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accession-icon GSE90450
Expression data from keratinocyte-specific Zfp36-deficient mouse skin treated with imiquimod
  • organism-icon Mus musculus
  • sample-icon 20 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

Tristetraprolin (TTP, encoded by Zfp36) regulates the mRNA stability of several important cytokines. Due to the critical role of this RNA-binding protein in the control of inflammation, TTP deficiency leads to the spontaneous development of a complex inflammatory syndrome. So far, this phenotype has been largely attributed to dysregulated production of TNF and IL-23 by myeloid cells such as macrophages or dendritic cells. Here, we generated mice with conditional deletion of TTP in keratinocytes. These mice developed exacerbated inflammation in the imiquimod-induced psoriasis model. Furthermore, these mice progressively developed a spontaneous pathology with systemic inflammation, psoriatic-like skin lesions and dactylitis. Finally, we provide evidence that keratinocyte-derived TNF productin drives the different pathological features. In summary, these findings expand current views on the initiation of psoriasis and related arthritis by revealing the keratinocyte-intrinsic role of TTP.

Publication Title

Tristetraprolin expression by keratinocytes controls local and systemic inflammation.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon GSE60003
Expression data from Control or ShSuz12 rat Intestinal epithelial cells IEC-6
  • organism-icon Rattus norvegicus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Genome 230 2.0 Array (rat2302)

Description

Polycomb-group proteins form multimeric protein complexes involved in transcriptional silencing. The Polycomb Repressive complex 2 (PRC2) contains the Suppressor of Zeste-12 protein (Suz12) and the histone methyltransferase Enhancer of Zeste protein-2 (Ezh2). This complex, catalyzing the di- and tri-methylation of histone H3 lysine 27, is essential for embryonic development and stem cell renewal. However, the role of Polycomb-group protein complexes in the control of the intestinal epithelial cell (IEC) phenotype is not known. We investigated the impact of Suz 12 depletion on gene expression in IEC-6 cells.

Publication Title

The histone H3K27 methylation mark regulates intestinal epithelial cell density-dependent proliferation and the inflammatory response.

Sample Metadata Fields

Cell line

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accession-icon SRP077873
Transcriptome analysis of mouse lung epithelial cells
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Mouse lung epithelial subpopulations (alveolar type 2, basal and airway luminal cells) freshly dissociated from mouse lung and trachea were isolated by FACS. RNA-seq gene expression profiling was used to determine gene signature from each population. Overall design: Cells were isolated from the small airway (SA) and large airway (LA) of 6 mouse lungs

Publication Title

Lung Basal Stem Cells Rapidly Repair DNA Damage Using the Error-Prone Nonhomologous End-Joining Pathway.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE54785
The acetylome regulators Hdac1 and Hdac2 differently modulate intestinal epithelial cell dependent homeostatic responses in experimental colitis
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Histone deacetylases (Hdac) remove acetyl groups from proteins, influencing global and specific gene expression. Hdacs control inflammation, as shown by Hdac inhibitor-dependent protection from DSS-induced murine colitis. While tissue-specific Hdac knockouts show redundant and specific functions, little is known of their intestinal epithelial cell (IEC) role. We have shown previously that dual Hdac1/Hdac2 IEC-specific loss disrupts cell proliferation and determination, with decreased secretory cell numbers and altered barrier function. We thus investigated how compound Hdac1/Hdac2 or Hdac2 IEC-specific deficiency alters the inflammatory response. Floxed Hdac1 and Hdac2 and villin-Cre mice were interbred. Compound Hdac1/Hdac2 IEC-deficient mice showed chronic basal inflammation, with increased basal Disease Activity Index (DAI) and deregulated Reg gene colonic expression. DSS-treated dual Hdac1/Hdac2 IEC-deficient mice displayed increased DAI, histological score, intestinal permeability and inflammatory gene expression. In contrast to double knockouts, Hdac2 IEC-specific loss did not affect IEC determination and growth, nor result in chronic inflammation. However, Hdac2 disruption protected against DSS colitis, as shown by decreased DAI, intestinal permeability and caspase-3 cleavage. Hdac2 IEC-specific deficient mice displayed increased expression of IEC gene subsets, such as colonic antimicrobial Reg3b and Reg3g mRNAs, and decreased expression of immune cell function-related genes. Our data show that Hdac1 and Hdac2 are essential IEC homeostasis regulators. IEC-specific Hdac1 and Hdac2 may act as epigenetic sensors and transmitters of environmental cues and regulate IEC-mediated mucosal homeostatic and inflammatory responses. Different levels of IEC Hdac activity may lead to positive or negative outcomes on intestinal homeostasis during inflammation

Publication Title

The acetylome regulators Hdac1 and Hdac2 differently modulate intestinal epithelial cell dependent homeostatic responses in experimental colitis.

Sample Metadata Fields

Specimen part

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accession-icon GSE47745
Expression data from intestine of HDAC1 and HDAC2 conditionally mutated mice
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Acetylation and deacetylation of histones and other proteins depend on the opposing activities of histone acetyltransferases and histone deacetylases (HDACs), leading to either positive or negative gene expression changes. The use of HDAC inhibitors (HDACi) has uncovered a role for HDACs in the control of proliferation, apoptosis and inflammation. However, little is known of the roles of specific HDACs in intestinal epithelial cells (IEC). We investigated the consequences of ablating both Hdac1 and Hdac2 in murine IECs gene expression.

Publication Title

HDAC1 and HDAC2 restrain the intestinal inflammatory response by regulating intestinal epithelial cell differentiation.

Sample Metadata Fields

Specimen part

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accession-icon GSE73355
The HLA-B*35 allele modulates ER stress, inflammation and proliferation in PBMCs from Limited Cutaneous Systemic Sclerosis patients
  • organism-icon Homo sapiens
  • sample-icon 15 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

The goal of this study was to assess whether the presence of HLA-B*35 contributes to activation of ER stress/UPR and inflammation in lcSScPAH PBMC.

Publication Title

The HLA-B*35 allele modulates ER stress, inflammation and proliferation in PBMCs from Limited Cutaneous Systemic Sclerosis patients.

Sample Metadata Fields

Specimen part

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accession-icon SRP051083
Transcriptome profiling of human lung cancer cell lines.
  • organism-icon Homo sapiens
  • sample-icon 160 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Purpose: The aim of this study is to compare different RNA extraction methods using a mixture design that allows the relative changes of the majority of genes profiled to be estimated. A number of samples were degraded to allow us to compare methods for dealing with more variable samples. Methods - Cell Culture: Lung adenocarcinoma cell lines NCI-H1975 and HCC827 from a range of passages (2-4) were grown on 3 separate occasions in RPMI media (Gibco) supplemented with Glutamax and 10% fetal calf serum to a 70% confluence. To replicate common experimental conditions cell lines were treated with 0.01% Dimethyl sulfoxide (Sigma), which is commonly used as a vehicle in drug treatment experiments. After 6 hours of treatment, cells were collected, snap-frozen on dry ice and stored at -80 degrees C until required. Methods - RNA preparation: Total RNA was extracted from between half a million and million cells using Total RNA Purification Kit (Norgen Biotek) with on-column DNAse treatment accorting to the kit instructions. RNA concentration for each pair of samples to be mixed was equalised to ~100 ng/µl using Qubit RNA BR Assay Kit (Life Technologies). Replicates were pooled in known proportions to obtain mixtures ranging from pure NCI-H1975 (100:0) to pure HCC827 (0:100) and intermediate mixtures ranging from 75:25 to 50:50 to 25:75 NCI-H1975:HCC827. All mixtures corresponding to the second replicate were split into two equal aliquots. One aliquot was left intact (we refer to this as the ''good'' replicate), while the second aliquot was degraded to produce known outlier samples by incubation at 37 degrees C for 7 days in a thermal cycler with a heated lid. 10 µl from each replicated mixture (both good and degraded) were used for Next Generation Sequencing library preparation using two kits: Illumina TruSeq Total Stranded RNA with Ribozero (TotalRNA) and Illumina TruSeq RNA v2 (mRNA) according to the manufacturer''s instructions. Completed libraries were sequenced on HiSeq 2500 with TruSeq SBS Kit v4- HS reagents (Illumina) as 100 bp single-end reads at the Australian Genome Research Facility (AGRF), Melbourne. Approximately 30 million 100 bp single-end reads were obtained for each sample. Reads were aligned to the human reference genome hg19 and mapped to known genomic features at the gene level using the Rsubread package (version 1.16.1) (Liao et al. 2013). Single reads were then summarized into gene-level counts using FeatureCounts (Liao et al. 2014). Overall design: Total RNA was extracted from lung adenocarcinoma cell lines NCI-H1975 and HCC827 (3 independent samples for each cell line) and mixed in known ratios. Both mRNA and Total RNA transcriptomes from these mixtures were profiled by RNA-Seq.

Publication Title

RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP083954
Transcriptome profiling of 5 human adenocarcinoma cell lines
  • organism-icon Homo sapiens
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Purpose: The aim of this study is to compare the transcriptome profiles of a limited number of lung cancer cell lines with the intention of selecting the two most similar cell lines for a mixture experiment (GSE64098). Methods - Cell Culture: Five lung adenocarcinoma cell lines (H2228, NCI-H1975, HCC827, H838 and A549) from a range of passages (2-4) were grown on 2 separate occasions in RPMI media (Gibco) supplemented with Glutamax and 10\% fetal calf serum to a 70\% confluence. To replicate common experimental conditions cell lines were treated with 0.01\% Dimethyl sulfoxide (Sigma), which is commonly used as a vehicle in drug treatment experiments. After 6 hours of treatment, cells were collected, snap-frozen on dry ice and stored at -80 degree C until required. Methods - RNA preparation: Total RNA was extracted from between half a million and million cells using Total RNA Purification Kit (Norgen Biotek) according to the kit instructions. RNA quality and concentration were assessed using Nanodrop and Tapestation RNA ScreenTape (Agilent) respectively. Methods - RNA-seq: 1 ug of total RNA from each sample were used for RNA-seq library preparation using TruSeq Total Stranded RNA with Ribozero (Illumina) according to manufacturer''s instructions. Completed libraries were sequenced on HiSeq 2000 with TruSeq SBS Kit v3- HS reagents (Illumina) as 100 bp single end reads at the Australian Genome Research Facility (AGRF), Melbourne. We obtained on average 28 million for each sample (range from 25 to 29 million). Reads were aligned to the human reference genome hg19 using the Rsubread package (version 1.16.1) (Liao et al. 2013). Single reads were then summarized into gene-level counts using FeatureCounts in the reverse-stranded mode (Liao et al. 2014). Overall design: Total RNA was extracted from lung adenocarcinoma cell lines H2228, NCI-H1975, HCC827, H838 and A549 (2 independent samples for each cell line). Total RNA transcriptome from these samples was profiled by RNA-seq.

Publication Title

RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods.

Sample Metadata Fields

Cell line, Subject

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accession-icon SRP120965
RNA-seq expression profiling of Grhl2-deficient and control murine lung epithelium at embryonic day E16.5
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Epithelial (CD31-CD45-EpCAM+) cells were isolated by FACS from Grhl2-deficient (Shh-Cre;Grhl2f/-) and control (Shh-Cre;Grhl2f/+) embryonic lungs at day E16.5 (3 biological replicates/genotype). Total RNA extracted from the samples was subjected to next-generation sequencing (NGS) library preparation using standard Illumina protocols. Completed libraries from individual samples were sequenced on a HiSeq2500 at the Australian Genome Research Facility. Overall design: RNA-seq was performed on Grhl2-deficient and control epithelium isolated from the lungs of E16.5 embryos (n=3 replicates/genotype/cell population).

Publication Title

Lung morphogenesis is orchestrated through Grainyhead-like 2 (Grhl2) transcriptional programs.

Sample Metadata Fields

Sex, Specimen part, Subject

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accession-icon GSE58095
Dissecting the heterogeneity of skin gene expression patterns in systemic sclerosis.
  • organism-icon Homo sapiens
  • sample-icon 59 Downloadable Samples
  • Technology Badge IconIllumina HumanHT-12 V4.0 expression beadchip

Description

We identified fibro-inflammatory and keratin gene expression signatures in systemic sclerosis skin.

Publication Title

Dissecting the heterogeneity of skin gene expression patterns in systemic sclerosis.

Sample Metadata Fields

Age, Specimen part, Race, Subject, Time

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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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