Prediction of Oxidative Stress- and Anticancer Drug-Induced Cellular Senescence Using Holotomography and Interpretable Attention-Based Contrastive Learning.

Authors

Avinash Yaganapu, Amarnath Singam, Sai Phani Parsa, Calvinique Lee, Jennifer Zheng, Sunjeet Saha, Jeong Hee Kim, Seungman Park, Mingon Kang

Published in

Small methods. Pages e71091. Oct 05, 2026. Epub Oct 05, 2026.

Abstract

Quantitative assessment of the complex morphological and structural changes of cells and organelles during senescence is critical for understanding the level of senescence, which is a key step toward developing anti-aging therapeutics and preventing aging-related diseases. Despite its importance, a robust quantitative method for evaluating cellular senescence is still lacking. Recent advances in holotomography (HT), a label-free imaging technique, address these challenges by enabling high-contrast cell imaging with subcellular specificity, which enables accurate detection and characterization of senescent cells, without the constraints of fluorescence-based imaging. In this study, we introduce an artificial intelligence (AI)-powered Biologically Interpretable Contrastive and Attention-based Network integrated with HT, called BICAN-HT, which leverages high-resolution images to analyze nucleus-cytoplasm interactions for biologically interpretable and quantitative assessment of cellular senescence with enhanced predictive power. We conducted in-depth experiments inducing cellular senescence using three stressors, including hydrogen peroxide (H₂O₂), doxorubicin (Dox), and palbociclib (Pal), across two different cell lines. BICAN-HT consistently outperformed current state-of-the-art models across multiple stress conditions and cell types, while capturing the morphological features and spatial relationships of cells and organelles within the nucleus and cytoplasm during senescence. BICAN-HT has the potential to transform aging research and accelerate the development of anti-aging interventions.

PMID:
42834621
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

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