Similar Datasets
Project description:Purpose: The purpose of this study is to detect activated or silenced genes during wild type (WT) and Rbm25-deficient bone marrow derived macrophages (BMDMs). Gene expression differences between two samples could be found using transcriptome profiling (RNA-seq) analysis. Methods: Mouse BMDMs were generated from bone marrow cells in RPMI-1640 medium with recombinant mouse M-CSF (20ng/ml). BMDMs were stained to confirm the surface expression of CD11b and F4/80. Cells with purity >97.5% were used for subsequent experiments. WT and Rbm25 deficient BMDMs were stimulated with LPS (100ng/ml) for 0, 2 or 8 hours, of which RNA profiles were generated by deep sequencing, using Illumina. Results: We mapped about 10 million sequence reads per sample to the mouse genome, identified hundreds of genes with significant mRNA variation between WT and Rbm25 deficient BMDMs.
2024-11-06 | GSE240157 | GEO
Project description:Next Generation Sequencing Facilitates Quantitative Analysis of wild type and Rbm25-deficient Bone Marrow Derived macrophages
| PRJNA1002379 | ENA
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare Transcriptomes of Sirt3 WT and KR macrophages with high-throughput data analysis Methods: Macrophages' mRNA profiles of 8 weeks-old wild-type (WT) and Sirt3 K223R mice were generated by deep sequencing, in quadruplicate, using Total RNA from each sample was quantified using a NanoDrop ND-1000 instrument. 1μg total RNA was used to prepare the sequencing library in the following steps: 1. Total RNA is enriched by oligo (dT) magnetic beads (rRNA removed); 2. RNA-seq library preparation using KAPA Stranded RNA-Seq Library Prep Kit (Illumina), which incorporates dUTP into the second cDNA strand and renders the RNA-seq library strand-specific. The completed libraries were qualified with Agilent 2100 Bioanalyzer and quantified by absolute quantification qPCR method. To sequence the libraries on the Illumina HiSeq 4000 instrument, the barcoded libraries were mixed, denatured to single stranded DNA in NaOH, captured on Illumina flow cell, amplified in situ, and subsequently sequenced for 150 cycles for both ends on Illumina HiSeq instrument. Results: Image analysis and base calling were performed using Solexa pipeline v1.8 (Off-Line Base Caller software, v1.8). Sequence quality was examined using the FastQC software. The trimmed reads (trimmed 5’, 3’-adaptor bases using cutadapt) were aligned to reference genome using Hisat2 software. The transcript abundances for each sample was estimated with StringTie , and the FPKM value for gene and transcript level were calculated with R package Ballgown. The differentially expressed genes and transcripts were filtered using R package Ballgown. The novel genes and transcripts were predicted from assembled results by comparing to the reference annotation using StringTie and Ballgown, then use CPAT to assess the coding potential of those sequences. Then use rMATS to detecting alternative splicing events and plots. Principle Component Analysis (PCA) and correlation analysis were based on gene expression level, Hierarchical Clustering, Gene Ontology, Pathway analysis, Gene Ontology, Pathway analysis, scatter plots and volcano plots were performed with the differentially expressed genes in R, Python or shell environment for statistical computing and graphics. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with 4 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 offer 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.
2022-01-29 | GSE195438 | GEO
Project description:Next Generation Sequencing Facilitates Quantitative Analysis of wild type and Rbm25-deficient Bone Marrow Derived macrophage Transcriptomes
| PRJNA1002385 | ENA
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.
2020-12-09 | GSE153512 | GEO
Project description:The goals of this study aim to reveal functional and phenotypic diversity of leukemia-associated macrophages in response to the microenvironmental cues in mouse T cell acute lymphoblastic leukemia Compare Transcriptomes of macrophages in T cell acute leukemia which are suggested as leukemia-associated macrophages (LAMs) with homeostasis
2015-03-01 | E-GEOD-63751 | biostudies-arrayexpress
Project description:PurposeNext-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived retinal 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.MethodsRetinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl(-/-)) mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. 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.ResultsUsing an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl(-/-) mice with BWA workflow and 34,115 transcripts with TopHat 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 (R(2)) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl(-/-) retina, 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 retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling.ConclusionsOur study represents the first detailed analysis of retinal 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.
| S-EPMC3233386 | biostudies-literature
Project description:Purpose:The purpose of this study is to detect activated or silenced genes during lncRNA-GMR-deficient Bone marrow-derived macrophages and the control cells. Gene expression differences between two samples could be found using transcriptome profiling (RNA-seq) analysis.Macrophages RNA profiles were generated by deep sequencing,using Illumina. Results: We mapped about 10 million sequence reads per sample to the mouse genome, identified hundreds of genes with significant mRNA variation between lncRNA-GMR-deficient macrophages and the control cells.
2020-12-21 | GSE134023 | GEO
Project description:Purpose: The purpose of this study is to detect activated or silenced genes during bone marrow derived macrophages (BMDMs) transfected with control siRNA or Acly-E14 siRNA. Gene expression differences between two samples could be found using transcriptome profiling (RNA-seq) analysis. Methods: Mouse BMDMs were generated from bone marrow cells in RPMI-1640 medium with recombinant mouse M-CSF (20ng/ml). BMDMs were stained to confirm the surface expression of CD11b and F4/80. Cells with purity >97.5% were used for subsequent experiments. BMDMs were transfected with control siRNA or Acly-E14 siRNA. 48 hour later, they were stimulated with LPS (100ng/ml) for 4 hours, of which RNA profiles were generated by deep sequencing, using Illumina. Results: We mapped about 10 million sequence reads per sample to the mouse genome, identified hundreds of genes with significant mRNA variation between BMDMs transfected with the indicated siRNAs.
2024-11-06 | GSE240188 | GEO
Project description:Purpose: The purpose of this study is to detect activated or silenced genes during bone marrow derived macrophages (BMDMs) transfected with control siRNA or Acly-E14 siRNA. Gene expression differences between two samples could be found using transcriptome profiling (RNA-seq) analysis. Methods: Mouse BMDMs were generated from bone marrow cells in RPMI-1640 medium with recombinant mouse M-CSF (20ng/ml). BMDMs were stained to confirm the surface expression of CD11b and F4/80. Cells with purity >97.5% were used for subsequent experiments. BMDMs were transfected with control ASO or Acly-E14 ASO. 48 hour later, they were stimulated with LPS (100ng/ml) for 4 hours, of which RNA profiles were generated by deep sequencing, using Illumina. Results: We mapped about 10 million sequence reads per sample to the mouse genome, identified hundreds of genes with significant mRNA variation between BMDMs transfected with the indicated ASOs.
2024-11-06 | GSE272304 | GEO