Project description:Primary human M1 and M2 macrophages were transfected with different human herpesvirus-derived viral miRNA and the impact on the cellular microRNAs was profiled using microarray. Viral miRNA-mediated impact was assessed on the host cellular microRNA profiles by miRNA microarray analysis. Five different viral miRNA representing 4 different human herpesviruses were overexpressed in M1 and M2 macrophages and the changes in cellular miRNA compared to control mimic were examined. Viral miRNA exhibit unique impact on M1 and M2 miRNA profiles.
Project description:Macrophages have distinct characteristics depending on their microenvironment. We performed proteomic analysis between M1 and M2 macrophages and found that cellular metabolism is the key regulator of macrophage function. We used microarray to support proteomic data between M1 and M2 macrophages. M1 macrophages are obtained using cell sorting of CD45+MHCII+CD8a-F4/80+ population from C57BL/6J bone marrow cell derived heterogenous cells under GM-CSF conditioning for 7 days. M2 macrophages are differentiated with 20% L929 cell supernatant for 7 days and sorted from CD45+F4/80+CD11b+ population.
Project description:We compare M1 and M2 stimuli in human macrophages. Total RNA obtained from autologous serum monocyte-derived macrophages exposed to key cytokines, Lps, Glucocorticoid receptor ligand or left untreated.
Project description:Macrophages have distinct characteristics depending on their microenvironment. We performed proteomic analysis between M1 and M2 macrophages and found that cellular metabolism is the key regulator of macrophage function. We used microarray to support proteomic data between M1 and M2 macrophages.
Project description:We reported exosome-guided phenotype switches between M1- and M2-polarized BMDMs. M1- or M2-polarized BMDMs were successfully reprogrammed to M2- or M1-phenotype via the treatment of exosomes obtained from M2- or M1-polarized BMDMs. In this uploaded information, the exosomes from M1- and M2-polarized BMDMs were analyzed by high-throughput sequencing.
Project description:Classically (M1) and alternatively activated (M2) macrophages play distinct roles in various physiological and disease processes. Understanding the gene transcription programs that contribute to macrophage polarization along the M1/M2 spectrum may lead to new tools to detect and therapeutically manipulate macrophage phenotype. Here, we define the M1 and M2 macrophage signature through mRNA microarray. The M1 macrophage signature was defined by 629 up-regulated and 732 down-regulated genes while the M2 macrophage signature was formed by 388 up-regulated and 425 down-regulated genes. While a subset of probes was common to both M1 and M2 cells, others were exclusive to each macrophage subset. The common M1/M2 pathways were characterized by changes in various transcriptional regulators and signaling partners, including increases in Kruppel-like Factor (Klf) 4, but decreases in Klf2. To identify M1 and M2 biomarkers that help discriminate these populations, we selected genes that were increased during M1 or M2 differentiation but decreased in the opposite population. Among top novel M1-distinct genes, we identified CD38, G-protein coupled receptor 18 (Gpr18) and Formyl peptide receptor 2 (Fpr2). Among top M2 genes, we found early growth response protein 2 (Egr2) and Myc. We validated these genes by Real-Time PCR and developed a CD38/Egr2-based flow cytometry assay that discriminates between M1 and M2 macrophages. Overall, this work defines the M1 and M2 signature and identifies several novel M1 and M2 genes that may be used to distinguish and manipulate M1 and M2 macrophages. Total RNA was prepared from bone marrow-derived macrophages of wild-type mice (n=2-3 independent mice) treated in M0, M1 or M2 conditions (n=2-3 replicates per condition originating from different mice)
Project description:Analysis of the effects of CNI-1493 treatment on M1 and M2 polarized macrophages. The purpose of this microarray is to identify genes that may be differentially expressed in M1 or M2 macrophages after treatment with CNI-1493. CNI-1493 is a known inhibitor of M1 macrophages but details of its molecular mechanism are unknown. The effect of CNI-1493 on M2 macrophages has yet to be explored, but we hypothesize that CNI-1493 treatment will attenuate pro-tumor properties of M2 macrophages. We demonstrate with this array that known macrophage markers are unchanged after treatment with CNI-1493, indicating that CNI-1493 does not change the macrophage phenotype on a transcriptional level. Additionally, no candidate genes to suggest how CNI-1493 may attenuate the pro-tumor effects of M2 macrophages are readily identifiable. Total RNA extracted from M1 or M2 macrophages after polarization with GM-CSF (25ng/ml) or M-CSF (25ng/ml) for 7 days, followed by addition of IFN-γ (20ng/ml) and LPS (100ng/ml) or IL-4 (40ng/ml) for 18 hours, respectively, from CD14+ human PBMCs, and treated with CNI-1493 (200nM)
Project description:To reveal the transcriptomes associated with M1 or M2-polarized Kupffer cells, the primary Kupffer cells isolated from mouse liver were treated with lipopolysaccharides or IL-4 and the gene expression patterns were analyzed by microarray. To study the role of RORα in Kupffer cell polarization, Kupffer cells were treated with RORα ligands and transcriptions were compared with those of the M1/M2 polarized Kupffer cells.
Project description:Classically (M1) and alternatively activated (M2) macrophages play distinct roles in various physiological and disease processes. Understanding the gene transcription programs that contribute to macrophage polarization along the M1/M2 spectrum may lead to new tools to detect and therapeutically manipulate macrophage phenotype. Here, we define the M1 and M2 macrophage signature through mRNA microarray. The M1 macrophage signature was defined by 629 up-regulated and 732 down-regulated genes while the M2 macrophage signature was formed by 388 up-regulated and 425 down-regulated genes. While a subset of probes was common to both M1 and M2 cells, others were exclusive to each macrophage subset. The common M1/M2 pathways were characterized by changes in various transcriptional regulators and signaling partners, including increases in Kruppel-like Factor (Klf) 4, but decreases in Klf2. To identify M1 and M2 biomarkers that help discriminate these populations, we selected genes that were increased during M1 or M2 differentiation but decreased in the opposite population. Among top novel M1-distinct genes, we identified CD38, G-protein coupled receptor 18 (Gpr18) and Formyl peptide receptor 2 (Fpr2). Among top M2 genes, we found early growth response protein 2 (Egr2) and Myc. We validated these genes by Real-Time PCR and developed a CD38/Egr2-based flow cytometry assay that discriminates between M1 and M2 macrophages. Overall, this work defines the M1 and M2 signature and identifies several novel M1 and M2 genes that may be used to distinguish and manipulate M1 and M2 macrophages.
Project description:Purpose: Macrophages are often classified into M1 ‘classical’ and M2 ‘alternatively-activated’ macrophages. Extracellular vesicles (EVs) are biomolecule carriers involved in cell-cell communication. Here, we provide a first insight into the complete small RNA cargo of human macrophage M1/M2 EVs. Methods: Monocyte-derived macrophages were polarised into M1 (GM-CSF+LPS+IFNγ) or M2 (M-CSF+IL-4+IL-13) and EVs isolated by size exclusion chromatography. EVs were characterised by nanoparticle tracking analysis, electron microscopy and ELISA. EV RNA samples were prepared for small RNA sequencing using Qiagen’s GIAseq small RNA Library Prep kit and sequenced on an Illumina NextSeq500, single end 75 bp. Functional enrichment analysis was performed using MIENTURNET, based on validated miR-target interactions from miRTarBase. Results: Many types of small non-coding RNAs were found in EVs from M1/M2 macrophages including miRNAs, isomiRs, tRNA fragments, piRNA, snRNA, snoRNA and yRNA fragments. Distinct differences were observed between M1 and M2 EVs, with higher relative abundance of miRNAs, and lower abundance of tRNA fragments in M1 EVs compared to M2 EVs. MicroRNA-target enrichment analysis identified several gene targets involved in gene expression and metabolic processes. Conclusions: M1 and M2 cells release EVs with distinct tRNA and miRNA cargo, which have the potential to contribute to the unique effect of these cell subsets on their microenvironment.