PLoS ONE (Jan 2021)

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

  • Alejandro Damián La Greca,
  • Nelba Pérez,
  • Sheila Castañeda,
  • Paula Melania Milone,
  • María Agustina Scarafía,
  • Alan Miqueas Möbbs,
  • Ariel Waisman,
  • Lucía Natalia Moro,
  • Gustavo Emilio Sevlever,
  • Carlos Daniel Luzzani,
  • Santiago Gabriel Miriuka

DOI
https://doi.org/10.1371/journal.pone.0253666
Journal volume & issue
Vol. 16, no. 6
p. e0253666

Abstract

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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.