Journal: Blood

Article Title: p16 INK4a deficiency promotes IL-4-induced polarization and inhibits proinflammatory signaling in macrophages

doi: 10.1182/blood-2010-10-313106

Figure Lengend Snippet: Microarray analysis using mRNA from p16−/− BMDM compared to p16+/+ BMDM showed (A) decreased mRNA expression of classically activated macrophages-associated genes and (B) increased mRNA expression of alternatively activated macrophages-associated genes. Data is expressed as fold change relative to p16+/+ BMDM. (C) Differential gene expression in p16−/− BMDM relative to p16+/+ BMDM was correlated with the changes induced in IL-4-induced p16+/+ AAMφ. The figure shows 2log values of the probesets significantly (p<0.05) regulated only in p16−/− BMDM (red dots), only in IL-4-polarized p16+/+ AAMφ (green dots) and by both conditions (blue dots), compared to p16+/+ BMDM. The X-axis represents differences in gene expression induced by IL-4, whereas the Y-axis represents the effect of p16INKa-deficiency. These comparisons are depicted in the schematic representation of the protocol in the corresponding colors. Pearson Correlation analysis was done for probesets differentially expressed by both conditions (blue). (D) Heat map of p16+/+ BMDM, p16−/− BMDM, IL-4-polarized p16+/+ and p16−/− AAMφ gene expression profiles. Colors fluctuate from blue (poorly expressed) to green (intermediate expression) and yellow (high expression). Additional information regarding gene description, fold induction, and p-value can be found in Table S3.

Article Snippet: We thank E. Vallez for mouse breeding, J. Brozek (Genfit SA, Loos, France) for microarray raw data analysis, T. Coevoet, N. Jouy and A. Lucas for technical assistance.

Techniques: Microarray, Expressing, Gene Expression

Journal: Blood

Article Title: p16 INK4a deficiency promotes IL-4-induced polarization and inhibits proinflammatory signaling in macrophages

doi: 10.1182/blood-2010-10-313106

Figure Lengend Snippet: Representation of the relative microarray intensity values from a selection of down-regulated genes in p16+/+ and p16−/− BMDM with or without polarization (AAMφ) by 15 ng/mL IL-4 from day 0 of differentiation. Statistically significant differences are indicated (a: p<0.05 compared to p16+/+ BMDM; b: p<0.05 compared to p16−/− BMDM; c: p<0.05 compared to p16+/+ AAMφ.)

Article Snippet: We thank E. Vallez for mouse breeding, J. Brozek (Genfit SA, Loos, France) for microarray raw data analysis, T. Coevoet, N. Jouy and A. Lucas for technical assistance.

Techniques: Microarray, Selection

Journal: BMC Ophthalmology

Article Title: MiRNAs regulate oxidative stress related genes via binding to the 3′ UTR and TATA-box regions: a new hypothesis for cataract pathogenesis

doi: 10.1186/s12886-017-0537-9

Figure Lengend Snippet: Heatmap shows differentially expressed mRNAs in cataractous lens samples compared with transparent lens samples. Six separate microarray assays were performed to determine the genome-wide mRNA expression in the central epithelium of transparent and cataractous human lenses. Microarray data were processed by CapitalBio Corporation. Heatmap shows differentially expressed mRNAs in cataractous lens samples compared with transparent lens samples. Relative expression value from high to low was shown by gradient of red to green in the heatmap. Colors indicate relative mRNA expression. Red and green indicate higher or lower expression of mRNAs relative to those in transparent lens samples, respectively. FDR (false discovery rate) adjusted p -value <0.05

Article Snippet: Microarray raw data were processed at CapitalBio Corporation.

Techniques: Microarray, Genome Wide, Expressing

Journal: Frontiers in Immunology

Article Title: Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis

doi: 10.3389/fimmu.2024.1326502

Figure Lengend Snippet: Overview of data processing steps. From the public databases, nine microarray datasets containing 250 patients with psoriasis were selected. According to the established methodologies, DEGs were filtered, and enrichment analysis, PPI network analysis, and supervised clustering were performed. Finally, the Xgboost classifier was constructed to predict the responses of stratified subtypes to commonly used biologics. DEGs, differentially expressed genes; GO, Gene Ontology; GSEA, Gene-set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MCODE, Molecular Complex Detection; PPI, protein–protein interaction.

Article Snippet: Normalized matrix files are downloaded directly from Illumina into raw microarray data.

Techniques: Microarray, Construct