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accession-icon GSE84154
Celastrol treatment on mouse embryonic fibroblasts (MEFs)
  • organism-icon Mus musculus
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Celastrol has been shown to sensitize leptin receptor signaling and reduce ER stress. Current microarray data provide the gene expression profile in mouse embryonic fibroblasts (MEFs) after Celastrol treatment compared with control.

Publication Title

Withaferin A is a leptin sensitizer with strong antidiabetic properties in mice.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon GSE51257
Functional heterogeneity of cancer-associated fibroblasts from human colon tumors shows specific prognostic gene expression signature
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Tumor growth and metastasis is controlled by paracrine signaling between cells of the tumor microenvironment and malignant cells. Cancer-associated fibroblasts (CAFs), are functionally important components of the tumor microenvironment. Although some steps involved in the cross-talk between these cells are known, there is still a lot that is not clear. Thus, the addition of, the consideration of microenvironment in the development of the disease, to the clinical and pathological procedures (currently admitted as the consistent value cancer treatments) could lay the foundations for the development of new treatment strategies to control the disease.

Publication Title

Functional heterogeneity of cancer-associated fibroblasts from human colon tumors shows specific prognostic gene expression signature.

Sample Metadata Fields

Specimen part

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accession-icon GSE108998
Allopregnanolone alters the gene expression profile of human glioblastoma cells
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Glioblastomas (GBM) are one of the most frequent and aggressive brain tumors. In these malignancies, progesterone (P4) promotes proliferation, migration, and invasion. The P4 metabolite allopregnanolone (3-THP) similarly promotes cell proliferation in the U87 human GBM cell line.

Publication Title

Allopregnanolone Alters the Gene Expression Profile of Human Glioblastoma Cells.

Sample Metadata Fields

Specimen part, Cell line, Treatment

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accession-icon GSE16455
Indolent MCL identified by genomic and gene expression profiling
  • organism-icon Homo sapiens
  • sample-icon 53 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Mantle cell lymphoma (MCL) is an aggressive neoplasm with poor outcome. However, some patients have an indolent disease (iMCL) and may not require intensive treatment at initial diagnosis. Diagnostic criteria to recognize these patients are not available. We hypothesized that the analysis of the genetic and expression features of the tumors may help to identify patients with an indolent clinical evolution and provide biomarkers that could be used in the clinical setting.

Publication Title

Genomic and gene expression profiling defines indolent forms of mantle cell lymphoma.

Sample Metadata Fields

Disease, Disease stage

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accession-icon GSE2378
Normal and glaucomatous astrocytes
  • organism-icon Homo sapiens
  • sample-icon 15 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U95A Array (hgu95a)

Description

Astrocytes from optic nerve head from donors with and without glaucoma

Publication Title

Differential gene expression in astrocytes from human normal and glaucomatous optic nerve head analyzed by cDNA microarray.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE40965
DICER1 hotspot mutations cause defective miRNA processing
  • organism-icon Mus musculus
  • sample-icon 28 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Recurrent somatic hotspot mutations of DICER1 appear to be clustered around each of four critical metal binding residues in the RNase IIIB domain of DICER1. This domain is responsible for cleavage of the 3 end of the 5p-miRNA strand of a pre-mRNA hairpin. To investigate the effects of these cancer-associated hotspot mutations we engineered mouse Dicer1-deficient ES cells to express wild-type and an allelic series of the mutant human DICER1 variants. Global miRNA and mRNA profiles from cells carrying the metal binding site mutations were compared to each other and wild-type human DICER1. The miRNA and mRNA profiles generated through the expression of the hotspot mutations were virtually identical, and the DICER1 hotspot mutation carrying cells were distinct from both wild-type and Dicer1-deficient cells. Further, miRNA profiles showed mutant DICER1 results in a dramatic loss in processing of mature 5p-miRNA strands but were still able to create 3p-strand miRNAs. Messenger-RNA profile changes were consistent with the loss of 5p-strand miRNAs and showed enriched expression for predicted targets of the lost 5p derived miRNAs. We therefore conclude that cancer-associated somatic hotspot mutations of DICER1, affecting any one of four metal binding residues in the RNase IIIB domain, are functionally equivalent with respect to miRNA-processing and are hypomorphic alleles, yielding a global loss in processing of mature 5p-strand miRNA. We further propose that this resulting 3p-strand bias in mature miRNA expression likely underpins the oncogenic potential of these hotspot mutations.

Publication Title

Cancer-associated somatic DICER1 hotspot mutations cause defective miRNA processing and reverse-strand expression bias to predominantly mature 3p strands through loss of 5p strand cleavage.

Sample Metadata Fields

Specimen part

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accession-icon SRP067378
Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis (PAGODA)
  • organism-icon Mus musculus
  • sample-icon 557 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

The extent of transcriptional diversity in mouse NPCs is likely to be influenced by a variety of unexamined factors that include programmed cell death, genomic mosaicism as well as a variety of “environmental” influences such as changes in exposure to signaling lipids. We therefore used scRNA-seq to assess a cohort of cortical NPCs from an embryonic mouse. We demonstrate that PAGODA (Pathway And Geneset OverDispersion Analysis) effectively recovers the known neuroanatomical and functional organization of NPCs, identifying multiple aspects of transcriptional heterogeneity within the developing mouse cortex that are difficult to discern by the existing heterogeneity analysis approaches. Overall design: Examination of mouse NPC transcriptional heterogeneity via single cell RNA-seq

Publication Title

Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE32305
Establishing a set of genes differentially expressed in benign versus malignant adrenocortical cells
  • organism-icon Bos taurus
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Bovine Genome Array (bovine)

Description

Models for tumorigenesis can be made by transforming normal cells with defined genetic elements. This allows us to determine that adrenocortical tumor development and progression follows a multistep model. Morever, we demonstrated that the order of genetic events has a great consequence on the phenotype of the resultant tumor. We performed transcriptomic analysis using cDNA microarrays to identify the molecular signature that might explain the distinctive in vivo phenotypes observed in response to both orders of the mutational events.

Publication Title

Acquisition order of Ras and p53 gene alterations defines distinct adrenocortical tumor phenotypes.

Sample Metadata Fields

Specimen part

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accession-icon SRP091680
Assessing characteristics of RNA amplification methods for single cell RNA sequencing
  • organism-icon Homo sapiens
  • sample-icon 124 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000, Illumina HiSeq 2500

Description

We conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. Our approach was to dilute bulk total RNA (from a single source) to levels bracketing single-cell levels of total RNA (10 pg and 100 pg) in replicates and amplifying the RNA to levels sufficient for RNA sequencing. Overall design: We performed replicate transcriptome amplifications of Universal Human Reference RNA (UHR) and Human Brain Reference RNA (HBR) that were diluted to single-cell and ten-cell abundances (10 and 100 picograms (pg.) total RNA or ~200,000 and 2 million mRNA molecules, respectively) and were amplified using three single-cell RNA amplification methods. Methods included the antisense RNA IVT protocol (aRNA), a custom C1 SMARTer protocol (SmartSeq Plus) performed on a Fluidigm C1 96-well chip, and a modified NuGen Ovation RNA sequencing protocol (NuGen). Bulk ribo-depleted UHR and HBR RNA were sequenced and served as a reference. The general experimental scheme was consistent for all dilution replicates; however, there were differences across experimental groups in the specifics of experimental protocols, necessitated by particular methodologies. Because of these experimental differences, head-to-head comparison of methods is not appropriate and our goal is to provide quantitative analyses of factors affecting individual methods. Current results should be used in experimental planning, data analysis, and method optimization rather than as a performance test of any particular method. Detailed experimental design: Each collaborating center obtained reference RNA with the same lot number for Universal Human Reference (UHR) RNA (Agilent 740000, Lot 0006141415) and Human Brain Reference (HBR) (Ambion AM6050, Lot-105P055201A) and performed replicate amplification using a single amplification method, detailed below. SmartSeq Plus: Reference RNA was diluted to an intermediate stock solution by serial dilution. A final 1000-fold dilution occurred on the C1 chip, such that individual wells in a given batch contained 9.99 pg. sampled from a common intermediate dilution. ERCC spike-in RNA mix 1 (Ambion 4456740) was also added for a final mass of approximately 7 femtograms (fg.) per sample, a 4,000,000x dilution from stock. Samples for each source RNA were prepared in single batches. After amplification, cDNA from the entire C1 96-well plate was quantified using picogreen. C1 chips with an average yield of less than 3 nanograms were discarded. The top 15 reactor wells by cDNA concentration were selected as representative 10 pg. samples for sequencing library preparation. Another 50 wells were selected by the same criteria. These were pooled in sets of 10, generating 5 100 pg. samples for each HBR and UHR. All samples for a given source were prepared in a single sequencing library preparation batch using Nextera XT C1 protocol. NuGen: HBR samples were prepared in a single batch using amplification protocol 1, generating 4 10 pg. and 4 100 pg. amplified replicates. UHR samples were prepared in two batches, using either amplification protocol 1 or 2, generating 15 10 pg. and 11 100 pg. samples. A single sequencing library preparation was performed for each batch of samples using either Lucigen NxSeq or NuGen Ovation Rapid protocol. aRNA: Amplification was performed as previously described (Morris J, Singh JM, Eberwine JH. Transcriptome analysis of single cells. J. Vis. Exp. [Internet]. 2011; Available from: http://www.jove.com/video/2634/transcriptome-analysis-of-single-cells). HBR samples were prepared in 4 batches from separate dilutions of reference RNA, generating 19 10 pg. and 3 100 pg. amplified replicates. ERCC spike-ins were added to 5 of the 10 pg. replicates before amplification at a dilution of 4,000,000x from stock. UHR samples were diluted and amplified in 2 batches from separate dilutions of reference RNA, generating 12 10 pg. and 7 100 pg. amplified replicates. A single sequencing library preparation was performed using Illumina TruSeq Stranded mRNA protocol modified to begin with amplified aRNA. A small numbers of reads were assigned to ERCC transcripts in replicates from the batch where ERCCs had been added that did not have spike-ins added (average of 0.5% of the number of reads assigned in spiked samples). 18 additional HBR 10 pg. replicates were amplified using aRNA for protocol optimization experiments. These samples were treated separately and were excluded from primary analysis. Bulk UHR and HBR: For each reference RNA, three sequencing libraries were generated from bulk material at the same laboratory as the SmartSeq Plus replicates. Cytoplasmic and mitochondrial ribosomal RNA (rRNA) were depleted using Ribo-Zero Gold as part of Illumina TruSeq Stranded Total RNA protocol. Samples were sequenced on Illumina HiSeq 2000. Because of differences in experimental design, direct comparison across methods of precision and the effect of input RNA abundance is difficult. For example, input RNA amount as a factor have different meanings for the different amplification methods: for SmartSeq Plus, because 100pg samples were constructed by pooling 10 pg. samples after cDNA amplification, any resulting effects involve library construction, while for aRNA and NuGen resulting effects reflect both cDNA amplification steps and library steps.

Publication Title

Assessing characteristics of RNA amplification methods for single cell RNA sequencing.

Sample Metadata Fields

Subject

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accession-icon SRP041753
Transriptional profiling upon heat shock and recovery in cells deficient for FBXW7 and their wild type counterpart.
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

FBXW7 modulates stress response by post-translational modification of HSF1 HSF1 orchestrates the heat-shock response upon exposure to heat stress and activates a transcriptional program vital for cancer cells. Genes positively regulated by HSF1 show increeased expression during heat shock while their expression is reduced during recovery. Genes negatively regulated by HSF1 show the opposite pattern. In this study we utilized the HCT116 FBXW7 KO colon cell line and its wild type counterpart to monitor gene expression changes during heat shock (42oC, 1 hour) and recovery (37oC for 2 hours post heat shock) using RNA sequencing. These results revealed that the heat-shock response pathway is prolonged in cells deficient for FBXW7. Overall design: Whole RNA was extracted from 1 million HCT116 WT or FBXW7KO cells using the RNAeasy kit (Qiagen) according to the manufacturer’s protocol. Poly-A+ (magnetic oligodT-containing beads (Invitrogen)) or Ribominus RNA was used for library preparation. cDNA preparation and strand-specific library construction was performed using the dUTP method. Libraries were sequenced on the Illumina HiSeq 2000 using 50bp single-read method. Differential gene expression analysis was performed for each matched recovery versus heat-shock pairs, separately in each biological replicate and cell line (WT or KO). Two types of comparisons were tested: (a) WT recovery vs WT heat shock, (b) FBXW7 KO recovery vs heat shock.

Publication Title

FBXW7 modulates cellular stress response and metastatic potential through ​HSF1 post-translational modification.

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)

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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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