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

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 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.

Deep MALDI-MS Spatial ‘Omics guided by Quantum Cascade Laser Mid-infrared Imaging Microscopy

“…Next, we explored small tissue morphologies like glomeruli in murine kidney to further validate QCL-IRI-based hyperspectral tissue segmentation. Gangliosides as markers of these functional filtration units are well-characterized, and autofluorescence-directed MSI has recently been used for their detailed molecular analysis 38 . Using ganglioside GM3 34:1;O2 (both negative mode ion images and molecular probabilistic maps (MPMs) 2 ) as a marker, we compared MALDI-qTOF-MSI and QCL-IRI for entire dried kidney cryosections by correlative MSI-IRI imaging ( Supplementary Fig.…”

“…2b). As ion suppression is likely different in these two areas of salt-rich kidney, ion intensities do not directly translate to quantities 28,38,47 . Nevertheless, cross-modality (semi-)quantitative analysis (Supplementary Fig.…”

“…In these mice, sulfatides, a family of sulfated glycosphingolipids, selectively accumulate in kidneys and other organs [26][27][28] . Because of this wellunderstood biology of ARSA-/-mice, we could use kidney sulfatides, whose masses, chemical sum formulae and structures (but not their quantities) are known, as qualitative ground truth for statistical evaluation of IRI-guided MSI methods, for method validation and for benchmarking against 4D LC-TIMS-MS sulfatide lipidomics, against previous sulfatide MSI studies and against CCS value prediction models 26,[28][29][30][31][32][33][34][35][36][37][38][39][40][41] .…”

Computational tools and algorithms for ion mobility spectrometry‐mass spectrometry

“…bruker.com/en/products-and-solutions/mass-spectrometry/mssoftware/scils-lab.html) for data acquired on TIMS-MS systems. A smaller but notable proportion of studies used custom software tools, written in MATLAB [129][130][131][132][133][134] or Python [135][136][137][138][139][140], for analysis of their IM-MSI data. Another tool, IM-MSIC, was developed as a set of scripts that coordinate between Skyline and vendor software (Agilent Mass Hunter/Mass Profiler) to produce ion images from IM-MSI data acquired on DTIMS-MS systems [141].…”

Combining imaging mass spectrometry and immunohistochemistry to analyse the lipidome of spinal cord inflammation

“…Thus, it is necessary to combine it with well-established techniques, in order to understand the huge amount of information produced in each experiment [ 21 ]. Among the different strategies tested, microdissection, micro-extraction [ 23 ], correlation with RAMAN spectroscopy [ 24 ], microscopy [ 25 ] etc., combination with immunohistochemistry (IHC), either in the same sample or in consecutive sections, seems the most promising technique (Fig. 1 ) [ 21 , 26 , 27 ].…”