Raw fastq files from single cell sequencing were processed using the CellRanger (10X Genomics Cell Ranger 7.0.1) pipeline. The reads were aligned to the CellRanger_GRCh38_2020-A (https://support.10xgenomics.com/ single-cell-gene-expression/software/release-notes/build#GRCh38 _2020A) human reference genome. The filtered feature-barcode matrices produced from the 10X pipeline were used for further downstream clustering and analysis. Unsupervised cell clustering was performed by Seurat (4.1.1) (9) in R. (4.1.0). For each sample, the filtered feature-barcode matrix produced from the CellRanger pipeline was read and converted to Seurat object using “Read10X” and “CreateSeuratObject” functions, respectively. Criteria were used to identify gel bead-in-emulsions (GEMs) likely containing mRNAs derived only from a single cell, where GEMs were retained if more than 200 genes and less than 2500 genes were detected. Genes were retained for downstream analysis if they were detected in minimum of three cells. For each sample, the ‘‘NormalizeData’’ was performed to normalize for gene expression followed by ‘‘FindVariableGene’’ function to identify a subset of genes, in this case limited to 2000 genes, that exhibit high cell-to-cell variation. The 8 samples in controland experiment groups were integrated using the ‘‘FindIntergrationAnchors’’ and ‘‘IntegrateData’’ functions with the dimension parameter set to 20. Next, the integrated dataset was scaled, and PCA was performed on this dataset. The first 20 principal components (PCs) were used to perform UMAP to place similar cells together in low-dimensional space. Then the shared nearest-neighbor graph (SNN) was constructed using the “FindNeighbors” function, using the first 20 PCs. The “FindClusters” function, which implements a graph-based Louvain algorithm was applied to identify 11 distinct clusters. The resolution was set to 0.2 for this dataset. The clusters were annotated with the top markers using “FindAllMarkers” function. Classic cell markers were selected as reference after extensive literature and database search. (example - Human Protein Atlas (10) and PanglaoDB (11)). The following genes were used as markers for cell type identity - MS4A1(B cells), CD8B, CD8A (T cells), LYZ (Dendritic cells), GNLY (NK cells), IL7R, BCL11B, MAL (T memory cells/T cells), SLC25A37 (Erythroid-like and erythroid precursor cells), GZMB, PTGDS, PPP1R14B (Plasmacytoid dendritic cells), HBB, HBA1, HBA2 (Red blood cells) and PPBP (Platelets). The differentially expressed genes between the cells from control and the children with MtD were calculated by “FindMarkers”.

Paper DOI - https://doi.org/10.3389/fimmu.2023.1142634