  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
