Foundry120 atlas

Dynamic changes in human single cell transcriptional signatures during fatal sepsis

Download from source ↗

Dataset overview

Participants 7
Samples 12
Reuse readiness 7.0/10 evidence-backed score

Dynamic changes in human single-cell transcriptional signatures during fatal sepsis.

Abstract

Systemic infections, especially in patients with chronic diseases, may result in sepsis: an explosive, uncoordinated immune response that can lead to multisystem organ failure with a high mortality rate. Patients with similar clinical phenotypes or sepsis biomarker expression upon diagnosis may have different outcomes, suggesting that the dynamics of sepsis is critical in disease progression. A within-subject study of patients with Gram-negative bacterial sepsis with surviving and fatal outcomes was designed and single-cell transcriptomic analyses of peripheral blood mononuclear cells (PBMC) collected during the critical period between sepsis diagnosis and 6 h were performed. The single-cell observations in the study are consistent with trends from public datasets but also identify dynamic effects in individual cell subsets that change within hours. It is shown that platelet and erythroid precursor responses are drivers of fatal sepsis, with transcriptional signatures that are shared with severe COVID-19 disease. It is also shown that hypoxic stress is a driving factor in immune and metabolic dysfunction of monocytes and erythroid precursors. Last, the data support CD52 as a prognostic biomarker and therapeutic target for sepsis as its expression dynamically increases in lymphocytes and correlates with improved sepsis outcomes. In conclusion, this study describes the first single-cell study that analyzed short-term temporal changes in the immune cell populations and their characteristics in surviving or fatal sepsis. Tracking temporal expression changes in specific cell types could lead to more accurate predictions of sepsis outcomes and identify molecular biomarkers and pathways that could be therapeutically controlled to improve the sepsis trajectory toward better outcomes.

Study facts

Organism
Homo sapiens
Platform
Illumina NovaSeq 6000
Age group
adult
Disease groups
—
Anatomical sites
peripheral blood mononuclear cells (PBMC)

Data availability

  • Raw counts
  • Processed matrix

File types MTXTSV

Files and samples

Strengths & limitations for reuse

Strengths

  • Raw reads are advertised
  • Raw counts are advertised
  • Processed matrices are advertised
  • Cell metadata are advertised
  • Participant mapping is available
  • 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 single-cell RNA-sequencing
Single-cell RNA-sequencing of human peripheral blood mononuclear cells

Section series_overall_design, offset —

assay.library_chemistry Chromium Next GEM Single Cell 3′ Reagent Kits v3.1
using the Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (10x Genomics)

Section extract_protocol, offset —

assay.platform Illumina NovaSeq 6000
"Illumina NovaSeq 6000": 12

Section instrument_model, offset —

assay.reference_genome GRCh38
Genome_build: GRCh38

Section geo_family_soft, offset —

assay.sequencing_type scrna_seq
Single-cell RNA-sequencing of human peripheral blood mononuclear cells

Section geo_family_soft, offset —

Cohort

FieldValueEvidence
cohort.age_group adult from source
GEO age characteristics: 35-40; 45-50; 90-95; 90-95; 65-70; 65-70; 65-70; 65-70; 45-50; 45-50; 70-75; 70-75

Section GEO family SOFT, offset —

cohort.non_ibd_controls 2 computed
GEO characteristics group 'non_ibd_controls': 2 subjects

Section GEO family SOFT, offset —

cohort.study_design longitudinal
A within-subject study of patients with Gram-negative bacterial sepsis with surviving and fatal outcomes was designed

Section series_overall_design, offset —

cohort.total_participants 7 computed
7 distinct subject/participant IDs across GEO samples

Section GEO family SOFT, offset —

Data_Assets

FieldValueEvidence
data_assets.cell_metadata True
_barcodes.tsv.gz

Section supplementary_files, offset —

data_assets.environment_or_container_info True
The Cell Ranger software pipeline (v3.1.0, 10x Genomics) was used

Section data_processing, offset —

data_assets.open_access True
Open access

Section fullTextUrlList, offset —

data_assets.participant_metadata True
"donor": "Tissue donor 1"

Section geo_family_soft, offset —

data_assets.processed_matrix True
GSM5102900_HC1_matrix.mtx.gz

Section supplementary_files, offset —

data_assets.raw_counts True
We obtained the feature-barcode matrix that contains gene expression counts in the outputs.

Section geo_family_soft, offset —

data_assets.raw_reads True inferred
"relationtype": "SRA"

Section geo_record_summary, offset —

Processing

FieldValueEvidence
processing.quality_control_reported True
viable cells confirmed by hemocytometer counting (>85% viable)

Section geo_family_soft, offset —

Specimens

FieldValueEvidence
specimens.anatomical_sites ['peripheral blood mononuclear cells (PBMC)']
performed single-cell transcriptomic analyses of peripheral blood mononuclear cells (PBMC)

Section summary, offset —

specimens.number_of_samples 12
"n_samples": 12

Section geo_record_summary, offset —

specimens.participant_to_sample_mapping_available True inferred
"donor": "Tissue donor 3"

Section sample characteristics, offset —