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Hemin Impairs Interferon Signaling in Periodontal Cells

GSE335283 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/06/17 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples
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)
Hemin has been shown to exert potent immunomodulatory effects in the oral microenvironment, yet its global transcriptional targets remain elusive. In this study, we performed high-throughput Bulk RNA-sequencing to investigate the transcriptomic reprogramming induced by Hemin in human oral epithelial cells (HSC-2) under inflammatory conditions.
Read the full summary ↓
Our transcriptomic analysis revealed that Hemin effectively reversed the inflammatory signature induced by IL-1β and TNF-α. Specifically, Hemin treatment profoundly and selectively paralyzed the interferon (IFN)-stimulated gene axis (e.g., CXCL10, CXCL11), alongside triggering compensatory NRF2-driven redox stress defenses (e.g., HMOX1). These RNA-seq data provide a comprehensive landscape demonstrating Hemin's role as a targeted repressor that fundamentally uncouples JAK-STAT interferon responses.
This dataset
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.

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 GSE335283_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1477843 and SRA study SRP709116. Searching any of these in the dataset finder brings you back here.

Study design
A cohort of 12 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run LP11001 … LP11026 and carry no group information.

Samples in this study
Datasets on a similar topic

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