High-Resolution Human Kidney Molecular Histology by Imaging Mass Spectrometry of Lipids

Abstract

For many years, traditional histology has been the gold standard for the diagnosis of many diseases. However, alternative and powerful techniques have appeared in recent years that complement the information extracted from a tissue section. One of the most promising techniques is imaging mass spectrometry applied to lipidomics. Here, we demonstrate the capabilities of this technique to highlight the architectural features of the human kidney at a spatial resolution of 10 μm. Our data demonstrate that up to seven different segments of the nephron and the interstitial tissue can be readily identified in the sections according to their characteristic lipid fingerprints and that such fingerprints are maintained among different individuals ( n = 32). These results set the foundation for further studies on the metabolic bases of the diseases affecting the human kidney.

Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry

“…Each of these approaches showed profound redistribution of lipid species in response to renal disease. To provide further insight into the lipidome and disease, technologies are linking specific lipid species with kidney FTUs based upon histology-informed segmentation of lipid IMS data (Martín-Saiz et al, 2021). These studies illustrate the utility of IMS to detect global lipidomic changes in disease and implicate diverse lipid classes in normal kidney function and pathogenesis.…”

“…In MALDI IMS workflows, tissue samples are coated with a matrix that assists with desorption and ionization of endogenous analytes (Franz and Michael, 2000). The tissue surface is then ablated using a laser in a raster pattern, where each laser spot produces a spectrum detecting hundreds to thousands of ions (Caprioli et al, 1997;Norris and Caprioli, 2013;Spraggins et al, 2019;Martín-Saiz et al, 2021). Spectral information from each laser spot (i.e., pixel) is reconstructed to show relative analyte intensity and distribution throughout the sample (Caprioli et al, 1997;Norris and Caprioli, 2013;Spraggins et al, 2019;Martín-Saiz et al, 2021).…”

“…The tissue surface is then ablated using a laser in a raster pattern, where each laser spot produces a spectrum detecting hundreds to thousands of ions (Caprioli et al, 1997;Norris and Caprioli, 2013;Spraggins et al, 2019;Martín-Saiz et al, 2021). Spectral information from each laser spot (i.e., pixel) is reconstructed to show relative analyte intensity and distribution throughout the sample (Caprioli et al, 1997;Norris and Caprioli, 2013;Spraggins et al, 2019;Martín-Saiz et al, 2021). DESI and nano-DESI workflows use ambient liquid extraction of small tissue regions at regularly spaced measurement regions followed by introduction to a mass spectrometer inlet or primary capillary for electrospray ionization (Roach et al, 2010;Eberlin et al, 2011).…”

“…Metabolomics has been applied to patient samples and kidney homogenates to great effect, and IMS has broadly characterized the spatial distribution of select small metabolites (Abbiss et al, 2019;Neumann et al, 2020;Zhang et al, 2020). Similarly, lipidomics has been used to characterize kidney homogenates, and IMS has generated spatial lipid maps (Rao et al, 2016;Lukowski et al, 2020;Zhang et al, 2020;Martín-Saiz et al, 2021). The next challenge will be to integrate these analytical modalities into workflows combining multiple spatially targeted MS technologies and to develop tools necessary to perform these analyses at scale across statistically relevant numbers of samples.…”

“…In addition to combining spatially targeted MS approaches, we anticipate that it will become more common for these data to be combined with other advanced molecular imaging technologies such as microscopy and spatial transcriptomics. Examples of this have already demonstrated integration of spatially targeted MS data with autofluorescence microscopy and multiplexed immunohistochemistry approaches such as imaging mass cytometry (IMC) and co-detection by indexing (CODEX) to molecularly characterize and discover markers for kidney FTUs and cell types (Patterson et al, 2018;Singh et al, 2019;Martín-Saiz et al, 2021;Neumann et al, 2021a,b). To enable these multimodal approaches, computational tools are emerging that automatically annotate, integrate, and mine molecular imaging data from orthogonal technologies for unbiased data interpretation and identification of candidate biomarkers (Van de Plas et al, 2015;Palmer et al, 2017;Vollnhals et al, 2017;Balluff et al, 2019;Race et al, 2020;Martín-Saiz et al, 2021;Tideman et al, 2021).…”