GEO series

Transcriptional basis of mouse and human dendritic cell heterogeneity revealed by single-cell profiling

GSE137710 Mus musculus (mouse) Single-cell RNA-seq 6 samples Submitted 2019/10/25 Platform GPL24676Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 6 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: 6 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)

Dendritic cells (DCs) play a critical role in orchestrating adaptive immune responses due to their unique ability to serve as a principal antigen-presenting cell type capable of initiating T cell responses. Classical dendritic cells have been divided into two subsets, cDC1 and cDC2, based on phenotypic markers and their distinct abilities to prime CD8 and CD4 T cells and direct their differentiation into effectors.

Read the full summary ↓

Whilst the transcriptional features of the cDC1 subset have been well characterized, the heterogeneity of cDC2 and the development and function of distinct cell types comprising this subset remain poorly understood. Unbiased single-cell RNA sequencing analysis of DCs, combined with genetic reporter expression and fate-mapping revealed two principal cDC2 lineages, their developmental pathways and transcriptional regulators, including the expression of T-bet and RORt, two key transcription factors known to define functional adaptive and innate lymphocyte subsets. The cDC2 lineages were characterized by distinct metabolic and functional programs with phenotypic conservation observed across tissues. Extending the findings in mice to humans revealed conserved characteristics of DC heterogeneity and the presence of the newly defined DC subsets in human autoimmune disease and cancer.

This dataset

Open GSE137710 in finder → Find more 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 GSE137710_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 PRJNA566350 and SRA study SRP222507. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 31668803) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 6 more — browse all 6 samples with per-sample file links →

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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.