3D Multiscale Biomolecular Human Reference Atlas Construction, Visualization and Usage [4 of 5]

Abstract Text
The Mapping Component at Indiana University (MC-IU) will develop and implement at scale a
socio-technical infrastructure that combines human expertise and machine learning algorithms
to construct, visualize, and use the Human Reference Atlas (HRA). It will continue to lead the
construction of ASCT+B tables (expert-curated, connected lists of anatomical structures, cell
types, plus biomarkers based on standard ontologies) and associated reference object libraries
that together define the evolving Human Reference Atlas. We will collaborate closely with the
other mapping component to incorporate cell-by-gene references (e.g., Azimuth) and other
cell-by-biomarker references and 3D reference objects as they become available. We will work
with the HIVE and other teams to link the atlas to experimental data and scholarly paper
evidence. We will work closely with 15 other international consortia to ensure the HRA meets
user needs and supports driving use cases that advance biomedical research and clinical
practice. In close collaboration with the HIVE and tissue data generating teams in HuBMAP and
beyond, we will define and incrementally extend data formats and ontologies for querying,
integrating, and sharing HRA data. We will incorporate modern machine learning and
human-in-the-loop approaches to accelerate the pace at which raw tissue imaging data is
converted into annotated and segmented tissue maps. Advanced spatial management will be
used to keep track of millions of cells derived in tissue data, to support spatial registration of
new tissue, and to enable efficient spatial queries and exploration in the HRA user interfaces.
We will develop a vasculature-based common coordinate system to better capture human
diversity and develop computational models of functional tissue units to link anatomical structure
to function.

Grant Number: 3OT2OD033756-01S4
NIH Institute/Center: NIH
Principal Investigator: KATY BORNER