Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
Islet
GSM4618782 GSM4618784 GSM4618786 GSM4618788
GSM4618781 GSM4618783 GSM4618785 GSM4618787
Description

Submission Date: Jun 16, 2020

Summary: Type 1 diabetes (T1D) is characterized by immune mediated destruction of insulin producing β cells. Biomarkers capable of identifying T1D risk and dissecting disease-related heterogeneity represent an unmet clinical need. Aims: Towards the goal of informing T1D biomarker strategies, we profiled different classes of RNAs in human islet-derived exosomes and identified RNAs that were differentially expressed under cytokine stress conditions. Human pancreatic islets were obtained from cadaveric donors and treated with/without IL-1β and IFN-γ to mimic the pro-inflammatory T1D milieu. Total RNA and small RNA sequencing were performed to identify long (mRNA and long non-coding RNAs) and different classes of small non-coding RNAs. RNAs with fold change ≥ 1.3 and p-value < 0.05 were considered as differentially expressed. mRNAs and miRNAs species represented the most abundant long and small RNA species, respectively. Expression patterns of each class of RNA were changed with cytokine treatment. Differentially expressed long RNAs and targets of small non-coding RNAs were predicted to be involved in insulin secretion, calcium signaling, necrosis and apoptosis. Our data provides the first comprehensive catalog of protein coding and non-coding RNAs in human islet-derived exosomes and identifies RNAs that are dysregulated under cytokine stress.

GEO Accession ID: GSE152615

PMID: 32820009

Description

Submission Date: Jun 16, 2020

Summary: Type 1 diabetes (T1D) is characterized by immune mediated destruction of insulin producing β cells. Biomarkers capable of identifying T1D risk and dissecting disease-related heterogeneity represent an unmet clinical need. Aims: Towards the goal of informing T1D biomarker strategies, we profiled different classes of RNAs in human islet-derived exosomes and identified RNAs that were differentially expressed under cytokine stress conditions. Human pancreatic islets were obtained from cadaveric donors and treated with/without IL-1β and IFN-γ to mimic the pro-inflammatory T1D milieu. Total RNA and small RNA sequencing were performed to identify long (mRNA and long non-coding RNAs) and different classes of small non-coding RNAs. RNAs with fold change ≥ 1.3 and p-value < 0.05 were considered as differentially expressed. mRNAs and miRNAs species represented the most abundant long and small RNA species, respectively. Expression patterns of each class of RNA were changed with cytokine treatment. Differentially expressed long RNAs and targets of small non-coding RNAs were predicted to be involved in insulin secretion, calcium signaling, necrosis and apoptosis. Our data provides the first comprehensive catalog of protein coding and non-coding RNAs in human islet-derived exosomes and identifies RNAs that are dysregulated under cytokine stress.

GEO Accession ID: GSE152615

PMID: 32820009

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

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Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.