Vandergrift Gregory W, Veličković Marija, Day Le Z, Gorman Brittney L, Williams Sarah M, Shrestha Bindesh, Anderton Christopher R
Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.
Waters Corporation, Milford, Massachusetts 01757, United States.
Anal Chem. 2025 Jan 14;97(1):392-400. doi: 10.1021/acs.analchem.4c04462. Epub 2024 Dec 21.
An increasing number of spatial multiomic workflows have recently been developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform the region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a new multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. This workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a poly(ethylene naphthalate)-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed the downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 μm × 200 μm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial- and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.
近年来，越来越多的空间多组学工作流程被开发出来。其中一些方法利用基于质谱成像（MSI）的初始空间代谢组学来为下游空间蛋白质组学的感兴趣区域（ROI）选择提供信息。然而，这些工作流程受到不同检测方法之间不同底物要求的限制，或者需要分析连续切片（即每种检测方法一个切片）。为了缓解这些问题，我们提出了一种新的多组学工作流程，该流程在对同一组织切片进行空间蛋白质组学分析之前，使用解吸电喷雾电离（DESI）-MSI来识别组织上具有代表性的空间代谢物模式。在此，我们用安装在聚（萘二甲酸乙二酯）膜载玻片上的模型哺乳动物组织（大鼠脑冠状切片）展示了这个工作流程。初始的DESI-MSI在METASPACE平台上产生了160个注释（SwissLipids）（错误发现率≤20%）。来自注释离子图像的分割图为同一样本的下游空间蛋白质组学表征的ROI选择提供了信息。DESI-MSI固有的非特异性底物要求和最小的样本破坏使得能够进行优化的下游空间蛋白质组学检测，每个ROI（200μm×200μm）能够可靠地检测到3888±240至4717±48个蛋白质。最后，我们展示了多组学信息的整合，我们发现神经酰胺定位与SMPD3丰度（神经酰胺合成蛋白）相关，并且我们还利用蛋白质丰度来解决代谢物异构体的模糊性。总体而言，将DESI-MSI整合到多组学工作流程中，可以从工作流程本身固有的每个质谱检测的优化实施中获得互补的空间和分子水平信息。
