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celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition

Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in...

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Detalles Bibliográficos
Autores principales: La Greca, Alejandro Damián, Pérez, Nelba, Castañeda, Sheila, Milone, Paula Melania, Scarafía, María Agustina, Möbbs, Alan Miqueas, Waisman, Ariel, Moro, Lucía Natalia, Sevlever, Gustavo Emilio, Luzzani, Carlos Daniel, Miriuka, Santiago Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8224851/
https://www.ncbi.nlm.nih.gov/pubmed/34166446
http://dx.doi.org/10.1371/journal.pone.0253666
Descripción
Sumario:Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.