Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease. However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Importantly, additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Here, we integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 μm) was used to determine lipid distributions within human glomeruli from a normal portion of fresh-frozen kidney cancer nephrectomy tissue revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types and substructures through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS, without sacrificing signal quality from either modality. Machine learning approaches were combined enabling cell type segmentation and identification based on MxIF data. This was followed by mining of cell type or cluster-associated MALDI IMS signatures using classification and interpretable machine learning. This allowed automated discovery of spatially specific molecular markers for glomerular cell types and substructures as well as lipids correlated to deep and superficial glomeruli. Overall, our work establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments providing a framework applicable to other kidney tissue features and organ systems.

中文翻译：

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通过集成成像质谱和多重免疫荧光显微镜分析肾小球细胞的原位分子谱


肾小球通过足细胞、系膜细胞、有孔内皮细胞和肾小球基底膜的配位过滤血液。细胞变化，例如足细胞丢失，与糖尿病肾病等病理有关。然而，关于特定细胞类型的原位分子谱以及这些谱如何随疾病变化，人们知之甚少。基质辅助激光解吸/电离成像质谱法 （MALDI IMS） 非常适合各种分子类别的非靶向组织作图分析。重要的是，其他成像模式可以与 MALDI IMS 集成，以将这些生物分子分布与特定细胞类型相关联。在这里，我们集成了结合 MALDI IMS 和多重免疫荧光 （MxIF） 显微镜的工作流程。高空间分辨率 MALDI IMS （5 μm） 用于确定新鲜冰冻肾癌肾切除术组织正常部分的人肾小球内的脂质分布，揭示肾小球内脂质异质性。质谱数据通过新方法与特定的肾小球细胞类型和亚结构相关联，这些方法使 MxIF 显微镜能够在 MALDI IMS 之后对同一组织切片进行，而不会牺牲任何一种模式的信号质量。结合机器学习方法，基于 MxIF 数据实现细胞类型分割和鉴定。然后，使用分类和可解释机器学习挖掘细胞类型或与集群相关的 MALDI IMS 特征。这允许自动发现肾小球细胞类型和亚结构的空间特异性分子标志物，以及与深部和浅表肾小球相关的脂质。 总体而言，我们的工作建立了一个工具箱，用于探测组织微环境中肾小球细胞类型和亚结构的分子特征，为其他肾脏组织特征和器官系统提供了一个框架。