Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli
Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli
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DOI：
10.1021/jasms.3c00033
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发表时间：
2023-06-15
影响因子：
3.2
通讯作者：
Spraggins，Jeffrey M. M.
中科院分区：
文献类型：
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作者：
Esselman，Allison B. B.;Patterson，Nathan Heath;Spraggins，Jeffrey M. M.
肾小球是肾单位的多细胞功能组织单位（FTU），负责血液过滤。每个肾小球都包含对其功能至关重要的多种亚结构和细胞类型。为了了解肾脏的正常衰老和疾病，需要在这些 FTU 内跨整个幻灯片图像进行高空间分辨率分子成像的方法。在这里，我们演示了一个工作流程，使用显微镜驱动的选定采样，对整个载玻片人肾组织内的所有肾小球进行 5 μm 像素大小的矩阵辅助激光解吸/电离成像质谱 (MALDI IMS)。这种高空间分辨率成像需要大量像素，从而增加了数据采集时间。自动化 FTU 特定组织采样可以对关键组织结构进行高分辨率分析，同时保持通量。使用配准自发荧光显微镜数据自动分割肾小球，并将这些分割转化为 MALDI IMS 测量区域。这使得能够从单个完整载玻片人肾组织切片中高通量采集 268 个肾小球。使用无监督的机器学习方法来发现肾小球亚区域的分子谱并区分健康和患病的肾小球。使用统一流形近似和投影 (UMAP) 和 k 均值聚类分析每个肾小球的平均光谱，产生 7 个不同组的分化的健康和患病肾小球。将逐像素均值聚类应用于所有肾小球，显示出位于每个肾小球内的子区域的独特分子特征。用于高空间分辨率分子成像的自动显微镜驱动、FTU 靶向采集可保持高通量，并能够以细胞分辨率快速评估整个载玻片图像，并识别与正常衰老和疾病相关的组织特征。
The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) andk-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wisek-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.