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RNA-seq profiling of LPS tolerance in monocytes, THP-1, fibroblasts, smooth muscle cells, and endothelial cells

GSE327840 Homo sapiens (human) RNA-seq (gene expression) 58 samples Submitted 2026/04/18 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 58 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
58 samples: 58 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.
Summary (written by the submitters)
Atherosclerosis – a chronic disease of the arteries characterized by lipid accumulation and chronic local inflammation – is the primary cause of most ischemic cardiovascular diseases. The inflammatory process can be limited by endotoxin tolerance. This study performed a comparative transcriptomic analysis of LPS tolerance in primary monocytes, the THP-1 monocyte line, fibroblasts, endothelial cells, and smooth muscle cells.
Read the full summary ↓
To model LPS tolerance, single and double LPS stimulation was performed. Sequencing of libraries obtained from total RNA after poly(A) selection was performed on the Illumina NextSeq 2000 platform. Primary monocytes and smooth muscle cells developed LPS tolerance; fibroblasts showed partial, mediator-dependent tolerance; THP-1 cells showed limited tolerance primarily for TNF; endothelial cells did not show tolerance for IL-8/CCL2. No conserved transcriptional tolerance signature was observed across tolerant cell types; overlap was limited to three genes (OAS1, IFIT2, IFIT3).
This dataset
Open GSE327840 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 GSE327840_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 PRJNA1452864 and SRA study SRP691413. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
58 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 58 sample titles: 58 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

+ 18 more — browse all 58 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.