GEO series

Differential response of human plasmacytoid pre-dendritic cells to SARS-CoV-2 variants

GSE294888 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2025/06/01 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 40 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: 40 samples: 8 conditions, mostly with about 5 replicates eachGroups 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)

SARS-CoV-2 variants have been involved in various waves of the COVID19 pandemic and showed different pathogenicity and inflammatory potential. Whether they can induce different patterns of innate immune activation in antigen-presenting cells is poorly understood. Here, we investigated the ability of primary plasmacytoid pre-dendritic cells (pDC), type 2 dendritic cells (DC2), and monocytes isolated from healthy blood donors to respond to SARS-CoV-2 variants.

Read the full summary ↓

Transcriptomic profiling using RNA sequencing revealed that pDC respond differentially to SARS-CoV-2 variants, while DC2 and monocytes do not. Functional studies showed that pDC undergo differential activation programs upon SARS-CoV-2 variant stimulation. The Alpha B220/95 and Delta variants induced P1-/P2-pDC effector phenotypes, characterized by strong IFN-α production. On the contrary, BA.1 variant turned pDC into a predominant T cell activating P3 phenotype, with less IFN-α and IFN-λ production, and stronger proinflammatory and CD4+T cell responses. Our results indicate that pDC activation and diversification pattern can be differentially controlled by SARS-CoV-2 variants, which may influence disease severity

This dataset

Open GSE294888 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 GSE294888_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 PRJNA1251981 and SRA study SRP579389. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

8 conditions, mostly with about 5 replicates each

pDC,NI, ×5 pDC,R848, ×5 pDC,Delta, ×5 pDC,BA.1, ×5 DC2,NI, ×5 DC2,R848, ×5 DC2,Delta, ×5 DC2,BA.1, ×5

Supports a between-group comparison across 40 samples.

How we read this: 8 replicated groups read from 40 sample titles; they account for 40 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

GSM8927717 pDC,NI,replicate1 pDC,NI,
GSM8927718 pDC,R848,replicate1 pDC,R848,
GSM8927719 pDC,Delta,replicate1 pDC,Delta,
GSM8927720 pDC,BA.1,replicate1 pDC,BA.1,
GSM8927721 DC2,NI,replicate1 DC2,NI,
GSM8927722 DC2,R848,replicate1 DC2,R848,
GSM8927723 DC2,Delta,replicate1 DC2,Delta,
GSM8927724 DC2,BA.1,replicate1 DC2,BA.1,
GSM8927725 DC2,NI,replicate2 DC2,NI,
GSM8927726 DC2,R848,replicate2 DC2,R848,
GSM8927727 DC2,Delta,replicate2 DC2,Delta,
GSM8927728 DC2,BA.1,replicate2 DC2,BA.1,
GSM8927729 pDC,NI,replicate2 pDC,NI,
GSM8927730 pDC,R848,replicate2 pDC,R848,
GSM8927731 pDC,Delta,replicate2 pDC,Delta,
GSM8927732 pDC,BA.1,replicate2 pDC,BA.1,
GSM8927733 pDC,NI,replicate3 pDC,NI,
GSM8927734 pDC,R848,replicate3 pDC,R848,
GSM8927735 pDC,Delta,replicate3 pDC,Delta,
GSM8927736 pDC,BA.1,replicate3 pDC,BA.1,
GSM8927737 pDC,NI,replicate4 pDC,NI,
GSM8927738 pDC,R848,replicate4 pDC,R848,
GSM8927739 pDC,Delta,replicate4 pDC,Delta,
GSM8927740 pDC,BA.1,replicate4 pDC,BA.1,
GSM8927741 pDC,NI,replicate5 pDC,NI,
GSM8927742 pDC,R848,replicate5 pDC,R848,
GSM8927743 pDC,Delta,replicate5 pDC,Delta,
GSM8927744 pDC,BA.1,replicate5 pDC,BA.1,
GSM8927745 DC2,NI,replicate3 DC2,NI,
GSM8927746 DC2,R848,replicate3 DC2,R848,
GSM8927747 DC2,Delta,replicate3 DC2,Delta,
GSM8927748 DC2,BA.1,replicate3 DC2,BA.1,
GSM8927749 DC2,NI,replicate4 DC2,NI,
GSM8927750 DC2,R848,replicate4 DC2,R848,
GSM8927751 DC2,Delta,replicate4 DC2,Delta,
GSM8927752 DC2,BA.1,replicate4 DC2,BA.1,
GSM8927753 DC2,NI,replicate5 DC2,NI,
GSM8927754 DC2,R848,replicate5 DC2,R848,
GSM8927755 DC2,Delta,replicate5 DC2,Delta,
GSM8927756 DC2,BA.1,replicate5 DC2,BA.1,

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