通过综合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特征。

IF 14.8 1区 医学 Q1 UROLOGY & NEPHROLOGY
Allison B Esselman, Felipe A Moser, Léonore Tideman, Lukasz G Migas, Katerina V 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
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引用次数: 0

摘要

肾小球通过荚膜细胞、间质细胞、栅栏状内皮细胞和肾小球基底膜的协调作用过滤血液。荚膜细胞脱落等细胞变化与糖尿病肾病等病变有关。然而,人们对特定细胞类型的原位分子特征以及这些特征如何随疾病发生变化知之甚少。基质辅助激光解吸电离成像质谱法(MALDI IMS)非常适合对各种分子类别进行非靶向组织绘图。重要的是,其他成像模式可与 MALDI IMS 集成,将这些生物分子分布与特定细胞类型联系起来。在这里,我们整合了 MALDI IMS 和多重免疫荧光(MxIF)显微镜的工作流程。利用高空间分辨率 MALDI IMS(5 μm)确定正常部分肾癌肾切除组织中人肾小球内的脂质分布,揭示了肾小球内脂质的异质性。通过新方法将质谱数据与特定的肾小球细胞类型和亚结构联系起来,这样就能在 MALDI IMS 之后在同一组织切片上进行 MxIF 显微镜检查,而不会牺牲两种模式的信号质量。结合机器学习方法,可根据 MxIF 数据进行细胞类型分割和识别。然后利用分类和可解释的机器学习挖掘细胞类型或集群相关的 MALDI IMS 特征。这样就能自动发现肾小球细胞类型和亚结构的空间特异性分子标记,以及与深层和浅层肾小球相关的脂质。总之,我们的工作为探测组织微环境中肾小球细胞类型和亚结构的分子特征建立了一个工具箱,提供了一个适用于其他肾组织特征和器官系统的框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In Situ molecular profiles of glomerular cells by integrated imaging mass spectrometry and multiplexed immunofluorescence microscopy.

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.

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来源期刊
Kidney international
Kidney international 医学-泌尿学与肾脏学
CiteScore
23.30
自引率
3.10%
发文量
490
审稿时长
3-6 weeks
期刊介绍: Kidney International (KI), the official journal of the International Society of Nephrology, is led by Dr. Pierre Ronco (Paris, France) and stands as one of nephrology's most cited and esteemed publications worldwide. KI provides exceptional benefits for both readers and authors, featuring highly cited original articles, focused reviews, cutting-edge imaging techniques, and lively discussions on controversial topics. The journal is dedicated to kidney research, serving researchers, clinical investigators, and practicing nephrologists.
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