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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| Vydáno v: | Journal of the American Society for Mass Spectrometry Ročník 34; číslo 7; s. 1305 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
05.07.2023
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| ISSN: | 1879-1123, 1879-1123 |
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| Abstract | 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. |
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| AbstractList | 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 k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-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.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 k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-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. 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. |
| Author | Colley, Madeline E Migas, Lukasz G Djambazova, Katerina V Patterson, Nathan Heath Esselman, Allison B Van de Plas, Raf Dufresne, Martin Spraggins, Jeffrey M |
| Author_xml | – sequence: 1 givenname: Allison B orcidid: 0000-0002-4122-6773 surname: Esselman fullname: Esselman, Allison B organization: Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States – sequence: 2 givenname: Nathan Heath orcidid: 0000-0002-0064-1583 surname: Patterson fullname: Patterson, Nathan Heath organization: Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States – sequence: 3 givenname: Lukasz G orcidid: 0000-0002-1884-6405 surname: Migas fullname: Migas, Lukasz G organization: Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, The Netherlands – sequence: 4 givenname: Martin orcidid: 0000-0002-1731-3666 surname: Dufresne fullname: Dufresne, Martin organization: Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States – sequence: 5 givenname: Katerina V orcidid: 0000-0002-2680-9014 surname: Djambazova fullname: Djambazova, Katerina V organization: Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States – sequence: 6 givenname: Madeline E orcidid: 0000-0002-9515-3493 surname: Colley fullname: Colley, Madeline E organization: Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States – sequence: 7 givenname: Raf orcidid: 0000-0002-2232-7130 surname: Van de Plas fullname: Van de Plas, Raf organization: Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, The Netherlands – sequence: 8 givenname: Jeffrey M orcidid: 0000-0001-9198-5498 surname: Spraggins fullname: Spraggins, Jeffrey M organization: Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States |
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| Keywords | glomeruli lipids unsupervised machine learning MALDI IMS human kidney high spatial resolution imaging high-throughput targeted whole slide imaging multimodal molecular imaging |
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