The healthy human body is composed of trillions of cells, whose molecular contents, spatial organization, interactions, and molecular contents are carefully regulated and precisely structured. While transformative advances in technology enable new types of anatomical, histological, spatial and single cell measurements, new methods for data integration are essential to discover commonalities across diverse human beings, and discover relationships between modalities across multiple scales. This requires novel computational frameworks, algorithms and scaled software. Here, we will leverage this unique opportunity to construct an integrated spatial and molecular map for the human body, by designing new methods to infer a Common Coordinate Framework (CCF) for both absolute positions and relative relationships, ?geolocate? new data points onto it, and enable querying and exploration from the biological community to derive new insights and hypotheses. We will (1) construct a CCF across multiple scales of human anatomy, a 3-d spatial representation of the human body with coordinate systems based on common features across individuals. To do this, we will develop a powerful suite of integration tools using a strategy based on ?semi-supervised? manifold alignment. (2) Develop analytical strategies to ?geolocate? new data onto this framework, thereby displaying the entirety of HuBMAP data on a conserved scaffold of human anatomy, suitable for both highly structured organs, distributed systems, and dynamical tissues; (3) Build infrastructure to allow users to flexibly explore and query this dataset, to identify relationships between molecular composition, tissue organization, and anatomical structure. Our MC will create an integrated atlas of a healthy human, representing a landmark public resource that will lead to transformative insights into the organization, interaction, and regulation of cells, tissues and organs across the human body.
The human body consists of trillions of single cells, but their identities and spatial organization are not well understood. We will develop computational methods to take diverse types of data, and integrate them into a reference ?atlas?, akin to a Google Maps for the human body.
