← BioTransfer GEO Dataset Finder
GEO series

TNIK signaling imprints CD8+ T memory formation early after priming

GSE127734 Mus musculus (mouse) RNA-seq (gene expression) 13 samples Submitted 2020/02/24 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 13 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
13 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)
Purpose: The goal of this study is to compare NGS-derived transcriptome profiling (RNA-seq) of naïve/ effector (D6)/ memory (D80) p14 T cell [wild-type (WT) and Tnik-/- (KO)] isolated form spleen of naïve or adoptively transferred and LCMV-WE immunized mice. Methods: Transcriptomic profiles of splenic naïve wild-type (WT) and Tnik-/- (KO) p14 T cells (8-week-old mice) or splenic effector (D6) and memory (D80) p14 T cells (post adoptive transfer and LCMV-WE immunization) were assessed in duplicates or triplicate by deep sequencing, using Illumina HiSeq 2500.
Read the full summary ↓
qRT–PCR validation was performed using TaqMan and SYBR Green assays. Results: We mapped about 30 million sequence reads per sample to the mouse genome (GRCm38 - mm10) and identified expressed transcripts in splenic WT and Tnik−/− p14 T cells. RNA-seq data confirmed stable expression of known housekeeping genes. Differentially expressed genes between the WT and Tnik−/− p14 T cells from naïve, D6 or D80 were identified with a fold change ≥1.5 and p-value <0.05. Hierarchical clustering and gene ontology analysis of differentially expressed genes uncovered genes that may contribute to regulation of stemness, transcriptional regulation, cell cycle regulation, survival and function of T cells. Conclusions: Our study represents the first detailed transcriptome analysis of WT versus Tnik-/- p14 T cells in the context of acute viral infection, with biologic replicates, generated by RNA-seq technology. Our results show that TNIK regulates T cell fate. Evaluation of mRNA content in effector (D6) and memory (D80) p14 T cells revealed that TNIK-deficiency during T cell activation enhances proliferation, terminal differentiation and glycolysis of effector T cells, while compromising steady-state metabolic activity of memory T cells. We conclude that TNIK imprints memory formation early after CD8+ T cell priming.
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 GSE127734_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 PRJNA525144 and SRA study SRP187295. Searching any of these in the dataset finder brings you back here.

Samples in this study

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

+ 13 more — browse all 13 samples with per-sample file links →

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.