• ← IL-6 + JAK/STAT inhibitors

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    10x Flex v2 scRNA-seq Data

    scRNA-seq data for this project was generated using 10x Genomics Flex v2 with a limited panel of 1,916 target genes. For data collection and processing details, see the Experimental Methods, and Analysis Methods sections.

    The .h5ad file for this project contains treatment metadata, in addition to cell type labels and QC metrics. Click the header below for descriptions of these metadata:

    Each file contains sample-level metadata, as well as cell-level cell type labels and QC metrics. The following values are stored in the .obs section of these .h5ad files as descriptions of observations:

    Process Identifiers
    batch_id: A GUID for the batch of samples processed together (e.g. B039)
    pool_id: A GUID for the pool of samples combined for Cell Hashing (e.g. B039-P1)
    chip_id: A GUID for the 10x Genomics chip the cells were loaded into (e.g. B039-P1C2)
    well_id: A GUID for the 10x Genomics well the cells were loaded into within the chip (e.g. B039-P1C2W4)
    *barcodes: A GUID for the individual cell
    original_barcodes: The original, sequence-based barcode generated by 10x Genomics Cell Ranger software
    plate_location: Well location within the 96-well plate for the sample (e.g. A7, B12)
    *used as the primary cell index in our .h5ad files

    Cell QC Metrics
    n_umis: Number of Unique Molecular Identifiers (unique molecules) detected
    n_genes: Number of genes with at least 1 UMI detected

    Treatment and Drug Metadata
    cyto_treatment: Cytokine treatment (IL6 or DMSO)
    drug_treatment: Unique identifier for the drug treatment applied
    drug_name: Name of the drug (as provided by ApexBio)
    drug_cas_number: CAS Number for the drug (as provided by ApexBio)
    drug_mw: Molecular weight of the drug (as provided by ApexBio)
    drug_solvent: Solvent for the drug (as provided by ApexBio)
    drug_pathway: Target pathway for the drug (as provided by ApexBio)
    drug_target: Specific Target for the drug (as provided by ApexBio)​​​​​​​
    drug_description: Description of the drug (as provided by ApexBio)
    drug_chembl_name: Name of the drug in the ChEMBL database (if available)
    drug_chembl_id: ChEMBL database ID for the drug (if available)
    drug_label: Label for the drug, based on ChEMBL, PubChem, and provided label

    Cell Labeling Results
    AIFI_L1: Final broad class cell type label (T cell)
    AIFI_L2: Final mid resolution cell type label (7 types)
    cell_type: T cell subtype label used for analysis to match Flow Cytometry populations (12 types)
     

    We are providing our scRNA-seq data in AnnData (.h5ad) format. For more details about AnnData, see the AnnData Documentation Page.

    The .h5ad file contains both normalized data as well as raw counts, which can be accessed in Python with:
    adata = adata.raw.to_adata()

    IL-6 + JAK/STAT inhibitor Flex v2 Data
    File NameDescriptionDownload Link
    il6_jak-stat_scrna-seq.h5ad 10x Flex v2 scRNA-seq data

    Differentially expressed genes

    We used the rank_genes_groups function in the scanpy package to perform Wilcoxon tests to compare scRNA-seq data between IL6 + drug treatments and the unstimulated control condition. The results of these pairwise comparisons are provided below, and descriptions of the columns are available by clicking on the header below:

    DEG Results columns
    cell_type:The T cell type used for differential expression test
    fg: Foreground drug treatment in the contrast
    bg: Background group (DMSO control) in the contrast
    gene: Gene symbol for the contrast test
    log2fc: Log2(Fold Change) of average expression between groups.
    padj: Adjusted P-value for the contrast test (using the Benjamini and Hochberg FDR method)
    pvalue: Original P-value generated by rank_genes_groups for the contrast test
    stat: Test statistic generated by rank_genes_groups for the contrast test

    IL-6 + JAK/STAT inhibitor DEGs
    File NameDescriptionDownload Link
    il6_jak-stat_wilcoxon_deg.csv IL-6 + Drug vs. Unstim. control DEGs

    Gene set enrichment analysis results

    After performing DEG analysis, we ranked DEGs for each result by nominal p-value and direction of change relative to the unstimulated control, then performed Gene Set Enrichment Analysis (GSEA) using the fgsea package for R against gene sets from MSigDB (Hallmark pathways) and Reactome. These results are used to display Pathway enrichment in our DEG Explorer visualization tool.

    Gene Set Enrichment Analysis results columns
    fg: Foreground cytokine in the contrast
    bg: Background group (PBS) in the contrast
    cell_type: T cell type used for differential expression test
    pathway: Pathway name
    NES: Normalized Enrichment Score computed by fgsea
    nomP: Original P-value computed by fgsea
    adjP: Adjusted P-value computed by fgsea
    n_leadingEdge: Number of leading edge genes reported by fgsea
    n_pathway_genes: Total number of genes in the pathway
    leadingEdge: Semicolon-separated list of leading edge genes reported by fgsea
    pathway_genes: Semicolon-separated list of all pathway genes

    IL-6 + JAK/STAT inhibitor GSEA
    File NameDescriptionDownload Link
    il6_jak-stat_hallmark_gsea.csv IL-6 + Drug vs. Unstim. Hallmark GSEA
    il6_jak-stat_reactome_gsea.csv IL-6 + Drug vs. Unstim. Reactome GSEA

    Phospho-Flow MFI and Frequency results

    After measuring and gating the flow cytometry data, the frequency of each population and the Mean Fluorescence Intensity (MFI) of each phospho-stat (pSTAT1, pSTAT3, and pSTAT5) were calculated and stored in CSV format. We provide both a table with values for frequency and MFI, as well as tables with metadata for the rows (samples/treatments) and columns (measures) for this table.

    IL-6 + JAK/STAT inhibitor pSTAT MFIs
    File NameDescriptionDownload Link
    il6_jak-stat_mfi_column-metadata.csv Column metadata for MFI table
    il6_jak-stat_mfi_row-metadata.csv Row metadata for MFI table
    il6_jak-stat_mfi_table.csv MFI and Frequency data table