Evidence map›Paper›PMID 42443462›Full record

ReviewNature reviews. Nephrology2026

Next-generation kidney tissue analysis - spatial omics and digital pathology.

Takahisa Yoshikawa, Roman D Bülow, Peter Boor, Motoko Yanagita

Abstract readReview
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Takahisa Yoshikawa *Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.ORCID http://orcid.org/0000-0003-3426-5982
Roman D Bülow *Institute for Pathology, RWTH Aachen University Hospital, Aachen, Germany.ORCID http://orcid.org/0000-0002-8527-7353
Peter BoorInstitute for Pathology, RWTH Aachen University Hospital, Aachen, Germany. pboor@ukaachen.de.ORCID http://orcid.org/0000-0001-9921-4284
Motoko YanagitaDepartment of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan. motoy@kuhp.kyoto-u.ac.jp.ORCID http://orcid.org/0000-0002-0339-9008

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

KidneyKidney DiseasesEpigenomicsGenomicsHumansImaging, Three-DimensionalMetabolomicsMultiomicsProteomicsSpatial Transcriptomics

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.