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accession-icon SRP166097
RNA-seq of bulk Treg and Tconv cells from murine liver and lymphoid tissues
  • organism-icon Mus musculus
  • sample-icon 381 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

With the aim of understanding how Treg cells in highly vascularized tissues are related to Treg cells in other organs, we performed RNA-seq analysis of bulk Treg and Tconv cells isolated from liver, blood, spleen, and the liver-draining portal lymph node. This revealed a clear separation of cell transcriptomes by both tissue and Treg/Tconv identity, with cells from the liver falling between blood- and spleen-derived cells. Compared to splenic Treg cells, hepatic Treg cells were enriched for genes related to proliferation and activation, and genes encoding chemokine and cytokine receptors. Overall design: RNA was extracted from FACS-purified Tconv and Treg cells from various tissues of Foxp3Thy1.1 mice. Each sample contains cells pooled from 3 mice. 2 cell types from each of 4 tissues x 3 replicates = 24 samples.

Publication Title

CD49b defines functionally mature Treg cells that survey skin and vascular tissues.

Sample Metadata Fields

Sex, Age, Specimen part, Cell line, Subject

View Samples
accession-icon SRP166106
RNA-seq of Treg and Tconv subsets from murine spleen
  • organism-icon Mus musculus
  • sample-icon 89 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

While unique subsets of Treg cells have been described in some non-lymphoid tissues, their relationship to Treg cells in secondary lymphoid organs and circulation remains unclear. We have identified a recirculating and highly suppressive effector Treg cell subset that expresses the a2 integrin, CD49b, and exhibits a unique tissue distribution. To identify genes and pathways enriched in CD49b+ Treg cells, we performed RNA-seq of splenic CD49b+ and CD49b- Treg cells that were of otherwise similar activation status based on expression of CD44 and CD62L. This revealed that splenic CD49b+ Treg cells express genes related to migration and activation, but are relatively depleted of genes whose expression is TCR-dependent in Treg cells. These results shed light on the identity and development of a functionally potent subset of mature effector Treg cells that recirculates through and surveys peripheral tissues. Overall design: RNA was extracted from FACS-purified splenic Tconv and Treg cells of different activation states from Foxp3GFP mice. 2 CD4+ T-cell lineages x 3 activation states x 4 replicates. There is no sample 3 (RNA was degraded); there are 23 samples in total.

Publication Title

CD49b defines functionally mature Treg cells that survey skin and vascular tissues.

Sample Metadata Fields

Sex, Age, Specimen part, Cell line, Subject

View Samples
accession-icon SRP165223
Single-cell RNA-seq of splenic Treg and Tconv cells
  • organism-icon Mus musculus
  • sample-icon 2 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

While unique subsets of Treg cells have been described in some non-lymphoid tissues, their relationship to Treg cells in secondary lymphoid organs and circulation remains unclear. We have identified a short-lived effector Treg cell subset that expresses the a2 integrin, CD49b, and exhibits a unique tissue distribution. Projection of the CD49b+ Treg signature onto the Treg phenotypic landscape as inferred by single-cell RNA-seq analysis, placed these cells at the apex of the Treg developmental trajectory. These results shed light on the identity and development of a functionally potent subset of mature effector Treg cells that recirculate through and survey peripheral tissues. Overall design: Single-cell RNA-seq libraries (10x Genomics) were prepared from FACS-purified Tconv and Treg cells from pooled spleens of Foxp3GFP mice.

Publication Title

CD49b defines functionally mature Treg cells that survey skin and vascular tissues.

Sample Metadata Fields

Sex, Age, Specimen part, Subject

View Samples
accession-icon SRP148597
Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment 3'' RNA Sequencing
  • organism-icon Homo sapiens
  • sample-icon 168 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 4000, Illumina HiSeq 2500

Description

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We created an immune map of breast cancer using single-cell RNA-seq data from 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph node. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer, with important implications for characterizing tumor-infiltrating immune cells.  Overall design: Single-cell RNA sequencing was performed on eight donors using the InDrop v2 protocol. For each donor populations of CD45+ immune cells were assayed for trancriptome-wide RNA-sequence. At least one replicate was taken for each donor.

Publication Title

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

Sample Metadata Fields

Specimen part, Subject

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accession-icon SRP148594
Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment - 5'' RNA sequencing and TCR sequencing
  • organism-icon Homo sapiens
  • sample-icon 29 Downloadable Samples
  • Technology Badge IconNextSeq 500, Illumina HiSeq 2500

Description

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We created an immune map of breast cancer using single-cell RNA-seq data from 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph node. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer, with important implications for characterizing tumor-infiltrating immune cells.  Overall design: Single-cell RNA sequencing was performed on three patients using the 10x genomics TCR profiling kits. For each patient, populations of T-cells were assayed for both TCR sequence and trancriptome-wide RNA-sequence. Two donors have a replicate experiment.

Publication Title

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

Sample Metadata Fields

Specimen part, Subject

View Samples
accession-icon GSE29751
Genomic Analysis of wig-1 Pathways
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

Analysis of wig-1 pathways via suppression of Wig-1 by antisense oligonucleotides

Publication Title

Genomic analysis of wig-1 pathways.

Sample Metadata Fields

Specimen part, Treatment

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accession-icon GSE36141
Gene expression data at 24hrs post-siRNA transfection for HCT116 cultures transfected with either DDX5si2008, DDX5si2053, or EBNA1si1666 siRNA's or mock transfected.
  • organism-icon Homo sapiens
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

HCT116 cells were transfected with two different siRNA's targeting either DDX5, an siRNA targeting EBNA1, or no siRNA (mock). The siRNA targeting EBNA1 is used as a negative control since HCT116 cells do not have the EBNA1 gene. RNA was obtained from cultures at 24hrs post-siRNA transfection using the Qiagen RNeasy Minikit (cat. # 74104) with on-column DNase digestion performed as per the manufacturer's protocol. The RNA samples were isolated at 24hrs post-siRNA transfection since this timepoint precedes an impaired G1-to-S phase cell cycle progression phenotype that is evident at 48hrs post-siRNA transfection and so may reveal gene expression changes occuring before this effect on cell cycle. RNA samples were submitted to the Cold Spring Harbor Laboratory Microarray Faciity where cDNA was prepared, labeled, and hybridized to Affymetrix GeneChip Human Gene 1.0 ST microarrays. Data from the arrays were processed using the RMA method with an up-to-data probe set definition (Biostatistics 4:249-264 and Nucleic Acids Research 33(20):e175. Gene set analysis was performed using generally applicable gene set enrichment (BMC Bioinformatics 10:161). The most differentially regulated gene ontology groups were selected with FDR q-value < 0.1.

Publication Title

DDX5 regulates DNA replication and is required for cell proliferation in a subset of breast cancer cells.

Sample Metadata Fields

Cell line

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accession-icon SRP047124
Analysis of allele-specific gene expression in total RNA from blood lymphocytes
  • organism-icon Homo sapiens
  • sample-icon 1 Downloadable Sample
  • Technology Badge IconIlluminaGenomeAnalyzerII

Description

Recently a genome of Russian individual (somatic DNA from blood) was sequenced (Skryabin et al. 2009). That study was continued to find a linkage between genetic differences in parental alleles and bias in biallelic expression of genes.

Publication Title

Individual genome sequencing identified a novel enhancer element in exon 7 of the CSFR1 gene by shift of expressed allele ratios.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE87567
Transcriptomic analysis of the the liver of Ppara KO mice
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

Livers from wild-type (WT) or Ppara knock-out (Ppara KO) C57Bl6 mice were used to prepare RNA which was then processed for analysis using MoGene-2_0-st Affymetrix microarrays according to standard procedures.

Publication Title

The logic of transcriptional regulator recruitment architecture at &lt;i&gt;cis&lt;/i&gt;-regulatory modules controlling liver functions.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE44967
IQGAP1 Scaffold-Kinase Interaction Blockade Selectively Targets Ras-MAP Kinase Driven Tumors
  • organism-icon Mus musculus
  • sample-icon 7 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

MAPK scaffolds, such as IQGAP1, assemble pathway kinases together to effect signal transmission and disrupting scaffold function therefore offers a potentially orthogonal approach to MAPK cascade inhibition. Consistent with this possibility, we observed an IQGAP1 requirement in Ras-driven tumorigenesis in mouse and human tissue. Delivery of the IQGAP1 WW peptide sequence that mediates Erk1/4 binding, moreover, disrupted IQGAP1-Erk1/2 interactions, abolished Ras/Raf-driven tumorigenesis, bypassed acquired resistance to the B-Raf inhibitor vemurafinib (PLX- 4032), and acts as a systemically deliverable therapeutic to significantly increase lifespan of tumor bearing mice. Scaffold-kinase interaction blockade (SKIB) acts by a mechanism distinct from direct kinase inhibition and represents a strategy to target over-active oncogenic kinase cascades in cancer.

Publication Title

IQGAP1 scaffold-kinase interaction blockade selectively targets RAS-MAP kinase-driven tumors.

Sample Metadata Fields

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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