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Astrocyte-enriched pan-brain aging transcriptome

GSE99791 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2017/12/11 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples
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)
Normal brain aging is marked by a cognitive decline, spurred by changes in cellular metabolism and homeostatic dysregulation, as well as modifications in synapses and neuronal connectivity. Astrocytes are well positioned as an effector of these changes, although how properties of astrocytes change with age remains unclear. Here, we address this question by profiling astrocytic gene expression from multiple brain regions of adult (4 month-old) and aged (2 years-old) mice.
Read the full summary ↓
We isolated mRNA from transgenic mice where ribosomes within astrocytes were genetically tagged (GFAP-cre x RPL22-HA, astrocyte ribotag). We then used RNA sequencing to identify and quantify the astrocyte-enriched mRNA, analyzing 4 different brain regions: visual cortex, somatomotor cortex, hypothalamus, and cerebellum. Overall, we find that astrocyte expression of inflammatory and immune response factors increase with age, as well as changes in genes associated with metabolism, cholesterol synthesis, and synaptogenesis. Going forward, these data contribute to an understanding of astrocyte diversity and provide insight into the role of astrocytes in normal aging.
This dataset
Open GSE99791 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 GSE99791_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 PRJNA389601 and SRA study SRP108790. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 29298427) ↗
Samples in this study

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+ 36 more — browse all 36 samples with per-sample file links →

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