ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis

• DOI: 10.1016/j.ydbio.2020.02.003
• Publication Date: 2020-02-14T02:18:24Z
• Authors: Heli Takko; Ceren Pajanoja; Kristen Kurtzeborn; Jenny Hsin; Satu Kuure; Laura Kerosuo
• Keywords: 0301 basic medicine; MATLAB; single cell analysis; Ilastik; Cell morphology analysis; cell shape; Article; User-friendly code; 03 medical and health sciences; Imaging, Three-Dimensional; Image Processing, Computer-Assisted; cell morphology analysis; Animals; Humans; Biochemistry, cell and molecular biology; Microscopy; Spatial Analysis; spatial localization; Tissue analysis; Single cell analysis; EMBRYO; Computational Biology; 3D cell segmentation; ShapeMetrics; Cell size; cell size; tissue analysis; cell segmentation pipeline; Cell shape; user-friendly code; Algorithms; Software; Spatial localization; Cell segmentation pipeline

The demand for single-cell level data is constantly increasing within life sciences. In order to meet this demand, robust cell segmentation methods that can tackle challenging in vivo tissues with complex morphology are required. However, currently available and volumetric analysis perform poorly on 3D images. Here, we generated ShapeMetrics, a MATLAB-based script segments cells and, by performing unbiased clustering using heatmap, separates the into subgroups according their morphological differences. be accurately segregated different biologically meaningful features such as ellipticity, longest axis, elongation, or ratio between volume surface area. Our machine learning based enables dissection of large amount novel from microscope images addition traditional information fluorescent biomarkers. Furthermore, spatially mapped back original locations tissue image help elucidate roles respective contexts. facilitate transition bulk accuracy, emphasize user-friendliness our method providing detailed step-by-step instructions through pipeline hence aiming reach users less experience computational biology.