Predicting Kinetochore Localization Using Pix2Pix Image Translation of Spindle Pole Body Foci

Recent advances in machine learning promise to revolutionize biological investigations by creating new methods for image analysis and generation. Deep learning models known as generative adversarial networks (GANs) have shown enormous promise in scientific applications, such as through super-resolution GANs that can artificially upscale image quality. However, GANs have not been applied extensively to predictive image modeling of cellular structures, such as the kinetochore. The kinetochore is the protein complex where spindle microtubules attach during cell division and is crucial in ensuring proper chromosome segregation. Here, we examine whether the Pix2Pix architecture, a type of GAN designed for paired-image translation, can be repurposed to generate predictive fluorescent microscopy images that can localize kinetochore proteins in the budding yeast S. cerevisiae. As a proof-of-concept, this architecture’s ability to create generated images that matched the ground truth based only on synthetic input images of a different microscopy channel was evaluated. Subsequently, this architecture’s robustness was evaluated through its performance on synthetic images with added noise, and then on actual microscopy images of the kinetochore. By comparing generated images with their ground truth targets on various metrics, we find that this architecture is well-suited to generative image modeling of the kinetochore, despite the loss of some fine detail mapping in later tests. Further refinements can improve the network’s accuracy and extend its applicability to other kinetochore protein complexes and during different phases of cell division. The development of predictive image modeling architectures such as the Pix2Pix can elucidate the spatial and temporal localization of pathological errors in cell division that result from kinetochore dysfunction, informing further investigations into the causes of human diseases such as cancer.Bachelor of Scienc