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accession-icon SRP132740
Integrating single-cell transcriptomic data across different conditions, technologies, and species
  • organism-icon Homo sapiens
  • sample-icon 13 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple datasets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq datasets based on common sources of variation, enabling the identification of shared populations across datasets and downstream comparative analysis. Implemented in our R toolkit Seurat (http://satijalab.org/seurat/), we use our approach to align scRNA-seq datasets of peripheral blood monocytes (PBMCs) under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across datasets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq datasets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution. Overall design: Human PBMCs were profiled using ddSeq and bulk RNA-seq. The ddSeq experiment was performed on unperturbed PBMCs. The bulk RNA-seq experiments were performed on both unperturbed and IFN-beta stimulated PBMC-derived populations (cDCs and pDCs) with three technical replicates.

Publication Title

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Sample Metadata Fields

Specimen part, Subject

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accession-icon SRP162235
Kinetics of Adult hematopoietic stem cell differentiation in vivo
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

Adult hematopoiesis has been studied in terms of progenitor differentiation potentials, whereas its kinetics in vivo is poorly understood. We combined inducible lineage tracing of endogenous adult hematopoietic stem cells (HSC) with flow cytometry and single-cell RNA sequencing to characterize early steps of hematopoietic differentiation in the steady state. Labeled cells, comprising primarily long-term HSC and some short-term HSC, produced megakaryocytic lineage progeny within one week, in a process that required only 2-3 cell divisions. Erythroid and myeloid progeny emerged simultaneously by 2 weeks, and included a progenitor population with expression features of both lineages. Myeloid progenitors at this stage showed diversification into granulocytic, monocytic and dendritic cell types, and rare intermediate cell states could be detected. In contrast, lymphoid differentiation was virtually absent within the first 3 weeks of tracing. These results show that continuous differentiation of HSC rapidly produces major hematopoietic lineages and cell types, and reveal fundamental kinetic differences between megakaryocytic, erythroid, myeloid and lymphoid differentiation. Overall design: We combined inducible lineage tracing of endogenous adult hematopoietic stem cells (HSC) with flow cytometry and single-cell RNA sequencing to characterize early steps of hematopoietic differentiation in the steady state.

Publication Title

Kinetics of adult hematopoietic stem cell differentiation in vivo.

Sample Metadata Fields

Specimen part, Subject

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accession-icon SRP065840
Genetic Diversity Through RNA Editing: Apobec1-mediated RNA editing in bulk and single cell macrophages and dendritic cells
  • organism-icon Mus musculus
  • sample-icon 26 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

RNA editing is a mutational mechanism that specifically alters the nucleotide content in sets of transcripts while leaving their cognate genomic blueprint intact. Editing has been detected from bulk RNA-seq data in thousands of distinct transcripts, but apparent editing rates can vary widely (from under 1% to almost 100%). These observed editing rates could result from approximately equal rates of editing within each individual cell in the bulk sample, or alternatively, editing estimates from a population of cells could reflect an average of distinct, biologically significant editing signatures that vary substantially between individual cells in the population. To distinguish between these two possibilities we have constructed a hierarchical Bayesian model which quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells and a cognate bulk sample consisting of ~ 106 cells. The model was applied to data from murine bone-marrow derived macrophages and dendritic cells, and predicted high variance for specific edited sites in both cell types tested. We then 1 validated these predictions using targeted amplification of specific editable transcripts from individual macrophages. Our data demonstrate substantial variance in editing signatures between single cells, supporting the notion that RNA editing generates diversity within cellular populations. Such editing-mediated RNA-level sequence diversity could contribute to the functional heterogeneity apparent in cells of the innate immune system. Overall design: 26 samples were subjected to RNA-seq: 24 single WT macrophages, and 2 bulk samples (Apobec1 WT and KO macrophages), consisting of 500,000-1 million cells each.

Publication Title

RNA editing generates cellular subsets with diverse sequence within populations.

Sample Metadata Fields

Specimen part, Cell line, Subject

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accession-icon GSE79831
Comparison of wild type mouse lung cancer cell lines to transfected cell lines with Spp1 sh RNA
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

We compared different mouse cancer cell lines to identify their unique cell signatures.

Publication Title

Tumor-derived osteopontin isoforms cooperate with TRP53 and CCL2 to promote lung metastasis.

Sample Metadata Fields

Cell line

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accession-icon SRP063876
Dose-dependent role of the cohesin complex in normal and malignant hematopoiesis [RNA-Seq]
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Cohesin complex members have recently been identified as putative tumor suppressors in hematologic and epithelial malignancies. The cohesin complex guides chromosome segregation, however cohesin-mutant leukemias do not show genomic instability. We hypothesized reduced cohesin function alters chromatin structure and disrupts cis-regulatory architecture of hematopoietic progenitors. We investigated the consequences of Smc3 deletion in normal and malignant hematopoiesis. Bi-allelic Smc3 loss induced bone marrow aplasia with premature sister chromatid separation, and revealed an absolute requirement for cohesin in hematopoietic stem cell function. In contrast, Smc3 haploinsufficiency increased self-renewal in vitro and in vivo including competitive transplantation. Smc3 haploinsufficiency reduced coordinated transcriptional output, including reduced expression of transcription factors and other genes associated with lineage commitment. Smc3 haploinsufficiency cooperated with Flt3-ITD to induce acute leukemia in vivo, with potentiated Stat5 signaling and altered nucleolar topology. These data establish a dose-dependency for cohesin in regulating chromatin structure and hematopoietic stem cell function. Overall design: mRNA-seq in murine c-kit+ cells for the following genotypes: Smc3 fl/+, Smc3 del/+, Flt3-ITD, Smc3 fl/del Flt3-ITD

Publication Title

Dose-dependent role of the cohesin complex in normal and malignant hematopoiesis.

Sample Metadata Fields

Specimen part, Subject

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accession-icon GSE59485
Expression data from bovine nucleus pulposus interverteral disc cells
  • organism-icon Bos taurus
  • sample-icon 9 Downloadable Samples
  • Technology Badge Icon Affymetrix Bovine Genome Array (bovine)

Description

Assessment of the putative differential gene expression profiles in high osmolality-treated bovine nucleus pulposus intervertebral disc cells for a short (5 h) and a long (24 h) time period. Identification of novel genes up- or down-regulated as an early or a late response to hyperosmotic stress.

Publication Title

Deficiency in the α1 subunit of Na+/K+-ATPase enhances the anti-proliferative effect of high osmolality in nucleus pulposus intervertebral disc cells.

Sample Metadata Fields

Specimen part

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accession-icon GSE139601
Transcriptomic profiling of the white adipose tissue (WAT) in ApoE3L.CETP mice fed a high fat diet (HFD) or a low fat diet (LFD) for three different time periods, or chow diet at baseline
  • organism-icon Mus musculus
  • sample-icon 25 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

The metabolic syndrome (MetS) is characterized by the presence of metabolic abnormalities that include abdominal obesity, dyslipidemia, hypertension, increased blood glucose/insulin resistance, hypertriglyceridemia and increased risk for cardiovascular disease (CVD). The ApoE*3Leiden.human Cholesteryl Ester Transfer Protein (ApoE3L.CETP) mouse model manifests several features of the MetS upon high fat diet (HFD) feeding. Moreover, the physiological changes in the white adipose tissue (WAT) contribute to MetS comorbidities. The aim of this study was to identify transcriptomic signatures in the gonadal WAT of ApoE3L.CETP mice in discrete stages of diet-induced MetS.

Publication Title

Transcriptome analysis of the adipose tissue in a mouse model of metabolic syndrome identifies gene signatures related to disease pathogenesis.

Sample Metadata Fields

Sex, Age, Specimen part

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accession-icon GSE58190
Tumor-mast cell transcriptional interactions in malignant pleural effusion
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st), Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Mast cells mediate malignant pleural effusion formation.

Sample Metadata Fields

Specimen part, Cell line

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accession-icon GSE58189
Gene expression profiling of mouse mast cells exposed to different cancer cell supernatants
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st), Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

Nave mast cells were cultured from murine bone marrow using incubation with IL-3 alone (samples 1-4) or IL-3 and KITL (samples 5-8).

Publication Title

Mast cells mediate malignant pleural effusion formation.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP060655
Differential expression profiles of type I JAK inhibitor persistent vs. naïve MPN cells
  • organism-icon Homo sapiens
  • sample-icon 33 Downloadable Samples
  • Technology Badge IconIonTorrentProton

Description

The type I JAK inhibitor ruxolitinib is approved for therapy of MPN patients but evokes resistance with longer exposure. Several novel type I JAK inhibitors were studied and we show that they uniformly induce resistance via a shared mechanism of JAK family heterodimer formation.Here we studied the expression profiles of SET2 cell lines persistent to several different type I JAK inhibitors in comparison to naive SET2 cells or in comparison to SET2 cells with acute exposure to ruxolitinib. Overall design: Analysis of RNA isolated from several type I JAK inhibitor SET2 cell lines in comparison to naïve SET2 cells

Publication Title

CHZ868, a Type II JAK2 Inhibitor, Reverses Type I JAK Inhibitor Persistence and Demonstrates Efficacy in Myeloproliferative Neoplasms.

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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