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CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Yang, Lu 
Costello, Ita 

Abstract

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D 'point-and-click' user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues.

Description

Keywords

3D cell detection, 4D image analysis, D. melanogaster, cell biology, developmental biology, ex vivo culture, live imaging, mouse, neural stem cells, zebrafish, Animals, Animals, Genetically Modified, Automation, Brain, Cell Division, Drosophila melanogaster, Embryo, Mammalian, Female, Image Processing, Computer-Assisted, Larva, Machine Learning, Male, Mice, Microscopy, Video, Mutation, Organoids, Phenotype, Reproducibility of Results, Retina, Time Factors, Time-Lapse Imaging, Tissue Culture Techniques, Zebrafish

Journal Title

Elife

Conference Name

Journal ISSN

2050-084X
2050-084X

Volume Title

9

Publisher

eLife Sciences Publications, Ltd
Sponsorship
Medical Research Council (MC_PC_12009)