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.
Enter gene symbol:

Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
Islet
GSM3439006 GSM3439007 GSM3439008 GSM3439009 GSM3439010 GSM3439011 GSM3439012
GSM3439013 GSM3439014 GSM3439015 GSM3439016 GSM3439017 GSM3439018 GSM3439019
Description

Submission Date: Oct 19, 2018

Summary: The risk of type 2 diabetes increases with age. Although changes in function and proliferation of aged beta cells resemble those preceding the development of diabetes, the contribution of beta cell aging and senescence remains unclear. The proportion of aged beta cells increases with animal age but even in young mice, senescent beta cells can be found. We showed that different models of insulin resistance accelerated aging and senescence marker expression in beta cells, BGal, led to loss of function and impaired glucose tolerance. Clearance of p16Ink4a+ cells, using the INK-ATTAC mouse model, ameliorated glucose metabolism, insulin secretion and decreased expression of aging, senescence and SASP genes in pancreatic islets. Senolytic drug ABT263 also improved glucose metabolism and beta cell identity when administered during insulin resistance. Human beta cells from diabetic and non-diabetic donors shared some of the same biology laying the foundation for translation into humans. These novel findings lay the framework to pursue senolysis of beta cells as a preventive and alleviating strategy for T2D.

GEO Accession ID: GSE121539

PMID: 31155496

Description

Submission Date: Oct 19, 2018

Summary: The risk of type 2 diabetes increases with age. Although changes in function and proliferation of aged beta cells resemble those preceding the development of diabetes, the contribution of beta cell aging and senescence remains unclear. The proportion of aged beta cells increases with animal age but even in young mice, senescent beta cells can be found. We showed that different models of insulin resistance accelerated aging and senescence marker expression in beta cells, BGal, led to loss of function and impaired glucose tolerance. Clearance of p16Ink4a+ cells, using the INK-ATTAC mouse model, ameliorated glucose metabolism, insulin secretion and decreased expression of aging, senescence and SASP genes in pancreatic islets. Senolytic drug ABT263 also improved glucose metabolism and beta cell identity when administered during insulin resistance. Human beta cells from diabetic and non-diabetic donors shared some of the same biology laying the foundation for translation into humans. These novel findings lay the framework to pursue senolysis of beta cells as a preventive and alleviating strategy for T2D.

GEO Accession ID: GSE121539

PMID: 31155496

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:

Select conditions:

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.
Select differential expression analysis method:
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.