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Molecular Signatures of Peripheral and Central Dental Pulp Subpopulations in Response to Streptococcus mutans: A Bulk RNA-Seq Analysis

GSE305125 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2026/03/02 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 36 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)
Pulp cells play a central role in the defence against cariogenic microorganisms, orchestrating key immunological processes. Peripheral pulp cells, located adjacent to dentin, encounter bacteria and toxins earlier than central cells. To understand the specific signalling pathways between pulp cells and their interplay with immune cells, this study aimed to analyse the transcriptomic profiles of two distinct pulp cell subpopulations - dentin-adherent cells (DACs) and central dental pulp cells (DPCs) - in coculture with Streptococcus mutans.
Read the full summary ↓
Primary cultures of both DACs and DPCs were obtained from healthy third molars of three female and three male donors aged 17 to 18. Cells were cocultured with viable S. mutans (2 × 10⁸ CFU/mL) for 6 hours (n = 6). Controls included γ-inactivated bacteria and unexposed cells. RNA was isolated, subjected to library preparation (Illumina® Stranded mRNA Prep) and transcriptome profiling was performed via paired-end RNA sequencing (Illumina NextSeq2000). Bioinformatic analysis included differential gene expression (DESeq2), gene set enrichment analysis (GSEA), and protein-protein interaction (PPI) network construction using STRING and Cytoscape. Batch effects were corrected, and significantly regulated genes (|log₂FC| > 1.5, padj < 0.05) were identified. Validation of DEGs was performed via reverse transcription quantitative polymerase chain reaction (RT-qPCR). RNA-Seq revealed a dynamic shift in the transcriptome of DACs and DPCs stimulated with S. mutans, while cells exposed to γ-inactivated or no bacteria did not. Although DACs and DPCs shared common DEGs (33 up, 8 down), several regulations were exclusive to DACs (22 up, 9 down) and DPCs (9 up, 25 down) highlighting a donor-independent functional specificity of the pulp subpopulations. Functional enrichment analysis revealed a strong and comparable activation of hypoxia-related pathways in both DPCs and DACs. However, DACs additionally showed enrichment in extracellular matrix organization and cytokine signaling, while DPCs were characterized by intracellular stress responses and protein folding pathways. Additionally, protein-protein-interaction analysis identified IL-6 as a key hub gene in DACs, while ANGPTL4 played a primary role in DPCs. Following exposure to S. mutans, mechanically isolated DACs and DPCs displayed distinct transcriptomic profiles, indicating functional heterogeneity in the pulpal immune response. DACs engaged immunomodulatory pathways, while DPCs were marked by cellular stress responses, suggesting divergent contributions to tissue defense and homeostasis.
This dataset
Open GSE305125 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 GSE305125_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 PRJNA1304476 and SRA study SRP608151. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
36 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 24 samples read as cases, 12 as controls.

How we read this: Read from 36 sample titles: 36 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
Datasets on a similar topic

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