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RNA-seq profiling of wild type and Trim33-/- dendritic cell-related progenitors

GSE194246 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/06/01 Platform GPL23479
At a glance

RNA-seq (gene expression) · mouse · 18 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
18 samples: 6 conditions, mostly in triplicateGroups 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
TRIM33 plays a critical role in regulating dendritic cell differentiation and homeostasis by modulating Irf8 and Bcl2l11 transcriptionCellular & molecular immunology · 2024
Summary (written by the submitters)
The development of dendritic cells (DCs) is regulated by complex transcriptional networks. DCs originate from the multipotent progenitors (MPPs) in the bone marrow, which could further give rise to common lymphoid progenitors (CLPs) and common dendritic cell progenitors (CDPs). Whereas CDPs, which could be further divided into CD115+ and CD115- populations, give rise to both conventional (cDC) and plasmacytoid DCs (pDCs), CLP is an addtional source of pDCs.
Read the full summary ↓
Transcriptome profiling of wild type and Trim33-/- MPPs, CD115- CDPs, and CLPs revealed a critical role of TRIM33 in the development of DCs.
Published in
TRIM33 plays a critical role in regulating dendritic cell differentiation and homeostasis by modulating Irf8 and Bcl2l11 transcription
Shen X, Li X, Wu T et al. · Cellular & molecular immunology 2024 · PMID 38822080 · doi:10.1038/s41423-024-01179-1
This dataset
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.

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 GSE194246_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA799935 and SRA study SRP356429. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
MPP_C ×3 MPP_K ×3 CD115-CDP_C ×3 CD115-CDP_K ×3 CLP_C ×3 CLP_K ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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
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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.