Construction and Usage of a Human Body Common Coordinate Framework Comprising Clinical, Semantic, and Spatial Ontologies

Abstract: The National Institutes of Health's (NIH) Human Biomolecular Atlas Program (HuBMAP) aims to create a comprehensive high-resolution atlas of all the cells in the healthy human body. Multiple laboratories across the United States are collecting tissue specimens from different organs of donors who vary in sex, age, and body size. Integrating and harmonizing the data derived from these samples and 'mapping' them into a common three-dimensional (3D) space is a major challenge. The key to making this possible is a 'Common Coordinate Framework' (CCF), which provides a semantically annotated, 3D reference system for the entire body. The CCF enables contributors to HuBMAP to 'register' specimens and datasets within a common spatial reference system, and it supports a standardized way to query and 'explore' data in a spatially and semantically explicit manner. [...] This paper describes the construction and usage of a CCF for the human body and its reference implementation in HuBMAP. The CCF consists of (1) a CCF Clinical Ontology, which provides metadata about the specimen and donor (the 'who'); (2) a CCF Semantic Ontology, which describes 'what' part of the body a sample came from and details anatomical structures, cell types, and biomarkers (ASCT+B); and (3) a CCF Spatial Ontology, which indicates 'where' a tissue sample is located in a 3D coordinate system. An initial version of all three CCF ontologies has been implemented for the first HuBMAP Portal release. It was successfully used by Tissue Mapping Centers to semantically annotate and spatially register 48 kidney and spleen tissue blocks. The blocks can be queried and explored in their clinical, semantic, and spatial context via the CCF user interface in the HuBMAP Portal.

• The paper introduces a comprehensive three-ontology structure (Clinical, Semantic, Spatial) to harmonize 3D tissue data.
• It successfully registers and semantically annotates 48 tissue samples, enabling multi-scale anatomical queries from organ to cell level.
• The modular design of the framework supports future integration and advances in precision medicine and systems biology research.

Construction and Usage of a Human Body Common Coordinate Framework: An Overview

Introduction

The effort of developing a comprehensive three-dimensional (3D) atlas of human cellular structured is embodied by the Human Biomolecular Atlas Program (HuBMAP), a project initiated by the National Institutes of Health (NIH). The paper under discussion delineates the construction and practical utility of a Common Coordinate Framework (CCF) to integrate and harmonize multimodal data from various tissue specimens into a semantically annotated 3D model. The CCF, as implemented within the HuBMAP framework, provides a systematic approach for spatial and semantic annotation of tissue data, offering significant potential for advancing both research and practical applications.

Methodology

The CCF construction deploys a tripartite ontology architecture: the CCF Clinical Ontology, the CCF Semantic Ontology, and the CCF Spatial Ontology, each offering specific insights into different dataset aspects. The Clinical Ontology captures demographic and metadata information, vital for sample characterization. The Semantic Ontology organizes anatomical structures, cell types, and biomarkers to support detailed partonomic hierarchies. Finally, the Spatial Ontology focuses on spatial data by delineating the shapes, sizes, and orientations of tissues within a 3D reference system. Together, these structures facilitate a robust framework allowing researchers to query, explore, and analyze large-scale tissue datasets meaningfully.

Key Findings

Preliminary implementations of the CCF have successfully registered and semantically annotated 48 tissue blocks, specifically from kidney and spleen samples, through the HuBMAP portal. These registered samples enable researchers to interrogate anatomical structure distributions at various scales, ranging from the organ level down to individual cells. The modular architecture of the CCF ensures its adaptability for diverse datasets, paving the way for future enhancement as new data and technologies emerge.

Implications

The organized alignment of clinical, semantic, and spatial data within the CCF framework opens substantial new investigative possibilities. Theoretically, the CCF enhances the ability to pose complex biological queries at unprecedented resolutions, thus potentially revolutionizing strategies in systems biology and clinical pathology. Practically, the tool facilitates integrative data analysis, crucial for personalized medicine initiatives and cross-consortia collaborations. The CCF exemplifies a versatile framework suitable for understanding normal human tissue organization, with prospective applications in pathophysiological contexts.

Future Directions

The expansion of the CCF framework to include additional tissues and variations reflective of human diversity remains a key challenge and opportunity. Integrating machine learning methodologies with bottom-up, data-driven processes could enhance both the fidelity and scalability of tissue annotation and mapping efforts. Moreover, ongoing technological advancements are expected to augment the current resolution capabilities of constructed 3D anatomical maps, promoting further refinement of the framework.

Conclusion

By bridging clinical, semantic, and spatial domains, the HuBMAP's CCF establishes a feasible blueprint for the comprehensive mapping of cellular components across the human body. This work marks a pivotal step towards achieving integrated, spatially-resolved biomedical data environments that could substantially benefit future research in anatomical pathology and precision medicine. The initiative underscores the potential of multi-institutional collaboration and technological integration in advancing the theoretical and practical frontiers of biomedical research.