Whole genome and whole transcriptome sequencing of Hepatosplenic T Cell Lymphoma tumor.
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ABSTRACT:
PROVIDER: EGAS00001003775 | EGA |
REPOSITORIES: EGA
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Project description:The goal of this study is to identify the transcriptome differences between the two major subtypes of diffuse large B cell lymphoma (DLBCL). DLBCL is the most common form of non-Hodgkin’s lymphoma and has two major subtypes: germinal center B-cell-like (GCB) and activated B-cell-like (ABC). When compared to the GCB form, ABC lymphomas respond much more poorly to current therapies. To investigate how gene expression changes might contribute to this aggressive phenotype, we have used RNA-Seq to profile the whole transcriptome in 8 DLBCL cell lines (4 GCB subtype, 4 ABC) that are derived from patient tumors. 1,545 genes are differentially expressed between subtypes (FDR < 0.05), approximately 7% of the transcriptome. The vast majority of these genes (81%, n = 1251) are more highly expressed in the ABC cell lines. In contrast, only 294 genes (19%) are more highly expressed in the GCB cell lines. Half (n = 765) of the genes with greater ABC subtype expression demonstrate very low read counts (< 5) in the GCB cell types. Conversely, only 21 genes that are more highly expressed in GCB are unique to that subtype. The prevalence of such “on/off” genes indicates that the major differences between ABC and GCB DLBCL are due almost exclusively to additional gene expression in ABC, rather than the two subtypes having divergent but equally active genetic programs. Measurement and comparison of gene expression in 8 cell lines representing the 2 subtypes of DLBCL. 4 cell lines are subtyped as ABC and 4 are subtyped as GCB. 2 replicates are present for each cell line. (Cell line OCI-Ly19 was not included in the analysis because its gene expression clustered in between the subtypes, probably due to its EBV+ status. However, its sequencing runs have been included for completeness.)
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Project description:Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole-transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2 M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis.
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Project description:BACKGROUND: The accumulation of somatic mutations in genes and molecular pathways is a major factor in the evolution of oral squamous cell carcinoma (OSCC), which sparks studies to identify somatic mutations with clinical potentials. Recently, massively parallel sequencing technique has started to revolutionize biomedical studies, due to the rapid increase in its throughput and drop in cost. Hence sequencing of whole transcriptome (RNA-Seq) becomes a superior approach in cancer studies, which enables the detection of somatic mutations and accurate measurement of gene expression simultaneously. METHODS: We used RNA-Seq data from tumor and matched normal samples to investigate somatic mutation spectrum in OSCC. RESULTS: By applying a sophisticated bioinformatic pipeline, we interrogated two tumor samples and their matched normal tissues and identified 70,472 tumor somatic mutations in protein-coding regions. We further identified 515 significantly mutated genes (SMGs) and 156 tumor-specific disruptive genes (TDGs), with six genes in both sets, including ANKRA2, GTF2H5, STOML1, NUP37, PPP1R26, and TAF1L. Pathway analysis suggested that SMGs were enriched in cell adhesion pathways, which are frequently indicated in tumor development. We also found that SMGs tend to be differentially expressed between tumors and normal tissues, implying a regulatory role of accumulation of genetic aberrations in these genes. CONCLUSIONS: Our finding of known tumor genes proves of the utility of RNA-Seq in mutation screening, and functional analysis of genes detected here would help understand the molecular mechanism of OSCC.
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