RevealingIn SituMolecular Profiles of Glomerular Cell Types and Substructures with Integrated Imaging Mass Spectrometry and Multiplexed Immunofluorescence Microscopy

质谱成像 马尔迪成像 电池类型 足细胞 原位 生物标志物发现 计算生物学 化学 质谱法 分子成像 细胞 病理 蛋白质组学 基质辅助激光解吸/电离 生物 生物化学 医学 体内 有机化学 色谱法 吸附 解吸 内分泌学 基因 蛋白尿 生物技术
作者
Allison B. Esselman,Felipe Moser,Leonoor Tideman,Lukasz G. Migas,Katerina Djambazova,Madeline E. Colley,Ellie L. Pingry,Nathan Heath Patterson,Melissa A. Farrow,Haichun Yang,Agnes B. Fogo,Mark de Caestecker,Raf Van de Plas,Jeffrey M. Spraggins
标识
DOI:10.1101/2024.02.21.581450
摘要

Abstract 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 (DKD). 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. Additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Herein, we demonstrate an integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 µm pixel size) was used to determine lipid distributions within human glomeruli, revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types 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. A combination of machine-learning approaches was assembled, enabling cell-type segmentation and identification based on MxIF data followed by the mining of cell type or cluster-associated MALDI IMS signatures using classification models and interpretable machine learning. This allowed the automated discovery of spatially specific biomarker candidates for glomerular substructures and cell types. Overall, the work presented here establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments and provides a framework that applies to other kidney tissue features and organ systems.

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