Cellular Differentiation of Human Monocytes Is Regulated by Cellular Differentiation of Human Monocytes Is Regulated by Time-Dependent Interleukin-4 Signaling and the Transcriptional Time-Dependent Interleukin-4 Signaling and the Transcriptional Regulator NCOR2 Regulator NCOR2

and activated. siRNA solutions were prepared in 1x siRNA Buffer (Dharmacon). hNCOR2 siRNA (Dharmacon) ON Target Plus pool was used for silencing (siRNA sequences targeting siRNA After removing the equilibration media the tubing system was filled with the siRNA solution or PBS. Subsequently, the cell suspension was filled into the chamber and incubated for 72h at 37 (cid:3) C and 5%CO 2 . After 72h, cells were directly lysed within the chambers by adding Trizol. qRT-PCR was performed to check silencing efficiency. five cell (in of gene expression of mean across all of two The resulting fractions were finally visualized in the form of stacked bar plots. Fold-Changes dendritic cell dataset. Fold-Changes were 1.3. genes, were specifically Mo-GM-CSF IL-4(0-72h/144h) and inflammatory dendritic cells, but overall lower expressed in all other investigated cell types. The approach was to find commonly expressed genes in inflammatory Mac as in Mo-GM-CSF and Mo-M-CSF. Fold-Changes were for inflammatory Mac to other cell types of the Affymetrix dataset. the minimum of of Mo-GM-CSF Mo-M-CSF 0.4, which were upregulated in the corresponding cell type compared to the overall mean with a Fold-Change > 1.5. The prediction was performed by considering ’’20kb centered around TSS’’ as ’’Pu-tative regulatory region,’’ by using’’ No motif collection’’ at ’’Motif collection,’’ and by setting the ‘‘Enrichment Score threshold’’ to 1.5. The resulting lists were filtered afterward to keep only those predicted regulators, which reached a normalized enrichment score (NES) > = 3 and which were identified in only one of the four cell types after the previous filtering step. remaining lists of genes were intersected, which identified 348 or 379 genes being commonly up- or downregulated in Mo-M-CSF and Mo-GM-CSF compared to CD14 + MO, respectively. The intersections were displayed in the form of Venn Diagrams. Additionally, the Fold-Changes against CD14 + MO for the union of the 348 and 379 genes (y-axes) were displayed versus the ranks of the corresponding Fold-Changes (x-axes), once ranked according to the values for Mo-M-CSF and once according to the values for Mo-GM-CSF. Second, DEG were determined directly between Mo-M-CSF and Mo-GM-CSF (Fold-Change > 2 or < (cid:1) 2 and FDR-adjusted p value < 0.05 using one way ANOVA). After reducing the results to a single probe per gene as described before, 124 genes being upregulated in Mo-GM-CSF compared to Mo-M-CSF, and 73 genes being upregulated in Mo-M-CSF compared to Mo-GM-CSF, were identified. Both lists were used as input to perform Gene Ontology Enrichment Analysis (GOEA) in PGS, where 646 as well as 444 significantly enriched GO terms (Enrichment p value < 0.05) were identified for the Mo-GM-CSF as well as Mo-M-CSF specific genes, respectively. Both lists of significant GO terms were used as input for REVIGO, which clusters semantically similar GO terms together and generates a representative GO term name to facilitate the interpretation of long GO term lists. The identified representative GO term names were then visualized as Treemaps, where each rectangle represents one of the representative GO term names. Those rectangles were further joined into ‘‘superclusters’’ by REVIGO, indicated by different colors and a common super-represen-tative GO term name. For simplicity, only the super-names were displayed. to the transcriptome data, lists of genes were generated, which had expression patterns being analogous to the observed functional outcomes between Mo-M-CSF, Mo-GM-CSF and Mo-GM-CSF IL-4(0-72h) . For YG bead uptake, the pattern was generated by requiring a Fold-Change > 1.3 between Mo-M-CSF and Mo-GM-CSF > 1.3 as well as between Mo-GM-CSF and Mo-GM-CSF IL-4(0-72h) . For migration, Fold-Changes > 1.3 were required between Mo-GM-CSF IL-4(0-72h) and Mo-GM-CSF, as well as between Mo-GM-CSF and Mo-M-CSF. For OCR/ECAR, Fold-Changes > 1.3 were required between Mo-GM-CSF IL-4(0-72h) and Mo-GM-CSF, and also between Mo-GM-CSF IL-4(0-72h) and Mo-M-CSF > 1.3, as well as a Fold-Change < 1.3 between Mo-GM-CSF and Mo-M-CSF. Using literature (references see manuscript), those lists were screened for genes, which were previously linked to either YG bead uptake, motility or OCR/ECAR, respectively. The resulting gene sets were displayed in the form of heatmaps, where rows were ordered based on hierarchical clustering using Euclidean distance and average linkage.