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Building a molecular portrait of the brain using mass spectrometry and deep learning

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BYLINE: Samantha Jones Toal

Newswise — Beckman Institute for Advanced Researchers in Science and Technology Jonathan Sweedlerchemistry teacher, and Fan Lamprofessor of bioengineering, explained how spatial omics technologies can reveal the molecular complexity of the brain at different scales.

Their research appeared this month in Natural methods.

The researchers and their colleagues used a biochemical imaging framework integrated with deep learning to create 3D molecular maps with cellular specificity to better understand how the brain functions in health and disease. Their research is supported by a $3 million grant from the National Institute on Aging of the National Institutes of Health.

“If you look at the brain chemically, it’s like a soup with a bunch of ingredients,” Lam said. “Understanding the biochemistry of the brain, how it is organized spatiotemporally, and how these chemical reactions support computing is essential to getting a better idea of ​​how the brain functions in health as well as during disease.”

To understand how the brain’s chemical ingredients interact with each other, researchers used a new imaging technique called mass spectrometry imaging to collect and analyze massive amounts of high-resolution data. They also used single-cell metabolomics and computational tools to extract data on individual molecules in single brain cells, enabling data acquisition at unprecedented speeds and scales.

“Most people feel that brain diseases such as depression and Alzheimer’s disease are caused by neurochemical imbalances,” Sweedler said. “But these imbalances are really difficult to study and it is difficult to understand how chemicals interact at different scales (for example, at the level of tissues and individual cells) during brain problems.”

According to Sweedler, creating 3D maps of chemical distributions with cell type specificity allows researchers to better understand the complex biochemistry of the brain, which in the long term should help combat currently incurable neurological diseases.

Single-cell metabolomics, a technology critical to researchers’ discoveries, has been named one of the “Seven technologies to watch in 2023“With CRISPR and the James Webb Space Telescope, talking about the high impact that these tools will continue to have when it comes to looking at cell-specific data,” Sweedler said.

The research would not have been possible without the collaborative nature of the Beckman Institute.

“It really amazes me how small interactions can turn into interesting research conversations and ultimately large-scale collaborative studies,” said first author Richard Xie, Beckman Institute Graduate Fellow. “The key is to be open-minded and interdisciplinary, because you can draw inspiration from another field. I am very excited about the progress being made in leveraging the groups’ different expertise to design tools to better describe the biochemical landscape of the brain.

Lam and Sweedler met at Xie’s request to discuss his work on single-cell and tissue mass spectrometry imaging. The team achieved a breakthrough in how informatics and computational methods could lead to a new type of multi-modal, multi-scale biochemical imaging, highlighted in their recent Nature Methods paper.


Editor’s notes:

The research described in this article, titled “Multi-scale biochemical mapping of the brain using deep learning-enhanced high-throughput mass spectrometry,” can be viewed online at https://doi.org/10.1038/s41592-024-02171-3.

Full author information is available in the article.

The research reported in this press release was supported by the National Institute on Aging of the National Institutes of Health under award number R01AG078797. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Media Contact: Jenna Kurtzweil, (email protected)



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