Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry

Yuxuan Richard Xie, Daniel C. Castro, Stanislav S. Rubakhin, Timothy J. Trinklein, Jonathan V. Sweedler, Fan Lam

Research output: Contribution to journalArticlepeer-review


Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

Original languageEnglish (US)
Pages (from-to)521-530
Number of pages10
JournalNature Methods
Issue number3
StatePublished - Mar 2024

ASJC Scopus subject areas

  • Molecular Biology
  • Biochemistry
  • Biotechnology
  • Cell Biology


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