Urine-derived cells: a promising diagnostic tool in Fabry disease patients is a research paper published in Scientific Reports (2018). On theSindex it has a DataRank of 1.6. It has been cited 38 times, with 32 citing works in its 1-hop citation network.
AbstractFabry disease is a lysosomal storage disorder resulting from impaired alpha-galactosidase A (α-Gal A) enzyme activity due to mutations in the GLA gene. Currently, powerful diagnostic tools and in vivo research models to study Fabry disease are missing, which is a major obstacle for further improvements in diagnosis and therapy. Here, we explore the utility of urine-derived primary cells of Fabry disease patients. Viable cells were isolated and cultured from fresh urine void. The obtained cell culture, modeling the renal epithelium, is characterized by patient-specific information. We demonstrate that this non-invasive source of patient cells provides an adequate cellular in vivo model as cells exhibit decreased α-Gal A enzyme activity and concomitant globotriaosylceramide accumulation. Subsequent quantitative proteomic analyses revealed dysregulation of endosomal and lysosomal proteins indicating an involvement of the Coordinated Lysosomal Expression and Regulation (CLEAR) network in the disease pathology. This proteomic pattern resembled data from our previously described human podocyte model of Fabry disease. Taken together, the employment of urine-derived primary cells of Fabry disease patients might have diagnostic and prognostic implications in the future. Our findings pave the way towards a more detailed understanding of pathophysiological mechanisms and may allow the development of future tailored therapeutic strategies.
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Base Score Contribution
0.550
From this paper's citation signal
Citation Network Contribution
1.0
From 28 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 35% comes from its base citations and 65% from the citation network (28 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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