Label-free identification of cell death mechanism using scattering-based microscopy and deep learning
Abstract
The detection of cell death and identification of its mechanism underpins many of the biological and medical sciences. A scattering microscopy based method is presented here for quantifying cell motility and identifying cell death in breast cancer cells using a label-free approach. We identify apoptotic and necrotic pathways by analyzing the temporal changes in morphological features of the cells. Moreover, a neural network was trained to identify the cellular morphological changes and classify cell death mechanisms automatically, with an accuracy of over 95%. A pre-trained network was tested on images of cancer cells treated with a different chemotherapeutic drug, which was not used for training, and it correctly identified cell death mechanism with ∼100% accuracy. This automated method will allow for quantification during the incubation steps without the need for additional steps, typically associated with conventional technique like fluorescence microscopy, western blot and ELISA. As a result, this technique will be faster and cost effective.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 04, 2023
- Source ID
- 10.1088/1361-6463/acf324
Entities
People
- Amit K Tiwari
- Aniruddha Ray
- Ashish Kharel
- Devinder Kaur
- Peuli Nath
- Richard E. Irving
- Saloni Malla
- Somaiyeh Khoubafarin
Organizations
- Air Force Office of Scientific Research
- Susan G. Komen for the Cure
- United States Department of Defense
- University of Toledo