Feature-based Classification of Protein Networks using Confocal Microscopy Imaging and Machine Learning

摘要： Fluorescence imaging has become a powerful tool to investigate complex subcellular structures such as cytoskeletal filaments. Advanced microscopes generate 3D imaging data at high resolution, yet tools for quantification of the complex geometrical patterns are largely missing. Here we present a computational framework to classify protein network structures. We developed a machine-learning method that combines state-of-the-art morphological quantification with protein network classification through morphologically distinct structural features enabling live imaging–based screening. We demonstrate applicability in a confocal laser scanning microscopy (CLSM) study differentiating protein networks of the FtsZ (filamentous temperature sensitive Z) family inside plant organelles (Physcomitrella patens).