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Imaging and spatially resolved mass spectrometry applications in nephrology

Abstract

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

Key points

  • Mass spectrometry (MS) is a versatile technology that enables the analysis of various classes of molecules, including proteins, metabolites, lipids and drugs; the resulting molecular data complement data from other omics approaches such as transcriptomics and genomics.

  • Bulk analysis of kidney samples with MS can obfuscate localized molecular changes at the functional tissue unit and cellular level; however, the results of these assays can complement spatially resolved MS data.

  • Spatially resolved MS methods, including MS imaging, have been used to identify key molecular signatures related to kidney function and disease progression at the cellular level.

  • Various spatially resolved and MS imaging approaches have been used to perform spatial and single-cell metabolomics and proteomics analyses of kidney samples; these approaches can be targeted or untargeted and can be used in a multimodal fashion.

  • Numerous emerging MS domains can be applied to nephrology studies, including highly multiplexed immunohistochemistry, ion mobility and three-dimensional imaging; these approaches can provide additional insights into the complex molecular mechanisms that occur within and between cells and functional tissue units.

  • Artificial intelligence and machine-learning tools are poised to have a substantial impact owing to their advantages in simplifying complex MS data, image processing and data integration, as well as aiding identification of disease markers, elucidation of connections between omics findings and rapid data interpretation.

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Fig. 1: Cell-specific and spatially explicit mass spectrometry approaches in nephrology.
Fig. 2: Examples of the use of mass spectrometry imaging approaches for spatial metabolomics and lipidomics of kidney tissue.
Fig. 3: Examples of use of mass spectrometry imaging approaches for intact protein imaging of kidney tissue.
Fig. 4: Utilization of on-tissue chemical derivatization to enhance sensitivity in spatial omics analysis.
Fig. 5: Multimodal images obtained from a single human kidney sample.

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Acknowledgements

B.L.G., N.R., K.S. and C.R.A. are supported as part of the Kidney Precision Medicine Project (KPMP), which is funded by the National Institute of Diabetes and Digestive and Kidney Diseases through 5U01DK114920-07. K.S. is also funded by a VA Merit Grant and the Department of Defense (CDMRP). E.K.N. and C.C.S. were supported by UC Davis, and C.C.S. is additionally supported by the UC Davis Deans Distinguished Graduate Fellowship.

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All authors researched the data for the article, made substantial contributions to discussions of the content and wrote, reviewed and edited the manuscript before submission.

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Correspondence to Christopher R. Anderton.

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K.S. reports holding equity in SygnaMap, Inc., serving as consultant for Bayer and Sanofi, and receiving research support from Boerhinger-Ingelheim. The other authors declare no competing interests.

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Glossary

Antigen retrieval

A technique that unmasks protein antigens in tissue samples so that enzymes or antibodies can access them (often used in analyses of formalin-treated tissue).

Derivatized

A chemical technique used to modify a molecular structure to improve its detection by mass spectrometry.

Dimensionality reduction

A statistics technique that reduces the number of variables (for example, principal component analysis).

Duty cycles

The rate at which consecutive measurements can be performed in an analysis.

Exoproteome

The extracellular protein content.

Lateral spatial resolution

The smallest distance a system can distinguish two adjacent objects.

Photocleavable peptide mass tags

(PC-MTs). Small organic molecules containing a unique peptide sequence with a known mass linked to a domain that cleaves when exposed to UV. These molecules are commonly linked to antibodies to enable antibody detection using mass spectrometry.

Raman spectroscopic imaging

An imaging technique that uses Stokes light scattering to image the chemical composition of samples.

Selective ion monitoring

Transmission of a targeted set of ions into a mass spectrometer for detection.

Spectral library matching

Identification of ion peaks in one mass spectrum using previously identified peaks in another empirically derived or in silico spectrum.

Tissue voxels

A measurement of volume in a structure that is to be imaged.

Water clusters

Clusters of water molecules in the gas phase.

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Gorman, B.L., Shafer, C.C., Ragi, N. et al. Imaging and spatially resolved mass spectrometry applications in nephrology. Nat Rev Nephrol 21, 399–416 (2025). https://doi.org/10.1038/s41581-025-00946-1

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