Towards a clinically-based common coordinate framework for the human gut cell atlas: the gut models.

Common coordinate framework Human cell atlas Human gut cell atlas

Journal

BMC medical informatics and decision making
ISSN: 1472-6947
Titre abrégé: BMC Med Inform Decis Mak
Pays: England
ID NLM: 101088682

Informations de publication

Date de publication:
15 02 2023
Historique:
received: 30 06 2022
accepted: 13 01 2023
entrez: 16 2 2023
pubmed: 17 2 2023
medline: 18 2 2023
Statut: epublish

Résumé

The Human Cell Atlas resource will deliver single cell transcriptome data spatially organised in terms of gross anatomy, tissue location and with images of cellular histology. This will enable the application of bioinformatics analysis, machine learning and data mining revealing an atlas of cell types, sub-types, varying states and ultimately cellular changes related to disease conditions. To further develop the understanding of specific pathological and histopathological phenotypes with their spatial relationships and dependencies, a more sophisticated spatial descriptive framework is required to enable integration and analysis in spatial terms. We describe a conceptual coordinate model for the Gut Cell Atlas (small and large intestines). Here, we focus on a Gut Linear Model (1-dimensional representation based on the centreline of the gut) that represents the location semantics as typically used by clinicians and pathologists when describing location in the gut. This knowledge representation is based on a set of standardised gut anatomy ontology terms describing regions in situ, such as ileum or transverse colon, and landmarks, such as ileo-caecal valve or hepatic flexure, together with relative or absolute distance measures. We show how locations in the 1D model can be mapped to and from points and regions in both a 2D model and 3D models, such as a patient's CT scan where the gut has been segmented. The outputs of this work include 1D, 2D and 3D models of the human gut, delivered through publicly accessible Json and image files. We also illustrate the mappings between models using a demonstrator tool that allows the user to explore the anatomical space of the gut. All data and software is fully open-source and available online. Small and large intestines have a natural "gut coordinate" system best represented as a 1D centreline through the gut tube, reflecting functional differences. Such a 1D centreline model with landmarks, visualised using viewer software allows interoperable translation to both a 2D anatomogram model and multiple 3D models of the intestines. This permits users to accurately locate samples for data comparison.

Sections du résumé

BACKGROUND
The Human Cell Atlas resource will deliver single cell transcriptome data spatially organised in terms of gross anatomy, tissue location and with images of cellular histology. This will enable the application of bioinformatics analysis, machine learning and data mining revealing an atlas of cell types, sub-types, varying states and ultimately cellular changes related to disease conditions. To further develop the understanding of specific pathological and histopathological phenotypes with their spatial relationships and dependencies, a more sophisticated spatial descriptive framework is required to enable integration and analysis in spatial terms.
METHODS
We describe a conceptual coordinate model for the Gut Cell Atlas (small and large intestines). Here, we focus on a Gut Linear Model (1-dimensional representation based on the centreline of the gut) that represents the location semantics as typically used by clinicians and pathologists when describing location in the gut. This knowledge representation is based on a set of standardised gut anatomy ontology terms describing regions in situ, such as ileum or transverse colon, and landmarks, such as ileo-caecal valve or hepatic flexure, together with relative or absolute distance measures. We show how locations in the 1D model can be mapped to and from points and regions in both a 2D model and 3D models, such as a patient's CT scan where the gut has been segmented.
RESULTS
The outputs of this work include 1D, 2D and 3D models of the human gut, delivered through publicly accessible Json and image files. We also illustrate the mappings between models using a demonstrator tool that allows the user to explore the anatomical space of the gut. All data and software is fully open-source and available online.
CONCLUSIONS
Small and large intestines have a natural "gut coordinate" system best represented as a 1D centreline through the gut tube, reflecting functional differences. Such a 1D centreline model with landmarks, visualised using viewer software allows interoperable translation to both a 2D anatomogram model and multiple 3D models of the intestines. This permits users to accurately locate samples for data comparison.

Identifiants

pubmed: 36793076
doi: 10.1186/s12911-023-02111-9
pii: 10.1186/s12911-023-02111-9
pmc: PMC9933383
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

36

Informations de copyright

© 2023. The Author(s).

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Auteurs

Albert Burger (A)

Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK. a.g.burger@hw.ac.uk.

Richard A Baldock (RA)

Division of Pathology, Centre for Comparative Pathology, Edinburgh Cancer Research Centre, Institute of Cancer and Genetics, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK.

David J Adams (DJ)

Experimental Cancer Genetics, Wellcome Sanger Institute, Hinxton, Cambridge, UK.

Shahida Din (S)

Edinburgh IBD Unit Western General Hospital, NHS Lothian, Edinburgh, UK.

Irene Papatheodorou (I)

European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, Cambridge, UK.

Michael Glinka (M)

Division of Pathology, Centre for Comparative Pathology, Edinburgh Cancer Research Centre, Institute of Cancer and Genetics, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK.

Bill Hill (B)

Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.

Derek Houghton (D)

Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.

Mehran Sharghi (M)

Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.

Michael Wicks (M)

Division of Pathology, Centre for Comparative Pathology, Edinburgh Cancer Research Centre, Institute of Cancer and Genetics, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK.

Mark J Arends (MJ)

Division of Pathology, Centre for Comparative Pathology, Edinburgh Cancer Research Centre, Institute of Cancer and Genetics, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK. m.arends@ed.ac.uk.

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Classifications MeSH