Strawbridge, S.E. orcid.org/0000-0003-0273-0835, Kurowski, A. orcid.org/0000-0003-0502-571X, Corujo-Simon, E. orcid.org/0000-0002-5490-369X et al. (3 more authors) (2023) insideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology. Biology Open, 12 (9). bio060055. ISSN 2046-6390
Abstract
A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells’ spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed insideOutside, for classifying interior and exterior points of a three-dimensional point-cloud, a common output from imaged cells within the early mouse embryo. We benchmark our method against other published methods to demonstrate that it yields greater accuracy in classification of nuclei from the pre-implantation mouse embryos and greater accuracy when challenged with local surface concavities. We have made MATLAB and Python implementations of the method freely available. This method should prove useful for embryology, with broader applications to similar data arising in the life sciences.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The author(s). Published by The Company of Biologists Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
Keywords: | Embryo; Inner cell mass; Machine learning; Pre-implantation; Quantitative biology; Trophectoderm; Animals; Mice; Algorithms; Cell Nucleus; Biological Science Disciplines; Blastocyst; Cell Differentiation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Funding Information: | Funder Grant number BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/R016925/1 BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL BB/V018647/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Sep 2023 14:08 |
Last Modified: | 04 Sep 2023 14:08 |
Status: | Published |
Publisher: | The Company of Biologists |
Refereed: | Yes |
Identification Number: | 10.1242/bio.060055 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202954 |