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

Clicks: 388
ID: 96566
2020
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Abstract
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 cell segmentation and volumetric analysis methods perform poorly on 3D images. Here, we generated ShapeMetrics, a MATLAB-based script that segments cells in 3D and, by performing unbiased clustering using a heatmap, separates the cells into subgroups according to their volumetric and morphological differences. The cells can be accurately segregated according to different biologically meaningful features such as cell ellipticity, longest axis, cell elongation, or the ratio between cell volume and surface area. Our machine learning based script enables dissection of a large amount of novel data from microscope images in addition to the traditional information based on fluorescent biomarkers. Furthermore, the cells in different subgroups can be spatially mapped back to their original locations in the tissue image to help elucidate their roles in their respective morphological contexts. In order to facilitate the transition from bulk analysis to single-cell level accuracy, we emphasize the user-friendliness of our method by providing detailed step-by-step instructions through the pipeline hence aiming to reach users with less experience in computational biology.
Reference Key
takko2020shapemetricsdevelopmental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Takko, Heli;Pajanoja, Ceren;Kurtzeborn, Kristen;Hsin, Jenny;Kuure, Satu;Kerosuo, Laura;
Journal Developmental biology
Year 2020
DOI
S0012-1606(20)30046-4
URL
Keywords
matlab cell size 3d cell segmentation ilastik shapemetrics cell morphology analysis cell segmentation pipeline cell shape single cell analysis spatial localization tissue analysis user-friendly code

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