Research Paper Volume 18 pp 349—368
Stage-dependent transcriptomic changes in human dermal fibroblast senescence model
- 1 Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience, Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan
- 2 DHC Corporation Laboratories, Division 2, 2-42 Hamada, Mihama-ku, Chiba 261-0025, Japan
Received: September 26, 2025 Accepted: March 11, 2026 Published: April 10, 2026
https://doi.org/10.18632/aging.206371How to Cite
Copyright: © 2026 Bordonaro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Cellular senescence is a fundamental biological process that underlies organismal aging and age-related diseases; however, the temporal dynamics of gene expression during senescence remain unclear. This study investigated gene expression changes across aging progression and the molecular mechanisms involved using three replicative senescence models of human dermal fibroblasts, designated as young, middle, and old. RNA sequencing transcriptome analysis with two independent analytical pipelines revealed that the transcriptome profiles of the young and middle stages were similar, whereas that of the old stage was distinct. Differential expression analysis showed significant gene expression changes between the young and middle stages, with differentially expressed genes (DEGs) increasing as senescence progressed. STRING and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated early activation of immune–inflammatory response clusters and decreases in structural maintenance genes. Using an alternative DEG set, non-negative matrix factorization (NMF) decomposed transcriptomes into distinct metagenes, with characteristic senescence-stage behaviors. NMF independently recapitulated immune–inflammatory activation and the loss of structural maintenance functions. These findings suggest that immune–inflammatory responses are engaged from early senescence, whereas cell adhesion and maintenance pathways decline progressively. These results identify early biomarkers and therapeutic targets, providing opportunities to delay or mitigate aging-related functional decline.
Abbreviations
ANOVA: Analysis of variance; DEG: Differentially expressed genes; ECM: Extracellular matrix; GO: Gene Ontology; HRP: Horseradish peroxidase; HSD: honest significant difference; IL: Interleukin; KEGG: Kyoto Encyclopedia of Genes and Genomes; MDS: Multi-dimensional scaling; NMF: Non-negative matrix factorization; NOD: Nucleotide-binding domain; padj: adjusted p-value; PC1: First principal component; PC2: Second principal component; PCA: Principal component analysis; PDL: Population doubling level; PPI: Protein–protein interaction; RNA-seq: RNA sequencing; RT-qPCR: Reverse transcription quantitative polymerase chain reaction; SA-β-gal: Senescence-associated β-galactosidase; SASP: Senescence-associated secretory phenotype; TPM: Transcripts Per Million.