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Image analysis using the Fluorescence Imaging of Nuclear Staining (FINS) algorithm
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  • Laura Bramwell,
  • Jack Spencer,
  • Ryan Frankum,
  • Emad Manni,
  • Lorna Harries
Laura Bramwell
University of Exeter Department of Clinical and Biomedical Science
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Jack Spencer
University of Exeter Department of Clinical and Biomedical Science
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Ryan Frankum
University of Exeter Department of Clinical and Biomedical Science
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Emad Manni
University of Exeter Department of Clinical and Biomedical Science
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Lorna Harries
University of Exeter Department of Clinical and Biomedical Science

Corresponding Author:[email protected]

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Abstract

Finding appropriate image analysis techniques for a particular purpose can be difficult. In the context of the analysis of immunocytochemistry images, where the key information lies in the number of nuclei containing co-localised fluorescent signals from a marker of interest, researchers often opt to use manual counting techniques because of the paucity of available tools. Here, we present the development and validation of the Fluorescence Imaging of Nuclear Staining (FINS) algorithm for the quantification of fluorescent signals from immunocytochemically-stained cells. The FINS algorithm is based on a variational segmentation of the nuclear stain channel and an iterative thresholding procedure to count co-localised fluorescent signal from nuclear proteins in other channels. We present experimental results comparing the FINS algorithm to the manual counts of seven researchers across a dataset of three human primary cell types which are immunocytochemically-stained for a nuclear marker (DAPI), a biomarker of cellular proliferation (Ki67) and a biomarker of DNA damage (γH2AX). The quantitative performance of the algorithm is analysed in terms of consistency with the manual count data and acquisition time. The FINS algorithm produces data consistent with that achieved by manual counting, but improves the process by reducing subjectivity and time. The algorithm is simple to use, based in software that is omnipresent in academia, and allows data review with its simple, intuitive user interface. We hope that, as the FINS tool is open source and is custom built for this specific application, it will streamline analysis of immunocytochemical images.