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A comparative analysis of the immunotranscriptomic features of DENV-1, -3, and -4 human challenge models

GSE333412 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2026/07/01 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 21 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
21 samples: 7 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)
Three different dengue human challenge models (DHIM) have been performed and provide an invaluable tool for studying the early virologic and immunologic features of dengue infection. In this work, we performed a comparative analysis of DHIM-1, DHIM-3, and DHIM-4 studies to determine shared and unique features of the transcriptional response to infection.
Read the full summary ↓
We identify a conserved transcriptional signature across all three DHIMs which is correlated with RNAemia. In order to validate the association of viral dose these signatures in vitro, we then exposed primary human PBMC to DENV-1 or DENV-3 at varying titers and performed bulk RNA sequencing.
This dataset
Open GSE333412 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 GSE333412_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 PRJNA1471053. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 conditions, mostly in triplicate
PBMC, Control, P0, biol ×3 PBMC, DENV1, P2, biol ×3 PBMC, DENV1, P3, biol ×3 PBMC, DENV1, P4, biol ×3 PBMC, DENV3, P2, biol ×3 PBMC, DENV3, P3, biol ×3 PBMC, DENV3, P4, biol ×3

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 21 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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