Cellular Senescence of Patient-derived Fibroblasts Reveals the Mid-old Stage as a Critical Window for Transcriptomic Signatures Linked to Alzheimer's Disease Biomarkers and Classification.

OBJECTIVE

Alzheimer's disease (AD) is strongly associated with aging, yet the interactions remain unclear. This study modeled replicative senescence in patient-derived fibroblasts to compare gene expression between AD dementia and controls across senescence stages and to evaluate whether stage-specific alterations reflect disease characteristics with diagnostic implications.

METHODS

Dermal fibroblasts from 13 AD dementia patients and 13 healthy controls were repeatedly passaged to induce replicative senescence and classified into young (passage 7), mid-old (passage 18), and old stages (passage 25-28). Transcriptomic profiling was performed by RNA sequencing, followed by stepwise gene extraction, machine learning-based classification, and correlation analyses with AD biomarkers.

RESULTS

Fibroblasts were successfully driven into replicative senescence, validated by SA-β-gal staining, increased expression of CDKN1A and CDKN2A, and transcriptomic age acceleration. From transcriptome data, 605 senescence-associated genes were identified, enriched in extracellular matrix remodeling, chromatin organization, and immune-related pathways. Machine learning classifiers trained on these genes achieved the highest accuracy at the mid-old stage above 0.9, markedly outperforming the young and old stages. In addition, among the most consistently selected mid-old genes, H2AC18, H1-2, and LTBP1 showed significant correlations with cortical amyloid burden and plasma pTau217, linking cellular transcriptomic changes to established AD biomarkers.

CONCLUSION

In summary, replicative senescence models of patient-derived fibroblasts revealed that transcriptomic differences between AD dementia and controls peak at the mid-old stage. This transitional window represents the most informative point for capturing disease-related alterations with strong biomarker relevance.