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The effect of antibiotics and propionate treatment on astrocyte transcription in APPPS1-21 mice

GSE295458 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2025/05/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 15 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
15 samples: 5 × VHL treated APPPS1-21 cortex vs 5 × ABX treated APPPS1-21 cortex vs 5 × Propionate treated APPPS1-21 cortexGroups 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)
Previously, we have shown that broad spectrum antibiotic treatment reduces reactive astrocyte phenotypes in the APPPS1-21 model of AD-related amyloidosis. We have also found that antibiotics selectively increases propionate levels and exogenous propionate treatment recapitulates phenotypes observed in antibiotic treated mice. In the current study, we wanted to assess astrocyte transcriptional state using bulk RNA sequencing.
Read the full summary ↓
To accomplish this we used translating ribosome affinity purification (TRAP) sequencing, a ribosomal protein L10a is fused to eGFP under the control of a cell type specific promoter in a transgenic mouse model. We crossed APPPS1-21 mice to the Aldh1l1-eGFP/Rpl10a bacTRAP mouse model and progeny were treated with antibiotics, propionate, or VHL and performed bulk TRAPseq.
This dataset
Open GSE295458 in finder → Find more mouse RNA-seq datasets →
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.

Series matrix ↓
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 GSE295458_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
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.

Raw reads on ENA ↗

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 PRJNA1254711 and SRA study SRP580811. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 × VHL treated APPPS1-21 cortex vs 5 × ABX treated APPPS1-21 cortex vs 5 × Propionate treated APPPS1-21 cortex

Supports a between-group comparison across 15 samples.

How we read this: 3 replicated groups read from 15 sample titles; they account for 15 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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