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

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 reso...

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Veröffentlicht in:Journal of the American Society for Mass Spectrometry Jg. 34; H. 7; S. 1305
Hauptverfasser: Esselman, Allison B, Patterson, Nathan Heath, Migas, Lukasz G, Dufresne, Martin, Djambazova, Katerina V, Colley, Madeline E, Van de Plas, Raf, Spraggins, Jeffrey M
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 05.07.2023
Schlagworte:
glomeruli
lipids
unsupervised machine learning
MALDI IMS
human kidney
high spatial resolution imaging
high-throughput
targeted
whole slide imaging
multimodal
molecular imaging
ISSN:1879-1123, 1879-1123
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Zusammenfassung: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) and -means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise -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.
Bibliographie:ObjectType-Article-1
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ISSN:1879-1123
1879-1123
DOI:10.1021/jasms.3c00033