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D-MAINS: A Deep-Learning Model for the Label-Free Detection of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence in Cancer Cells

Authors :
Sarah He
Muhammed Sillah
Aidan R. Cole
Apoorva Uboveja
Katherine M. Aird
Yu-Chih Chen
Yi-Nan Gong
Source :
Cells, Vol 13, Iss 12, p 1004 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Background: Identifying cells engaged in fundamental cellular processes, such as proliferation or living/death statuses, is pivotal across numerous research fields. However, prevailing methods relying on molecular biomarkers are constrained by high costs, limited specificity, protracted sample preparation, and reliance on fluorescence imaging. Methods: Based on cellular morphology in phase contrast images, we developed a deep-learning model named Detector of Mitosis, Apoptosis, Interphase, Necrosis, and Senescence (D-MAINS). Results: D-MAINS utilizes machine learning and image processing techniques, enabling swift and label-free categorization of cell death, division, and senescence at a single-cell resolution. Impressively, D-MAINS achieved an accuracy of 96.4 ± 0.5% and was validated with established molecular biomarkers. D-MAINS underwent rigorous testing under varied conditions not initially present in the training dataset. It demonstrated proficiency across diverse scenarios, encompassing additional cell lines, drug treatments, and distinct microscopes with different objective lenses and magnifications, affirming the robustness and adaptability of D-MAINS across multiple experimental setups. Conclusions: D-MAINS is an example showcasing the feasibility of a low-cost, rapid, and label-free methodology for distinguishing various cellular states. Its versatility makes it a promising tool applicable across a broad spectrum of biomedical research contexts, particularly in cell death and oncology studies.

Details

Language :
English
ISSN :
20734409
Volume :
13
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Cells
Publication Type :
Academic Journal
Accession number :
edsdoj.361366edb0db42e89a5a3a855c51684d
Document Type :
article
Full Text :
https://doi.org/10.3390/cells13121004