Automated detection of apoptotic versus nonapoptotic cell death using label‐free computational microscopy
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 (
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 09, 2022
- Source ID
- 10.1002/jbio.202100310
Entities
People
- Amit K Tiwari
- Aniruddha Ray
- Ashish Kharel
- Devinder Kaur
- Hamed Sari‐sarraf
- Jared Neil Wolfe
- Marwa Hassan
- Md Alamgir Kabir
- Peuli Nath
- Saloni Malla
- Zachary Joseph Kreis
Organizations
- Susan G. Komen for the Cure
- Texas Tech University
- United States Department of Defense
- University of Toledo