美国伊利诺伊大学香槟分校Fan Lam等研究人员合作通过深度学习增强型高通量质谱技术绘制大脑多尺度生化图谱。相关论文于2024年2月16日在线发表在《自然—方法学》杂志上。

研究人员利用MEISTER这一实验和计算质谱（MS）综合框架展示了全脑和单细胞生化图谱的互补性。这个框架整合了基于深度学习的重构技术（可将高分辨质谱加速15倍）、多模态配准技术（可创建三维分子分布）和数据整合方法（可将细胞特异性质谱拟合到三维数据集）。

研究人员对具有数百万像素的组织以及从大鼠大脑中获取的大量单细胞群进行了详细的脂质图谱成像。研究人员根据细胞亚群和细胞的解剖起源，确定了区域特异性脂质含量和细胞特异性脂质定位。这个工作流程为未来开发用于大脑生化特征描述的多尺度技术绘制了蓝图。

据介绍，空间组学技术可以揭示大脑分子的复杂性。虽然质谱成像（MSI）提供了化合物的空间定位，但通过具有单细胞分辨率的MSI进行全脑范围的三维综合生化图谱分析尚未实现。

附：英文原文

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

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

Issue&Volume: 2024-02-16

Abstract: 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.

DOI: 10.1038/s41592-024-02171-3

Source: https://www.nature.com/articles/s41592-024-02171-3