An open access, machine learning pipeline for high-throughput quantification of cell morphology

Cell morphology is influenced by many factors and can be used as a phenotypic marker. Here we describe a machine-learning-based protocol for high-throughput morphological measurement of human fibroblasts using a standard fluorescence microscope and the pre-existing, open access software ilastik for cell body identification, ImageJ/Fiji for image overlay, and CellProfiler for morphological quantification. Because this protocol overlays nuclei with their cell bodies and relies on coloration differences, it can be broadly applied to other cell types beyond fibroblasts. For details on the use and execution of this protocol, please also refer to Leung et al. (2022).(1)