Immune checkpoint landscape of human atherosclerosis and influence of cardiometabolic factors

Barcia Durán, J.G., Das, D., Gildea, M. et al. Immune checkpoint landscape of human atherosclerosis and influence of cardiometabolic factors. Nat Cardiovasc Res (2024). https://doi.org/10.1038/s44161-024-00563-4

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Human carotid plaque scRNA-seq analysis

Raw data and processed count matrices for this study have been deposited in GEO under the accession numbers GSE246315, GSE235437, and GSE224273

Code and associated files

Description of analysis pipeline and associated files:

1. Create seurat object Read in cellranger generated count matrices and generates a list of seurat objects
2. CITE-seq sample processing Process and QC the single CITE-seq sample (GSM7502475_Sample38)
3. Filter and QC QC and filtering of low quality barcodes for each sample and exports a list of filtered seurat objects
4. Integration and reference annotation Map each sample to CITE-seq PBMC reference and integrate
5. Clustering Dimensionality reduction, clustering, marker gene identification for each cluster
6. Subset major cell types Subset major cell types (myeloid, NK, B, T, CD4 T, CD8 T, DN T, DP T)
7. Subclustering Dimensionality reduction, clustering, and marker gene identification of cells in each major population
8. Reclassification of DNT cells T-cell clustering diagnostics and reclassification of double negative T-cells (DNT) as CD4 or CD8
9. Celltypist Annotate cells with celltypist
10. Cellchat Run cellchat

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Human PBMC CITE-seq analysis

Raw data and processed count matrices for this study have been deposited in GEO under the accession number GSE246317

Code and associated files

Description of analysis pipeline and associated files:

1. Pre-processing and filtering of 4h post-treatment samples Read in cellranger generated count matrices, inspect quality, and filter low quality barcodes
2. Pre-processing and filtering of 24h post-treatment samples Read in cellranger generated count matrices, inspect quality, and filter low quality barcodes
3. Integration and clustering Integrate, and cluster anti-CTLA4 and anti-PD1 samples. Identify marker genes for each cluster
4. Protein data clustering Use protein expression and clustering to annotate WNN clusters with major cell type identities
5. T-cell gating Divide T-cells into CD4, CD8, DN T, and DP T, based on expression of CD4 and CD8 protein
6. Subclustering Dimensionality reduction, clustering, and marker gene identification of cells in each major population

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Human PBMC scRNA-seq analysis

Raw data and processed count matrices for this study have been deposited in GEO under the accession number GSE272294

Code and associated files

Description of analysis pipeline and associated files:

1. Create seurat object and filter Read in cellranger generated count matrices, filter low quality barcodes, and save a list of seurat objects
2. Mark multiplets Mark multiplets using DoubletFinder
3. Remove proliferation associated genes Remove cell-cycle/proliferative genes for integration and clustering
4. Integration Integrate data
5. Dimensionality reduction and clustering Remove marked multiplets and cluster
6. Celltypist Annotate cells with celltypist
7. Differential expression analysis Marker gene identification for each cluster. Differential expression analysis of immune checkpoint genes between type 2 and no diabetes NT samples for each cell type. The latter was run after subclustering and annotation
8. Subset major cell types Add celltypist labels and subset major cell types (myeloid, NK, B, T, CD4 T, CD8 T, DN T, DP T)
9. Subclustering Dimensionality reduction, clustering, and marker gene identification of cells in each major population
10. Cellchat Run cellchat

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Mouse scRNA-seq analysis

Raw data and processed count matrices for this study have been deposited in GEO under the accession numbers GSE272294, GSE141038, GSE161494, GSE168389, and GSE253555

Code and associated files

Description of analysis pipeline and associated files:

1. Process and integrate Read in cellranger generated count matrices, filter low quality barcodes, integrate, cluster, identify marker genes per cluster
2. Identify proliferation associated genes Identify cell-cycle/proliferative genes for removal prior to integration and clustering
3. Differential expression analysis Differential expression analysis of immune checkpoint genes between NT and LL. The latter was run after subclustering and annotation
4. Cellchat Run cellchat

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Human coronary scRNA-seq analysis

Raw data and processed count matrices for this study have been deposited in GEO under the accession number GSE264666

Code and associated files

Description of analysis pipeline and associated files:

1. Create seurat object and filter Read in cellranger generated count matrices, filter low quality barcodes, detect and mark multiplets via scDblFinder
2. Integrate Remove multiplets, integrate, cluster, and identify marker genes per cluster. Annotate CD45- clusters and subcluster CD45+ cells
3. Subset major cell types Subset major cell types (myeloid, NK, B, T, CD4 T, CD8 T, DN T, DP T) from CD45+ cells
4. Subcluster Dimensionality reduction, clustering, and marker gene identification of cells in each major population
5. Plaque vs control Differential expression analysis of plaque vs control