TOOLS AND RESOURCES CytoCensus, mapping cell identity and division in tissues and organs using machine learning Martin Hailstone1, Dominic Waithe2, Tamsin J Samuels1, Lu Yang1, Ita Costello3, Yoav Arava4, Elizabeth Robertson3, Richard M Parton1,5, Ilan Davis1,5* 1Department of Biochemistry, University of Oxford, Oxford, United Kingdom; 2Wolfson Imaging Center & MRC WIMM Centre for Computational Biology MRC Weather all Institute of Molecular Medicine University of Oxford, Oxford, United Kingdom; 3The Dunn School of Pathology,University of Oxford, Oxford, United Kingdom; 4Department of Biology, Technion - Israel Institute of Technology, Haifa, Israel; 5Micron Advanced Bioimaging Unit, Department of Biochemistry, University of Oxford, Oxford, United Kingdom 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. *For correspondence:
[email protected] Competing interest: See Introduction page 25 Complex tissues develop through regulated proliferation and differentiation of a small number of Funding: See page 25 stem cells.