The NIH’s Human BioMolecular Atlas Program (HuBMAP) recently released a wealth of 3D molecular imaging and genetic data mapping several organs – and all of it is publicly available. Data related to the kidney was borne out of the HuBMAP-funded Biomolecular Multimodal Imaging Center at Vanderbilt University.
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. You can travel around inside a piece of tissue and explore different cells and microenvironments.”
“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 breaking 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.
Caprioli and Jeff Spraggins, Ph.D., a research assistant professor of biochemistry and director of the HuBMAP Tissue Mapping Centers, are principal investigators on a four-year $5.5 million HuBMAP award supporting the new imaging center at Vanderbilt.¹ Their initial organ of focus has been the kidney.
Together with Alvin Powers, M.D., director of the Vanderbilt Diabetes Center, and Kevin Schey, Ph.D., a professor of biochemistry, ophthalmology and visual sciences at Vanderbilt, the team has also earned $4.4 million in HuBMAP funding to map the pancreas and eye.
Eighteen Tissue Mapping Centers across the country contributed over 300 datasets to HuBMAP’s inaugural data release that also maps the heart, large intestine, small intestine, lymph nodes, spleen and thymus.
“Once each of the centers collects maps across various organs, they will work with biocomputational research teams to integrate and mine these multimodal data at scale,” Spraggins explained. As a final step, teams of computer scientists are charged with designing interactive visualization tools. The HuBMAP Consortium has 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. This requires that teams 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 with Spraggins has developed new technologies for biomolecular spatial analyses. The team built 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 is 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 integrate different image types to discover the relationships between cells and molecules in tissue.”
Building a Useable Framework
HuBMAP is available for use by the scientific community and the general public through its data portal. The portal is an invaluable tool, say the researchers. HuBMAP could help surgeons explain diseases processes and interventions to patients, drug developers identify where particular genes are highly expressed, or even students understand cellular anatomy.
Researchers are already benefiting from the integrated data. Said Spraggins, “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.”
“We’re seeing further differentiation of cell subtypes that would otherwise go missed.”
HuBMAP’s initial focus on healthy tissue could also lead to a clearer benchmark for studying disease states, Caprioli said. “When you bring millions of cells together in an organ, they communicate, and even the same cell types are not necessarily the same molecularly. 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.”
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.