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Canonical and isomiR products of miR-142 enhance dendritic cell reprogramming [lenti_hairpins]

GSE328357 Homo sapiens (human) RNA-seq (gene expression) 114 samples Submitted 2026/07/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 114 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
114 samples: 57 conditions, mostly in duplicateGroups 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)
MicroRNAs (miRNAs) regulate gene expression and generate isoforms (isomiRs) with distinct targeting properties, but their roles in cellular specification and reprogramming remain poorly understood. Here, we investigated miRNAs and isomiRs during direct reprogramming to conventional type 1 dendritic cells (cDC1s), identifying miR-124 and miR-142 as facilitators of the process.
Read the full summary ↓
The canonical miR-124-3p strand induced a permissive chromatin landscape enriched for cooperative transcription factor motifs, thereby transiently increasing reprogramming efficiency. In contrast, miR-142 enhanced lineage fidelity by repressing fibroblast identity and activating cDC1-specific programs through cooperating isomiRs that drove XCR1 expression and increased type III interferon production. In addition, miR-142 mimics enhanced cancer cell reprogramming and anti-tumor immunity in vivo. Finally, we developed RNA-based cDC1 reprogramming using transcription factor mRNAs combined with miRNA mimics. Together, these findings uncover miRNA isoform diversity as a programmable regulatory layer in immune cell specification and establish RNA-driven reprogramming as a non-viral strategy for cancer immunotherapy.
This dataset
Open GSE328357 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 GSE328357_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 PRJNA1454836 and SRA study SRP692714. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
57 conditions, mostly in duplicate
DC1_d3_A_Villani ×2 DC1_d3_B_Villani ×2 DC1_d3_C_Villani ×2 DC2_d3_A_Villani ×2 DC2_d3_B_Villani ×2 DC2_d3_C_Villani ×2 pDC_d3_A_Villani ×2 pDC_d3_B_Villani ×2 +49 more

Supports a between-group comparison across 114 samples.

How we read this: 57 replicated groups read from 114 sample titles; they account for 114 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

+ 74 more — browse all 114 samples with per-sample file links →

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.