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Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Huw Summers Orcid Logo, John W. Wills, Paul Rees Orcid Logo

Cell Reports Methods, Volume: 2, Issue: 11, Start page: 100348

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

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DOI (Published version): 10.1016/j.crmeth.2022.100348

Abstract

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular rela...

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Published in: Cell Reports Methods
ISSN: 2667-2375
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62019
first_indexed 2022-11-24T10:33:22Z
last_indexed 2024-11-14T12:20:11Z
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spelling 2024-10-18T16:29:49.4338491 v2 62019 2022-11-24 Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2022-11-24 EAAS Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function. Journal Article Cell Reports Methods 2 11 100348 Elsevier BV 2667-2375 cell imagings; patial statistics; tissue analysis; cytometry 21 11 2022 2022-11-21 10.1016/j.crmeth.2022.100348 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-10-18T16:29:49.4338491 2022-11-24T10:30:13.8871729 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Huw Summers 0000-0002-0898-5612 1 John W. Wills 2 Paul Rees 0000-0002-7715-6914 3 62019__25886__4da1a2842c19423aaa159538fd29d7bb.pdf 62019.pdf 2022-11-24T10:32:43.3866811 Output 4456322 application/pdf Version of Record true This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
spellingShingle Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
Huw Summers
Paul Rees
title_short Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
title_full Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
title_fullStr Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
title_full_unstemmed Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
title_sort Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis
author_id_str_mv a61c15e220837ebfa52648c143769427
537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv a61c15e220837ebfa52648c143769427_***_Huw Summers
537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Huw Summers
Paul Rees
author2 Huw Summers
John W. Wills
Paul Rees
format Journal article
container_title Cell Reports Methods
container_volume 2
container_issue 11
container_start_page 100348
publishDate 2022
institution Swansea University
issn 2667-2375
doi_str_mv 10.1016/j.crmeth.2022.100348
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
document_store_str 1
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description Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function.
published_date 2022-11-21T05:21:44Z
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