Project description:Bone marrow cells (BM) were isolated and primed with M-CSF (M BMDM) or GM-CSF (GM BMDM) for 7 days. M-BMDMs were treated with IL6 (20 ng/ml) for 3 h or 24 h while GM-BMDMs were treated for 3 h. The difference between BM, M-BMDM, and GM-BMDM were analyzed to describe the phenotye and function of each population. Moreover, by RNA-seq analysis, the influence of IL6 treatment on GM-BMDMs and M-BMDMs were analyzed.
Project description:Bone marrow cells were isolated, primed with M-CSF (M-BMDM) or GM-CSF (GM-BMDM) and cultured for 7 days. The proteomic difference between GM-BMDM and M-BMDM were analyzed to describe the phenotye and function of two types of macrophages.
Project description:We performed RNA-seq to characterize the transcriptome of tumor-associated macrophages (TAMs), bone marrow-derived monocytes from healthy and tumor-bearing mice (BMDM-Ts/BMDM-Hs) and, macrophages from healthy mammary fat pad (MGMs).
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived BMDM transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Methods: BMDM mRNA profiles of 6-week-old wild-type (WT) and METTL14 knockout (M14−/−) mice with or without LPS treatment were generated by deep sequencing, in triplicate, using Illumina Hi-Seq 4000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the mouse genome (build mm9) and identified 11,902 transcripts in WT and M14−/− BMDMs with BWA workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 20% of the transcripts showed differential expression between the WT and M14−/− BMDMs, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to BMDM function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of BMDM transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
Project description:The aim of the study was to investigate the effects of ionizing radiation on CT-2A cells at the protein level, proteomics analyses were performed on CT-2A cells irradiated with 5 Gy. To investigate the effect of glioma-secreted extracellular vesicles (EVs) on bone-marrow-derived macrophages (BMDM) at the protein level, BMDM were exposed to EVs for 48 hours.
Multiplexed mass spectrometry-based proteomics was performed using TMTpro barcoding reagents and the SPS-MS3 method on an Orbitrap Lumos mass spectrometer.
Samples were labeled as follows:
CT-2A CTRL samples: CT2A_Cells_CTRL_1 (126), CT2A_Cells_CTRL_2 (127n)
CT-2A IR samples: CT2A_Cells_IR_1 (127c), CT2A_Cells_IR_2 (128n)
BMDM samples: Macrophage_CTRL_1 (128c), Macrophage_CTRL_2 (129n)
BMDM+EV samples: Macrophage_Plus_EV_1 (130c), Macrophage_Plus_EV_2 (131n)
BMDM+irEV samples: Macrophage_Plus_irEV_1 (131c), Macrophage_Plus_irEV_2 (132n)
Project description:To explore the mechanism of inflammation caused by glucomannan with increasing degree of acetyl substitution (acGM) in bone marrow derived macrophages (BMDM), BMDM was treated with acGM-0.2 and acGM-1.8 for 24h.
Project description:In this study, we make used of mRNA-seq and its ability to reliably quantify isoforms, integrating this data with ribosome profiling and LC-MS/MS, to assign ribosome footprints and peptides at the isoform level. We leverage the principle that most cell types, and even tissues, predominantly express a single principal isoform to set isoform-level mRNA-seq quantifications as priors to guide and improve allocation of footprints or peptides to isoforms. Through tightly integrated mRNAseq, ribosome footprinting and/or LC-MS/MS proteomics we demonstrate that a principal isoform can be identified in over 80% of gene products in homogenous HEK293 cell culture and over 70% of proteins detected in complex human brain tissue. Defining isoforms in experiments with matched RNA-seq and translatomic/proteomic data increases the functional relevance of such datasets and will further broaden our understanding of multi-level control of gene expression. In this PRIDE submission you will find the raw files for the HEK293 cell proteomics. Files for the human brain proteomics can be found at PXD005445. We have also uploaded a zip file that contains the input files for our HEK293 cell analysis, and the isoform level output files – there is a separate folder within the zip files for these. The data used to create the manuscript figures is in the Rdata file. Code for assigning peptides and footprints to isoforms can be found on Github here: https://github.com/rkitchen/EMpire
