GEO series

Temporally resolved single-cell transcriptomics defines immune dysfunction trajectories in glioblastoma

GSE232040 Mus musculus (mouse) Single-cell RNA-seq 167 samples Submitted 2023/12/21 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 167 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: 167 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)

Immune cells sense their environment and adapt their state. Deciphering cellular trajectories unfolding over time is fundamental for understanding biology. Empirical in vivo genomic technologies measuring both cell state and time are lacking. Here, we present Zman-seq, a single-cell technology tracking transcriptomic dynamics across time by introducing fluorescent time stamps into immune cells.

Read the full summary ↓

We applied Zman-seq to resolve immune cell state-transitions of the dysfunctional tumor immune microenvironment (TME) of glioblastoma (GBM). By measuring the exposure time of infiltrating immune cells in the TME, Zman-seq defines the cellular and molecular trajectories underlying immune incompetence in GBM. Within 24 hours, cytotoxic NK cells transitioned to a dysfunctional program regulated by Tgfb1, while monocytes differentiated into immunosuppressive macrophages in 36-48 hours. Anti-TREM2 antagonistic antibody reshaped the TME by redirecting monocytes to pro-inflammatory macrophages. Zman-seq is a broadly applicable technology enabling empirical measurements of differentiation trajectories guiding the development of more efficacious immunotherapies.

This dataset

Open GSE232040 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 GSE232040_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 PRJNA970693 and SRA study SRP436640. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38134933) ↗

Samples in this study

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+ 167 more — browse all 167 samples with per-sample file links →

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