression output were removed using the number of transcripts, genes, and the percentage of mitochondrial genes for each cell.

Section Methods—Bioinformatics—Preprocessing: from FASTQ to gene expression table, offset —

Specimens

FieldValueEvidence
specimens.anatomical_sites ['proximal colon', 'distal colon', 'rectum']
For isolation of colon cells, proximal, distal colon, and rectum were collected from each time point.

Section Single-Cell RNA-seq, offset —
specimens.inflamed_status_available True
We analyzed the initiation and disappearance of inflammation at the single-cell level using a mouse model of colitis.

Section Single Cell RNA-Sequencing Analysis on a Mouse Colitis Model, offset —
specimens.number_of_cells 14624
We profiled 14,624 cells in total from 18 mice at 6 different time points.

Section Single Cell RNA-Sequencing Analysis on a Mouse Colitis Model, offset —