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scRNAseq of monocytes from Trained immunity experiments

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Dataset overview

Participants 3
Samples None
Reuse readiness 5.0/10 evidence-backed score

Single-cell RNA sequencing reveals induction of distinct trained-immunity programs in human monocytes.

Zhang B, Moorlag SJ, Dominguez-Andres J, Bulut Ö, Kilic G, Liu Z, van Crevel R, Xu CJ, et al. · The Journal of clinical investigation · 2022

Abstract

Trained immunity refers to the long-lasting memory traits of innate immunity. Recent studies have shown that trained immunity is orchestrated by sustained changes in epigenetic marks and metabolic pathways, leading to an altered transcriptional response to a second challenge. However, the potential heterogeneity of trained-immunity induction in innate immune cells has not been explored. In this study, we demonstrate cellular transcriptional programs in response to 4 different inducers of trained immunity in monocyte populations at single-cell resolution. Specifically, we identified 3 monocyte subpopulations upon the induction of trained immunity, and replicated these findings in an in vivo study. In addition, we found gene signatures consistent with these functional programs in patients with ulcerative colitis, sepsis, and COVID-19, suggesting the impact of trained-immunity programs in immune-mediated diseases.

doi:10.1172/jci147719 ↗ PMID 35133977 ↗ PMC8970681 ↗

Study facts

Organism
Homo sapiens
Platform
—
Age group
—
Disease groups
—
Anatomical sites
—

Data availability

  • Analysis code

File types CSVTSV

  • E-MTAB-9702.idf.txt
  • E-MTAB-9702.sdrf.txt
  • Metadata_TrainedImmunity.csv
  • Plate_Barcode.csv
  • RMC-SM-003_HM3JFBGX9_S5_R2.ReadCounts.tsv
  • RMC-SM-005_HM3JFBGX9_S6_R2.ReadCounts.tsv
  • RMC-SM-006_HM3JFBGX9_S7_R2.ReadCounts.tsv
  • RMC-SM-007_HM3JFBGX9_S8_R2.ReadCounts.tsv
  • RMC-SM-008_HGVNYBGX9_S1_R2.ReadCounts.tsv
  • RMC-SM-009_HGVNYBGX9_S2_R2.ReadCounts.tsv
  • RMC-SM-011_HGVNYBGX9_S4_R2.ReadCounts.tsv
  • RMC-SM-012_HGVNYBGX9_S5_R2.ReadCounts.tsv
  • RMC-SM-013_HGVNYBGX9_S6_R2.ReadCounts.tsv
  • RMC-SM-014_HGVNYBGX9_S7_R2.ReadCounts.tsv
  • RMC-SM-015_HGVNYBGX9_S8_R2.ReadCounts.tsv
  • RMC-SM-016_HGVNYBGX9_S9_R2.ReadCounts.tsv
  • RMC-SM-017_HGVNYBGX9_S10_R2.ReadCounts.tsv
  • RMC-SM-018_HLWF5BGX9_S5_R2.ReadCounts.tsv
  • RMC-SM-019_HLWF5BGX9_S6_R2.ReadCounts.tsv
  • RMC-SM-020_H5VYVBGXB_S9_R2.ReadCounts.tsv
  • RMC-SM-021_H5VYVBGXB_S10_R2.ReadCounts.tsv
  • RMC-SM-022_H5VYVBGXB_S2_R2.ReadCounts.tsv
  • RMC-SM-023_HCLG2BGXB_S4_R2.ReadCounts.tsv
  • RMC-SM-024_HCLG2BGXB_S5_R2.ReadCounts.tsv
  • RMC-SM-025_HG277BGXB_S4_R2.ReadCounts.tsv
  • RMC-SM-026_HG277BGXB_S5_R2.ReadCounts.tsv
  • RMC-SM-027_H5VYVBGXB_S7_R2.ReadCounts.tsv
  • RMC-SM-028_H5VYVBGXB_S8_R2.ReadCounts.tsv
  • RMC-SM-029_H2MFKBGXB_S5_R2.ReadCounts.tsv
  • RMC-SM-030_H2MFKBGXB_S6_R2.ReadCounts.tsv
  • RMC-SM-031_H5VYVBGXB_S3_R2.ReadCounts.tsv
  • RMC-SM-032_H5VYVBGXB_S4_R2.ReadCounts.tsv
  • RMC-SM-033_HG277BGXB_S6_R2.ReadCounts.tsv
  • RMC-SM-034_H5VYVBGXB_S5_R2.ReadCounts.tsv
  • RMC-SM-035_H5VYVBGXB_S1_R2.ReadCounts.tsv
  • RMC-SM-036_H55YJBGXB_S3_R2.ReadCounts.tsv
  • RMC-SM-037_H55YJBGXB_S2_R2.ReadCounts.tsv
  • RMC-SM-038_HG277BGXB_S7_R2.ReadCounts.tsv
  • RMC-SM-039_HG277BGXB_S8_R2.ReadCounts.tsv
  • RMC-SM-040_HG277BGXB_S9_R2.ReadCounts.tsv
  • RMC-SM-041_H55YJBGXB_S4_R2.ReadCounts.tsv
  • RMC-SM-042_H55YJBGXB_S10_R2.ReadCounts.tsv
  • RMC-SM-043_H55YJBGXB_S1_R2.ReadCounts.tsv
  • RMC-SM-044_H55YJBGXB_S5_R2.ReadCounts.tsv
  • RMC-SM-045_H55YJBGXB_S6_R2.ReadCounts.tsv
  • RMC-SM-046_H55YJBGXB_S7_R2.ReadCounts.tsv
  • RMC-SM-047_HG277BGXB_S10_R2.ReadCounts.tsv

Strengths & limitations for reuse

Strengths

  • Analysis code is available
  • Participant counts are documented

Limitations

  • Not documented: raw counts are advertised
  • 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.

Cohort

FieldValueEvidence
cohort.non_ibd_controls 3 inferred
PBMCs from 3 donors were isolated

Section Methods – Sample collection and cell sorting, offset —
cohort.total_participants 3
PBMCs from 3 donors were isolated

Section Methods – Sample collection and cell sorting, offset —

Data_Assets

FieldValueEvidence
data_assets.analysis_code True
The code and scripts used in this study are available in GitHub

Section Data and materials availability, offset —
data_assets.open_access True
"isOpenAccess": "Y"

Section Publication metadata, offset —

Processing

FieldValueEvidence
processing.batch_correction_reported True
These 2 steps corrected batch effects

Section Methods – Data integration and clustering of the in vitro study, offset —
processing.cell_type_annotation_method marker genes and CellMarker database
we used a double-checking strategy for the inference by comparing data-derived marker genes with public databases

Section Methods – Cell type (cluster) annotation, offset —
processing.doublet_detection_reported True
cells with number of detected genes less than 100 or more than 7,000 were removed to avoid empty wells or doublets

Section Methods – Reads processing and quality control of in vitro study, offset —
processing.quality_control_reported True
low-quality cells were further filtered based on the following strategy

Section Methods – Reads processing and quality control of in vitro study, offset —

Specimens

FieldValueEvidence
specimens.number_of_cells 4362
we profiled the transcriptomic profile of 4,362 monocytes/macrophages

Section Results – scRNA-seq profiling of trained monocytes and macrophages, offset —