Dataset Information

Next Generation Sequencing Facilitates Quantitative Analysis of lymphoma cells‘ Transcriptomes from the lymph node tissues of Myc-E and Myc-ECreCD19Trib3F/+ mice

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to analysis the differiational genes and pathways in Myc-E and Myc-ECreCD19Trib3F/+ mice‘ lymphoma cells by using NGS-derived lymphoma transcriptome profiling (RNA-seq). Methods: The Myc-E and Myc-ECreCD19Trib3F/+ mice' lymphoma cells were isolated, then these cells' mRNA profiles were generated by deep sequencing, in triplicate, using Illumina HiSeq 4000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with following methods: Alignment by using HISAT2 v2.1, IGV was used to to view the mapping result by the Heatmap, histogram, scatter plot or other stytle, FPKM was then calculated to estimate the expression level of genes in each sample, DEGseq v1.18.0 was used for differential gene expression analysis between two samples with non biological replicates and Function Enrichment Analysis including GO enrichment analysis and KEGG . Conclusions: Our study represents the first detailed analysis of Myc-E and Myc-ECreCD19Trib3F/+ mice lymphoma cells' 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE126738 | GEO | 2020/10/02

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to analysis the differiational genes and pathways in WT and WT+CXCL14 mammary endothelial cells by using NGS-derived transcriptome profiling (RNA-seq). Methods: Recombinant mouse CXCL14 protein in 50 μL of PBS (50μg/kg) was injected into the tail vein every 3 days for 2 weeks. The mice in the control group were treated with an identical volume of PBS. The mice were sacrificed by excessive anaesthesia 14 days after cytokine administration. Mammary glands of 6-week-old female virgin mice were harvested and single-cell suspension was obtained by Collagenase III-mediated digestion of minced tissue, followed by trypsin-EDTA and DNaseI sequential incubation before filtering through 70 μm cell strainers. The following antibodies in 1:200 dilutions were used: rat anti-mouse CD16/CD32, FITC-conjugated hematopoietic lineage cocktail, APC-conjugated CD31. All analysis and sorting were performed using a FACS Aria III sorter. The purity of sorted population was routinely checked and ensured to be > 95%. WT and WT+CXCL14 endothelial cells' mRNA profiles were generated by deep sequencing, in triplicate, using Illumina HiSeq 4000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with following methods: Alignment by using HISAT2 v2.1, IGV was used to to view the mapping result by the Heatmap, histogram, scatter plot or other stytle, FPKM was then calculated to estimate the expression level of genes in each sample, DEGseq v1.18.0 was used for differential gene expression analysis between two samples with non biological replicates and Function Enrichment Analysis including GO enrichment analysis and KEGG . Conclusions: Our study represents the first detailed analysis of WT and WT+CXCL14 endothelial cells' 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:MYC translocations are the biologic hallmark of Burkitt lymphomas but also occur in other mature B-cell lymphomas. If accompanied by chromosomal breaks targeting the BCL2 and/or BCL6 oncogenes, these MYC translocation-positive (MYC+) lymphomas are called double-hit lymphomas (DHLs); otherwise, the term single-hit lymphoma (SHL) is applied. In order to characterize the biologic features of these MYC+ lymphomas other than Burkitt lymphomas, we explored, after exclusion of molecular Burkitt lymphoma (mBL) as defined by gene expression profiling (GEP), the molecular, pathological and clinical aspects of 80 MYC translocation (MYC+) lymphomas (31 SHL, 26 BCL2+/MYC+, 14 BCL6+/MYC+, 6 BCL2+/BCL6+/MYC+ and 3 MYC+ lymphomas with unknown BCL6 status). Comparison of SHL and DHL revealed no difference in frequency of MYC partner (IG/non-IG), genomic complexity or MYC expression and no differences in GEP. DHL showed a more frequent GCB-like GEP and higher IGH and MYC mutation rates. GEP revealed 130 differentially expressed genes between BCL6+/MYC+ and BCL2+/MYC+ DHL. BCL2+/MYC+ DHL showed a more frequent GCB-like GEP. Analysis of all lymphomas according to MYC partner (IG/non-IG) revealed no substantial differences. In contrast to mBL and lymphomas without MYC break, SHL and DHL patients had similar poor outcome. Our data suggest that after excluding mBL, MYC+ lymphomas could be biologically widely lumped without further need for subclassification. 32 diffuse large B-cell lymphoma samples were hybridized to HG-U133A Affymetrix GeneChips. In addition, this study contains 30 already published samples, which contribute to GSE4475 (Hummel et al. 2006 (PMID 16760442)), as well as 18 already published samples from GSE22470 (Salaverria et al. 2011 (PMID 21487109)). No re-normalisation of the published samples was performed. The complete dataset representing: (1) the 32 diffuse large B-cell lymphoma Samples, (2) the 30 Samples from GSE4475 and (3) the 18 Samples from GSE22470, is linked below as a supplementary file

Dataset Information

Next Generation Sequencing Facilitates Quantitative Analysis of lymphoma cells‘ Transcriptomes from the lymph node tissues of Myc-E and Myc-ECreCD19Trib3F/+ mice

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure