Foundry120 atlas

An immune-cell signature of bacterial sepsis (Patient PBMCs)

Download from source ↗

Dataset overview

Participants 45
Samples None
Reuse readiness 6.9/10 evidence-backed score

Bulk and Single-Cell Transcriptomic Reveals Shared Key Genes and Patterns of Immune Dysregulation in Both Intestinal Inflammatory Disease and Sepsis.

Abstract

Inflammatory bowel disease (IBD) and Sepsis are both characterised by immune dysregulation. Notably, IBD is a factor in the increase in septic infections. However, these two conditions' shared molecular and pathophysiological mechanisms remain unclear. We used 'limma' and 'WGCNA' analyses to identify common DEGs between these two conditions. Single-cell RNA sequencing further assessed immune cell heterogeneity. We used machine learning algorithms to construct and identify diagnostic markers for Sepsis, which we then validated using receiver operating characteristic curve (ROC) analysis. A mouse model of IBD combined with Sepsis was constructed, and real-time PCR and western blot validated the expression of BCL2A1 and CEBPB. It was found that 58 shared DEGs identified in both IBD and Sepsis were highly enriched in immune and inflammation-related pathways. Single-cell analysis revealed that CD14<sup>+</sup> monocytes (or IL1B<sup>+</sup> macrophages) primarily express these hub genes. Both conditions significantly increased the proportion of this cell type compared to healthy controls. Finally, BCL2A1 and CEBPB were identified as potential biomarkers that have strong diagnostic potential. Furthermore, we confirmed that levels of BCL2A1 and CEBPB were elevated in mice with IBD complicated by Sepsis through real-time PCR and observed that IBD exacerbates the progression of Sepsis. We conclude that IL1B<sup>+</sup> macrophages expressing high levels of these hub genes play a key role in the immune dysregulation associated with both IBD and Sepsis. The overlapping gene expression and pathway alterations in these cells indicate shared common molecular mechanisms, suggesting new strategies for targeted therapeutic interventions.

Study facts

Organism
Platform
Age group
Disease groups
Anatomical sites
blood

Data availability

  • Raw counts
  • Processed matrix

File types CLUSTERDOCUMENTATIONEXPRESSION MATRIXMETADATA

Files and samples

Strengths & limitations for reuse

Strengths

  • 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.sequencing_type scrna_seq
single-cell RNA-sequencing

Section description, offset —

Cohort

FieldValueEvidence
cohort.non_ibd_controls 16
16 healthy samples

Section Materials and Methods > Data Collection and Processing, offset —

cohort.total_participants 45 inferred
SCP548, consisting of 29 sepsis samples and 16 healthy samples

Section Materials and Methods > Data Collection and Processing, offset —

Data_Assets

FieldValueEvidence
data_assets.cell_metadata True
scp_meta_updated.txt

Section study_files, offset —

data_assets.open_access True
"public": true

Section dataset-authority, offset —

data_assets.participant_metadata True
Clinical_Data_Reyes_et_al_NATURE_MED.xlsx

Section study_files, offset —

data_assets.processed_matrix True
scp_gex_matrix.csv.gz

Section study_files, offset —

data_assets.raw_counts True
scp_gex_matrix_raw.csv.gz

Section study_files, offset —

data_assets.raw_reads False inferred
study_files

Section study_files, offset —

Processing

FieldValueEvidence
processing.batch_correction_reported True
The ‘Harmony’ package was then employed to eliminate batch effects across different samples

Section Analysis of Single‐Cell RNA Sequence Data, offset —

processing.cell_type_annotation_method Annotation using classical/known cell markers
Seven primary cell categories were then identified within the PBMCs ... using classical markers

Section Accumulation of the Hub Genes in CD14 + Monocytes in Sepsis Patients, offset —

processing.normalization_method Seurat normalization using UMI counts and mitochondrial gene percentage
The Seurat software package (version 4.3.0) was employed to normalise the expression matrix and obtain scaled data by considering the UMI counts of each sample and the percentage of mitochondria genes.

Section Analysis of Single‐Cell RNA Sequence Data, offset —

processing.quality_control_reported True
A total of 43,294 cells were acquired following standardised data processing and quality filtering

Section Analysis of Single‐Cell RNA Sequence Data, offset —

Specimens

FieldValueEvidence
specimens.anatomical_sites ['blood']
we used single-cell RNA-sequencing to profile the blood of people with sepsis

Section description, offset —

specimens.number_of_cells 126351 from source
cell_count=126351

Section structured repository metadata, offset —