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Partial beta cell loss induces transient compensatory proliferation II

GSE256518 Mus musculus (mouse) RNA-seq (gene expression) 34 samples Submitted 2025/02/28 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 34 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
34 samples: 9 conditions, mostly with about 4 replicates eachGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Loss of functional β-cells is a pivotal event in the dysregulation of glucose homeostasis observed in diabetes. While the plasticity of β-cells in response to increased metabolic demand has been extensively studied, the common drivers of β-cell proliferation and the molecular mechanisms governing regeneration in humans remain elusive.
Read the full summary ↓
Here, we investigated the proliferative response following targeted chemical ablation of β-cells using diphtheria toxin (DT) and streptozotocin (STZ) in mice. Hemizygous expression of the RIP-DTR transgene enabled efficient partial ablation of β-cells, leading to close to 50% β-cell loss five days post ablation, stabilizing at this level up to 10 weeks post ablation. Transcriptomic analysis revealed a dynamic molecular landscape following ablation, with early activation of tissue remodeling and clearance of dead cells, followed by transient activation of regenerative signaling pathways. Furthermore, we observed a distinct proliferative boost in the immediate aftermath of ablation, characterized by upregulation of cell cycle progression markers. Interestingly, long-term follow-up revealed compound stress from partial β-cell loss and high-fat diet feeding induced a robust proliferative response, albeit with a negative impact on β-cell signature. Additionally, single low-dose STZ injections induced partial β-cell loss and compensatory proliferation, with the regulatory landscape deregulated by STZ injection in the long term. The compound stress of high-fat diet and STZ ablation further altered the islet transcriptional landscape, particularly affecting metabolic pathways. Overall, our study provides insights into the dynamic response of β-cells to partial ablation and highlights the impact of compound stressors on islet function and metabolism, shedding light on potential strategies for promoting β-cell regeneration in diabetes. Loss of functional β-cells is a pivotal event in the dysregulation of glucose homeostasis observed in diabetes. While the plasticity of β-cells in response to increased metabolic demand has been extensively studied, the common drivers of β-cell proliferation and the molecular mechanisms governing regeneration in humans remain elusive. Here, we investigated the proliferative response following targeted chemical ablation of β-cells using diphtheria toxin (DT) and streptozotocin (STZ) in mice. Hemizygous expression of the RIP-DTR transgene enabled efficient partial ablation of β-cells, leading to close to 50% β-cell loss five days post ablation, stabilizing at this level up to 10 weeks post ablation. Transcriptomic analysis revealed a dynamic molecular landscape following ablation, with early activation of tissue remodeling and clearance of dead cells, followed by transient activation of regenerative signaling pathways. Furthermore, we observed a distinct proliferative boost in the immediate aftermath of ablation, characterized by upregulation of cell cycle progression markers. Interestingly, long-term follow-up revealed compound stress from partial β-cell loss and high-fat diet feeding induced a robust proliferative response, albeit with a negative impact on β-cell signature. Additionally, single low-dose STZ injections induced partial β-cell loss and compensatory proliferation, with the regulatory landscape deregulated by STZ injection in the long term. The compound stress of high-fat diet and STZ ablation further altered the islet transcriptional landscape, particularly affecting metabolic pathways. Overall, our study provides insights into the dynamic response of β-cells to partial ablation and highlights the impact of compound stressors on islet function and metabolism, shedding light on potential strategies for promoting β-cell regeneration in diabetes. Loss of functional β-cells is a pivotal event in the dysregulation of glucose homeostasis observed in diabetes. While the plasticity of β-cells in response to increased metabolic demand has been extensively studied, the common drivers of β-cell proliferation and the molecular mechanisms governing regeneration in humans remain elusive. Here, we investigated the proliferative response following targeted chemical ablation of β-cells using diphtheria toxin (DT) and streptozotocin (STZ) in mice. Hemizygous expression of the RIP-DTR transgene enabled efficient partial ablation of β-cells, leading to close to 50% β-cell loss five days post ablation, stabilizing at this level up to 10 weeks post ablation. Transcriptomic analysis revealed a dynamic molecular landscape following ablation, with early activation of tissue remodeling and clearance of dead cells, followed by transient activation of regenerative signaling pathways. Furthermore, we observed a distinct proliferative boost in the immediate aftermath of ablation, characterized by upregulation of cell cycle progression markers. Interestingly, long-term follow-up revealed compound stress from partial β-cell loss and high-fat diet feeding induced a robust proliferative response, albeit with a negative impact on β-cell signature. Additionally, single low-dose STZ injections induced partial β-cell loss and compensatory proliferation, with the regulatory landscape deregulated by STZ injection in the long term. The compound stress of high-fat diet and STZ ablation further altered the islet transcriptional landscape, particularly affecting metabolic pathways. Overall, our study provides insights into the dynamic response of β-cells to partial ablation and highlights the impact of compound stressors on islet function and metabolism, shedding light on potential strategies for promoting β-cell regeneration in diabetes.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE256518_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1079820 and SRA study SRP491450. Searching any of these in the dataset finder brings you back here.

Study design
9 conditions, mostly with about 4 replicates each
CTRL HFD 10W ×5 STZ 75mg/kg HFD 10WPA ×4 STZ 75mg/kg SD 10WPA ×4 DT 5 DPA ×4 DT 30 DPA ×4 DT SD 10WPA ×4 CTRL, ×3 CTRL SD 10W ×3 +1 more

Supports a between-group comparison across 34 samples.

How we read this: 9 replicated groups read from 34 sample titles; they account for 34 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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