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accession-icon SRP055932
Cross-species transcriptome profiling identifies new alveolar epithelial type I cell-specific genes
  • organism-icon Homo sapiens
  • sample-icon 4 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Diseases involving the distal lung alveolar epithelium include chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF) and lung adenocarcinoma. Accurate labeling of specific cell types is critical for determining the contribution of each to pathogenesis of these diseases. The distal lung alveolar epithelium is comprised of two cell types, alveolar epithelial type 1 (AT1) and type 2 (AT2) cells. While cell type-specific markers, most prominently surfactant protein C (SFTPC), have allowed detailed studies of AT2 cell differentiation and their roles in disease, studies of AT1 cells have been hampered by lack of genes with expression unique to AT1 cells. To address this, we performed genome-wide expression profiling of multiple rat organs alongside purified rat AT2, AT1 and in vitro differentiated AT1-like cells, resulting in identification of 54 candidate AT1 cell markers. Cross-referencing with genes upregulated in human in vitro differentiated AT1-like cells narrowed the potential list to 18 candidate genes. Testing the top four candidate genes at RNA and protein levels revealed GRAM domain 2 (GRAMD2), a protein of unknown function, as unique to AT1 cells, while SCNN1G within lung is restricted to AT1 cells. RNAseq confirmed that GRAMD2 is transcriptionally silent in human AT2 cells. Immunofluorescence of mouse alveoli verified that GRAMD2 expression is restricted to the plasma membrane of AT1 cells. These new AT1 cell-specific genes, with GRAMD2 as a leading candidate, will enhance AT1 cell isolation, investigation of alveolar epithelial cell differentiation potential, and contribution of AT1 cells to distal lung diseases. Overall design: RNAseq of purified primary human alveolar epithelial type 2 (AT2) and in vitro differentiated type 1 (AT1-like) cells.

Publication Title

Cross-Species Transcriptome Profiling Identifies New Alveolar Epithelial Type I Cell-Specific Genes.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE3330
Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping
  • organism-icon Mus musculus
  • sample-icon 60 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Expression 430A Array (moe430a)

Description

Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. To map gene regulatory pathways, we used microarray-derived gene expression measurements in 60 individuals of an F2 sample segregating for diabetes. We performed correlation analysis among ~40,000 expression traits. By combining correlation among expression traits and linkage mapping information, we were able to identify regulatory networks, make functional predictions to uncharacterized genes, and characterize novel members of known pathways. Using 36 seed traits, we found evidence of coordinate regulation of 160 G-protein coupled receptor (GPCR) pathway expression traits. Of the 160 traits, 50 had their major LOD peak within 8 cM of a locus on chromosome 2, and 81 others had a secondary peak in this region. A previously uncharacterized Riken cDNA clone, which showed strong correlation with stearoyl CoA desaturase 1 expression, was experimentally validated to be responsive to conditions that regulate lipid metabolism. Using linkage mapping, we identified multiple genes whose expression is under the control of transcription regulatory loci. Trait-correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we only studied mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping only.

Publication Title

Combined expression trait correlations and expression quantitative trait locus mapping.

Sample Metadata Fields

No sample metadata fields

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accession-icon SRP058841
Tunable protein synthesis by transcript isoforms in human cells (Transcript Isoforms in Polysomes sequencing: TrIP-seq)
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2500

Description

Eukaryotic genes generate multiple mRNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex as each isoform can be translated differently. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and translational output of each transcript isoform. We extracted a panel of 5' and 3' untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5' untranslated regions exert robust translational control between cell lines, while 3' untranslated regions can confer cell-type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels. Overall design: Total cytoplasmic and eight polysomal fractions of RNA were purified from HEK 293T cells in biological duplicate. Ribosomal RNA was depleted using Ribo-Zero (Human/Mouse/Rat; Epicenter) and libraries were prepared using the TruSeq RNA v2 kit (RS-122-2001; Illumina) skipping the polyA selection step. Reads are paired-end 75bp and sequencing adapters are GATCGGAAGAGCACACGTCTGAACTCCAGTCAC (read1) and AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT (read2).

Publication Title

Tunable protein synthesis by transcript isoforms in human cells.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE34757
Microarray analysis of Saccharomyces cerevisiae expressing the Hsp90 mutant hsc82-W296A
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

Altered mRNA levels of HBT1 were observed in S. cerevisiae cells expressing hsc82-W296A compared to WT HSC82. We conducted microarray analysis to determine the extent of other changes in that strain.

Publication Title

Identification of an Hsp90 mutation that selectively disrupts cAMP/PKA signaling in Saccharomyces cerevisiae.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE93351
Expression data from human embryonic stem cells, progenitors, and differentiated neurons
  • organism-icon Homo sapiens
  • sample-icon 17 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Previous studies have reported that human pluripotent stem cells (hPSCs) generate dorsal forebrain, cortical-like neurons under default differentiation in the absence of patterning morphogens. Novel bioinformatic analyses of whole transcriptome data allow us to examine these cells' regional specification more comprehensively. Furthermore, these tools allow us to ask how well hPSNs mimic their endogenous counterparts during various stages of in vivo human brain development.

Publication Title

Default Patterning Produces Pan-cortical Glutamatergic and CGE/LGE-like GABAergic Neurons from Human Pluripotent Stem Cells.

Sample Metadata Fields

Sex, Specimen part, Time

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accession-icon GSE19415
Expression data from primary ovine fetal turbinate cells infected with Orf Virus IA82 and deletion mutant OV-IA82024
  • organism-icon Ovis aries
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Bovine Genome Array (bovine)

Description

Reverse genetics has been widely used to investigate function of viral genes. In the present study we investigated the gene expression profile of a primary ovine cell (OFTu) in response to infection with the wild type (OV-IA82) and deletion mutant virus (OV-IA82024) aiming to determine possible functions for ORFV024 during ORFV infection.

Publication Title

A novel inhibitor of the NF-{kappa}B signaling pathway encoded by the parapoxvirus orf virus.

Sample Metadata Fields

Specimen part

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accession-icon SRP116018
Whole-organism clone-tracing using single-cell sequencing
  • organism-icon Danio rerio
  • sample-icon 160 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

We present ScarTrace, a single-cell sequencing strategy that allows us to simultaneously quantify information on clonal history and cell type for thousands of single cells obtained from different organs from adult zebrafish. Using this approach we show that all blood cells types in the kidney marrow arise from a small set of multipotent embryonic. In contrast, we find that cells in the eyes, brain, and caudal tail fin arise from many embryonic progenitors, which are more restricted and produce specific cell types in the adult tissue. Next we use ScarTrace to explore when embryonic cells commit to forming either left or right organs using the eyes and brain as a model system. Lastly we monitor regeneration of the caudal tail fin and identify a subpopulation of resident macrophages that have a clonal origin that is distinct from other blood cell types. Overall design: Single cell sequencing data from cells isolated from zebrafish organs (whole kidney marrow, forebrain, hindbrain, left eye, right eye, left midbrain, right midbrain, and regenerated fin). For each cell, we provide libraries with transcritpome and with clonal information, respectively.

Publication Title

Whole-organism clone tracing using single-cell sequencing.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE68454
Systems analysis of uterine and tumor microenvironments
  • organism-icon Mus musculus
  • sample-icon 63 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice.

Sample Metadata Fields

Age, Specimen part

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accession-icon GSE68433
Systems analysis of uterine microenvironment 4, 6, 8, 10, 11 or 12 days after fertilization
  • organism-icon Mus musculus
  • sample-icon 33 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

Analysis of uterine microenvironment at gene expression level. The hypothesis tested in the present study was that Tregs orchestrated the immune reponse triggered in presence of embryo

Publication Title

Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice.

Sample Metadata Fields

Age, Specimen part

View Samples
accession-icon GSE68434
Systems analysis of B16 tumor microenvironment 4 or 14 days after tumor inoculation
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

Analysis of B16 tumor microenvironment at gene expression level. The hypothesis tested in the present study was that Tregs orchastrated the immune reponse triggered in presence of tumors

Publication Title

Regulatory T Cells Orchestrate Similar Immune Evasion of Fetuses and Tumors in Mice.

Sample Metadata Fields

Age, 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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