A Reference Library for Characterizing Protein Subcellular Localizations by Image-Based Machine Learning - Andrews, David (University of Dundee, 2021)
Libraries composed of 789,011 and 523,319 optically validated reference confocal micrographs of 17 and 20 EGFP fusion proteins localized at key cell organelles as landmarks in murine and human cells were generated for assignment of subcellular localization in mammalian cells. For each image of individual cells, 160 morphology and statistical features were used to train a random forests classifier to automatically assign the localization of proteins and dyes in both cell types and to analyze the sequence requirements that specify subcellular localization of a model tail-anchor (TA) protein. Automated assignment of subcellular localizations for a library of TA proteins with randomly mutated C-terminal targeting sequences enabled the discovery of motifs responsible for targeting TA proteins to mitochondria, endoplasmic reticulum and the late secretory pathway. Analysis of directed mutants enabled refinement of subcellular localization motifs that characterize cellular sub-compartments.
Screen Description
EGFP fusion proteins of organelle marker proteins were stably expressed in eukaryotic cells NMuMG.
Keywords: FOS: Biological sciences