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SARS-CoV-2 nucleocapsid induces hyperinflammation and vascular leakage through the Toll-like receptor signaling axis in macrophages

GSE327545 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/06/25 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 16 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
16 samples: 8 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)
Nearly 20% of SARS-CoV-2 infections result in ICU admission, often driven by an imbalance between antiviral responses and inflammatory signaling, leading to uncontrolled cytokine secretion. The SARS-CoV-2 nucleocapsid (N) protein is a known immune antagonist, but its role in macrophage-driven cytokine storms is unclear. We demonstrate that N functions in a stimulus-specific manner, specifically amplifying extracellular and dampening intracellular RNA sensing.
Read the full summary ↓
Moreover, we show that this is a conserved feature of pathogenic betacoronaviruses through distinct mechanisms. Our interaction networks with SARS-CoV-2 variant N proteins suggest the Delta variant N drives inflammation through interactions with several proteins, most notably, cGAS. Profiling of secreted cytokines revealed that the N proteins disrupt the secretome in a variant-specific manner. Most notably, we found that supernatants from the Delta variant N-expressing macrophages dramatically disrupt heart endothelial barriers, implicating N in COVID-19-associated cardiac complications. Our findings highlight N-mediated immune imbalance as a driver of severe COVID-19 and identify N as a promising therapeutic target to mitigate hyperinflammation.
This dataset
Open GSE327545 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 GSE327545_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 PRJNA1451192 and SRA study SRP690613. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in duplicate
Parental THP-1 macrophage-like cells, No Trea… ×2 Parental THP-1 macrophage-like cells, CL097 t… ×2 Parental THP-1 macrophage-like cells, polyI:C… ×2 Parental THP-1 macrophage-like cells, polyI:C… ×2 N-expressing THP-1 macrophage-like cells, No … ×2 N-expressing THP-1 macrophage-like cells, CL0… ×2 N-expressing THP-1 macrophage-like cells, pol… ×2 N-expressing THP-1 macrophage-like cells, pol… ×2

Supports a case/control comparison: 4 samples read as cases, 4 as controls.

How we read this: 8 replicated groups read from 16 sample titles; they account for 16 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
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