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arXiv · 1910.00662

Enhancing high-content imaging for studying microtubule networks at large-scale

Hao-Chih Lee·Sarah T Cherng·Riccardo Miotto·Joel T Dudley
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Abstract

Given the crucial role of microtubules for cell survival, many researchers have found success using microtubule-targeting agents in the search for effective cancer therapeutics. Understanding microtubule responses to targeted interventions requires that the microtubule network within cells can be consistently observed across a large sample of images. However, fluorescence noise sources captured simultaneously with biological signals while using wide-field microscopes can obfuscate fine microtubule structures. Such requirements are particularly challenging for high-throughput imaging, where researchers must make decisions related to the trade-off between imaging quality and speed. Here, we propose a computational framework to enhance the quality of high-throughput imaging data to achieve fast speed and high quality simultaneously. Using CycleGAN, we learn an image model from low-throughput, high-resolution images to enhance features, such as microtubule networks in high-throughput low-resolution images. We show that CycleGAN is effective in identifying microtubules with 0.93+ AUC-ROC and that these results are robust to different kinds of image noise. We further apply CycleGAN to quantify the changes in microtubule density as a result of the application of drug compounds, and show that the quantified responses correspond well with known drug effects

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Hao-Chih Lee, Sarah T Cherng, Riccardo Miotto, Joel T Dudley. 2019-10-01. Enhancing high-content imaging for studying microtubule networks at large-scale. https://arxiv.org/abs/1910.00662

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