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Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D

John W. Wills Orcid Logo, Jack Robertson, Pani Tourlomousis, Clare M.C. Gillis, Claire Barnes Orcid Logo, Michelle Miniter Orcid Logo, Rachel E. Hewitt, Clare E. Bryant, Huw Summers Orcid Logo, Jonathan J. Powell, Paul Rees Orcid Logo

Cell Reports Methods, Volume: 3, Issue: 2, Start page: 100398

Swansea University Authors: Claire Barnes Orcid Logo, Huw Summers Orcid Logo, Paul Rees Orcid Logo

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DOI (Published version): 10.1016/j.crmeth.2023.100398

Abstract

Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and com...

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Published in: Cell Reports Methods
ISSN: 2667-2375
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62581
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spelling v2 62581 2023-02-06 Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D 024232879fc13d5ceac584360af8742c 0000-0003-1031-7127 Claire Barnes Claire Barnes true false a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2023-02-06 MEDE Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows. Journal Article Cell Reports Methods 3 2 100398 Elsevier BV 2667-2375 label free; cell segmentation; tissue; confocal microscopy; immunofluorescence; single cell; quantitative; 2D; 3D; digital pathology 1 2 2023 2023-02-01 10.1016/j.crmeth.2023.100398 http://dx.doi.org/10.1016/j.crmeth.2023.100398 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) EPSRC, BBSRC, MRC, Wellcome Trust EP/N013506/1, BB/P026818/1, MR/R005699/1, 108045/Z/15/Z 2023-04-21T12:42:38.5353257 2023-02-06T10:53:36.1298275 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering John W. Wills 0000-0002-4347-5394 1 Jack Robertson 2 Pani Tourlomousis 3 Clare M.C. Gillis 4 Claire Barnes 0000-0003-1031-7127 5 Michelle Miniter 0000-0002-8480-2726 6 Rachel E. Hewitt 7 Clare E. Bryant 8 Huw Summers 0000-0002-0898-5612 9 Jonathan J. Powell 10 Paul Rees 0000-0002-7715-6914 11 62581__27154__b55af2a7c26d41108f5942c7944ba415.pdf 62581.VOR.pdf 2023-04-21T12:37:33.4311033 Output 7873243 application/pdf Version of Record true Distributed under the terms of a Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
spellingShingle Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
Claire Barnes
Huw Summers
Paul Rees
title_short Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
title_full Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
title_fullStr Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
title_full_unstemmed Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
title_sort Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D
author_id_str_mv 024232879fc13d5ceac584360af8742c
a61c15e220837ebfa52648c143769427
537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv 024232879fc13d5ceac584360af8742c_***_Claire Barnes
a61c15e220837ebfa52648c143769427_***_Huw Summers
537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Claire Barnes
Huw Summers
Paul Rees
author2 John W. Wills
Jack Robertson
Pani Tourlomousis
Clare M.C. Gillis
Claire Barnes
Michelle Miniter
Rachel E. Hewitt
Clare E. Bryant
Huw Summers
Jonathan J. Powell
Paul Rees
format Journal article
container_title Cell Reports Methods
container_volume 3
container_issue 2
container_start_page 100398
publishDate 2023
institution Swansea University
issn 2667-2375
doi_str_mv 10.1016/j.crmeth.2023.100398
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
url http://dx.doi.org/10.1016/j.crmeth.2023.100398
document_store_str 1
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description Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows.
published_date 2023-02-01T12:42:37Z
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score 11.013148

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