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Integrative and Quantitative Analysis of Tumor Progression Reveals Fundamental Properties of Glioblastoma

GSE114456 Mus musculus (mouse) Single-cell RNA-seq 152 samples Submitted 2024/05/01 Platform GPL13112
At a glance

Single-cell RNA-seq · mouse · 152 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
152 samples: 6 conditions, mostly with about 37 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)
Principle of tumor growth is one fundamental aspect of cancer biology and it remains to be superficially understood. Here we developed a set of genetic and mathematic tools allow integrative analysis of mouse glioblastoma (GBM) progression. Quantitative temporal imaging of mouse GBM and mathematic modeling suggest that mouse GBM grows exponentially, which led to the discovery that a sustainable exponential growth requires outward migrating brain tumor stem cells (BTSCs).
Read the full summary ↓
Quantitative single cell tracing of BTSCs in vivo unravels symmetric expansion of BTSCs, asymmetric differentiation, and a non-reversible differentiation process during tumor progression. Mosaic tracing of BTSCs and non-BTSCs reveals a cellular temporal dynamic changes and cellular turnover. These experimentally collected parameters were integrated step by step to a quantitative mathematic model of GBM growth, which faithfully predicts tumor cellular architecture, tumor response to chemotherapy and BTSC therapy. Bulk and single cell RNA-Seq reveals distinct molecular hierarchy and molecular heterogeneity in BTSCs, which was confirmed by analyzing human GBM single cell RNA-Seq results. This study reveals basic developmental principles that govern tumor development, which provides novel understanding of cancer development, tumor heterogeneity and new approaches to treat GBM.
This dataset
Open GSE114456 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 GSE114456_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 PRJNA471501 and SRA study SRP147371. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 37 replicates each
RS1 ×38 GS1 ×37 RS2 ×37 GS2 ×33 GB1 ×4 RB1 ×3

Supports a between-group comparison across 152 samples.

How we read this: 6 replicated groups read from 152 sample titles; they account for 152 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

+ 112 more — browse all 152 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

Share this dataset

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.