Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry

Abstract

The kidney functions through the coordination of approximately one million multifunctional nephrons in 3-dimensional space. Molecular understanding of the kidney has relied on transcriptomic, proteomic, and metabolomic analyses of kidney homogenate, but these approaches do not resolve cellular identity and spatial context. Mass spectrometry analysis of isolated cells retains cellular identity but not information regarding its cellular neighborhood and extracellular matrix. Spatially targeted mass spectrometry is uniquely suited to molecularly characterize kidney tissue while retaining in situ cellular context. This review summarizes advances in methodology and technology for spatially targeted mass spectrometry analysis of kidney tissue. Profiling technologies such as laser capture microdissection (LCM) coupled to liquid chromatography tandem mass spectrometry provide deep molecular coverage of specific tissue regions, while imaging technologies such as matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) molecularly profile regularly spaced tissue regions with greater spatial resolution. These technologies individually have furthered our understanding of heterogeneity in nephron regions such as glomeruli and proximal tubules, and their combination is expected to profoundly expand our knowledge of the kidney in health and disease.

Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection

“…Human kidney is an incredibly complex organ with several distinct cell types and functional tissue units, and hundreds of different diseases have various pathophysiological effects throughout the cortex and the medulla. Hence, several methodologies are typically used concurrently with both high spatial and high mass-resolution imaging for confident, clinically relevant analyses . Pathogenesis of disease and trauma has broad-ranging consequences spanning hypertension, metabolic stress and injury, and endothelial apoptosis.…”

“…Hence, several methodologies are typically used concurrently with both high spatial and high mass resolution imaging for confident, clinically relevant analyses. 51 Pathogenesis of disease and trauma have broad ranging consequences spanning hypertension, metabolic stress and injury, as well as endothelial apoptosis. This occurs within the glomeruli, distal and proximal tubules, and crosses the interfacial region into the medullary regions systemically effecting the entire renal system.…”

Spatially resolved proteomics via tissue expansion

“…The improved spatial resolution extends applicability of ProteomEx as it is more biologically relevant for analysis of heterogeneity of biological tissues. For example, ~100 µm resolution is more appropriate for analysis of functional tissue units (FTU) 38 , e.g., renal glomeruli [39][40][41] , colonic crypts 39 , human lung blood vessels 36 , which are typically limited by 100 µm in size. Furthermore, Piehowski et al demonstrated that 100 µm resolution was sufficient to characterize region-specific bioactivity and unique tissue microenvironments within the mouse Wnt5a-null uterine tissue 30 .…”

Application of Machine Learning in Spatial Proteomics

“…The major obstacles in integrating data from multiple modalities are the existence of batch effects and the distinct feature spaces of different omics data. , Integrating MS-based and imaging-based spatial proteomics approaches allows simultaneous analyses of multiple organelles at different time points, which can help understand disease processes. Advanced data analysis pipelines for such spatiotemporal analyses are desired to better integrate spatial and temporal maps of proteome changes during disease progression. , Since the occurrence of disease changes multiple interacting components of biological systems (such as RNA, proteins, lipids, and metabolites), multiomics integration incorporating spatial proteomics can give unprecedented insights into cell functionality. , Combining spatial proteomics and other omics data enables comprehensive studies of the heterogeneity in various diseases, and numerous related investigations have been conducted. ,− The development of analytical tools that provide workflows for multimodal data integration is currently a big challenge . Moreover, the lack of unified and systematic data analysis tools hampers the comparative analyses of changes in protein spatial patterns under different conditions .…”

“…8,202−204 The development of analytical tools that provide workflows for multimodal data integration is currently a big challenge. 202 Moreover, the lack of unified and systematic data analysis tools hampers the comparative analyses of changes in protein spatial patterns under different conditions. 22 The development and application of ML models which are designed to take multiple conditions into consideration can facilitate the exploration of the subcellular proteomic dynamics.…”