Abstract
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.
Highlights
In the past few years there has been an increasing interest in artificial intelligence
We defined a cell death model in all cell lines used in this work -three pluripotent stem cell (PSC) lines and four cancer cell (CC) lines- by incubating them with camptothecin (CPT), a topoisomerase I inhibitor
We have previously demonstrated that this molecule induces a very rapid cell death signaling in human embryonic stem cells that derives in apoptosis [21]
Summary
In the past few years there has been an increasing interest in artificial intelligence. Deep learning (DL) models inspired in neural networks (NN) have proved to be powerful. These models, called convolutional neural networks (CNN), employ backpropagation algorithms to reconfigure its parameters in successive layers while attempting to represent the input data [2], allowing them to classify complex and large sets of information, including digital images. Cell death is a complex event found in normal and pathological contexts [5]. For this reason, it is widely studied in biomedical research and it is a hallmark of many experiments, in the context of drug discovery [6, 7]. There is need for time and money in order to perform
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