Convolutional Neural Networks for Classifying Chromatin Morphology in Live-Cell Imaging.

Journal: Methods in molecular biology (Clifton, N.J.)
Published Date: Jan 1, 2022

Abstract

Chromatin is highly structured, and changes in its organization are essential in many cellular processes, including cell division. Recently, advances in machine learning have enabled researchers to automatically classify chromatin morphology in fluorescence microscopy images. In this protocol, we develop user-friendly tools to perform this task. We provide an open-source annotation tool, and a cloud-based computational framework to train and utilize a convolutional neural network to automatically classify chromatin morphology. Using cloud compute enables users without significant resources or computational experience to use a machine learning approach to analyze their own microscopy data.

Authors

  • Kristina Ulicna
    Institute of Structural and Molecular Biology, University College London, London, UK.
  • Laure T L Ho
    Institute of Structural and Molecular Biology, University College London, London, UK.
  • Christopher J Soelistyo
    Institute of Structural and Molecular Biology, University College London, London, UK.
  • Nathan J Day
    Institute of Structural and Molecular Biology, University College London, London, UK.
  • Alan R Lowe
    London Centre for Nanotechnology, University College London, 17-19 Gordon Street, London, WC1H 0AH, UK; Institute for the Physics of Living Systems, University College London, Gower Street, London, WC1E 6BT, UK; Institute for Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK. Electronic address: a.lowe@ucl.ac.uk.

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