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accession-icon GSE98588
Genetically-defined Diffuse Large B-cell Lymphoma Subsets Arise by Distinct Pathogenetic Mechanisms and Predicts Outcome
  • organism-icon Homo sapiens
  • sample-icon 137 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

We obtained gene experssion profiles of 52 newly diagnosed diffuse large B-cell lymphoma (DLBCL).

Publication Title

Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes.

Sample Metadata Fields

Specimen part

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accession-icon E-MEXP-1269
Transcription profiling by array of three human multiple myeloma cell lines treated with 5-aza-2-deoxycytidine and/or trichostatin A
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

To identify epigenetically silenced genes in multiple myeloma (MM) cell lines and to determine the effects of 5-aza-2-deoxycytidine and trichostatin A on gene expression. We treated 3 multiple myeloma cell lines (MM1, NCI-H929, U266) with 5-aza-2-deoxycytidine and/or trichostatin A.

Publication Title

Genome-wide transcriptional response to 5-aza-2'-deoxycytidine and trichostatin a in multiple myeloma cells.

Sample Metadata Fields

Specimen part, Disease, Cell line

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accession-icon GSE110811
Distinct Gene Expression Profiles Define Anaplastic Grade in Retinoblastoma
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 2.0 ST Array (hugene20st)

Description

Morbidity and mortality associated with retinoblastoma have decreased drastically in recent decades, in large part due to better prediction of high-risk disease and appropriate treatment stratification. High-risk histopathologic features and severe anaplasia both predict the need for more aggressive treatment; however, not all centers are able to easily assess tumor samples for degree of anaplasia. Instead, identification of genetic signatures able to distinguish among anaplastic grades and thus predict high versus low risk retinoblastoma would facilitate appropriate risk stratification in a wider patient population. A better understanding of genes dysregulated in anaplasia would also yield valuable insights into pathways underlying the development of more severe retinoblastoma. Here, we present the histopathologic and gene expression analysis of 28 retinoblastoma cases using microarray analysis. Tumors of differing anaplastic grade show clear differential gene expression, with significant dysregulation of unique genes and pathways in severe anaplasia. Photoreceptor and nucleoporin expression in particular are identified as highly dysregulated in severe anaplasia and suggest particular cellular processes contributing to the development of increased retinoblastoma severity. A limited set of highly differentially expressed genes are also able to accurately predict severe anaplasia in our dataset. Together, these data contribute to the understanding of the development of anaplasia and facilitate the identification of genetic markers of high-risk retinoblastoma.

Publication Title

Distinct Gene Expression Profiles Define Anaplastic Grade in Retinoblastoma.

Sample Metadata Fields

Specimen part

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accession-icon GSE32497
GENOME-WIDE CpG ISLAND METHYLATION ANALYSIS IN NON-SMALL CELL LUNG CANCER PATIENTS
  • organism-icon Homo sapiens
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Genome-wide CpG island methylation analyses in non-small cell lung cancer patients.

Sample Metadata Fields

Specimen part, Disease, Cell line, Treatment

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accession-icon GSE32496
GENOME-WIDE CpG ISLAND METHYLATION ANALYSIS IN NON-SMALL CELL LUNG CANCER PATIENTS [Affymetrix expression data]
  • organism-icon Homo sapiens
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Epigenetic changes largely contribute to the regulation of gene expression in cancer cells. DNA methylation is part of the epigenetic gene regulation complex which is relevant for the pathogenesis of cancer. We performed a genome-wide search for methylated CpG islands in tumors and corresponding non-malignant lung tissue samples of 101 stage I-III non-small cell lung cancer (NSCLC) patients by combining methylated DNA immunoprecipitation and microarray analysis using NimbleGens 385K Human CpG Island plus Promoter arrays. By testing for differences in methylation between tumors and corresponding non-malignant lung tissues, we identified 298 tumor-specifically methylated genes. From many of these genes epigenetic regulation was unknown so far. Gene Ontology analysis revealed an over-representation of genes involved in regulation of gene expression and cell adhesion. Expression of 182 of 298 genes was found to be upregulated after 5-aza-2-deoxycytidine (Aza-dC) and/or trichostatin A (TSA) treatment of 3 NSCLC cell lines by Affymetrix microarray analysis. In addition, methylation of selected genes in primary NSCLCs and corresponding non-malignant lung tissue samples were analyzed by methylation-sensitive high resolution melting analysis (MS-HRM). Our results obtained by MS-HRM analysis confirmed our data obtained by MeDIP-chip analysis. Moreover, by comparing methylation results from MeDIP-chip analysis with clinico-pathological parameters of the patients we observed methylation of HOXA2 as potential parameter for shorter disease-free survival of NSCLC patients. In conclusion, using a genome-wide approach we identified a large number of tumor-specifically methylated genes in NSCLC patients. Our results stress the importance of DNA methylation for the pathogenesis of NSCLCs.

Publication Title

Genome-wide CpG island methylation analyses in non-small cell lung cancer patients.

Sample Metadata Fields

Cell line, Treatment

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accession-icon GSE19337
Listeria monocytogenes induces T cell receptor unresponsiveness via its pore-forming toxin listeriolysin O
  • organism-icon Mus musculus
  • sample-icon 3 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

The success of many pathogens relies on their ability to circumvent the innate and adaptive immune defenses. How bacterial pathogens subvert host responses is not clear. Cholesterol-dependent cytolysins (CDCs) represent an expansive family of homologous pore-forming toxins produced by more than 20 Gram-positive bacterial species. Here we show that listeriolysin O (LLO), a prototype CDC produced by Listeria monocytogenes, inhibits antigen receptor-induced T cell proliferation. In vivo proliferation of OT II T cells was highly diminished in the presence of wild type but not the LLO-deficient bacteria. T cells pre-exposed to LLO ex vivo were also impaired in proliferation upon TCR activation in vivo and in vitro. Our results suggest that LLO-induced T cell unresponsiveness is due to the sub-threshold activation of T cells via the induction of a calcium-NFAT dependent transcriptional program that drives the expression of negative regulators of TCR signaling.

Publication Title

Listeria monocytogenes induces T cell receptor unresponsiveness through pore-forming toxin listeriolysin O.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon SRP067124
Comparative analysis of single-cell RNA sequencing methods
  • organism-icon Mus musculus
  • sample-icon 743 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500, Illumina HiSeq 2000, Illumina HiSeq 1500

Description

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods and provides a framework for benchmarking further improvements of scRNA-seq protocols. Overall design: J1 mESC in two replicates per library preparation method.

Publication Title

A systematic evaluation of single cell RNA-seq analysis pipelines.

Sample Metadata Fields

Cell line, Subject

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accession-icon SRP108854
zUMIs: a fast and flexible pipeline for RNA sequencing data with UMIs
  • organism-icon Homo sapiens
  • sample-icon 81 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 1500

Description

Background Single-cell RNA-sequencing (scRNA-seq) experiments typically analyze hundreds or thousands of cells after amplification of the cDNA. The high throughput is made possible by the early introduction of sample-specific bar codes (BCs), and the amplification bias is alleviated by unique molecular identifiers (UMIs). Thus, the ideal analysis pipeline for scRNA-seq data needs to efficiently tabulate reads according to both BC and UMI. Findings zUMIs is a pipeline that can handle both known and random BCs and also efficiently collapse UMIs, either just for exon mapping reads or for both exon and intron mapping reads. If BC annotation is missing, zUMIs can accurately detect intact cells from the distribution of sequencing reads. Another unique feature of zUMIs is the adaptive downsampling function that facilitates dealing with hugely varying library sizes but also allows the user to evaluate whether the library has been sequenced to saturation. To illustrate the utility of zUMIs, we analyzed a single-nucleus RNA-seq dataset and show that more than 35% of all reads map to introns. Also, we show that these intronic reads are informative about expression levels, significantly increasing the number of detected genes and improving the cluster resolution. Conclusions zUMIs flexibility makes if possible to accommodate data generated with any of the major scRNA-seq protocols that use BCs and UMIs and is the most feature-rich, fast, and user-friendly pipeline to process such scRNA-seq data. Overall design: HEK293T cells were sequenced using the mcSCRB-seq protocol (Bagnoli et al., 2017)

Publication Title

zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs.

Sample Metadata Fields

Cell line, Subject

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accession-icon GSE105778
Regulation of Glucose Uptake and Inflammation by FOXO1 and FOXO3 in Skeletal Muscle
  • organism-icon Mus musculus
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.1 ST Array (mogene21st)

Description

Forkhead box class O (FoxO) transcription factors regulate whole body energy metabolism, skeletal muscle mass and substrate switching. To elucidate the role of FOXO in skeletal muscle, dominant negative (dn) constructs for FOXO1 (FOXO1dn) or FOXO3 (FOXO3dn) were transfected by electroporation into mouse tibialis anterior muscle and glucose uptake, signal transduction, and glucose stimulated gene expression profiles were assessed. Results were compared against contralateral control transfected muscle.

Publication Title

Regulation of glucose uptake and inflammation markers by FOXO1 and FOXO3 in skeletal muscle.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon SRP067154
Sequencing Universal Human Reference RNA by Smart-seq and early barcoding library preparation methods
  • organism-icon Homo sapiens
  • sample-icon 11 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq1500

Description

Many library preparation methods are available for gene expression quantification. Here, we sequenced and analysed Universal Human Reference RNA (UHRR) prepared using Smart-Seq2, TruSeq (public data) and a protocol using unique molecular identifiers (UMIs) that all include the ERCC spike-in mRNAs to investigate the effects of amplification bias on expression quantification. Overall design: UHRR 10 and 12 replicates for Smart-seq2 and UMI-seq library preparation methods, respectively.

Publication Title

The impact of amplification on differential expression analyses by RNA-seq.

Sample Metadata Fields

No sample metadata fields

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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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