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eIF3 interactions with miRNAs involved in translational control of early T cell activation

GSE328211 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2026/06/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
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.
Summary (written by the submitters)
Early T cell activation induces extensive remodeling of the cellular transcriptome and proteome. We previously showed using a transcriptome-wide crosslinking approach that human translation initiation factor eIF3 directly interacts with a select set of immune-related mRNAs shortly after T cell activation. We also found that eIF3 binding to the 3’-untranslated regions (3’-UTRs) of the TCRA and TCRB mRNAs encoding the T cell receptor alpha and beta subunits dynamically regulates a burst in their translation.
Read the full summary ↓
MicroRNAs (miRNAs) add an additional layer of regulation by fine-tuning both mRNA and protein expression. Although miRNA expression is dynamically regulated during T cell activation, how miRNAs interact with core regulatory pathways in primary T cells remains poorly understood. Here, we reexamined the eIF3-RNA crosslinking experiments in Jurkat cells and probed eIF3 function to investigate miRNA-mediated translational regulation during T cell activation. We found that human eIF3 interacts with multiple mature miRNAs in activated Jurkat cells, including members of the miR-17-92 cluster. These interactions also occur in primary T cells, as shown by RNA immunoprecipitation followed by qPCR (RIP-qPCR). Knocking out the miR-17-92 cluster led to a delay in ILR2A (CD25) cell surface expression in Jurkat cells and reduced activation-associated cell size increase in primary T cells during early T cell activation. In parallel, we used mass spectrometry analysis of activated primary T cells to identify eIF3-interacting factors, and surprisingly found no evidence of Argonaute binding. Together, these findings provide evidence that eIF3-miRNA interactions may play an unappreciated role in translational control during early T cell activation.
This dataset
Open GSE328211 in finder → Find more human 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 GSE328211_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: 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 PRJNA1454361 and SRA study SRP692369. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in triplicate
WT_0h ×3 KO_Clone1_0h ×3 KO_Clone2_0h ×3 WT_5h ×3 KO_Clone1_5h ×3 KO_Clone2_5h ×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
Datasets on a similar topic

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