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
CD11c High Islet Macrophages
GSM3052355 GSM3052356
GSM3052347 GSM3052351 GSM3052353
F480 High Islet Macrophages
GSM3052361 GSM3052362
GSM3052358 GSM3052359
Description

Submission Date: Mar 19, 2018

Summary: Inflammation is a key component of the pathogenesis of obesity-associated type 2 diabetes (T2D). However, the nature of T2D-associated islet inflammation and its impacts on T2D-associated beta cell abnormalities remain poorly defined. Using both diet-induced and genetically modified T2D animal models, we explore immune components of islet inflammation and define their roles in regulating beta cell function and proliferation. Our studies show that T2D-associated islet inflammation is uniquely dominated by macrophages, without the involvement of adaptive immune cells. We identify two islet macrophage populations, characterized by their distinct phenotypes, anatomical distributions and functional properties. Obesity induces a local expansion of intra-islet macrophages, independent of the replenishment from circulating monocytes. In contrast, the abundance of peri-islet macrophages is negligibly affected by obesity. Functionally, intra-islet macrophages impair beta cell function in a cell-cell contact dependent manner. In contrast, both intra- and peri-islet macrophage populations are able to promote beta cell proliferation. Together, these data provide a definitive view of the genesis of T2D-associated islet inflammation and define specific roles of islet macrophages in regulating beta cell function and proliferation.

GEO Accession ID: GSE112002

PMID: 30595478

Description

Submission Date: Mar 19, 2018

Summary: Inflammation is a key component of the pathogenesis of obesity-associated type 2 diabetes (T2D). However, the nature of T2D-associated islet inflammation and its impacts on T2D-associated beta cell abnormalities remain poorly defined. Using both diet-induced and genetically modified T2D animal models, we explore immune components of islet inflammation and define their roles in regulating beta cell function and proliferation. Our studies show that T2D-associated islet inflammation is uniquely dominated by macrophages, without the involvement of adaptive immune cells. We identify two islet macrophage populations, characterized by their distinct phenotypes, anatomical distributions and functional properties. Obesity induces a local expansion of intra-islet macrophages, independent of the replenishment from circulating monocytes. In contrast, the abundance of peri-islet macrophages is negligibly affected by obesity. Functionally, intra-islet macrophages impair beta cell function in a cell-cell contact dependent manner. In contrast, both intra- and peri-islet macrophage populations are able to promote beta cell proliferation. Together, these data provide a definitive view of the genesis of T2D-associated islet inflammation and define specific roles of islet macrophages in regulating beta cell function and proliferation.

GEO Accession ID: GSE112002

PMID: 30595478

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.