GEO series

Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h [TGFB1_IL6-48h]

GSE75110 Mus musculus (mouse) Single-cell RNA-seq 130 samples Submitted 2015/11/20 Platform GPL9250

At a glance

Single-cell RNA-seq · mouse · 130 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: 130 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)

Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or polarized in vitro under either pathogenic or non-pathogenic differentiation conditions.

Read the full summary ↓

Computational analysis reveals a spectrum of cellular states in vivo, including a self-renewal state, Th1-like effector/memory states and a dysfunctional/senescent state. Relating these states to in vitro differentiated Th17 cells, unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four novel genes: Gpr65, Plzp, Toso and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity, and can identify targets for selective suppression of pathogenic Th17 cells while sparing non-pathogenic tissue-protective ones.

This dataset

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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 GSE75110_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 PRJNA302458 and SRA study SRP066321. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 26607794) ↗

Samples in this study

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+ 130 more — browse all 130 samples with per-sample file links →

Datasets on a similar topic

GSE75111 Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h [TGFB1_IL6-48h-IL-17A/GFP+] 151 samples shares il6-48h, tgfb1, th17, differentiated, single-cell, vitro
GSE75109 Single-cell transcriptional profiling of Th17 cells, differentiated in vitro for 48h [IL1B_IL6_IL23-48h-IL-17A/GFP+] 139 samples shares th17, differentiated, single-cell, vitro
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