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Peculiar transcriptional reprogramming with functional impairment of dendritic cells upon exposure to transformed HTLV-1-infected cells

GSE266976 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2024/09/05 Platform GPL16791
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: 18 samples, each with a distinct title — no replicated groupsGroups 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
Peculiar transcriptional reprogramming with functional impairment of dendritic cells upon exposure to transformed HTLV-1-infected cellsPLoS pathogens · 2024
Summary (written by the submitters)
Manipulation of immune cell functions, independently of direct infection of these cells, emerges as a key process in viral pathophysiology. Chronic infection by Human T-cell Leukemia Virus type 1 (HTLV-1) is associated with immune dysfunctions, including misdirected responses of dendritic cells (DCs). Here, we interrogate the ability of HTLV-1-infected T cells to indirectly manipulate human DC functions.
Read the full summary ↓
We show that upon coculture with chronically infected T cells, monocyte-derived DCs (MDDCs) fail to fully mature. We further show that exposure to HTLV-1-infected T cells induces a unique transcriptional signature in MDDCs, which differs from a typical maturation program, and which is correlated with a dampened ability of HTLV-1-exposed MDDCs to subsequently respond to restimulation. Induction of this tolerogenic behavior is not strictly dependent on capture of HTLV-1 viral particles by MDDCs, nor on cell-cell contacts between HTLV-1-infected T cells and MDDCs, but is instead the result of a molecular dialogue between HTLV-1-infected T cells and MDDCs upon coculture, illustrating how HTLV-1 might indirectly induce a local tolerogenic immune microenvironment suitable for its own persistence.
Published in
Peculiar transcriptional reprogramming with functional impairment of dendritic cells upon exposure to transformed HTLV-1-infected cells
Carcone A, Mortreux F, Alais S et al. · PLoS pathogens 2024 · PMID 39283919 · doi:10.1371/journal.ppat.1012555
This dataset
Open GSE266976 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 GSE266976_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 PRJNA1108877 and SRA study SRP506318. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39283919) ↗
Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. 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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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.