Toward a digital urinary biopsy for early risk stratification in diabetic kidney disease: Anchoring shed cells, vesicles, and cell-free DNA to spatial kidney cell states.

Authors

Juan Wu, Ping Sheng, Ying Lu, Ke Chen

Published in

Clinical and translational medicine. Volume 16. Issue 11.

Abstract

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease worldwide. However, albuminuria and estimated glomerular filtration rate (eGFR), which define and stage DKD, report established injury and do not resolve the cellular heterogeneity underlying variable progression. Urine, the only biofluid to traverse every nephron segment, carries shed cells, extracellular vesicles (EVs) and cell-free DNA (cfDNA), each retaining information about its cell of origin.
This review develops the concept of a digital urinary biopsy, in which the three compartments are read together against spatial single-cell kidney atlases to infer non-invasively which cell states are active and whether niche-associated programs are engaged, an approach we term atlas-anchored molecular profiling. For each compartment, we tabulate study population, sample size, processing, assay, performance and validation status. Because shed-cell RNA, selectively sorted vesicle cargo and cfDNA methylation differ in origin, measurement scale and bias, each requires its own reference matrix, sampling function and background terms. All three carry renal information in feasibility studies, yet none is a validated clinical assay in DKD. Moreover, a cfDNA methylation reference at the required resolution is lacking, and no named computational method has been tested on urine.
We therefore present the framework as a structured research hypothesis and define the required validation, from analytical and clinical validity to prospective demonstration of utility, with non-albuminuric DKD, which albuminuria-based screening misses, as the index use case. It offers a testable route to cell-resolved, non-invasive staging of DKD rather than a demonstrated capability.

PMID:
42949643
Bibliographic data and abstract were imported from PubMed on 29 Oct 2026.

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