Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness
Fig 2
Generation of single-cell profiles and data analysis workflow.
(a) A flow chart illustrating the key steps of image-based cell profiling and data analysis workflow. (b) Schematic representation of the image segmentation process: cells were labelled with WGA and DAPI to visualise cytoplasm and nuclei. Extracted cell outlines were used to quantify the intensity of E-cadherin, vimentin or cytokeratins within each cell. (c) Illustrations of the measurements calculated for each cell: geometric parameters, intensity and texture of the fluorescent signal, and measurements of the context. (d) Summary of the final dataset. Left column indicates the proportion of cells captured at each stiffness level. Morphological and contextual features of the cells were calculated (left); additionally, intensity and texture of the fluorescent biomarker signals were measured (right). Each row of the heatmaps corresponds to a single cell and each column represents a single feature. Z-score normalisation was applied to each feature to allow for direct comparison regardless of the scale. Right column indicates z-score values.