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Updated: Oct 9, 2025
Flow Cytometric Analysis of Apoptotic Biomarkers in Actinomycin D-Treated SiHa Cervical Cancer Cells
Published on: August 26, 2021
Automated detection of apoptotic versus nonapoptotic cell death using label-free computational microscopy
Md Alamgir Kabir1, Ashish Kharel2, Saloni Malla3
1Department of Physics and Astronomy, University of Toledo, Toledo, OH, USA.
Abstract:
Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high-throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning-based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging-based screening tools.
Insights
This study introduces a novel, low-cost method using lensless digital holography to distinguish between apoptotic, necrotic, and other non-apoptotic cell death pathways. The technique achieves over 93% accuracy in classifying cell death mechanisms, offering a valuable tool for biological research.
Area of Science:
- Cell Biology
- Biophysics
- Biotechnology
Background:
- Distinguishing cell death mechanisms (apoptotic vs. non-apoptotic) is crucial for understanding cell signaling, disease pathogenesis, and therapeutic development.
- Current high-content imaging methods can be expensive and complex.
Purpose of the Study:
- To develop a novel, high-throughput, label-free method for identifying and classifying cell death processes.
- To offer a cost-effective alternative to existing cell death detection tools.
Main Methods:
- Utilized lensless digital holography to capture temporal changes in mammalian cell morphology.
- Induced different cell death processes using known cytotoxic agents.
- Employed a deep learning algorithm for automated classification of cell death mechanisms.
Main Results:
- Achieved over 93% accuracy in classifying cell death as apoptotic, necrotic, or other non-apoptotic pathways.
- Demonstrated that morphological changes over time are unique to specific cell death processes.
- The developed method is label-free and has a low estimated cost (<$250).
Conclusions:
- Lensless digital holography combined with deep learning provides an accurate and efficient method for cell death mechanism identification.
- This approach offers a low-cost, high-throughput alternative for cell death screening.
- The technique has broad applications in drug discovery, disease research, and understanding cellular responses.
