Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli

被引:13
|
作者
Esselman, Allison B. B. [1 ,2 ]
Patterson, Nathan Heath [1 ,3 ]
Migas, Lukasz G. G. [1 ,4 ]
Dufresne, Martin [1 ,3 ]
Djambazova, Katerina V. V. [1 ,5 ]
Colley, Madeline E. E. [1 ,3 ]
van de Plas, Raf [1 ,3 ,4 ]
Spraggins, Jeffrey M. M. [1 ,2 ,3 ,5 ]
机构
[1] Vanderbilt Univ, Mass Spectrometry Res Ctr, Nashville, TN 37232 USA
[2] Vanderbilt Univ, Dept Chem, Nashville, TN 37232 USA
[3] Vanderbilt Univ, Dept Biochem, Nashville, TN 37232 USA
[4] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 Delft, Netherlands
[5] Vanderbilt Univ, Dept Cell & Dev Biol, Nashville, TN 37232 USA
基金
美国国家科学基金会; 美国国家卫生研究院;
关键词
MALDI IMS; multimodal; molecular imaging; high spatial resolution imaging; human kidney; whole slide imaging; high-throughput; glomeruli; targeted; lipids; unsupervised machine learning; AGE; GLOMERULOSCLEROSIS; BIOPSY;
D O I
10.1021/jasms.3c00033
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The glomerulus is a multicellular functional tissue unit(FTU)of the nephron that is responsible for blood filtration. Each glomeruluscontains multiple substructures and cell types that are crucial fortheir function. To understand normal aging and disease in kidneys,methods for high spatial resolution molecular imaging within theseFTUs across whole slide images is required. Here we demonstrate aworkflow using microscopy-driven selected sampling to enable 5 & mu;mpixel size matrix-assisted laser desorption/ionization imaging massspectrometry (MALDI IMS) of all glomeruli within whole slide humankidney tissues. Such high spatial resolution imaging entails largenumbers of pixels, increasing the data acquisition times. AutomatingFTU-specific tissue sampling enables high-resolution analysis of criticaltissue structures, while concurrently maintaining throughput. Glomeruliwere automatically segmented using coregistered autofluorescence microscopydata, and these segmentations were translated into MALDI IMS measurementregions. This allowed high-throughput acquisition of 268 glomerulifrom a single whole slide human kidney tissue section. Unsupervisedmachine learning methods were used to discover molecular profilesof glomerular subregions and differentiate between healthy and diseasedglomeruli. Average spectra for each glomerulus were analyzed usingUniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthyand diseased glomeruli. Pixel-wise k-means clusteringwas applied to all glomeruli, showing unique molecular profiles localizedto subregions within each glomerulus. Automated microscopy-driven,FTU-targeted acquisition for high spatial resolution molecular imagingmaintains high-throughput and enables rapid assessment of whole slideimages at cellular resolution and identification of tissue featuresassociated with normal aging and disease.
引用
收藏
页码:1305 / 1314
页数:10
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