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accession-icon GSE12075
The impact of microRNAs on protein output
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 17 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

The impact of microRNAs on protein output.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE12003
4 days cultured progenitors and 8 days cultured mature neutrophils from WT vs miR-223 null neutrophils
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This array analysis is to study developmental time course of the regulation of target messages expression during culture of murine neutrophils versus miR-223 null neutrophils. Culture media was SILAC-IMDM for MS analysis.

Publication Title

The impact of microRNAs on protein output.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE12001
Wild-type neutrophils and miR-223 null neutrophils
  • organism-icon Mus musculus
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This array analysis is to study the regulation of target messages expression in murine neutrophils versus miR-223 null neutrophils.

Publication Title

The impact of microRNAs on protein output.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE11973
Wild-type cultured neutrophils versus miR-223 null cultured neutrophils
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This array analysis is to study the regulation of target messages expression in in vitro cultured murine neutrophils versus miR-223 null neutrophils. Culture media was SILAC-IMDM for MS analysis.

Publication Title

The impact of microRNAs on protein output.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE49384
Transcriptional targets of Hippo signaling in mammalian cells
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

The Hippo pathway is an emerging signaling cascade involved in the regulation of organ size control. It consists of evolutionally conserved protein kinases that are sequentially phosphorylated and activated. The active Hippo pathway subsequently phosphorylates a transcription coactivator, YAP, which precludes its nuclear localization and transcriptional activation. Identification of transcriptional targets of YAP in diverse cellular contexts is therefore critical to the understanding of the molecular mechanisms in which the Hippo pathway restricts tissue growth.

Publication Title

Hippo signaling regulates microprocessor and links cell-density-dependent miRNA biogenesis to cancer.

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 GSE57864
Gene expression in diploid and evolved tetraploid RPE-1 and BJ-1 cells
  • organism-icon Homo sapiens
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Both diploid RPE-1 and BJ-1 cells were made tetraploid by transient treatment with the cytokinesis inhibitor DCD. Proliferating tetraploids from both BJ-1 and RPE-1 were selected and isolated. The gene expression profiles of the proliferating tetraploid cells were then compared to the diploids from which they originated.

Publication Title

Cytokinesis failure triggers hippo tumor suppressor pathway activation.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE57769
Gene expression profile of diploid and tetraploid mouse primary hepatocyte
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Genetically unstable tetraploid cells can promote tumorigenesis. Recent estimates suggest that ~37% of human tumors have undergone a genome-doubling event during their development. This potentially oncogenic effect of tetraploidy is countered by a p53-dependent barrier to proliferation. However, the cellular defects and corresponding signaling pathways that trigger growth suppression in tetraploid cells are not known. Here we combine genome-scale RNAi screening and in vitro evolution approaches to demonstrate that cytokinesis failure activates the Hippo tumor suppressor pathway in cultured cells as well as in naturally occurring tetraploid cells in vivo. Induction of the Hippo pathway is triggered in part by extra centrosomes, which alter small G-protein signaling and activate LATS2 kinase; LATS2 in turn stabilizes p53 and inhibits the transcriptional regulators YAP and TAZ. These findings define an important tumor suppression mechanism. Furthermore, our experiments uncover adaptations that allow nascent tumor cells to bypass this inhibitory regulation.

Publication Title

Cytokinesis failure triggers hippo tumor suppressor pathway activation.

Sample Metadata Fields

Age, Specimen part

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accession-icon GSE55560
Hippo Pathway Activity Influences Liver Cell Fate
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Hippo pathway activity influences liver cell fate.

Sample Metadata Fields

Specimen part, Time

View Samples
accession-icon GSE55558
Analysis of Liver Progenitor Cells Generated from Yap Expression [Set 1]
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Hippo signaling is highly associated with activity in the stem cell compartment of many epithelial tissues. In this study, we examined if Hippo signaling inhibition (by inducing Yap expression) could convert differentiated cells into a progenitor like phenotype. Organoid cells derived from mouse livers under various conditions, wild-type, Yap ON (Plus Dox), and Yap ON then OFF (Minus Dox) was examined.

Publication Title

Hippo pathway activity influences liver cell fate.

Sample Metadata Fields

Specimen part

View Samples
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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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