ReviewNature reviews. Nephrology2026
Next-generation kidney tissue analysis - spatial omics and digital pathology.
Review in Nature reviews. Nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
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Abstract
Understanding pathological processes is crucial for the diagnosis, prediction and prognostication of kidney diseases. Although progress has been made in non-invasive diagnostic approaches, many pathological processes and diseases mainly manifest in the kidney tissue, necessitating comprehensive analyses of kidney samples. Cellular and molecular analyses of the kidney, including single-cell omics approaches, have identified novel disease mechanisms. However, these non-spatial methods lack information on cellular localization and tissue organization, which is crucial for understanding cell-cell interactions. Such information is particularly important for the kidney, which has many different cell types and intricate architecture. Developments in spatial omics technologies address this challenge by enabling the simultaneous analysis of molecular profiles and spatial context. Advances in three-dimensional imaging technologies, including non-destructive approaches, may provide additional layers of structural information. Integration of spatial omics technologies, such as transcriptomics, epigenomics and metabolomics, with imaging and computational pathology approaches, such as pathomics, has the potential to further advance understanding of the pathophysiology of kidney diseases. In the future, such approaches could become part of the diagnostic workflow in pathology. In the meantime, they hold great promise to aid the identification of diagnostic, prognostic and predictive biomarkers as well as novel therapeutic targets, and thereby facilitate drug discovery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.