Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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

View ORCID ProfileMartin Hailstone, Dominic Waithe, View ORCID ProfileTamsin J Samuels, Lu Yang, Ita Costello, Yoav Arava, Elizabeth J Robertson, Richard M Parton, View ORCID ProfileIlan Davis
doi: https://doi.org/10.1101/137406
Martin Hailstone
1Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Martin Hailstone
Dominic Waithe
2Wolfson Imaging Center & MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tamsin J Samuels
1Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Tamsin J Samuels
Lu Yang
1Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ita Costello
4The Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yoav Arava
5Department of Biology, Technion – Israel Institute of Technology, Haifa 32000
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elizabeth J Robertson
4The Dunn School of Pathology, University of Oxford, South Parks Road, Oxford OX1 3RE, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Richard M Parton
1Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
3Micron Advanced, Bioimaging Unit, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: richard.parton@bioch.ox.ac.uk ilan.davis@bioch.ox.ac.uk
Ilan Davis
1Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
3Micron Advanced, Bioimaging Unit, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ilan Davis
  • For correspondence: richard.parton@bioch.ox.ac.uk ilan.davis@bioch.ox.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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 these 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.

Summary Hailstone et al. develop CytoCensus, a “point-and-click” supervised machine-learning image analysis software to quantitatively identify defined cell classes and divisions from large multidimensional data sets of complex tissues. They demonstrate its utility in analysing challenging developmental phenotypes in living explanted Drosophila larval brains, mammalian embryos and zebrafish organoids. They further show, in comparative tests, a significant improvement in performance over existing easy-to-use image analysis software.

Figure
  • Download figure
  • Open in new tab

Highlights

  • CytoCensus: machine learning quantitation of cell types in complex 3D tissues

  • Single cell analysis of division rates from movies of living Drosophila brains in 3D

  • Diverse applications in the analysis of developing vertebrate tissues and organoids

  • Outperforms other image analysis software on challenging, low SNR datasets tested

Footnotes

  • The manuscript has been significantly revised to focus both on CytoCensus (formerly QBrain) and new improved exemplars (Drosophila, Mouse, Zebrafish) with reduced emphasis on Syncrip characterisation.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted August 16, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
CytoCensus: mapping cell identity and division in tissues and organs using machine learning
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
CytoCensus: mapping cell identity and division in tissues and organs using machine learning
Martin Hailstone, Dominic Waithe, Tamsin J Samuels, Lu Yang, Ita Costello, Yoav Arava, Elizabeth J Robertson, Richard M Parton, Ilan Davis
bioRxiv 137406; doi: https://doi.org/10.1101/137406
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
CytoCensus: mapping cell identity and division in tissues and organs using machine learning
Martin Hailstone, Dominic Waithe, Tamsin J Samuels, Lu Yang, Ita Costello, Yoav Arava, Elizabeth J Robertson, Richard M Parton, Ilan Davis
bioRxiv 137406; doi: https://doi.org/10.1101/137406

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Developmental Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3571)
  • Biochemistry (7514)
  • Bioengineering (5473)
  • Bioinformatics (20664)
  • Biophysics (10250)
  • Cancer Biology (7925)
  • Cell Biology (11563)
  • Clinical Trials (138)
  • Developmental Biology (6558)
  • Ecology (10129)
  • Epidemiology (2065)
  • Evolutionary Biology (13526)
  • Genetics (9493)
  • Genomics (12784)
  • Immunology (7869)
  • Microbiology (19429)
  • Molecular Biology (7609)
  • Neuroscience (41854)
  • Paleontology (306)
  • Pathology (1252)
  • Pharmacology and Toxicology (2178)
  • Physiology (3247)
  • Plant Biology (6993)
  • Scientific Communication and Education (1290)
  • Synthetic Biology (1941)
  • Systems Biology (5404)
  • Zoology (1107)