GEO series

A tumor-associated photoreceptor signature unifies distinct central nervous system malignancies [scRNA-seq]

GSE273440 Mus musculus (mouse) Single-cell RNA-seq 13 samples Submitted 2026/02/07 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 13 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 13 samples: 5 × CrxGFP_E vs 4 × CrxGFP_P vs 2 × CrxGFP_E15 vs 2 × CrxGFP_P05Groups 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: A tumor-associated photoreceptor signature unifies distinct central nervous system malignanciesCancer cell · 2026

Summary (written by the submitters)

Pineoblastoma is a clinically aggressive childhood brain tumor that segregates into molecularly distinct subgroups. Mechanisms governing pineoblastoma pathogenesis, including developmental origins, remain poorly defined. Herein, we resolved the cellular composition of pineoblastoma and other pineal parenchymal tumors at single-cell resolution, aligning malignant cells with pineal gland development to retrace cellular origins.

Read the full summary ↓

Integrative computational analyses mapped divergent pineoblastoma subgroups to cycling progenitors of the pinealocyte lineage. Lineage-specific perturbation of suspected drivers provoked the generation of genetically accurate preclinical models representing distinct molecular subgroups, while uncovering an oncogenic photoreceptor program conserved across pineoblastoma, retinoblastoma, and Group 3 medulloblastoma. Transcriptional activity of this program was acquired from cellular origin, incriminating photoreceptor identity as a common developmental vulnerability, substantiated as a strong dependency. Illuminated through multidisciplinary analysis of an uncommon, heterogeneous pediatric brain tumor, these advances motivate future studies evaluating developmentally encoded transcriptional programs of malignancy as potential therapeutic liabilities in clinically challenging entities.

Published in

A tumor-associated photoreceptor signature unifies distinct central nervous system malignancies

Gudenas BL, Ahmad ST, Englinger B et al. · Cancer cell 2026 · PMID 41791379 · doi:10.1016/j.ccell.2026.02.010

This dataset

Open GSE273440 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 GSE273440_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1141927 and SRA study SRP523132. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41791379) ↗

Study design

5 × CrxGFP_E vs 4 × CrxGFP_P vs 2 × CrxGFP_E15 vs 2 × CrxGFP_P05

Supports a between-group comparison across 13 samples.

How we read this: 4 replicated groups read from 13 sample titles; they account for 13 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

GSM8428412 CrxGFP_E15_Rep2 CrxGFP_E15
GSM8428413 CrxGFP_E17 CrxGFP_E
GSM8428414 CrxGFP_E18 CrxGFP_E
GSM8428415 CrxGFP_P05_Rep1 CrxGFP_P05
GSM8428416 CrxGFP_P05_Rep2 CrxGFP_P05
GSM8428417 CrxGFP_P10 CrxGFP_P
GSM8428418 CrxGFP_E13 CrxGFP_E
GSM8428419 CrxGFP_E14 CrxGFP_E
GSM8428420 CrxGFP_E15_Rep1 CrxGFP_E15
GSM8428421 CrxGFP_E16 CrxGFP_E
GSM8428422 CrxGFP_P01 CrxGFP_P
GSM8428423 CrxGFP_P16 CrxGFP_P
GSM8428424 CrxGFP_P21 CrxGFP_P

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