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Age-Related Retinal Degeneration Resulting from the Deletion of Shp2 Tyrosine Phosphatase in Photoreceptor

GSE269514 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/08/08 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 3 × ko_bead vs 3 × wt_bead vs 3 × ko_input vs 3 × wt_inputGroups 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.
Paper
Age-related retinal degeneration resulting from the deletion of Shp2 tyrosine phosphatase in photoreceptor neuronsCell death & disease · 2024
Summary (written by the submitters)
Shp2, a critical SH2-domain-containing tyrosine phosphatase, is essential for cellular regulation and implicated in metabolic disruptions, obesity, diabetes, Noonan syndrome, LEOPARD syndrome, and cancers. This study focuses on Shp2 in rod photoreceptor cells, revealing its enrichment, particularly in rods. Deletion of Shp2 in rods leads to age-dependent photoreceptor degeneration.
Read the full summary ↓
Shp2 targets occludin (OCLN), a tight junction protein, and its deletion reduces OCLN expression in the retina and retinal pigment epithelium (RPE). Isolation of actively translating mRNAs from rods lacking Shp2, followed by RNA sequencing, reveals alterations in cell cycle regulation. Altered retinal metabolism is observed in retinal cells lacking Shp2. Our studies indicate that Shp2 is crucial for maintaining the structure and function of photoreceptors.
Published in
Age-related retinal degeneration resulting from the deletion of Shp2 tyrosine phosphatase in photoreceptor neurons
Rajala A, Rajala R, Bhat MA et al. · Cell death & disease 2024 · PMID 39117618 · doi:10.1038/s41419-024-06924-y
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 GSE269514_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 PRJNA1122170 and SRA study SRP513044. Searching any of these in the dataset finder brings you back here.

Study design
3 × ko_bead vs 3 × wt_bead vs 3 × ko_input vs 3 × wt_input

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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
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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.