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

Show full item record



Permalink

http://hdl.handle.net/10138/315822

Citation

Takko , H , Pajanoja , C , Kurtzeborn , K , Hsin , J , Kuure , S & Kerosuo , L 2020 , ' ShapeMetrics: a userfriendly pipeline for 3D cell segmentation and spatial tissue analysis ' , Developmental Biology , vol. 462 , no. 1 , pp. 7-19 . https://doi.org/10.1016/j.ydbio.2020.02.003

Title: ShapeMetrics: a userfriendly pipeline for 3D cell segmentation and spatial tissue analysis
Author: Takko, Heli; Pajanoja, Ceren; Kurtzeborn, Kristen; Hsin, Jenny; Kuure, Satu; Kerosuo, Laura
Contributor: University of Helsinki, Department of Biochemistry and Developmental Biology
University of Helsinki, Department of Biochemistry and Developmental Biology
University of Helsinki, Helsinki Institute of Life Science HiLIFE
University of Helsinki, STEMM - Stem Cells and Metabolism Research Program
University of Helsinki, Staff Services
Date: 2020-06-01
Language: eng
Number of pages: 13
Belongs to series: Developmental Biology
ISSN: 0012-1606
URI: http://hdl.handle.net/10138/315822
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.
Subject: 1182 Biochemistry, cell and molecular biology
ShapeMetrics
3D cell segmentation
spatial localization
cell shape
single cell analysis
user-friendly code
tissue analysis
cell segmentation pipeline
cell morphology analysis
cell size
MATLAB
Ilastik
Tissue analysis
Single cell analysis
Spatial localization
Cell segmentation pipeline
EMBRYO
Cell shape
Cell size
User-friendly code
Cell morphology analysis
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
1_s2.0_S0012160620300464_main.pdf 13.51Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record