The NIH’s Human BioMolecular Atlas Program (HuBMAP) is a monumental effort to create an open-access, 3D molecular guide to the healthy human body. The program unites software engineers, computational biologists, microscopists, pathologists, and scores of other experts to map the body at cellular resolution.
“It’s like Google Earth for the body.”
“Essentially, it’s like Google Earth for the body. You can travel around inside a piece of tissue and explore different cells and microenvironments. It’s going to break open our understanding of disease at an unprecedented level,” said Richard Caprioli, Ph.D., professor and director of the Mass Spectrometry Research Center at Vanderbilt University Medical Center.
To date, 19 research teams have been awarded HuBMAP funding. Caprioli and Jeff Spraggins, Ph.D., research assistant professor of biochemistry, are principal investigators on a $1.5 million HuBMAP award to launch a new Tissue Mapping Center at Vanderbilt.¹
Caprioli and Spraggins’ initial organ of focus is the kidney and they are working with newly-funded Tissue Mapping Centers across the country to analyze other organ tissues.
“Once each of the centers collects maps across various organs, there are other research teams focused on integrating all of those data,” Spraggins explained. As a final step, teams of computer scientists are charged with designing interactive visualization tools. The HuBMAP Consortium recently detailed their full approach in Nature.
More Than “Overlaying Images”
HubMAP’s overarching goal is to allow researchers anywhere to study the relationship between tissue organization and function. It requires teams to capture high-resolution molecular profiles of cells to understand how they behave in different “cellular neighborhoods,” Spraggins said.
Caprioli brings decades of mass spectrometry experience to the effort and is working with Spraggins to develop new biomolecular spatial analyses. They will spend the next two years building a platform that combines mass spectrometry with other imaging modalities (such as multiplexed immunohistochemistry and autofluorescence microscopy) to molecularly characterize healthy tissues.
The final product will be a navigable computer-based fusion of all imaging technologies collected. “This is not just overlaying images. It is taking image data pixel by pixel and mathematically combining them,” Caprioli said. Added Spraggins, “In general terms, we’re using machine learning to understand relationships between different image types.”
Building a Framework
In just the first few months of funding, the Vanderbilt team has begun building the necessary infrastructure and generating pilot data.
Said Spraggins, “We have some of the first atlases already. What we’re finding is that when we use the imaging mass spectrometry approach, we’re seeing further differentiation of cell subtypes that would otherwise go missed.”
Such findings in healthy tissue could lead to a clearer benchmark for studying disease states. “When you bring millions of cells together in an organ, they communicate, and even the same cell types are not necessarily the same molecularly,” Caprioli said. “To understand disease to the extent we want, we have to know what each cell type does normally in the context of the tissue environment.”
“When you bring millions of cells together in an organ, they communicate, and even the same cell types are not necessarily the same molecularly.”
Over the next several years, the researchers hope to integrate further imaging modalities into their analyses. They are working toward including MRI and CT data, as an example, through a collaboration with Vanderbilt’s ImageVU, a de-identified database of patient images.