Zentgraf, M. | bioRxiv Science

bioRxiv2025

Single-cell dissection of urine-derived stem cell hierarchies reveals robust chondrogenesis and clinical scalability

Developing of models of human cartilage and bone growth is essential for the study of growth disorders and to advance towards personalized therapeutic interventions. The majority of in vitro strategies depend on the use of invasively obtained mesenchymal stem cells (MSCs) or the laborious generation of induced pluripotent stem cells (iPSCs). We have now established urine-derived stem cells (USCs) as a non-invasive stem cell source capable of osteogenic and robust chondrogenic spheroid differentiation. Single-cell RNA sequencing of USCs revealed a hierarchy originating from parietal epithelial cells of the kidney, with a proliferative TOP2A subpopulation governed by MYC and E2F4 regulatory networks. Pseudo-time analysis of chondrogenic USCs uncovered alternative chondrogenic differentiation trajectories with an ALDH1A2 intermediate state and a TIMP3-expressing chondrocyte-like subpopulation as the major endpoint, exhibiting cartilage-specific gene ontologies. In conclusion, a streamlined, xeno-free culture and differentiation protocol was developed, thereby establishing the basis for clinical-grade cell expansion and cartilage matrix formation. This positions USCs as a powerful tool for studying cartilage biology and a potential platform for development and use in regenerative therapies.

genetics↗

bioRxiv2023

Extending ligand efficacy indices with compound pharmacokinetic characteristics towards holistic Compound Quality Scores

The suitability of a small molecule to become an oral drug is often assessed by simple physicochemical rules, the application of ligand efficacy scores (combining physicochemical properties with potency) or by multi-parameter composite scores based on physicochemical compound properties. These rules and scores are empirical and typically lack mechanistic background, such as information on pharmacokinetics (PK). We introduce a new type of Compound Quality Scores (specifically called dose-scores and cmax-scores), which explicitly include predicted or when available experimentally determined PK parameters, such as volume of distribution, clearance and plasma protein binding. Combined with on-target potency, these scores are surrogates for an estimated dose or the corresponding cmax. These Compound Quality Scores allow for prioritization of compounds in test cascades, and by integrating machine learning based potency and PK predictions, these scores allow prioritization for synthesis. We demonstrate the complementary and in most cases the superiority to existing efficiency metrics (such as ligand efficiency scores) by project examples.

pharmacology and toxicology↗