Publication
Authors
Katy Börner, Philip D. Blood, Jonathan C. Silverstein, Matthew Ruffalo, Rahul Satija, Sarah A. Teichmann, Gloria J. Pryhuber, Ravi S. Misra, Jean Fan, John W. Hickey, Griffin M. Weber, Andreas Bueckle, Bruce W. Herr II, et al., HRA Team
Journal
Nature Methods , Vol. 22 , pp. 845–860 (2025)
PubMed
PMID 40082611

A reference atlas is only as useful as the coordinate system it’s built on. For the human body — with its 37 trillion cells, dozens of organs, and enormous variation across individuals — building that coordinate system from scratch is one of the most ambitious undertakings in modern biology.

This paper describes HuBMAP’s 3D Human Reference Atlas (HRA) v2.0: what it contains, how it was built, and how researchers can use it to map their own data.


What the Atlas Contains

HRA v2.0 is a comprehensive, multi-scale map of the healthy adult human body linking anatomy, cell types, and molecular biomarkers:

  • 4,499 unique anatomical structures
  • 1,195 cell types
  • 2,089 biomarkers (genes, proteins, lipids)
  • 33 ASCT+B tables covering major organ systems
  • 65 3D reference objects linked to standard ontologies

Every element is connected — from whole organs down to the molecular signatures that define individual cell populations.


How It Was Built

The atlas is built around a Common Coordinate Framework (CCF) that allows data from different labs, technologies, and tissue donors to be registered to the same anatomical space. Experts from 20+ consortia contributed to the knowledge graphs, ontology mappings, and 3D models that make up the framework.

The CCF enables three registration pathways for new data: cell type classification systems, validated antibody panels, and direct spatial tissue registration. Whichever path a dataset enters through, it lands in the same coordinate space — making cross-study comparisons possible in a way they haven’t been before.


Why It Matters

The HRA is designed to outlast any single experiment. As a shared reference, it becomes the baseline against which aging, disease, and perturbation can be measured. New datasets from anywhere in the world can be registered to the atlas, immediately gaining the context of everything already mapped.

My work at the Pittsburgh Supercomputing Center has contributed to the computational infrastructure supporting HuBMAP. Seeing that infrastructure now underpinning an atlas of this scale — 247 contributors, dozens of institutions, a single coherent coordinate system — is exactly the kind of outcome that makes large-scale collaboration worth the complexity.