Search

Additional file 1: Table S1. Clinical characteristic of healthy control subjects and IgAN patients for scRNA-seq. Table S2. Oxford classification of IgAN patients for scRNA-seq. Figure S1. FACS for PBMCs and scRNA-seq QC results. (A) Representative FACS data of CD45+ PBMCs for scRNA-seq. (B) Distribution plots of gene count to cell number from four scRNA-seq experiments. Each scRNA-seq experiment has four PBMC samples with at last one sample from CTRL. (C, D, E) Summarized results of the RNA counts (C), RNA feature numbers (D), and mitochondria RNA percentages (E) of four scRNA-seq. (F) Correlation between RNA counts and RNA feature number from individual samples. (G) Bar graphs of cell numbers from each sample before and after QC. (H) Summary of cell numbers before and after QC in CTRL, IgAN, and all samples. (I) Pie chart of cell percentages from each sample. Figure S2. Data integration of four scRNA-seq results with Seurat V3.0. (A) UMAP illustration of PBMCs from four scRNA-seq results before and after data integration colored by experimental batches. (B) Individual UMAP illustration of PBMCs after data integration colored by cell-type annotation. Figure S3. UMAP illustrations of the representative marker genes used for cell type annotation. CD3D, CD4, IL6SThigh for cluster-0 Naive CD4 T cells; CD14 for cluster-1 classical monocytes: CD3D, CD4, IL6STlow for cluster-2 Effector CD4 T cells; NCAM1, FCGR3A for cluster-3 natural killer cells; CD8A, CD8B, IL6STlow for cluster-4 Effector CD8 T cells; CD8A, CD8B, IL6SThigh for cluster-5 Na��ve CD8 T cells; KLRB1, KLRG1, GMZK, TRDC, CD8A for cluster-6 gd-T/MAIT/Effector T cells. CD19, IGHD, IGHM for cluster-7 B cells; Foxp3 for cluster-8 Treg cells; FCGR3A+, CD14- for cluster-9 non-classical monocytes; XFA1, MX1 for cluster-10 neutrophils; FCER1A, HLA-DRA, HLA-DPA1 for cluster-11 Myeloid DC; STMN1, MKI67 for cluster-12 Plasmacytoid DC1; IRF8, ITM2C for cluster-13 Plasmacytoid DC2; JCHAIN, CD38 for cluster-14 plasma cells. Figure S4. Dot plot for the marker gene expression levels and percentages in cell clusters. Figure S5. UMAP and cell population bar graphs of each sample from CTRL and IgAN. (A) UMAP illustrations of each PBMC sample colored by cell type. (B) Stacked bar graphs showing the cell-type composition in each PBMC sample. Colors indicate different cell types. CTRL1-6: healthy donors; 61P-63P, 65P-67P, 69P-72P: IgAN patients. Figure S6. Heatmaps of the relative communication strength of the inferred signaling pathways in NK and classical monocytes of CTRL and IgAN. (A) Comparison of signaling pathways between NK and classical monocytes in CTRL (Left) and IgAN (Right). (B) Comparison of BAFF and IL1 pathways between CTRL (Left) and IgAN (Right). Figure S7. Integration of scRNA-seq with GWAS genes in immune cells from CTRL and IgAN. The expression levels of GWAS genes were shown in each cell cluster of CTRL and IgAN.

OBJETIVO: IDENTIFICAR LOS FUNDADORES Y DISEMINADORES DEL CONCEPTO ¿EL BIEN COMÚN¿ Y REVISAR SU EVOLUCIÓN DURANTE UN PERIODO PROLONGADO DE TIEMPO (1993-2018). MÉTODO: SE REALIZA UN ANÁLISIS BIBLIOMÉTRICO DE ESTE CONCEPTO BASADO EN LOS 861 ARTÍCULOS DE LA BASE DE DATOS DE LA WEB OF SCIENCE (WOS) PARA HACER EL ESTUDIO DESCRIPTIVO DEL MISMO. RESULTADOS: LA TENDENCIA REFLEJA QUE EL USO DEL CONCEPTO DEL BIEN COMÚN EN ÁREAS DE NEGOCIOS, DE LA ECONOMÍA, DE GESTIÓN, DE ESTUDIOS DE DESARROLLO, DE ESTUDIOS URBANOS Y DE LA GEOGRAFÍA HA AUMENTADO DESDE EL AÑO 2007 HASTA LA ACTUALIDAD. TAMBIÉN SE OBSERVA QUE EN EL ANÁLISIS DE LOS AUTORES LA GRAN MAYORÍA DE ÉSTOS ESTÁN ADSCRITOS A UNIVERSIDADES LOCALIZADAS EN DIFERENTES CONTINENTES DEL MUNDO. ESTO REFLEJA LA DIVERSIDAD GEOGRÁFICA QUE PRESENTA EL BIEN COMÚN.