The MEISTER framework for multiscale biochemical profiling using high-mass-resolution MS enhanced by computational methods. Credit: Yuxuan Richard Xie et al.
Researchers from the Beckman Institute for Advanced Science and Technology have published their methods for utilizing spatial omics technology to reveal the intricacy of the human brain at different scales. The findings will aid future researchers in understanding how the brain functions, both in a healthy state and when a disease is present.
Published in Nature Methods, the technique involves using biochemical imaging frameworks integrated with deep learning. This combination of methods provides 3D molecular maps, with cell specificity, to understand how a human brain functions under various conditions.
“If you look at the brain chemically, it’s like a soup with a bunch of ingredients,” said Fan Lam, professor of bioengineering. “Understanding the biochemistry of the brain, how it organizes spatiotemporally, and how those chemical reactions support computing is critical to having a better idea of how the brain functions in health as well as during disease.”
To provide the imaging, researchers relied on mass spectrometry imaging to capture high-resolution data that they paired with single-cell metabolomics and computational tools to analyze and extract information regarding individual cells in the brain. The combination of techniques allowed for data acquisition at high speeds on large scales.
“Most people have a feeling that brain diseases such as depression and Alzheimer’s are caused by neurochemical imbalances,” said Jonathan Sweedler, professor of chemistry. “But those imbalances are really hard to study and it’s difficult to understand how chemicals interact at different scales (for example, at the tissue level and individual cell level) during problems in the brain.”
Creating these 3D maps of chemical distributions within the brain with cell-type specificity allows researchers to understand the complex biochemistry within the brain. Utilization of the technique could allow for a better understanding of complex neurological diseases.