← BioTransfer GEO Dataset Finder
GEO series

Effect of deletion of SPTLC1 on gene expression and differentiation of mouse Th17 cells in vitro

GSE254602 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2024/03/21 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 8 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
Sphingolipid biosynthesis is essential for metabolic rewiring during T(H)17 cell differentiationScience advances · 2024
Summary (written by the submitters)
Th17 cells are implicated in autoimmune diseases and several metabolic processes are shown to be important for their development and function. However, the role of de novo sphingolipid biosynthesis in their differentiation and function is less known.
Read the full summary ↓
To investigate the role of SPTLC1(major subunit of serine palmitoyl transferase enzyme (SPT) that catalyzes the first and rate limiting step of de novo sphingolipid synthesis) in mouse Th17 cell differentiation, we have activated the WT and SPTLC1 defecient (KO) naïve CD4+ T cells, isolated from mouse spleen and lymph nodes, in Th17 differentiating condition in the precence and absence of NAC ( N-acetyl cysteine) and 3-KDS (3-ketodihydrosphingosine).
Published in
Sphingolipid biosynthesis is essential for metabolic rewiring during T(H)17 cell differentiation
Abimannan T, Parthibane V, Le SH et al. · Science advances 2024 · PMID 38657065 · doi:10.1126/sciadv.adk1045
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 GSE254602_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 PRJNA1071114 and SRA study SRP486880. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly in triplicate
WT Th17 biol ×3 KO Th17 biol ×3 WT Th17 with 3-KDS biol ×3 KO Th17 with 3-KDS biol ×3 WT Th17 with NAC biol ×3 KO Th17 with NAC biol ×3 WT naïve CD4+ T cells biol ×3 KO naïve CD4+ T cells biol ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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
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.