Single-cell atlas of human urine-derived stem cell chondrogenesis enables a non-invasive, xeno-free platform for translational cartilage and skeletal disease research

Single-cell atlas of human urine-derived stem cell chondrogenesis enables a non-invasive, xeno-free platform for translational cartilage and skeletal disease research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-cell atlas of human urine-derived stem cell chondrogenesis enables a non-invasive, xeno-free platform for translational cartilage and skeletal disease research Alexander Schulz, Emily M. Brockmann, Miriam Zentgraf, Andreas S. Baur, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8230463/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Clinically compatible, non-invasively harvested stem cell sources are needed to model human skeletal disorders and advance regenerative strategies. Human urine-derived stem cells (USCs) offer patient-specific accessibility, yet their developmental hierarchy and chondrogenic mechanisms remain poorly defined. Methods USCs were isolated, expanded and profiled in detail using single-cell RNA sequencing with lineage reconstruction to map progenitor states and differentiation trajectories. Chondrogenesis was induced in two-dimensional (2D) and three-dimensional (3D) systems. To support translational use, we established a fully xeno-free expansion and differentiation workflow using autologous human serum and benchmarked it against conventional serum-based conditions. Results Single-cell analysis resolved a structured USC hierarchy originating from MYC/E2F4-regulated TOP2A⁺ proliferative progenitors, progressing through an ALDH1A2⁺ retinoic-acid–responsive intermediate, and culminating in TIMP3⁺ chondrocyte-like cells exhibiting high transcriptional similarity to native cartilage. This trajectory featured coordinated activation of canonical chondrogenic regulators (SOX9, SOX5, SOX6) and enrichment of extracellular matrix programs associated with cartilage formation. Under xeno-free autologous serum conditions, USCs preserved proliferative capacity, enhanced mesenchymal condensation, and generated matrix‑rich cartilage-like constructs in 2D and 3D with superior maturation signatures compared with standard culture conditions. Conclusions We provide a mechanistic single-cell atlas of human USC chondrogenesis and establish USCs as a non-invasive, patient-compatible, and fully xeno-free stem cell platform for translational cartilage research, skeletal disease modelling, and personalized regenerative medicine applications. single-cell RNA sequencing urine-derived stem cells chondrogenesis xeno-free culture cartilage biology skeletal dysplasia modeling translational regenerative medicine disease modelling tissue engineering adult stem cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Developmental disorders of the skeleton have major implications for human health, but their underlying mechanisms remain only partially understood 1 . They manifest clinically as impaired longitudinal bone growth leading to short stature and affecting 2.3% of the general population 2 . Growth disorders can be attributed to various etiologies, including complex genetic syndromes 3-6 , endocrine defects 7-9 , skeletal dysplasias 10,11 , chronic diseases 12 , and idiopathic short stature 13,14 . At the cellular level, the pathology may originate from any zone of the growth plate (reserve, proliferative, or hypertrophic) or from the metaphysis and epiphysis 15,16 . Disruption of the function of the cells that comprise the cartilage (chondrocytes) and the composition of the extracellular matrix (ECM) have been shown to impair the organization of growth plates, leading to abnormal bone elongation 15 . Known genetic causes include genes encoding growth plate matrix proteins, such as COL2A1 associated with spondyloepiphyseal dysplasia and hypochondrogenesis 17 , or ACAN resulting in premature growth plate closure 18 . Defects in the key regulators of cartilage cells such as Indian hedgehog, underlying brachydactyly type A1 and acrocapital femoral dysplasia 17 are also well known. Within the aforementioned spectrum, skeletal dysplasias are of particular pertinence, with achondroplasia representing a prevalent form affecting more than 360,000 individuals worldwide 19 . However, research on disorders of cartilage and bone formation is hampered by the limited access to primary human chondrogenic tissue, as it requires invasive procedures with potential harmful side-effects 20 . Consequently, the functional study of skeletal disorders and the development of patient-specific therapies is challenged by a paucity of suitable and broadly accessible cellular models. The majority of in vitro models used for studying chondrogenesis utilize either induced pluripotent stem cells (iPSCs) or mesenchymal stem cells (MSCs). MSCs, which possess the capacity to differentiate into cartilage, are obtained through an invasive procedure from tissue sources such as adipose tissue, bone marrow, or synovial fluid 21,22 . In contrast, iPSCs offer the benefit of a pluripotent and renewable source. However, these methods are expensive, time-consuming, and laborious, which limits their availability 23,24 . Moreover, their therapeutic value is further constrained by intrinsic tumorigenicity 25 . Consequently, both MSC and iPSC approaches exhibit significant practical limitations when modeling and treating genetic skeletal disorders, particularly in cases requiring repeated sampling, or in patient-specific studies. Urine-derived stem cells (USCs) have emerged as a promising alternative to overcome these challenges. These can be obtained without the need for surgical procedures and easily propagated and mantained 26 . Despite increasing interest in their cartilage regenerative capabilities 27-29 , the application of USCs as a model system for cartilage and skeletal diseases has remained largely unexplored, particularly in the context of developmental skeletal disorders. In addition, the conventional culture of USCs, as initially identified in their original discovery 30 , is complex and reliant on animal-derived products, preventing potential use in cell-therapeutic approaches due to the inherent risks associated with the transfer of animal factors to patients 31 . We here propose USCs as a platform for investigating human chondrogenesis and modeling of genetic skeletal disorders. Furthermore, a simple, xeno-free culture and chondrogenic differentiation method for USCs is presented, which has the potential to pave the way for personalized, non-surgical medicine and cartilage regeneration. Methods Urine stem cell isolation and culture USCs were isolated and expanded by modifying an existing protocol from Culenova et al 32 . Briefly, urine was centrifuged at 300 g, cell pellet was washed with PBS 1% Penicillin Streptomycin (Gibco) and pelleted cells cultured after repeated centrifugation. Culture medium consisted of a 1:1 mixture of DMEM high glucose (Gibco), containing 1 % NEAA (Gibco) and 1 % Penicilin Streptomycin (Gibco), and KSFM (Gibco) with addition of 15 % FBS (Sigma Aldrich or Roth) and 5 ng/mL bFGF (Peprotech) or humankine thermostable bFGF (Sigma). For an animal free workflow, we harvested USCs in the same manner, this time resupending the cellular pellet in either Alpha MEM Eagle medium (PAN Biotech) supplemented 5 ng/mL fibroblast growth factor 2 (FGF2, Sino Biological) with 10% FBS (Sigma-Aldrich or Roth) as the FBS-Control, or in 10 % autologous human serum. All cell lines generated for this study were from the same donor, and no passages greater 5 were used. Culture medium was exchanged three times per week. Proliferation curves To compare proliferation, we seeded FBS- and HS-derived USCs of the same passages in triplicates onto 96 well plates at a density of 1000 cells per well, and counted cells manually with trypan blue exclusion every second day. Doubling time was calculated for cells in the exponential growth phase (day 4-8) as the following: Urine stem cell differentiation and staining Chondrogenic differentiation was induced with the StemPro Chondrogenesis Kit (Gibco). Osteogenic induction was facilitated with an osteogenic medium described by zhang et al 33 . Differentiation lasted for two weeks with media change three times per week. To stain glycosaminoglycans in chondrogenic, and calcium deposits in osteogenic cells, we fixed cells with 4 % paraformaldehyde, washed with PBS, and stained for one hour with alcian blue or alizarin red s staining solution. After a final washing step with PBS images were taken under a VertA1 (Zeiss) microscope. A separate USC line, harvested at a different time point from the same donor, was cultured for 2 days in Alpha MEM Eagle medium (PAN Biotech) supplemented with 10% fetal bovine serum (FBS, Sigma-Aldrich or Roth) and 5 ng/mL fibroblast growth factor 2 (FGF2, Sino Biological), and served as the non-induced control. For a comparative pellet culture between HS- or FBS-USCs, we centrifuged 250000 USCs at 300 g for 10 min in 15 ml falcon tubes (Corning) and incubated with in house made chondrogenic media. The media recipe was: DMEM HG with pyruvate (Gibco), human recombinant insulin 6.25 μg/mL (Merck), human holo-transferrin 6.25 μg/mL (Sigma Aldrich), sodium selenite 6.7 ng/mL (Sigma Aldrich), 10 % Serum (autologous human serum or FBS, Roth) 10 ng/mL recombinant human TGF-Beta 1 (Peprotech), 100 ng/mL recombinant human IGF1, 100 nm Dexmethason (Sigma Aldrich), 50 µg/mL ascorbate-6-phosphate (Sigma Aldrich). Alcian blue quantification was performed by lysing cells for 2 hours at room temperature with 6M Guanidine hydrochloride (Sigma). Absorbance was measured at 650 nm. For normalization to DNA content, DNA was extracted from control wells with the DNeasy Blood & Tissue Kit (Qiagen). Single-cell RNA sequencing and data Processing USCs and chondrogenic USCs were subjected to single-cell RNA sequencing (scRNA-seq) to characterize transcriptomic profiles in undifferentiated and chondrogenically differentiated states. Cell fixation, barcoding, cDNA amplification and Libraries were prepared using Parse Biosciences Parse Evercode WT V3 Workflow and sequenced on Illumina NovaSeq X Plus. FASTQ files were processed using Trailmaker TM pipeline module (https://app.trailmaker.parsebiosciences.com/, pipeline v1.5.0, Parse Biosciences). Raw count matrices and associated metadata were processed in Seurat (version 5.3.0) 34 in R. Cells with 99th percentile of gene counts or Unique Molacular Identifier (UMI) counts, or >95th percentile mitochondrial reads, were excluded. Data were normalized (LogNormalize), variable features (n = 2,000) identified (vst), and counts scaled while regressing out nCount_RNA and percent.mt. PCA (dims 1–20) was followed by UMAP visualization and Louvain clustering (resolution = 0.5). Cluster markers were identified via Wilcoxon testing (min.pct = 0.25). For visualization of data we used Seurat´s DotPlot, VlnPlot or DimPlots and the R packages ggplot2 (version 3.5.2) 35 , ggrepel (version 0.9.6) 36 , viridis (version 0.6.5) 37 and RColorBrewer (version 1.1.3) 38 . Tissue origin and cell-type annotation Marker lists for human urinary and renal tissues were curated from CellMarker 2.0 39 , filtered for uniqueness across tissues, and scored per cell using Seurat’s AddModuleScore. Complementary AUC enrichment was computed with AUCell (version 1.30.1) 40 on the normalized data. To assign putative cell identities, we leveraged a well-annotated human kidney atlas (Tisch et al. 41 ) as a reference. Briefly, the atlas and our USC Seurat object were first normalized and dimensionally reduced in parallel. We then computed integration anchors between the reference and query using FindTransferAnchors. Cell-type labels stored in the reference metadata were projected onto the query via TransferData. Finally, we summarized mapping confidence by computing the median prediction.score.max within each Seurat cluster to guide downstream cluster ordering and interpretation. Stemness inference and transcription ‐ factor activity mapping CytoTRACE2 (version 1.1.0) 42 was run on the top 10,000 variable genes (counts slot) to assign each cell a continuous stemness score. Cells were binned into five quantile‐defined stemness groups. We inferred per‐cell transcription‐factor activities using VIPER (version 1.42.0) 43 with high‐confidence DoRothEA (version 1.20.0) 44 A/B regulons on the Seurat‐normalized data slot. TF activities were merged into the metadata alongside CytoTRACE2 scores and UMAP coordinates. For each stemness group (the five quintile bins), we computed the mean activity of every TF and selected the top three TFs uniquely highest in each group. To visualize regulatory dynamics, we computed Spearman correlations between TF activities and CytoTRACE2 scores across all cells, identifying the top 20 TFs most strongly associated with stemness. Cell cycle analysis Cell cycle phase assignment was performed using the built-in Seurat gene sets for S phase and G2/M phase genes. The Seurat function CellCycleScoring was applied to the processed data, utilizing the predefined sets of cell cycle genes for the S and G2/M phases. Each cell was classified into one of the four phases: G1, S, G2/M, or unassigned. Phase information was then merged with cluster identities, and the proportion of cells in each cell cycle phase per cluster was calculated. Bulk RNA-sequencing RNA was extracted from USC samples with RNeasy Mini Kit (Qiagen)with DNAse digestion, according to manufacturer. A cDNA library was generated for each sample using the Illumina stranded mRNA kit. Paired-end sequencing of the libraries with a fragment length of 159bp was conducted on an Illumina NovaSeq6000 platform. Raw data was then converted into reads and demultiplexed using Illumina Dragen Software (v. 3.8.4). Unwanted RNA was filtered from the reads using bwa mem (version 0.7.17-r1188, arXiv:1303.3997) in combination with samtools (version 1.17 45 ) and converted back into fastq format using the SamToFastq tool from GATK (version 4.2.1.0 46 ). Reads from each sequencing lane were aligned individually to the hg38 reference genome with Ensembl (release 110 47 ) gene annotations using STAR (version 2.7.10a 48 ). For each sample, resulting alignments were combined using the MergeSamFiles command from Picard (version 2.25.4, http://broadinstitute.github.io/picard/). Gene quantification in form of a count matrix was generated using featureCounts (version 2.0.1 49 ) and Ensembl gene annotations corresponding to those of the alignment reference. Alignment and quantification levels were used for quality assessment. Differential gene expression analysis Raw gene-level count data were obtained from bulk RNA-sequencing of three undifferentiated and three differentiated (osteogenic or chondrogenic) USC samples. Differential gene expression (DGE) analysis was conducted using the DESeq2 package (version 1.48.1) 50 in R (version 4.5). For each comparison, gene-level counts were used to construct a DESeqDataSet object, with group labels assigned as the experimental condition. The DESeq2 pipeline was run with default parameters using DESeq(), and differential expression results were extracted using the results() function, specifying contrasts such that log2 fold changes represented upregulation in the differentiated condition relative to WT. Genes with an adjusted p-value (Benjamini-Hochberg method) less than 0.05 were considered statistically significant. Gene annotations were retrieved by mapping Ensembl IDs to HGNC symbols using the org.Hs.eg.db package (version 3.21.0) 51 . Differential expression results were visualized using volcano plots created with the ggplot2 35 and ggrepel 36 packages. Sample metadata was generated to define the experimental condition associated with each sample (WT, chWT, oWT), and this metadata was used to create a DESeqDataSet object using the DESeq2 package. The dataset was then log-transformed using the regularized log (rlog) transformation to stabilize variance across the dynamic range of expression levels. To improve PCA interpretability and reduce noise, features (genes) with negligible or zero variance were removed. Columns (genes) with variance less than 1e-6 or equal to zero were filtered out. Rows (samples) with zero variance across retained genes were also removed. PCA was performed on the transposed, filtered, and normalized gene expression matrix using the base R prcomp() function with centering and scaling enabled. The first three principal components were extracted for visualization. The resulting PCA scores were visualized using the plotly package 52 to generate an interactive 3D scatter plot. The final plot was exported as an HTML widget using the htmlwidgets package 53 . Reverse transcriptase quantitative PCR RNA was extracted from USC samples with RNeasy Mini Kit (Qiagen) with DNAse digestion, according to manufacturer. Alternatively, we used NucleoSpin RNA Plus (Machery Nagel) with gRNA removal column. We made sure to use samples prepared with the same procdures for comperative anlysis. For cDNA Synthesis LunaScript RT SuperMix Kit (New Englang Biolabs) was used. RT-qPCR was performed with TaqMan Master-Mix and predesigned Assays (Applied Biosystems) on the Quantstudio 12K Flex Platform (Applied Biosystems). RPLP0 served as housekeeping gene. Values were calculated with the delta delta CT (ddCT) method and four technical replicates. Statistics (unpaired welch´s t-test of ddCT values) and visualization were performed in R studio and Graphpad Prism (version 10). We always used three biological replicates per condition. 3D-culture and immunofluorescence 20000 USCs were seeded in each well of a 96 well Nunclon Sphera ultra low attachment cell culture plate (ThermoFisher Scientific) and centrifuged for 10 minutes at 300 g to enable scaffold free cellular self-aggregation. Cells were cultured for 3 weeks with StemPro Chondrogenesis Kit (Gibco). As an undifferentiated control we used USCs from the same donor (different batch), which were cultured 2 days in Alpha MEM Eagle (PAN Biotech) with 10 % FBS (Sigma Aldrich or Roth) and 5 ng/mL FGF2 (SinoBiological). For immunofluorescent staining, spheres were fixed for one hour with 4 % paraformaldehyde (PFA) at room temperature. In the following, the spheroids were permeabilized with Triton X-100 for 30 minutes and then blocked with 1 % bovine serum albumin (Sigma-Aldrich) in PBS for 45 minutes. For primary antibody incubation anti-aggrecan (ab3778, abcam) was used in a concentration of 1:500 for one hour, followed by one hour of secondary anti-mouse antibody (AlexaFluor 488, Invitrogen) in a 1:500 dilution, with DAPI (1:1000, thermo fisher scientific). After washing with PBS spheroids were mounted with aqua polymount (Polysciences) on concave slides (Paul Marienfeld). Pictures were taken with the Axio Imager 2 with Apotome (Zeiss) and 25 Z-stacks. We used the same exposure times for differentiated and undifferentiated spheroids (DAPI: 10 ms, Aggrecan: 35 ms). The same image enhancements for each condition, including normalization to DAPI, were performed with FIJI 54 . Merging undifferentiated and chondrogenic USC datasets Undifferentiated and chondrogenic USC Seurat objects were annotated with a stage metadata field (“undiff” vs. “chondrogenic”). The two objects were merged and reprocessed end-to-end (NormalizeData, FindVariableFeatures, ScaleData, RunPCA, RunUMAP, FindNeighbors, FindClusters) to generate a unified embedding for downstream trajectory and mapping analyses. Results comparing chondrogenic and undifferentiated samples were obtained from DESeq2 analysis (adjusted p-value 1). For proving concordance with bulk RNA-Sequencing, we extracted significantly upregulated genes and calculated a gene module score in the merged Seurat object. The AddModuleScore function was applied to quantify the expression of this gene set across cells. Resulting module scores were visualized on a UMAP embedding using the FeaturePlot function (Additional file 1: Figure S2D, E). Construction of healthy cartilage reference atlas and label transfer onto merged USC dataset We assembled a multi‐sample single‐cell reference from publicly available human cartilage data 55 . For each of the three selected samples, raw count matrices, feature, and barcode files were imported into Seurat. Cells with < 200 genes, 10% mitochondrial reads were excluded. Each sample was SCTransformed (regressing out percent.mt), then merged into a single Seurat object. We first applied the same QC filters as for our USC objects to the published cartilage reference to generate a high-quality “healthy cartilage” atlas. Cells annotated as normal cartilage were subsetted, and all assay layers were collapsed into a single RNA assay. This object was then log-normalized (NormalizeData), variable features were identified (FindVariableFeatures), data were scaled (ScaleData), and PCA (dims 1–30), neighbor-graph construction, clustering, and UMAP were performed. The merged undifferentiated + chondrogenic USC object underwent the same SCT-assay preprocessing. We then selected the top 3,000 integration features across both objects and computed label-transfer anchors with FindTransferAnchors (normalization.method = "SCT"). Finally, reference cluster identities were projected onto the USC dataset via TransferData, yielding per-cell predicted cluster labels and confidence scores, which were appended to the USC metadata (Additional file 1: Figure S2F-H). Mapping and correlation of USCs to chondrogenic reference atlas and pseudotime inference We loaded the processed human cartilage reference and the mapped USC dataset into Seurat (SCTransform normalization). To identify “cluster 4–like” USCs, we labeled any cell whose transferred reference cluster equaled “4” and whose PredictionScore > 0.7 (Additional file 1: Figure S2I). Next, we computed the mean SCT embedding for reference cluster 4 and, for each USC cell, calculated the Pearson correlation between its SCT profile and the cluster 4 embedding. These correlation coefficients were then overlaid on the merged UMAP embedding to show how closely individual USCs resemble cluster 4 in principal component space (Additional file 1: Figure S2J, K). For pseudotime analysis, we ran Slingshot (version 2.16.0) 56 on the UMAP data, using the mitotic ( TOP2A ⁺) cluster as the root. Slingshot automatically detected terminal states. We computed pseudotime values per cell, appended them to the USC metadata, and projected them onto the UMAP. Finally, we annotated clusters based on their top marker genes (FindAllMarkers) and performed Gene Ontology biological process enrichment (clusterProfiler, version 4.16.0) 57 . We mapped DE genes to Entrez IDs, ran enrichGO with Benjamini–Hochberg correction, and selected the top ten GO–BP terms per cluster. These process annotations, and their positions on the UMAP, were summarized and visualized using cluster centroids Chondrogenic cluster annotation and functional characterization To identify clusters with chondrogenic potential, we integrated transcriptomic similarity to a reference chondrocyte population, GO enrichment, and expression of canonical marker genes. Average “cluster 4–like” scores (Pearson correlation to reference cluster 4) were computed per cluster and visualized with VlnPlot. The Z-score normalized, scaled average expression of chondrogenic markers ( SOX5 , SOX6 , SOX9 ) were calculated and depicted in a heatmap. GO biological process enrichment was filtered to include only terms related to chondrocyte differentiation (GO:0032330, GO:0002062), and enrichment values were visualized with ggplot2 across clusters. Results USCs are parietal epithelial cells derived from MYC - and E2F4 - driven stem cells We performed scRNA-Seq to identify and characterize USCs. After standard Seurat 34 QC procedures and clustering of 19,024 cells, nine transcriptionally distinct clusters were identified (UMAP, Figure 1A, B, Additional file 1: Figure S1A–E). We confirmed the kidney origin of USCs by comparing with established urinary and renal gene‑sets (CellMarker 2.0 39 ) (Figure 1C). Anchor-based data transfer from a published kidney atlas 41 revealed a parietal epithelial cell (PEC) identity of USCs. The expression of PEC-specific markers was observed to be highest in cluster 4, which also demonstrated the highest PEC‐marker expression and mapping confidence (Figure 1C–F). However, given that only two clusters showed a PEC marker gene expression signature, and USCs were previously reported to have multipotent differentiation capabilities 58 , we investigated the drivers of stemness in these cells. The application of CytoTRACE2 42 -based stemness scoring revealed a negative correlation between PEC identity and high potency levels, particularly in the TOP2A ⁺ cluster in G₂/M phase. Analysis of transcription factor activity using VIPER, further identified E2F4 and MYC as master regulators of the stem-like state, whereas KLF4 activity marked quiescent PEC-like clusters (Figure 1G–I, Additional file 1: Figure S2B). USCs show osteogenic and chondrogenic differentiation potential In order to develop cellular models for growth disorders, we analyzed and confirmed the differentiation potential of USCs towards osteogenic and chondrogenic differentiation. CytoTRACE2 potency UMAP revealed that a considerable proportion of cells showing a multipotency expression profile (Figure 2A), suggestive of the capacity to generate multiple cell types. Thus, USCs were cultured in differentiation media for 2 weeks, followed by staining of osteogenic-induced cells positive for calcium deposits for their osteogenic potential. Chondrogenic cultures were positive for glycosaminoglycans (GAG) and formed chondrogenic aggregates, resembling normal chondrogenic differentiation 59 (Figure 2B). Subsequently, we sought to ascertain whether these cells also express bona fide markers for osteoblast and chondrocytes. Bulk RNA-Seq and reverse transcription quantitative polymerase chain reaction (RT-qPCR) revealed expressed gene patterns of chondrocyte and osteoblast lineage specification (Figure 2C-F, Additional file 1: Figure S2C). Chondrogenic cultures exhibited a more pronounced upregulation of lineage-specific genes (Figure 2D) in comparison to osteogenic cultures (Figure 2C), (osteogenic: 2/4, chondrogenic 4/4 canonical genes), while both conditions increased expression of genes associated with terminal differentiation, as confirmed by qPCR (Figure 2E, F). In order to confirm these results from monolayer cultures in 3D, we generated 3-week chondrospheres through cell-aggregation in low attachment plates. After differentiation, we detected strong aggrecan immunofluorescence (Figure 2G, additional file 2) as a marker of articular cartilage, confirming that USCs form bona fide cartilage matrix in 3D culture. Taken together, our data demonstrates that USCs can differentiate along both osteogenic and especially chondrogenic pathways. Chondrogenic differentiation of USCs at single cell resolution To unravel how urine-derived stem cells (USCs) acquire a chondrogenic identity at the cellular level, we analyzed single-cell RNA sequencing data from undifferentiated and chondrogenically differentiated monolayer cultures (Figure 3). This approach allowed us to trace individual transcriptional trajectories and determine whether USC differentiation recapitulates key steps of endochondral ossification, the natural process driving cartilage formation during skeletal development. We first integrated 10,325 cells from the chondrogenic stage with a healthy cartilage reference atlas (Additional file 1: Figure S1F–J). This enabled us to compare USC-derived cells to genuine chondrocytes and detect subpopulations that resemble the cartilage lineage (Figure 3A). This finding indicates that USCs undergo a transcriptional maturation process, mirroring early-to-late chondrogenic commitment. To better understand how USCs transition from an undifferentiated to a chondrogenic state, we next analyzed how gene expression evolves along the trajectory. Pseudo time analysis using Slingshot revealed distinct differentiation trajectories emerging from proliferative TOP2A ⁺ stem-like cells and progressing towards chondrocyte-like end states (Figure 3B). This allowed us to pinpoint which genes become activated or silenced as cells progress toward a chondrocyte-like phenotype. We therefore looked for genes, differentially expressed in individual clusters along pseudo time. In the middle of our pseudo time, a subpopulation upregulated the expression of ALDH1A2 , a gene involved in retinoic acid signaling. This means that retinoic acid is involved in chondrogenic modulation of USCs, highlighting the importance of vitamin metabolites in the differentiation process. At the end of the trajectory, we identified two distinct group of cells characterized by high expression of TIMP3 , or CDH1 (Figure 3C). TIMP3 is associated with extracellular matrix organization, whereas CDH1 facilitates cell adhesion. Both of these features indicate that the cells are acquiring structural and regulatory functions typical of mature cartilage tissue, underscoring that USCs differentiation process recapitulates bona fide chondrogenesis. To further strengthen these findings, we performed a Gene Ontology (GO) enrichment analysis and summarized the top 10 terms per cluster (Figure 3C). Late-stage clusters with chondrocyte-like features (clusters 2, 3, 7) showed enrichment for processes related to ECM remodeling, cytoskeletal organization, and metabolic adaptation, including autophagy and energy metabolism pathways. These results again highlight the upregulation of TIMP3 , together with TAGLN , as key markers of a matrix-producing, contractile phenotype emerging during chondrogenic maturation. To assess if bona fide chondrogenic biological processes are active in these cells, we specifically looked for chondrocyte specific GO-terms. Here, the ALDH1A2 ⁺ intermediate was enriched for the GO-term “chondrocyte differentiation”. Again, suggesting a crucial function of retinoic acid related chondrogenesis modulation in USCs. Cluster 3, which contains most of aforementioned TIMP3 ⁺ cells, showed even stronger enrichment for the term “chondrocyte differentiation”, in addition to “regulation of chondrocyte differentiation” (Figure 3F), supporting resemblance of chondrocyte transcriptional programs in chondrogenic USCs. Importantly, when we compared each cluster to a reference atlas of native chondrocytes, cluster 3 exhibited the highest transcriptional similarity. This confirms that these late-stage TIMP3 ⁺ cells represent the terminally differentiated end point of the USC chondrogenic trajectory, mimicking transcriptional gene expression programs active in chondrocytes. We next examined the expression of the chondrogenic transcription factors SOX9 , SOX5 , and SOX6 , known as the core regulatory trio of cartilage formation, to investigate the underlying transcription factor activity driving chondrogenesis in USCs. These genes were predominantly expressed in late clusters along pseudo time (Figure 3B, D, E), confirming that USC-derived chondrocyte-like cells activate the canonical transcriptional network that drives cartilage lineage commitment. This transcriptional alignment with native chondrocytes supports the authenticity of the differentiation process. To validate the single-cell findings at the bulk level, we quantified by RT-qPCR the expression of genes that resemble USCs bona fide chondrogenesis best. We chose TIMP3 , as its expression was associated with chondrocyte related GO-terms (cluster 3, Figure 3F) and chondrocyte similarity (Figure 3A), and SOX9 , as its expression increased in concordance with pseudo time (Figure 3B, D, E). Both SOX9 and TIMP3 were strongly upregulated after differentiation (Figure 3G, H), confirming the transcriptional activation observed in cluster 3. TIMP3 , in particular, is an extracellular matrix regulator crucial for cartilage maintenance, further highlighting the functional maturation of these cells, driven by the chondrogenic master transcription factor SOX9 . Together, these results show that USC differentiation proceeds through defined transcriptional stages, from proliferative progenitors to mature chondrocyte-like cells. This is accompanied by activation of canonical cartilage transcription factors and functional pathways. The emergence of a TIMP3 ⁺ cluster with strong chondrocyte similarity and chondrogenesis related gene expression profile demonstrates that USCs can effectively recapitulate the molecular program of endochondral chondrogenesis. Potential for clinical application of USCs To assess the clinical suitability of urine-derived stem cells (USCs), we established a xeno-free culture protocol by replacing fetal bovine serum (FBS) with autologous human serum (HS) during isolation and expansion (Supplementary Figure 3A, B). This modification eliminates animal-derived components, a critical requirement for translational use in regenerative medicine. When we compared transcriptional markers between FBS- and HS-expanded USCs, HS cultures showed increased expression of MYC (Figure 4A, B). Elevated MYC is associated with proliferative, stem-like activity, whereas KLF4 supports maintenance of a parietal epithelial cell (PEC)-like identity (as shown in Figure 1D-I). Together, these findings indicate that HS expansion sustains the native USC phenotype, retaining both stemness and renal lineage features. Maintaining this balanced phenotype is desirable because it preserves the cells’ proliferative capacity and differentiation competence, both essential for efficient chondrogenic induction and clinical scalability. To ensure that HS expansion does not trigger reprogramming toward an undesired pluripotent state, we measured OCT4 , a canonical pluripotency transcription factor that can become aberrantly reactivated in dedifferentiating renal cells. OCT4 expression remained unchanged (Figure 4C) between FBS- and HS-expanded USCs. Additionally, proliferation appeared to be slightly reduced (Additional file 1: Figure S3C), supporting that cells were not undergoing malignant transformation. Taken together, these data show that HS conditions preserve differentiation competence without inducing pluripotency. We next evaluated whether xeno-free expansion affects the chondrogenic potential of USCs by inducing differentiation in monolayer culture, to observe differences in cellular morphology in both culture conditions. HS-expanded USCs (chHS) formed dense multicellular condensates, characteristic of mesenchymal condensation during early cartilage development, whereas FBS-expanded controls (chFBS) exhibited weaker condensation (Figure 4D). We then quantified GAG content to observe differences in matrix deposition. FBS-expanded cells accumulated more GAGs (Figure 4E), suggesting that FBS drives matrix deposition, whereas HS supports the initial cellular organization required for lineage commitment. Because three-dimensional culture more closely mimics in vivo cartilage formation, we performed pellet differentiation of HS- and FBS-expanded cells. Under HS conditions, expression of TOP2A , the gene upregulated in proliferative progenitors, remained stable following induction, whereas it was markedly reduced under FBS (Figure 4F). This indicates that HS preserves a pool of proliferative progenitors during early chondrogenesis, while FBS drives premature cell-cycle exit. We then moved on validating the expression of genes appearing to reflect USCs chondrogenic program in our single cell atlas (Figure 3), to see if xeno-free cultivation improves functional chondrocyte maturation in USCs. As differentiation progressed, HS pellets exhibited higher expression of the master chondrogenic regulator SOX9 (Figure 4G) and markedly increased levels of terminal associated genes TIMP3 and CDH1 (Figure 4H, I). The upregulated expression of TIMP3 corresponds to the late pseudo time chondrocyte-similar (Figure 3A) and chondrocyte related GO-term related state (Figure 3F) identified in our single-cell analysis, confirming that HS expansion promotes robust and complete chondrogenic differentiation. The increased CDH1 expression, a gene involved in cellular aggregation, in 3D is in line with the observed chondrogenic aggregates in 2D (Figure 4D), delivering orthogonal evidence that FBS diminishes chondrogenic outcomes of USC differentiation. Together, these results demonstrate that autologous human serum supports xeno-free USC expansion while maintaining a proliferative, MYC -positive progenitor pool. During differentiation, HS-expanded cells efficiently activate SOX9 -driven chondrogenic programs and progress toward TIMP3 ⁺/ CDH1 ⁺ terminal states, indicating faithful recapitulation of cartilage maturation. Thus, making xeno-free cultured USCs an ideal starting material for chondrocyte regeneration approaches. Discussion Our goal was to establish a non-invasive, physiologically relevant, and translational model of human cartilage development by leveraging urine-derived stem cells (USCs). By integrating high-resolution single-cell transcriptomic profiling, functional lineage differentiation assays, and a xeno-free expansion protocol, we aimed to delineate the cellular hierarchy and transcriptional programs underlying USC chondrogenesis, evaluate their potential to recapitulate native cartilage formation, and develop a clinically compatible platform for modeling genetic skeletal disorders and advancing personalized regenerative therapies. To establish USCs as a well-defined biological system, it was necessary to resolve their cellular heterogeneity and origin. We began by deconstructing the cellular composition of bulk urine-derived stem cells to define cellular diversity. Single-cell RNA sequencing of USCs revealed nine transcriptionally distinct clusters, delineating a structured progenitor hierarchy rather than a uniform population (Figure 1A, B). To definitively confirm their source within the kidney, we mapped our data against a reference atlas, which confirmed their parietal epithelial cell (PEC) origin (Figure 1C-F), reinforcing the concept that USCs retain a tissue-specific transcriptional memory after ex vivo expansion, a process in line with behavior of adult stem cells 60 . This tissue-specific memory is beneficial as it provides a stable molecular identity that may preserve lineage fidelity during differentiation, enhancing the reproducibility of experiments and increasing their relevance for modeling kidney- or cartilage-related pathologies. Furthermore, defining both the heterogeneity and origin of USCs establishes a foundation for rational selection and manipulation of specific subpopulations to optimize chondrogenic or osteogenic differentiation for translational applications. Next, we sought to understand the regulatory drivers behind the proliferative and quiescent compartments we observed. CytoTRACE and VIPER analyses identified MYC and E2F4 as central regulators of the proliferative compartment (Figure 1G-I). While MYC is classically associated with oncogenesis 61 , here it appears to drive physiologically controlled cell-cycle re-entry, supporting expansion without evidence of transformation 62 . Interestingly, although E2F4 is often viewed as a repressor in classical cell-cycle control 63 , in certain developmental or stem-cell contexts it can act as a transcriptional activator. For example, in mouse embryonic stem cells, E2F4 promotes expansion by directly activating cell cycle genes independently of the RB family, and E2F4 knockout slows S-phase entry and reduces viability 64 . This is particularly significant because E2F4 has not previously been implicated in maintaining controlled proliferative activity in adult, non-pluripotent stem cell populations and its activation in USCs highlights a previously unrecognized mechanism that supports stable expansion. KLF4 activity marked the quiescent PEC-like clusters (Figure 1E-I), reflecting a retention of renal lineage traits, suggesting that these cells maintain a baseline functional identity even under proliferative stress. This balance between proliferative and quiescent states is beneficial because it preserves both the stem-like potential and tissue-specific memory of USCs, enabling predictable differentiation into osteogenic and chondrogenic lineages while maintaining a physiologically relevant transcriptional profile. By identifying the molecular drivers of these distinct compartments, we gain insight into how USCs can be selectively manipulated to enhance expansion, lineage commitment, and ultimately, translational utility. Collectively, these data define USCs as a hierarchically organized progenitor population with proliferative and quiescent, lineage-primed cells. This architecture provides the mechanistic transcription factor activity driving their observed multipotency and establishes a foundation for subsequent chondrogenic studies. Having defined their progenitor identity, we moved beyond transcriptional profiles and functionally validate their capacity to generate the skeletal lineages relevant to our model, osteogenic and chondrogenic fates. Complementary bulk RNA-Seq and RT-qPCR analyses showed upregulation of canonical osteogenic and chondrogenic markers (Figure 2C-F), demonstrating that USCs can faithfully execute mesodermal lineage-specific programs. This functional validation is important because transcriptional similarity alone does not guarantee actual differentiation potential. Confirming that USCs can produce mature lineage-specific phenotypes strengthens their relevance as a cellular model. Moreover, this predisposition aligns with their renal epithelial origin, which may prime them for mesenchymal-like transitions under appropriate cues. Parietal epithelial cells, the presumed source of USCs, have been shown to undergo epithelial-to-mesenchymal transition and adopt progenitor-like phenotypes in vitro and in vivo 65,66 , suggesting that these cells harbor latent plasticity that may facilitate skeletal lineage reprogramming in our differentiation assays. This context-dependent flexibility likely underlies the ability of USCs to efficiently adopt osteogenic and chondrogenic fates, bridging epithelial and mesenchymal lineages. This indicates that USCs are not only transcriptionally competent but also functionally poised for skeletal differentiation, increasing their reliability for modeling cartilage and bone development, studying disease mechanisms, and ultimately serving as a scalable and patient-specific platform for regenerative applications. As our results showed a marked bias toward chondrogenesis, we employed complementary 2D and 3D culture systems because they provide different but essential insights. Monolayer cultures allowed analysis of gene expression and cellular morphology during differentiation, while 3D spheroid cultures recapitulated key morphogenetic events such as mesenchymal condensation and extracellular matrix (ECM) deposition. To confirm that the matrix produced in 3D was authentic cartilage, we performed staining for the cartilage‐specific proteoglycan aggrecan, a definitive marker of articular cartilage extracellular matrix structure and function 67 . The strong aggrecan deposition observed validates that our 3D differentiated tissues replicate key compositional features of native cartilage, thereby supporting their relevance for developmental and regenerative applications (Figure 2G). This identifies USCs as a powerful tool for studying cartilage biology. Their intrinsic preference toward chondrogenesis is particularly attractive, as it indicates that these cells naturally favor to recreate cartilage-specific microenvironments, enabling more reliable modeling of cartilage development, disease processes, and potentially regenerative therapies. While bulk assays confirmed differentiation potential, we aimed to unravel the precise sequence of cellular events and identify key transitional states during chondrogenesis at single-cell resolution. We therefore performed pseudo time analysis (Figure 3B), which revealed the chondrogenic trajectory emanating from TOP2A ⁺ progenitors, converging through an ALDH1A2 ⁺ intermediate enriched for chondrocyte-specific programs (Figure 3C-F). The presence of this intermediate in undifferentiated populations suggests pre-patterning at the metabolic level via retinoic acid signaling 68 , highlighting the nuanced interplay between cellular state and lineage bias. This finding uncovers early regulatory checkpoints that may prime cells for chondrogenic commitment, providing mechanistic insight into how lineage bias is established. By defining these transitional states, we gain the ability to predict, manipulate, or enhance chondrogenic differentiation, thereby improving the fidelity of USCs as a model for cartilage development and disease, and informing strategies for regenerative therapies. A critical question was whether the endpoint of our chondrogenic differentiation resembled genuine chondrocytes. Terminal differentiation yielded discrete chondrocyte-like subpopulations: a TIMP3 ⁺, and a CDH1 ⁺ cluster (Figure 3A-D). TIMP3, a tissue inhibitor of metalloproteinases abundantly expressed in mature cartilage 69 , plays a crucial role in maintaining extracellular matrix integrity and protecting against proteolytic degradation 70 . Its strong induction therefore signifies the acquisition of a regulatory, matrix-stabilizing phenotype characteristic of functional chondrocytes. In parallel, the emergence of CDH1⁺ cells reflects activation of intercellular adhesion mechanisms. E-cadherin ( CDH1 ) has been shown to enhance chondrogenic differentiation in human mesenchymal stem cell aggregates, acting together with N-cadherin to promote mesenchymal condensation and lineage commitment 71 . This underscores the importance of CDH1 in facilitating mesenchymal condensation, an early and indispensable step of chondrogenic differentiation. Together, the coordinated expression of these structural and regulatory markers indicates that USCs undergo a structured, stepwise maturation process akin to endochondral development. This single-cell resolution map reveals not only the hierarchical differentiation of USCs but also actionable molecular targets, such as TIMP3 -mediated matrix regulation and CDH1 -associated adhesion, which may be leveraged to modulate differentiation or tissue formation. Since a primary advantage of USCs is their non-invasive nature, we engineered a xeno-free protocol to make the entire procedure clinically relevant. We first asked whether xeno-free expansion alters the fundamental identity of USCs. HS-expanded USCs modestly increased MYC and KLF4 expression compared with FBS, while OCT4 remained unchanged, confirming safety with respect to pluripotency activation (Figure 4A-C). This coordinated, increased, expression of stemness-associated and renal lineage-associated transcription factors is especially advantageous because it allows USCs to expand efficiently while retaining their original renal identity, minimizing the risk of dedifferentiation or acquisition of unintended phenotypes. This makes HS-expanded USCs a safer and more reliable starting material for translational applications, including patient-specific disease modeling and regenerative therapies. We then rigorously compared the differentiation efficacy of HS- versus FBS-expanded cells to ensure that the clinical suitability did not compromise functional outcomes. During 2D differentiation, HS-expanded cells formed pronounced mesenchymal condensates, indicative of bona fide chondrogenesis 59 , whereas FBS-expanded cells accumulated more bulk glycosaminoglycans (Figure 4D-E), reflecting accelerated ECM deposition. This comparison is beneficial because it highlights that xeno-free conditions do not simply mimic conventional culture but instead preserve a more physiologically relevant developmental program. To resolve this apparent discrepancy and better approximate in vivo cartilage formation, we switched to 3D pellet differentiation and gene expression analysis. In this context, HS-expanded USCs maintained TOP2A expression while robustly activating chondrogenic programs, corresponding to the late pseudotime chondrocyte-like states identified in our single-cell analyses (Figure 4F-I). This finding is particularly advantageous because it demonstrates that xeno-free expansion strikes an optimal balance, by retaining a pool of proliferative progenitors necessary for long-term differentiation capacity while allowing faithful progression toward mature, functional chondrocytes. This balance ensures that USCs can serve as a reproducible, clinically compatible platform for cartilage modeling and regenerative applications, combining safety, scalability, and developmental fidelity. Conclusion To conclude, our study successfully establishes urine-derived stem cells (USCs) as a non-invasive, physiologically relevant, and translational model of human cartilage development. By combining single-cell transcriptomic mapping with functional differentiation in 2D, spheroid, and pellet cultures under xeno-free conditions, we delineated a hierarchical structure of USC chondrogenesis, from proliferative MYC / E2F4 -active TOP2A ⁺ progenitors through ALDH1A2 ⁺ intermediates to terminal TIMP3 ⁺ chondrocyte-like cells. This framework not only recapitulates key stages of native cartilage formation but also confirms that USCs preserve both lineage fidelity and differentiation potential during expansion. Together, these findings demonstrate that USCs provide a robust, clinically compatible platform for modeling genetic skeletal disorders and advancing personalized regenerative therapies, effectively bridging mechanistic insight with translational application. Declarations Ethics approval and consent to participate All procedures were in accordance with the ethical standards of the FAU Erlangen-Nürnberg (reference number: 180_15 Bc) and the Helsinki Declaration. This study was approved by the Ethics Committee of the Friedrich-Alexander-University Erlangen-Nürnberg under two institutional review board approvals: (i) project title: “Identification and functional characterization of novel genetic causes of autosomal-dominant short stature”, approval number 180_15 BC, approval date: 14 July 2025; and (ii) project title: “Elucidation of genetic causes and pathomechanisms of rare, inherited diseases”, approval date: 21 September 2022. Written informed consent for participation in the study and for the use of biological samples was obtained from all participants and/or their parents or legally authorized representatives in the case of minors. Consent for publication Consent for publication: Not applicable. The manuscript does not contain any identifiable individual patient data. Availability of data and materials All sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) database. scRNA-Seq of USCs: accession code GSE307232. Bulk-RNA-Seq of USCs: accession code GSE306729. All the R codes used to generate the results of this study are publicly available on GitHub: https://github.com/alexschulzcell/USC-paper. A preprint of this work has been deposited on bioRxiv (doi: 10.1101/2025.09.26.678723). This preprint has not undergone peer review. Competing interests The authors declare no competing interests. Funding This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – [TH896 7-1]. Authors' contributions A.S: performed the experiments, processed and analyzed the data, and wrote the original draft, E.M.B: processed the raw sequencing data. M.Z: revised figures and contributed to xenofree media development, A.S.B: revised manuscript, S.U: uploaded all sequencing data, A.B.E: assisted with sample collection, M.D: Helped with scRNA-Seq data processing, S.Z: Contributed to scRNA-Seq data gathering, C.T.T: project P.I, acquired funding, supervised research and study design, and revised manuscript Acknowledgements We thank Mohammad Deen Hayatu, as well as Evelyn Galsterer for excellent technical assistance. An AI-based language model (ChatGPT, OpenAI) was used for language editing of this manuscript; all scientific content was provided and verified by the authors. References Formosa, M. M. et al. A Roadmap to Gene Discoveries and Novel Therapies in Monogenic Low and High Bone Mass Disorders. Front Endocrinol (Lausanne) 12 , 709711, doi:10.3389/fendo.2021.709711 (2021). Sharma, L., Rani, D., Kanchan, T. & et al. in StatPearls [Internet] (StatPearls Publishing, 2025). Butler, M. G. Prader-Willi syndrome: current understanding of cause and diagnosis. Am J Med Genet 35 , 319-332, doi:10.1002/ajmg.1320350306 (1990). Clement-Jones, M. et al. The short stature homeobox gene SHOX is involved in skeletal abnormalities in Turner syndrome. Human Molecular Genetics 9 , 695-702, doi:10.1093/hmg/9.5.695 (2000). Noonan, J. A. Noonan syndrome and related disorders: alterations in growth and puberty. Rev Endocr Metab Disord 7 , 251-255, doi:10.1007/s11154-006-9021-1 (2006). Price, S. M., Stanhope, R., Garrett, C., Preece, M. A. & Trembath, R. C. The spectrum of Silver-Russell syndrome: a clinical and molecular genetic study and new diagnostic criteria. J Med Genet 36 , 837-842 (1999). Lindsay, R., Feldkamp, M., Harris, D., Robertson, J. & Rallison, M. Utah Growth Study: Growth standards and the prevalence of growth hormone deficiency. The Journal of Pediatrics 125 , 29-35, doi:https://doi.org/10.1016/S0022-3476(94)70117-2 (1994). Kotake, M. et al. Short stature due to growth hormone deficiency associated with Cushing's disease and ulcerative colitis. Horm Metab Res 28 , 565-569, doi:10.1055/s-2007-979854 (1996). Al-Omari, A. F. & Omer, Z. K. The impact of juvenile hypothyroidism on stature. J Med Life 16 , 1211-1214, doi:10.25122/jml-2022-0303 (2023). Seema, Abbas, S., Ahsan, M. N. & Asghar, M. S. Frequency of skeletal dysplasia in children with short stature presenting to endocrine clinic: An observational study. J Family Med Prim Care 11 , 3143-3147, doi:10.4103/jfmpc.jfmpc_2393_21 (2022). Sentchordi-Montané, L. et al. High prevalence of variants in skeletal dysplasia associated genes in individuals with short stature and minor skeletal anomalies. Eur J Endocrinol 185 , 691-705, doi:10.1530/eje-21-0557 (2021). Kyle, U. G., Shekerdemian, L. S. & Coss-Bu, J. A. Growth failure and nutrition considerations in chronic childhood wasting diseases. Nutr Clin Pract 30 , 227-238, doi:10.1177/0884533614555234 (2015). Ranke, M. B. Towards a Consensus on the Definition of Idiopathic Short Stature. Hormone Research 45 , 64-66, doi:10.1159/000184851 (2008). Wit, J. M. et al. Idiopathic short stature: definition, epidemiology, and diagnostic evaluation. Growth Horm IGF Res 18 , 89-110, doi:10.1016/j.ghir.2007.11.004 (2008). Argente, J., Tatton-Brown, K., Lehwalder, D. & Pfäffle, R. Genetics of Growth Disorders—Which Patients Require Genetic Testing? Frontiers in Endocrinology Volume 10 - 2019 , doi:10.3389/fendo.2019.00602 (2019). Voller, T., Cameron, P., Watson, J. & Phadnis, J. The growth plate: anatomy and disorders. Orthopaedics and Trauma 34 , 135-140, doi:https://doi.org/10.1016/j.mporth.2020.03.006 (2020). Phornphutkul, C. & Gruppuso, P. A. Disorders of the growth plate. Current Opinion in Endocrinology, Diabetes and Obesity 16 (2009). Nilsson, O. et al. Short Stature, Accelerated Bone Maturation, and Early Growth Cessation Due to Heterozygous Aggrecan Mutations. The Journal of Clinical Endocrinology & Metabolism 99 , E1510-E1518, doi:10.1210/jc.2014-1332 (2014). Foreman, P. K. et al. Birth prevalence of achondroplasia: A systematic literature review and meta-analysis. American Journal of Medical Genetics Part A 182 , 2297-2316, doi:https://doi.org/10.1002/ajmg.a.61787 (2020). Phull, A. R., Eo, S. H., Abbas, Q., Ahmed, M. & Kim, S. J. Applications of Chondrocyte-Based Cartilage Engineering: An Overview. Biomed Res Int 2016 , 1879837, doi:10.1155/2016/1879837 (2016). Neybecker, P. et al. Respective stemness and chondrogenic potential of mesenchymal stem cells isolated from human bone marrow, synovial membrane, and synovial fluid. Stem Cell Res Ther 11 , 316, doi:10.1186/s13287-020-01786-5 (2020). Kangari, P., Talaei-Khozani, T., Razeghian-Jahromi, I. & Razmkhah, M. Mesenchymal stem cells: amazing remedies for bone and cartilage defects. Stem Cell Res Ther 11 , 492, doi:10.1186/s13287-020-02001-1 (2020). Moy, A. B., Kamath, A., Ternes, S. & Kamath, J. The Challenges to Advancing Induced Pluripotent Stem Cell-Dependent Cell Replacement Therapy. Med Res Arch 11 , doi:10.18103/mra.v11i11.4784 (2023). Doss, M. X. & Sachinidis, A. Current Challenges of iPSC-Based Disease Modeling and Therapeutic Implications. Cells 8 , doi:10.3390/cells8050403 (2019). Lee, A. S., Tang, C., Rao, M. S., Weissman, I. L. & Wu, J. C. Tumorigenicity as a clinical hurdle for pluripotent stem cell therapies. Nat Med 19 , 998-1004, doi:10.1038/nm.3267 (2013). Zhang, D., Wei, G., Li, P., Zhou, X. & Zhang, Y. Urine-derived stem cells: A novel and versatile progenitor source for cell-based therapy and regenerative medicine. Genes Dis 1 , 8-17, doi:10.1016/j.gendis.2014.07.001 (2014). Wan, S. et al. Extracellular Vesicles from Hypoxic Pretreated Urine-Derived Stem Cells Enhance the Proliferation and Migration of Chondrocytes by Delivering miR-26a-5p. Cartilage 13 , 19476035221077401, doi:10.1177/19476035221077401 (2022). Sun, J. et al. Comparison of chondrogenesis-related biological behaviors between human urine-derived stem cells and human bone marrow mesenchymal stem cells from the same individual. Stem Cell Research & Therapy 12 , 366, doi:10.1186/s13287-021-02370-1 (2021). Pei, M. et al. Matrix from urine stem cells boosts tissue-specific stem cell mediated functional cartilage reconstruction. Bioactive Materials 23 , 353-367, doi:https://doi.org/10.1016/j.bioactmat.2022.11.012 (2023). Zhang, Y. et al. Urine derived cells are a potential source for urological tissue reconstruction. J Urol 180 , 2226-2233, doi:10.1016/j.juro.2008.07.023 (2008). Bui, H. T. H., Nguyen, L. T. & Than, U. T. T. Influences of Xeno-Free Media on Mesenchymal Stem Cell Expansion for Clinical Application. Tissue Eng Regen Med 18 , 15-23, doi:10.1007/s13770-020-00306-z (2021). Culenova, M. et al. Isolation, Culture and Comprehensive Characterization of Biological Properties of Human Urine-Derived Stem Cells. Int J Mol Sci 22 , doi:10.3390/ijms222212503 (2021). Zhang, C. et al. Reno-protection of Urine-derived Stem Cells in A Chronic Kidney Disease Rat Model Induced by Renal Ischemia and Nephrotoxicity. International Journal of Biological Sciences 16 , 435-446, doi:10.7150/ijbs.37550 (2020). Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology 42 , 293-304, doi:10.1038/s41587-023-01767-y (2024). Valero-Mora, P. M. ggplot2: Elegant Graphics for Data Analysis. Journal of Statistical Software, Book Reviews 35 , 1 - 3, doi:10.18637/jss.v035.b01 (2010). Slowikowski, K. ggrepel: Automatically Position Non-Overlapping Text Labels with 'ggplot2'. (2024). Garnier, S. et al. (Zenodo, 2023). RColorBrewer: ColorBrewer Palettes v. 1.1-3 (R package, 2022). Hu, C. et al. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Research 51 , D870-D876, doi:10.1093/nar/gkac947 (2022). Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nature Methods 14 , 1083-1086, doi:10.1038/nmeth.4463 (2017). Tisch, A. et al. Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms. Heliyon 10 , doi:10.1016/j.heliyon.2024.e38567 (2024). Kang, M. et al. Mapping single-cell developmental potential in health and disease with interpretable deep learning. bioRxiv , 2024.2003.2019.585637, doi:10.1101/2024.03.19.585637 (2024). Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet 48 , 838-847, doi:10.1038/ng.3593 (2016). Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Res 29 , 1363-1375, doi:10.1101/gr.240663.118 (2019). Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10 , doi:10.1093/gigascience/giab008 (2021). McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20 , 1297-1303, doi:10.1101/gr.107524.110 (2010). Dyer, S. C. et al. Ensembl 2025. Nucleic Acids Res 53 , D948-d957, doi:10.1093/nar/gkae1071 (2025). Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29 , 15-21, doi:10.1093/bioinformatics/bts635 (2013). Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30 , 923-930, doi:10.1093/bioinformatics/btt656 (2014). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15 , 550, doi:10.1186/s13059-014-0550-8 (2014). Carlson, M. org.Hs.eg.db: Genome wide annotation for Human. (2024). Sievert, C. Interactive Web-Based Data Visualization with R, plotly, and shiny. (2020). Vaidyanathan, R. et al. htmlwidgets: HTML Widgets for R. 1.6.4 (2023). Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nature Methods 9 , 676-682, doi:10.1038/nmeth.2019 (2012). Huang, C. et al. Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis. Genes & Diseases 12 , 101241, doi:https://doi.org/10.1016/j.gendis.2024.101241 (2025). Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19 , 477, doi:10.1186/s12864-018-4772-0 (2018). Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A Journal of Integrative Biology 16 , 284-287, doi:10.1089/omi.2011.0118 (2012). Bharadwaj, S. et al. Multipotential differentiation of human urine-derived stem cells: Potential for therapeutic applications in urology. Stem Cells 31 , 1840-1856, doi:10.1002/stem.1424 (2013). Tacchetti, C. et al. Cell condensation in chondrogenic differentiation. Experimental Cell Research 200 , 26-33, doi:https://doi.org/10.1016/S0014-4827(05)80067-9 (1992). Royall, L. N. & Jessberger, S. How stem cells remember their past. Current Opinion in Cell Biology 69 , 17-22, doi:https://doi.org/10.1016/j.ceb.2020.12.008 (2021). Dhanasekaran, R. et al. The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. Nat Rev Clin Oncol 19 , 23-36, doi:10.1038/s41571-021-00549-2 (2022). Jha, R. K., Kouzine, F. & Levens, D. MYC function and regulation in physiological perspective. Front Cell Dev Biol 11 , 1268275, doi:10.3389/fcell.2023.1268275 (2023). Trimarchi, J. M. & Lees, J. A. Sibling rivalry in the E2F family. Nature Reviews Molecular Cell Biology 3 , 11-20, doi:10.1038/nrm714 (2002). Hsu, J. et al. E2F4 regulates transcriptional activation in mouse embryonic stem cells independently of the RB family. Nature Communications 10 , 2939, doi:10.1038/s41467-019-10901-x (2019). Swetha, G., Chandra, V., Phadnis, S. & Bhonde, R. Glomerular parietal epithelial cells of adult murine kidney undergo EMT to generate cells with traits of renal progenitors. J Cell Mol Med 15 , 396-413, doi:10.1111/j.1582-4934.2009.00937.x (2011). Naito, S., Pippin, J. W. & Shankland, S. J. The glomerular parietal epithelial cell’s responses are influenced by SM22 alpha levels. BMC Nephrology 15 , 174, doi:10.1186/1471-2369-15-174 (2014). Alberton, P. et al. Aggrecan Hypomorphism Compromises Articular Cartilage Biomechanical Properties and Is Associated with Increased Incidence of Spontaneous Osteoarthritis. International Journal of Molecular Sciences 20 , 1008 (2019). Zhu, L. et al. Variants in ALDH1A2 reveal an anti-inflammatory role for retinoic acid and a new class of disease-modifying drugs in osteoarthritis. Science Translational Medicine 14 , eabm4054, doi:10.1126/scitranslmed.abm4054. Morris, K. J., Cs-Szabo, G. & Cole, A. A. Characterization of TIMP-3 in human articular talar cartilage. Connect Tissue Res 51 , 478-490, doi:10.3109/03008201003686958 (2010). Green, J. et al. Suramin analogues protect cartilage against osteoarthritic breakdown by increasing levels of tissue inhibitor of metalloproteinases 3 (TIMP-3) in the tissue. Bioorganic & Medicinal Chemistry 92 , 117424, doi:https://doi.org/10.1016/j.bmc.2023.117424 (2023). Wang, X., Zhang, Y. & Yang, J. Transcriptome analysis reveals synergistic modulation of E-cadherin/N-cadherin in hMSC aggregates chondrogenesis. Genes & Genomics 45 , 681-692, doi:10.1007/s13258-022-01362-6 (2023). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8230463","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575107805,"identity":"27367c7f-2f3a-47c3-a472-928a53a65042","order_by":0,"name":"Alexander Schulz","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Schulz","suffix":""},{"id":575107806,"identity":"070cf34c-0849-4ab4-9425-9b6c48a2f637","order_by":1,"name":"Emily M. Brockmann","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"M.","lastName":"Brockmann","suffix":""},{"id":575107807,"identity":"2b5cc10b-4801-4447-b6e6-2c2685d37627","order_by":2,"name":"Miriam Zentgraf","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Miriam","middleName":"","lastName":"Zentgraf","suffix":""},{"id":575107808,"identity":"97f84389-f7d4-4e85-b14f-0b757bc6f183","order_by":3,"name":"Andreas S. Baur","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"S.","lastName":"Baur","suffix":""},{"id":575107809,"identity":"145ab62d-bce0-4137-8842-b8307d4a6e00","order_by":4,"name":"Steffen Uebe","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Steffen","middleName":"","lastName":"Uebe","suffix":""},{"id":575107810,"identity":"97dfa1e8-a436-4a47-aa75-aae62421e494","order_by":5,"name":"Arif B. Ekici","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Arif","middleName":"B.","lastName":"Ekici","suffix":""},{"id":575107811,"identity":"22e10685-1a61-4194-b08f-ba3d9b48c6c7","order_by":6,"name":"Mark Dedden","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Dedden","suffix":""},{"id":575107812,"identity":"92e6e663-9be4-4b98-a2ab-0cc97abd2250","order_by":7,"name":"Sebastian Zundler","email":"","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Zundler","suffix":""},{"id":575107813,"identity":"ec02eb3d-5e2c-4887-bc39-802a9bd09d3b","order_by":8,"name":"Chrisitian T. Thiel","email":"data:image/png;base64,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","orcid":"","institution":"Friedrich-Alexander-Universität Erlangen-Nürnberg","correspondingAuthor":true,"prefix":"","firstName":"Chrisitian","middleName":"T.","lastName":"Thiel","suffix":""}],"badges":[],"createdAt":"2025-11-28 12:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8230463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8230463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100561010,"identity":"a50c96e4-97b0-41ee-912b-8448550c8b4a","added_by":"auto","created_at":"2026-01-19 08:43:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19592376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional identity and stemness architecture of USCs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Uniform Manifold Approximation and Projection (UMAP) of USCs with biological top markers annotated per cluster.\u003c/p\u003e\n\u003cp\u003e(B) Dotplot of biological top marker expression across cluster.\u003c/p\u003e\n\u003cp\u003e(C) AUC-based enrichment scores of bladder and kidney-specific expression profiles projected onto UMAP space.\u003c/p\u003e\n\u003cp\u003e(D) UMAP of predicted cell types from label transfer of kidney reference cell types. (Cells of ascending loop of Henle = ALH, of the parietal epithelium = PECs, of late proximal tubule = LPT, of distal thin limb = DTL, of proximal tubule = PT).\u003c/p\u003e\n\u003cp\u003e(E) Dotplot showcasing expression of PEC markers among clusters.\u003c/p\u003e\n\u003cp\u003e(F) The mapping confidence of individual clusters to the PECs/LPT/DTL of reference.\u003c/p\u003e\n\u003cp\u003e(G) CytoTRACE2 scoring of individual clusters.\u003c/p\u003e\n\u003cp\u003e(H) Cell cycle phase proportions of individual clusters.\u003c/p\u003e\n\u003cp\u003e(I) Top 3 TFs per stemness group overlaid onto CytoTRACE2 score (Score) based UMAP in a percentage-based color gradient manner. (darker boxes = more stemness and vice versa.)\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/f972026d3133c00c0e945c2b.png"},{"id":100560896,"identity":"80f9c5dc-5676-4f5a-8a96-40e41a753f60","added_by":"auto","created_at":"2026-01-19 08:43:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19763552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional differentiation capacity of USCs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) CytoTRACE2 potency UMAP depicting differentiation capacities across cells.\u003c/p\u003e\n\u003cp\u003e(B) GAC and calcium staining of uninduced (negative) or induced (positive) cells. (osteo= osteogenic differentiation stained with alcian blue, chondro= chondrogenic differentiation stained with alizarin red).\u003c/p\u003e\n\u003cp\u003e(C, D): Volcanoplots of osteogenic/chondrogenic differentiation, with genes expressed canonically in osteoblasts/chondrocytes labbelled (genes highlighted: adjusted p-values \u0026lt; 0.05, log2 fold change \u0026gt; ± 1). In osteogenic USCs 2/4 genes are positively upregulated, in chondrogenic 4/4.\u003c/p\u003e\n\u003cp\u003e(E) \u003cem\u003eSPP1\u003c/em\u003eexpression levels in undifferentiated (USC) and osteogenic (oUSC) cells as measured by RT-qPCR. RQ undifferentiated = 0.149 ± 0.006, differentiated = 1.00 ± 0.0.06, n = 3, p \u0026lt; 0.0001, Welch’s t-test.\u003c/p\u003e\n\u003cp\u003e(F) RQ values of \u003cem\u003eCOL10A1\u003c/em\u003e of USCs (USC) and chondrogenic (chUSC) cells. Bar with undetermined CT-values or exceeding 40 cycles, was marked (=ND). \u003cem\u003eCOL10A1\u003c/em\u003e, RQ undifferentiated = 0.030 ± 0.013, differentiated = 1.00 ± 0.07, n = 3, p \u0026lt; 0.01, Welch´s t-test.\u003c/p\u003e\n\u003cp\u003e(G) Immunofluorescence staining of uninduced (negative), or chondrogenically induced spheroids (sphere 1-3). (Scale bar represents 200 µm).\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/9e45ceed446a78c34b92139d.png"},{"id":100561066,"identity":"827da570-3d3c-4755-a2cf-6835def33414","added_by":"auto","created_at":"2026-01-19 08:43:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10159676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell lineage trajectories toward chondrocyte-like states.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Violinplot highlighting correlation to chondrocyte reference cluster across merged undifferentiated and chondrogenic USC object clusters. (Cluster 3 pearson R = 0.164 ± 0.092, n = 2753).\u003c/p\u003e\n\u003cp\u003e(B) UMAP of merged USC object with slingshot pseudotime gradient and chondrogenic lineage curves.\u003c/p\u003e\n\u003cp\u003e(C) UMAP of the merged USC object with top marker annotation, chondrogenic lineage curves and summary of biological process GO-terms.\u003c/p\u003e\n\u003cp\u003e(D) UMAP of the merged USC object with cluster number annotation\u003c/p\u003e\n\u003cp\u003e(E) Heatmap, depicting the z-score normalized expression of SOX- transcription factors among clusters.\u003c/p\u003e\n\u003cp\u003e(F) Dotplot depicting clusters, enriched for chondrocyte related GO-terms. G/H: \u003cem\u003eSOX9\u003c/em\u003e(RQ undifferentiated = 0.017 ± 0.001, differentiated = 1.00 ± 0.23, n = 3, p \u0026lt; 0.01, Welch´s t-test) and \u003cem\u003eTIMP3\u003c/em\u003e(RQ undifferentiated = 0.011 ± 0.013, differentiated = 1.00 ± 0.91, n = 3, p \u0026lt; 0.05, Welch´s t-test) expression levels in undifferentiated and chondrogenic cells as measured by RT-qPCR in chondrogenic cells and undifferentiated controls. Bar with undetermined CT-values or exceeding 40 cycles, was highlighted (=ND).\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/c516ab39fde59f632b70e600.png"},{"id":100561198,"identity":"3f2d695b-319d-4898-827f-4ca89f783ce1","added_by":"auto","created_at":"2026-01-19 08:43:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6866088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eXeno-free (HS) expansion enhances clinically relevant chondrogenesis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B): \u003cem\u003eKLF4 \u003c/em\u003e(RQ FBS = 1.00 ± 0.06, RQ HS = 1.80 ± 0.39, n = 3, p \u0026lt; 0.05\u003cem\u003e, \u003c/em\u003eWelch´s t-test),\u003cem\u003e MYC (\u003c/em\u003eRQ FBS = 1.00 ± 0.11, RQ HS = 1.25 ± 0.12, n = 3, p \u0026lt; 0.05\u003cem\u003e, \u003c/em\u003eWelch´s t-test) and\u003cem\u003e OCT4\u003c/em\u003e (RQ: FBS = 1.00 ± 0.17, HS = 0.90 ± 0.12, n = 3, p = 0.44, Welch t-test) expression levels in FBS-USC (FBS) and HS-USC (HS) cells as measured by RT-qPCR\u003c/p\u003e\n\u003cp\u003e(C) Alcian blue quantification of chondrogenic FBS-USC (chFBS) or chondrogenic HS-USC (chHS).\u003c/p\u003e\n\u003cp\u003e(D) Alcian blue staining of chFBS or chHS (OD650/DNA in FBS = 0.01 ± 0.0003, OD650/DNA in HS = 0.006 ± 0.0004, n = 4; p \u0026lt; 0.0001, unpaired t-test).\u003c/p\u003e\n\u003cp\u003e(E, F, G, H) RQ values of \u003cem\u003eTOP2A\u003c/em\u003e (FBS: RQ undifferentiated = 3.28 ± 0.72; RQ differentiated = 0.54 ± 0.08, n = 3, p \u0026lt; 0.001, Welch´s t-test), \u003cem\u003eSOX9\u003c/em\u003e (RQ chFBS = 0.61 ± 0.02; RQ chHS = 1.39 ± 0.07; n = 3; p \u0026lt; 0.0001, Welch´s t-test), \u003cem\u003eTIMP3\u003c/em\u003e (RQ chFBS = 0.19 ± 0.04; RQ chHS = 1.81 ± 0.36; n = 3; p \u0026lt; 0.001, Welch´s t-test) and \u003cem\u003eCDH1\u003c/em\u003e (RQ chFBS = 0.18 ± 0.03; RQ chHS = 1.81 ± 0.25; n = 3; p \u0026lt; 0.001, Welch´s t-test) of uninduced USCs (USC) and chondrogenic (chUSC) cells in pellet culture, comparing FBS or HS culture conditions. Bar with undetermined CT-values or exceeding 40 cycles, was marked (=ND).\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/eb84698df6b6e386b80a0ffa.png"},{"id":101397591,"identity":"92cbec0f-4e35-436b-a00c-a2adc2709e64","added_by":"auto","created_at":"2026-01-29 09:31:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":50753860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/2b96406b-6b1f-495d-9fb8-0b0c4eb05d3e.pdf"},{"id":100561100,"identity":"774d2700-88ed-49d4-87bd-55d3dc19417b","added_by":"auto","created_at":"2026-01-19 08:43:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":397563,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/02a95ca3f0efb231637e5943.pdf"},{"id":100561175,"identity":"7d97393b-a8e4-4606-b489-86dcb569bd94","added_by":"auto","created_at":"2026-01-19 08:43:57","extension":"avi","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5478548,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.avi","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/b08e357e3f3702439a02301e.avi"},{"id":100560825,"identity":"4d1740b2-2538-4fa7-b93b-281b4bd64f5a","added_by":"auto","created_at":"2026-01-19 08:43:51","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2926953,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8230463/v1/b66d5c4db59139d453cee51c.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell atlas of human urine-derived stem cell chondrogenesis enables a non-invasive, xeno-free platform for translational cartilage and skeletal disease research","fulltext":[{"header":"Background","content":"\u003cp\u003eDevelopmental disorders of the skeleton have major implications for human health, but their underlying mechanisms remain only partially understood\u003csup\u003e1\u003c/sup\u003e. They manifest clinically as impaired longitudinal bone growth leading to short stature and affecting 2.3% of the general population\u003csup\u003e2\u003c/sup\u003e. Growth disorders can be attributed to various etiologies, including complex genetic syndromes\u003csup\u003e3-6\u003c/sup\u003e, endocrine defects\u003csup\u003e7-9\u003c/sup\u003e, skeletal dysplasias\u003csup\u003e10,11\u003c/sup\u003e, chronic diseases\u003csup\u003e12\u003c/sup\u003e, and idiopathic short stature\u003csup\u003e13,14\u003c/sup\u003e. At the cellular level, the pathology may originate from any zone of the growth plate (reserve, proliferative, or hypertrophic) or from the metaphysis and epiphysis\u003csup\u003e15,16\u003c/sup\u003e. Disruption of the function of the cells that comprise the cartilage (chondrocytes) and the composition of the extracellular matrix (ECM) have been shown to impair the organization of growth plates, leading to abnormal bone elongation\u003csup\u003e15\u003c/sup\u003e. Known genetic causes include genes encoding growth plate matrix proteins, such as \u003cem\u003eCOL2A1\u003c/em\u003e associated with spondyloepiphyseal dysplasia and hypochondrogenesis\u003csup\u003e17\u003c/sup\u003e, or \u003cem\u003eACAN\u003c/em\u003e resulting in premature growth plate closure\u003csup\u003e18\u003c/sup\u003e. Defects in the key regulators of cartilage cells such as Indian hedgehog, underlying brachydactyly type A1 and acrocapital femoral dysplasia\u003csup\u003e17\u003c/sup\u003e are also well known. Within the aforementioned spectrum, skeletal dysplasias are of particular pertinence, with achondroplasia representing a prevalent form affecting more than 360,000 individuals worldwide\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, research on disorders of cartilage and bone formation is hampered by the limited access to primary human chondrogenic tissue, as it requires invasive procedures with potential harmful side-effects\u003csup\u003e20\u003c/sup\u003e. Consequently, the functional study of skeletal disorders and the development of patient-specific therapies is challenged by a paucity of suitable and broadly accessible cellular models. The majority of \u003cem\u003ein vitro\u003c/em\u003e models used for studying chondrogenesis utilize either induced pluripotent stem cells (iPSCs) or mesenchymal stem cells (MSCs). MSCs, which possess the capacity to differentiate into cartilage, are obtained through an invasive procedure from tissue sources such as adipose tissue, bone marrow, or synovial fluid\u003csup\u003e21,22\u003c/sup\u003e. In contrast, iPSCs offer the benefit of a pluripotent and renewable source. However, these methods are expensive, time-consuming, and laborious, which limits their availability\u003csup\u003e23,24\u003c/sup\u003e. Moreover, their therapeutic value is further constrained by intrinsic tumorigenicity\u003csup\u003e25\u003c/sup\u003e. Consequently, both MSC and iPSC approaches exhibit significant practical limitations when modeling and treating genetic skeletal disorders, particularly in cases requiring repeated sampling, or in patient-specific studies.\u003c/p\u003e\n\u003cp\u003eUrine-derived stem cells (USCs) have emerged as a promising alternative to overcome these challenges. These can be obtained without the need for surgical procedures and easily propagated and mantained\u003csup\u003e26\u003c/sup\u003e. Despite increasing interest in their cartilage regenerative capabilities\u003csup\u003e27-29\u003c/sup\u003e, the application of USCs as a model system for cartilage and skeletal diseases has remained largely unexplored, particularly in the context of developmental skeletal disorders. In addition, the conventional culture of USCs, as initially identified in their original discovery\u003csup\u003e30\u003c/sup\u003e, is complex and reliant on animal-derived products, preventing potential use in cell-therapeutic approaches due to the inherent risks associated with the transfer of animal factors to patients\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe here propose USCs as a platform for investigating human chondrogenesis and modeling of genetic skeletal disorders. Furthermore, a simple, xeno-free culture and chondrogenic differentiation method for USCs is presented, which has the potential to pave the way for personalized, non-surgical medicine and cartilage regeneration.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eUrine stem cell isolation and culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUSCs were isolated and expanded by modifying an existing protocol from Culenova et al\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. Briefly, urine was centrifuged at 300 g, cell pellet was washed with PBS 1% Penicillin Streptomycin (Gibco) and pelleted cells cultured after repeated centrifugation. Culture medium consisted of a 1:1 mixture of DMEM high glucose (Gibco), containing 1 % NEAA (Gibco) and 1 % Penicilin Streptomycin (Gibco), and KSFM (Gibco) with addition of 15 % FBS (Sigma Aldrich or Roth) and 5 ng/mL bFGF (Peprotech) or humankine thermostable bFGF (Sigma). For an animal free workflow, we harvested USCs in the same manner, this time resupending the cellular pellet in either Alpha MEM Eagle medium (PAN Biotech) supplemented 5 ng/mL fibroblast growth factor 2 (FGF2, Sino Biological) with 10% FBS (Sigma-Aldrich or Roth) as the FBS-Control, or in 10 % autologous human serum. All cell lines generated for this study were from the same donor, and no passages greater 5 were used. Culture medium was exchanged three times per week.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProliferation curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo compare proliferation, we seeded FBS- and HS-derived USCs of the same passages in triplicates onto 96 well plates at a density of 1000 cells per well, and counted cells manually with trypan blue exclusion every second day. Doubling time was calculated for cells in the exponential growth phase (day 4-8) as the following:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1768579291.png\" width=\"664\" height=\"100\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrine stem cell differentiation and staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChondrogenic differentiation was induced with the StemPro Chondrogenesis Kit (Gibco). Osteogenic induction was facilitated with an osteogenic medium described by zhang et al \u003csup\u003e33\u003c/sup\u003e. Differentiation lasted for two weeks with media change three times per week. To stain glycosaminoglycans in chondrogenic, and calcium deposits in osteogenic cells, we fixed cells with 4 % paraformaldehyde, washed with PBS, and stained for one hour with alcian blue or alizarin red s staining solution. After a final washing step with PBS images were taken under a VertA1 (Zeiss) microscope. A separate USC line, harvested at a different time point from the same donor, was cultured for 2 days in Alpha MEM Eagle medium (PAN Biotech) supplemented with 10% fetal bovine serum (FBS, Sigma-Aldrich or Roth) and 5 ng/mL fibroblast growth factor 2 (FGF2, Sino Biological), and served as the non-induced control.\u003c/p\u003e\n\u003cp\u003eFor a comparative pellet culture between HS- or FBS-USCs, we centrifuged 250000 USCs at 300 g for 10 min in 15 ml falcon tubes (Corning) and incubated with in house made chondrogenic media. The media recipe was: DMEM HG with pyruvate (Gibco), human recombinant insulin 6.25 \u0026mu;g/mL (Merck), human holo-transferrin 6.25 \u0026mu;g/mL (Sigma Aldrich), sodium selenite 6.7 ng/mL (Sigma Aldrich), 10 % Serum (autologous human serum or FBS, Roth) 10 ng/mL recombinant human TGF-Beta 1 (Peprotech), 100 ng/mL recombinant human IGF1, 100 nm Dexmethason (Sigma Aldrich), 50 \u0026micro;g/mL ascorbate-6-phosphate (Sigma Aldrich).\u003c/p\u003e\n\u003cp\u003eAlcian blue quantification was performed by lysing cells for 2 hours at room temperature with 6M Guanidine hydrochloride (Sigma). Absorbance was measured at 650 nm. For normalization to DNA content, DNA was extracted from control wells with the DNeasy Blood \u0026amp; Tissue Kit (Qiagen).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA sequencing and data Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUSCs and chondrogenic USCs were subjected to single-cell RNA sequencing (scRNA-seq) to characterize transcriptomic profiles in undifferentiated and chondrogenically differentiated states. Cell fixation, barcoding, cDNA amplification and Libraries were prepared using Parse Biosciences Parse Evercode WT V3 Workflow and sequenced on Illumina NovaSeq X Plus. FASTQ files were processed using Trailmaker\u003csup\u003eTM\u003c/sup\u003e pipeline module (https://app.trailmaker.parsebiosciences.com/, pipeline v1.5.0, Parse Biosciences). Raw count matrices and associated metadata were processed in Seurat (version 5.3.0)\u003csup\u003e34\u003c/sup\u003e in R. Cells with \u0026lt;1st or \u0026gt;99th percentile of gene counts or Unique Molacular Identifier (UMI) counts, or \u0026gt;95th percentile mitochondrial reads, were excluded. Data were normalized (LogNormalize), variable features (n = 2,000) identified (vst), and counts scaled while regressing out nCount_RNA and percent.mt. PCA (dims 1\u0026ndash;20) was followed by UMAP visualization and Louvain clustering (resolution = 0.5). Cluster markers were identified via Wilcoxon testing (min.pct = 0.25). For visualization of data we used Seurat\u0026acute;s DotPlot, VlnPlot or DimPlots and the R packages ggplot2 (version 3.5.2)\u003csup\u003e35\u003c/sup\u003e, ggrepel (version 0.9.6)\u003csup\u003e36\u003c/sup\u003e, viridis (version 0.6.5)\u003csup\u003e37\u003c/sup\u003e and RColorBrewer (version 1.1.3)\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue origin and cell-type annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarker lists for human urinary and renal tissues were curated from CellMarker 2.0\u003csup\u003e39\u003c/sup\u003e, filtered for uniqueness across tissues, and scored per cell using Seurat\u0026rsquo;s AddModuleScore. Complementary AUC enrichment was computed with AUCell (version 1.30.1)\u003csup\u003e40\u003c/sup\u003e on the normalized data. To assign putative cell identities, we leveraged a well-annotated human kidney atlas (Tisch et al.\u003csup\u003e41\u003c/sup\u003e) as a reference. Briefly, the atlas and our USC Seurat object were first normalized and dimensionally reduced in parallel. We then computed integration anchors between the reference and query using FindTransferAnchors. Cell-type labels stored in the reference metadata were projected onto the query via TransferData. Finally, we summarized mapping confidence by computing the median prediction.score.max within each Seurat cluster to guide downstream cluster ordering and interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStemness inference and transcription\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003efactor activity mapping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCytoTRACE2 (version 1.1.0)\u003csup\u003e42\u003c/sup\u003e was run on the top 10,000 variable genes (counts slot) to assign each cell a continuous stemness score. Cells were binned into five quantile‐defined stemness groups. We inferred per‐cell transcription‐factor activities using VIPER (version 1.42.0)\u003csup\u003e43\u003c/sup\u003e with high‐confidence DoRothEA (version 1.20.0)\u003csup\u003e44\u003c/sup\u003e A/B regulons on the Seurat‐normalized data slot. TF activities were merged into the metadata alongside CytoTRACE2 scores and UMAP coordinates. For each stemness group (the five quintile bins), we computed the mean activity of every TF and selected the top three TFs uniquely highest in each group. To visualize regulatory dynamics, we computed Spearman correlations between TF activities and CytoTRACE2 scores across all cells, identifying the top 20 TFs most strongly associated with stemness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell cycle analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell cycle phase assignment was performed using the built-in Seurat gene sets for S phase and G2/M phase genes. The Seurat function CellCycleScoring was applied to the processed data, utilizing the predefined sets of cell cycle genes for the S and G2/M phases. Each cell was classified into one of the four phases: G1, S, G2/M, or unassigned. Phase information was then merged with cluster identities, and the proportion of cells in each cell cycle phase per cluster was calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBulk RNA-sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was extracted from USC samples with RNeasy Mini Kit (Qiagen)with DNAse digestion, according to manufacturer. A cDNA library was generated for each sample using the Illumina stranded mRNA kit. Paired-end sequencing of the libraries with a fragment length of 159bp was conducted on an Illumina NovaSeq6000 platform. Raw data was then converted into reads and demultiplexed using Illumina Dragen Software (v. 3.8.4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnwanted RNA was filtered from the reads using bwa mem (version 0.7.17-r1188, arXiv:1303.3997) in combination with samtools (version 1.17\u003csup\u003e45\u003c/sup\u003e) and converted back into fastq format using the SamToFastq tool from GATK (version 4.2.1.0\u003csup\u003e46\u003c/sup\u003e). Reads from each sequencing lane were aligned individually to the hg38 reference genome with Ensembl (release 110\u003csup\u003e47\u003c/sup\u003e) gene annotations using STAR (version 2.7.10a\u003csup\u003e48\u003c/sup\u003e). For each sample, resulting alignments were combined using the MergeSamFiles command from Picard (version 2.25.4, http://broadinstitute.github.io/picard/). Gene quantification in form of a count matrix was generated using featureCounts (version 2.0.1\u003csup\u003e49\u003c/sup\u003e) and Ensembl gene annotations corresponding to those of the alignment reference. Alignment and quantification levels were used for quality assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw gene-level count data were obtained from bulk RNA-sequencing of three undifferentiated and three differentiated (osteogenic or chondrogenic) USC samples. Differential gene expression (DGE) analysis was conducted using the DESeq2 package (version 1.48.1)\u003csup\u003e50\u003c/sup\u003e in R (version 4.5). For each comparison, gene-level counts were used to construct a DESeqDataSet object, with group labels assigned as the experimental condition. The DESeq2 pipeline was run with default parameters using DESeq(), and differential expression results were extracted using the results() function, specifying contrasts such that log2 fold changes represented upregulation in the differentiated condition relative to WT. Genes with an adjusted p-value (Benjamini-Hochberg method) less than 0.05 were considered statistically significant. Gene annotations were retrieved by mapping Ensembl IDs to HGNC symbols using the org.Hs.eg.db package (version 3.21.0)\u003csup\u003e51\u003c/sup\u003e. Differential expression results were visualized using volcano plots created with the ggplot2\u003csup\u003e35\u003c/sup\u003e and ggrepel\u003csup\u003e36\u003c/sup\u003e packages. Sample metadata was generated to define the experimental condition associated with each sample (WT, chWT, oWT), and this metadata was used to create a DESeqDataSet object using the DESeq2 package. The dataset was then log-transformed using the regularized log (rlog) transformation to stabilize variance across the dynamic range of expression levels. To improve PCA interpretability and reduce noise, features (genes) with negligible or zero variance were removed. Columns (genes) with variance less than 1e-6 or equal to zero were filtered out. Rows (samples) with zero variance across retained genes were also removed. PCA was performed on the transposed, filtered, and normalized gene expression matrix using the base R prcomp() function with centering and scaling enabled. The first three principal components were extracted for visualization. The resulting PCA scores were visualized using the plotly package\u003csup\u003e52\u003c/sup\u003e to generate an interactive 3D scatter plot. The final plot was exported as an HTML widget using the htmlwidgets package\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse transcriptase quantitative PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA was extracted from USC samples with RNeasy Mini Kit (Qiagen) with DNAse digestion, according to manufacturer. Alternatively, we used NucleoSpin RNA Plus (Machery Nagel) with gRNA removal column. We made sure to use samples prepared with the same procdures for comperative anlysis. For cDNA Synthesis LunaScript RT SuperMix Kit (New Englang Biolabs) was used. RT-qPCR was performed with TaqMan Master-Mix and predesigned Assays (Applied Biosystems) on the Quantstudio 12K Flex Platform (Applied Biosystems). \u003cem\u003eRPLP0\u003c/em\u003e served as housekeeping gene. Values were calculated with the delta delta CT (ddCT) method and four technical replicates. Statistics (unpaired welch\u0026acute;s t-test of ddCT values) and visualization were performed in R studio and Graphpad Prism (version 10). We always used three biological replicates per condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3D-culture and immunofluorescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e20000 USCs were seeded in each well of a 96 well Nunclon Sphera ultra low attachment cell culture plate (ThermoFisher Scientific) and centrifuged for 10 minutes at 300 g to enable scaffold free cellular self-aggregation. Cells were cultured for 3 weeks with StemPro Chondrogenesis Kit (Gibco). As an undifferentiated control we used USCs from the same donor (different batch), which were cultured 2 days in Alpha MEM Eagle (PAN Biotech) with 10 % FBS (Sigma Aldrich or Roth) and 5 ng/mL FGF2 (SinoBiological).\u003c/p\u003e\n\u003cp\u003eFor immunofluorescent staining, spheres were fixed for one hour with 4 % paraformaldehyde (PFA) at room temperature. In the following, the spheroids were permeabilized with Triton X-100 for 30 minutes and then blocked with 1 % bovine serum albumin (Sigma-Aldrich) in PBS for 45 minutes. For primary antibody incubation anti-aggrecan (ab3778, abcam) was \u0026nbsp; used in a concentration of 1:500 for one hour, followed by one hour of secondary anti-mouse antibody (AlexaFluor 488, Invitrogen) in a 1:500 dilution, with DAPI (1:1000, thermo fisher scientific). After washing with PBS spheroids were mounted with aqua polymount (Polysciences) on concave slides (Paul Marienfeld). Pictures were taken with the Axio Imager 2 with Apotome (Zeiss) and 25 Z-stacks. We used the same exposure times for differentiated and undifferentiated spheroids (DAPI: 10 ms, Aggrecan: 35 ms). The same image enhancements for each condition, including normalization to DAPI, were performed with FIJI\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMerging undifferentiated and chondrogenic USC datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUndifferentiated and chondrogenic USC Seurat objects were annotated with a stage metadata field (\u0026ldquo;undiff\u0026rdquo; vs. \u0026ldquo;chondrogenic\u0026rdquo;). The two objects were merged and reprocessed end-to-end (NormalizeData, FindVariableFeatures, ScaleData, RunPCA, RunUMAP, FindNeighbors, FindClusters) to generate a unified embedding for downstream trajectory and mapping analyses. Results comparing chondrogenic and undifferentiated samples were obtained from DESeq2 analysis (adjusted p-value \u0026lt; 0.01, log2 fold change \u0026gt; 1). For proving concordance with bulk RNA-Sequencing, we extracted significantly upregulated genes and calculated a gene module score in the merged Seurat object. The AddModuleScore function was applied to quantify the expression of this gene set across cells. Resulting module scores were visualized on a UMAP embedding using the FeaturePlot function (Additional file 1: Figure S2D, E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of healthy cartilage reference atlas and label transfer onto merged USC dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assembled a multi‐sample single‐cell reference from publicly available human cartilage data\u003csup\u003e55\u003c/sup\u003e. For each of the three selected samples, raw count matrices, feature, and barcode files were imported into Seurat. Cells with \u0026lt; 200 genes, \u0026lt; 3 cells per gene, or \u0026gt; 10% mitochondrial reads were excluded. Each sample was SCTransformed (regressing out percent.mt), then merged into a single Seurat object. We first applied the same QC filters as for our USC objects to the published cartilage reference to generate a high-quality \u0026ldquo;healthy cartilage\u0026rdquo; atlas. Cells annotated as normal cartilage were subsetted, and all assay layers were collapsed into a single RNA assay. This object was then log-normalized (NormalizeData), variable features were identified (FindVariableFeatures), data were scaled (ScaleData), and PCA (dims 1\u0026ndash;30), neighbor-graph construction, clustering, and UMAP were performed. The merged undifferentiated\u0026thinsp;+\u0026thinsp;chondrogenic USC object underwent the same SCT-assay preprocessing. We then selected the top 3,000 integration features across both objects and computed label-transfer anchors with FindTransferAnchors (normalization.method = \u0026quot;SCT\u0026quot;). Finally, reference cluster identities were projected onto the USC dataset via TransferData, yielding per-cell predicted cluster labels and confidence scores, which were appended to the USC metadata (Additional file 1: Figure S2F-H).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping and correlation of USCs to chondrogenic reference atlas and pseudotime inference\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe loaded the processed human cartilage reference and the mapped USC dataset into Seurat (SCTransform normalization). To identify \u0026ldquo;cluster 4\u0026ndash;like\u0026rdquo; USCs, we labeled any cell whose transferred reference cluster equaled \u0026ldquo;4\u0026rdquo; and whose PredictionScore \u0026gt; 0.7 (Additional file 1: Figure S2I). Next, we computed the mean SCT embedding for reference cluster 4 and, for each USC cell, calculated the Pearson correlation between its SCT profile and the cluster 4 embedding. These correlation coefficients were then overlaid on the merged UMAP embedding to show how closely individual USCs resemble cluster 4 in principal component space (Additional file 1: Figure S2J, K). For pseudotime analysis, we ran Slingshot (version 2.16.0)\u003csup\u003e56\u003c/sup\u003e on the UMAP data, using the mitotic (\u003cem\u003eTOP2A\u003c/em\u003e⁺) cluster as the root. Slingshot automatically detected terminal states. We computed pseudotime values per cell, appended them to the USC metadata, and projected them onto the UMAP. Finally, we annotated clusters based on their top marker genes (FindAllMarkers) and performed Gene Ontology biological process enrichment (clusterProfiler, version 4.16.0)\u003csup\u003e57\u003c/sup\u003e. We mapped DE genes to Entrez IDs, ran enrichGO with Benjamini\u0026ndash;Hochberg correction, and selected the top ten GO\u0026ndash;BP terms per cluster. These process annotations, and their positions on the UMAP, were summarized and visualized using cluster centroids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChondrogenic cluster annotation and functional characterization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify clusters with chondrogenic potential, we integrated transcriptomic similarity to a reference chondrocyte population, GO enrichment, and expression of canonical marker genes. Average \u0026ldquo;cluster 4\u0026ndash;like\u0026rdquo; scores (Pearson correlation to reference cluster 4) were computed per cluster and visualized with VlnPlot. The Z-score normalized, scaled average expression of chondrogenic markers (\u003cem\u003eSOX5\u003c/em\u003e, \u003cem\u003eSOX6\u003c/em\u003e, \u003cem\u003eSOX9\u003c/em\u003e) were calculated and depicted in a heatmap. GO biological process enrichment was filtered to include only terms related to chondrocyte differentiation (GO:0032330, GO:0002062), and enrichment values were visualized with ggplot2 across clusters.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eUSCs are parietal epithelial cells derived from \u003cem\u003eMYC\u003c/em\u003e- and \u003cem\u003eE2F4\u003c/em\u003e- driven stem cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed scRNA-Seq to identify and characterize USCs. After standard Seurat\u003csup\u003e34\u003c/sup\u003e QC procedures and clustering of 19,024 cells, nine transcriptionally distinct clusters were identified (UMAP, Figure 1A, B, Additional file 1: Figure S1A\u0026ndash;E). We confirmed the kidney origin of USCs by comparing with established urinary and renal gene‑sets (CellMarker 2.0\u003csup\u003e39\u003c/sup\u003e) (Figure 1C). Anchor-based data transfer from a published kidney atlas\u003csup\u003e41\u003c/sup\u003e revealed a parietal epithelial cell (PEC) identity of USCs. The expression of PEC-specific markers was observed to be highest in cluster 4, which also demonstrated the highest PEC‐marker expression and mapping confidence (Figure 1C\u0026ndash;F).\u003c/p\u003e\n\u003cp\u003eHowever, given that only two clusters showed a PEC marker gene expression signature, and USCs were previously reported to have multipotent differentiation capabilities\u003csup\u003e58\u003c/sup\u003e, we investigated the drivers of stemness in these cells. The application of CytoTRACE2\u003csup\u003e42\u003c/sup\u003e-based stemness scoring revealed a negative correlation between PEC identity and high potency levels, particularly in the \u003cem\u003eTOP2A\u003c/em\u003e⁺\u0026nbsp;cluster in G₂/M phase. Analysis of transcription factor activity using VIPER, further identified \u003cem\u003eE2F4\u003c/em\u003e and \u003cem\u003eMYC\u003c/em\u003e as master regulators of the stem-like state, whereas \u003cem\u003eKLF4\u003c/em\u003e activity marked quiescent PEC-like clusters (Figure 1G\u0026ndash;I, Additional file 1: Figure S2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUSCs show osteogenic and chondrogenic differentiation potential\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to develop cellular models for growth disorders, we analyzed and confirmed the differentiation potential of USCs towards osteogenic and chondrogenic differentiation. CytoTRACE2 potency UMAP revealed that a considerable proportion of cells showing a multipotency expression profile (Figure 2A), suggestive of the capacity to generate multiple cell types. Thus, USCs were cultured in differentiation media for 2 weeks, followed by staining of osteogenic-induced cells positive for calcium deposits for their osteogenic potential. Chondrogenic cultures were positive for glycosaminoglycans (GAG) and formed chondrogenic aggregates, resembling normal chondrogenic differentiation\u003csup\u003e59\u003c/sup\u003e (Figure 2B).\u003c/p\u003e\n\u003cp\u003eSubsequently, we sought to ascertain whether these cells also express bona fide markers for osteoblast and chondrocytes. Bulk RNA-Seq and reverse transcription quantitative polymerase chain reaction (RT-qPCR) revealed expressed gene patterns of chondrocyte and osteoblast lineage specification (Figure 2C-F, Additional file 1: Figure S2C). Chondrogenic cultures exhibited a more pronounced upregulation of lineage-specific genes (Figure 2D) in comparison to osteogenic cultures (Figure 2C), (osteogenic: 2/4, chondrogenic 4/4 canonical genes), while both conditions increased expression of genes associated with terminal differentiation, as confirmed by qPCR (Figure 2E, F). In order to confirm these results from monolayer cultures in 3D, we generated 3-week chondrospheres through cell-aggregation in low attachment plates. After differentiation, we detected strong aggrecan immunofluorescence (Figure 2G, additional file 2) as a marker of articular cartilage, confirming that USCs form bona fide cartilage matrix in 3D culture. Taken together, our data demonstrates that USCs can differentiate along both osteogenic and especially chondrogenic pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChondrogenic differentiation of USCs at single cell resolution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo unravel how urine-derived stem cells (USCs) acquire a chondrogenic identity at the cellular level, we analyzed single-cell RNA sequencing data from undifferentiated and chondrogenically differentiated monolayer cultures (Figure 3). This approach allowed us to trace individual transcriptional trajectories and determine whether USC differentiation recapitulates key steps of endochondral ossification, the natural process driving cartilage formation during skeletal development. We first integrated 10,325 cells from the chondrogenic stage with a healthy cartilage reference atlas (Additional file 1: Figure S1F\u0026ndash;J). This enabled us to compare USC-derived cells to genuine chondrocytes and detect subpopulations that resemble the cartilage lineage (Figure 3A). This finding indicates that USCs undergo a transcriptional maturation process, mirroring early-to-late chondrogenic commitment.\u003c/p\u003e\n\u003cp\u003eTo better understand how USCs transition from an undifferentiated to a chondrogenic state, we next analyzed how gene expression evolves along the trajectory. Pseudo time analysis using Slingshot revealed distinct differentiation trajectories emerging from proliferative \u003cem\u003eTOP2A\u003c/em\u003e⁺\u0026nbsp;stem-like cells and progressing towards chondrocyte-like end states (Figure 3B). This allowed us to pinpoint which genes become activated or silenced as cells progress toward a chondrocyte-like phenotype. We therefore looked for genes, differentially expressed in individual clusters along pseudo time. In the middle of our pseudo time, a subpopulation upregulated the expression of \u003cem\u003eALDH1A2\u003c/em\u003e, a gene involved in retinoic acid signaling. This means that retinoic acid is involved in chondrogenic modulation of USCs, highlighting the importance of vitamin metabolites in the differentiation process. At the end of the trajectory, we identified two distinct group of cells characterized by high expression of \u003cem\u003eTIMP3\u003c/em\u003e, or \u003cem\u003eCDH1\u0026nbsp;\u003c/em\u003e(Figure 3C). \u003cem\u003eTIMP3\u0026nbsp;\u003c/em\u003eis\u003cem\u003e\u0026nbsp;\u003c/em\u003eassociated with extracellular matrix organization, whereas \u003cem\u003eCDH1\u0026nbsp;\u003c/em\u003efacilitates\u003cem\u003e\u0026nbsp;\u003c/em\u003ecell adhesion. Both of these features indicate that the cells are acquiring structural and regulatory functions typical of mature cartilage tissue, underscoring that USCs differentiation process recapitulates bona fide chondrogenesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further strengthen these findings, we performed a Gene Ontology (GO) enrichment analysis and summarized the top 10 terms per cluster (Figure 3C). Late-stage clusters with chondrocyte-like features (clusters 2, 3, 7) showed enrichment for processes related to ECM remodeling, cytoskeletal organization, and metabolic adaptation, including autophagy and energy metabolism pathways. These results again highlight the upregulation of \u003cem\u003eTIMP3\u003c/em\u003e, together with \u003cem\u003eTAGLN\u003c/em\u003e, as key markers of a matrix-producing, contractile phenotype emerging during chondrogenic maturation.\u003c/p\u003e\n\u003cp\u003eTo assess if \u003cem\u003ebona fide\u003c/em\u003e chondrogenic biological processes are active in these cells, we specifically looked for chondrocyte specific GO-terms. Here, the \u003cem\u003eALDH1A2\u003c/em\u003e⁺\u0026nbsp;intermediate was enriched for the GO-term \u0026ldquo;chondrocyte differentiation\u0026rdquo;. Again, suggesting a crucial function of retinoic acid related chondrogenesis modulation in USCs. Cluster 3, which contains most of aforementioned \u003cem\u003eTIMP3\u003c/em\u003e⁺\u0026nbsp;cells, showed even stronger enrichment for the term \u0026ldquo;chondrocyte differentiation\u0026rdquo;, in addition to \u0026ldquo;regulation of chondrocyte differentiation\u0026rdquo; (Figure 3F), supporting resemblance of chondrocyte transcriptional programs in chondrogenic USCs. Importantly, when we compared each cluster to a reference atlas of native chondrocytes, cluster 3 exhibited the highest transcriptional similarity. This confirms that these late-stage \u003cem\u003eTIMP3\u003c/em\u003e⁺\u0026nbsp;cells represent the terminally differentiated end point of the USC chondrogenic trajectory, mimicking transcriptional gene expression programs active in chondrocytes.\u003c/p\u003e\n\u003cp\u003eWe next examined the expression of the chondrogenic transcription factors \u003cem\u003eSOX9\u003c/em\u003e, \u003cem\u003eSOX5\u003c/em\u003e, and \u003cem\u003eSOX6\u003c/em\u003e, known as the core regulatory trio of cartilage formation, to investigate the underlying transcription factor activity driving chondrogenesis in USCs. These genes were predominantly expressed in late clusters along pseudo time (Figure 3B, D, E), confirming that USC-derived chondrocyte-like cells activate the canonical transcriptional network that drives cartilage lineage commitment. This transcriptional alignment with native chondrocytes supports the authenticity of the differentiation process.\u003c/p\u003e\n\u003cp\u003eTo validate the single-cell findings at the bulk level, we quantified by RT-qPCR the expression of genes that resemble USCs bona fide chondrogenesis best. We chose \u003cem\u003eTIMP3\u003c/em\u003e, as its expression was associated with chondrocyte related GO-terms (cluster 3, Figure 3F) and chondrocyte similarity (Figure 3A), and \u003cem\u003eSOX9\u003c/em\u003e, as its expression increased in concordance with pseudo time (Figure 3B, D, E). Both \u003cem\u003eSOX9\u003c/em\u003e and \u003cem\u003eTIMP3\u003c/em\u003e were strongly upregulated after differentiation (Figure 3G, H), confirming the transcriptional activation observed in cluster 3. \u003cem\u003eTIMP3\u003c/em\u003e, in particular, is an extracellular matrix regulator crucial for cartilage maintenance, further highlighting the functional maturation of these cells, driven by the chondrogenic master transcription factor \u003cem\u003eSOX9\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTogether, these results show that USC differentiation proceeds through defined transcriptional stages, from proliferative progenitors to mature chondrocyte-like cells. This is accompanied by activation of canonical cartilage transcription factors and functional pathways. The emergence of a \u003cem\u003eTIMP3\u003c/em\u003e⁺\u0026nbsp;cluster with strong chondrocyte similarity and chondrogenesis related gene expression profile demonstrates that USCs can effectively recapitulate the molecular program of endochondral chondrogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential for clinical application of USCs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the clinical suitability of urine-derived stem cells (USCs), we established a xeno-free culture protocol by replacing fetal bovine serum (FBS) with autologous human serum (HS) during isolation and expansion (Supplementary Figure 3A, B). This modification eliminates animal-derived components, a critical requirement for translational use in regenerative medicine.\u003c/p\u003e\n\u003cp\u003eWhen we compared transcriptional markers between FBS- and HS-expanded USCs, HS cultures showed increased expression of \u003cem\u003eMYC\u0026nbsp;\u003c/em\u003e(Figure 4A, B). Elevated \u003cem\u003eMYC\u003c/em\u003e is associated with proliferative, stem-like activity, whereas \u003cem\u003eKLF4\u003c/em\u003e supports maintenance of a parietal epithelial cell (PEC)-like identity (as shown in Figure 1D-I). Together, these findings indicate that HS expansion sustains the native USC phenotype, retaining both stemness and renal lineage features. Maintaining this balanced phenotype is desirable because it preserves the cells\u0026rsquo; proliferative capacity and differentiation competence, both essential for efficient chondrogenic induction and clinical scalability.\u003c/p\u003e\n\u003cp\u003eTo ensure that HS expansion does not trigger reprogramming toward an undesired pluripotent state, we measured \u003cem\u003eOCT4\u003c/em\u003e, a canonical pluripotency transcription factor that can become aberrantly reactivated in dedifferentiating renal cells. \u003cem\u003eOCT4\u003c/em\u003e expression remained unchanged (Figure 4C) between FBS- and HS-expanded USCs. Additionally, proliferation appeared to be slightly reduced (Additional file 1: Figure S3C), supporting that cells were not undergoing malignant transformation. Taken together, these data show that HS conditions preserve differentiation competence without inducing pluripotency.\u003c/p\u003e\n\u003cp\u003eWe next evaluated whether xeno-free expansion affects the chondrogenic potential of USCs by inducing differentiation in monolayer culture, to observe differences in cellular morphology in both culture conditions. HS-expanded USCs (chHS) formed dense multicellular condensates, characteristic of mesenchymal condensation during early cartilage development, whereas FBS-expanded controls (chFBS) exhibited weaker condensation (Figure 4D). We then quantified GAG content to observe differences in matrix deposition. FBS-expanded cells accumulated more GAGs (Figure 4E), suggesting that FBS drives matrix deposition, whereas HS supports the initial cellular organization required for lineage commitment.\u003c/p\u003e\n\u003cp\u003eBecause three-dimensional culture more closely mimics in vivo cartilage formation, we performed pellet differentiation of HS- and FBS-expanded cells. Under HS conditions, expression of \u003cem\u003eTOP2A\u003c/em\u003e, the gene upregulated in proliferative progenitors, remained stable following induction, whereas it was markedly reduced under FBS (Figure 4F). This indicates that HS preserves a pool of proliferative progenitors during early chondrogenesis, while FBS drives premature cell-cycle exit.\u003c/p\u003e\n\u003cp\u003eWe then moved on validating the expression of genes appearing to reflect USCs chondrogenic program in our single cell atlas (Figure 3), to see if xeno-free cultivation improves functional chondrocyte maturation in USCs. As differentiation progressed, HS pellets exhibited higher expression of the master chondrogenic regulator \u003cem\u003eSOX9\u003c/em\u003e (Figure 4G) and markedly increased levels of terminal associated genes \u003cem\u003eTIMP3\u003c/em\u003e and \u003cem\u003eCDH1\u003c/em\u003e (Figure 4H, I). The upregulated expression of \u003cem\u003eTIMP3\u003c/em\u003e corresponds to the late pseudo time chondrocyte-similar (Figure 3A) and chondrocyte related GO-term related state (Figure 3F) identified in our single-cell analysis, confirming that HS expansion promotes robust and complete chondrogenic differentiation. The increased \u003cem\u003eCDH1\u0026nbsp;\u003c/em\u003eexpression, a gene involved in cellular aggregation, in 3D is in line with the observed chondrogenic aggregates in 2D (Figure 4D), delivering orthogonal evidence that FBS diminishes chondrogenic outcomes of USC differentiation.\u003c/p\u003e\n\u003cp\u003eTogether, these results demonstrate that autologous human serum supports xeno-free USC expansion while maintaining a proliferative, \u003cem\u003eMYC\u003c/em\u003e-positive progenitor pool. During differentiation, HS-expanded cells efficiently activate \u003cem\u003eSOX9\u003c/em\u003e-driven chondrogenic programs and progress toward \u003cem\u003eTIMP3\u003c/em\u003e⁺/\u003cem\u003eCDH1\u003c/em\u003e⁺ terminal states, indicating faithful recapitulation of cartilage maturation. Thus, making xeno-free cultured USCs an ideal starting material for chondrocyte regeneration approaches.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur goal was to establish a non-invasive, physiologically relevant, and translational model of human cartilage development by leveraging urine-derived stem cells (USCs). By integrating high-resolution single-cell transcriptomic profiling, functional lineage differentiation assays, and a xeno-free expansion protocol, we aimed to delineate the cellular hierarchy and transcriptional programs underlying USC chondrogenesis, evaluate their potential to recapitulate native cartilage formation, and develop a clinically compatible platform for modeling genetic skeletal disorders and advancing personalized regenerative therapies.\u003c/p\u003e\n\u003cp\u003eTo establish USCs as a well-defined biological system, it was necessary to resolve their cellular heterogeneity and origin. We began by deconstructing the cellular composition of bulk urine-derived stem cells to define cellular diversity. Single-cell RNA sequencing of USCs revealed nine transcriptionally distinct clusters, delineating a structured progenitor hierarchy rather than a uniform population (Figure 1A, B). To definitively confirm their source within the kidney, we mapped our data against a reference atlas, which confirmed their parietal epithelial cell (PEC) origin (Figure 1C-F), reinforcing the concept that USCs retain a tissue-specific transcriptional memory after ex vivo expansion, a process in line with behavior of adult stem cells\u003csup\u003e60\u003c/sup\u003e. This tissue-specific memory is beneficial as it provides a stable molecular identity that may preserve lineage fidelity during differentiation, enhancing the reproducibility of experiments and increasing their relevance for modeling kidney- or cartilage-related pathologies. Furthermore, defining both the heterogeneity and origin of USCs establishes a foundation for rational selection and manipulation of specific subpopulations to optimize chondrogenic or osteogenic differentiation for translational applications.\u003c/p\u003e\n\u003cp\u003eNext, we sought to understand the regulatory drivers behind the proliferative and quiescent compartments we observed. CytoTRACE and VIPER analyses identified \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eE2F4\u003c/em\u003e as central regulators of the proliferative compartment (Figure 1G-I). While MYC is classically associated with oncogenesis\u003csup\u003e61\u003c/sup\u003e, here it appears to drive physiologically controlled cell-cycle re-entry, supporting expansion without evidence of transformation\u003csup\u003e62\u003c/sup\u003e. Interestingly, although \u003cem\u003eE2F4\u003c/em\u003e is often viewed as a repressor in classical cell-cycle control\u003csup\u003e63\u003c/sup\u003e, in certain developmental or stem-cell contexts it can act as a transcriptional activator. For example, in mouse embryonic stem cells, \u003cem\u003eE2F4\u003c/em\u003e promotes expansion by directly activating cell cycle genes independently of the RB family, and E2F4 knockout slows S-phase entry and reduces viability\u003csup\u003e64\u003c/sup\u003e. This is particularly significant because \u003cem\u003eE2F4\u003c/em\u003e has not previously been implicated in maintaining controlled proliferative activity in adult, non-pluripotent stem cell populations and its activation in USCs highlights a previously unrecognized mechanism that supports stable expansion. \u003cem\u003eKLF4\u003c/em\u003e activity marked the quiescent PEC-like clusters (Figure 1E-I), reflecting a retention of renal lineage traits, suggesting that these cells maintain a baseline functional identity even under proliferative stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis balance between proliferative and quiescent states is beneficial because it preserves both the stem-like potential and tissue-specific memory of USCs, enabling predictable differentiation into osteogenic and chondrogenic lineages while maintaining a physiologically relevant transcriptional profile. By identifying the molecular drivers of these distinct compartments, we gain insight into how USCs can be selectively manipulated to enhance expansion, lineage commitment, and ultimately, translational utility. Collectively, these data define USCs as a hierarchically organized progenitor population with proliferative and quiescent, lineage-primed cells. This architecture provides the mechanistic transcription factor activity driving their observed multipotency and establishes a foundation for subsequent chondrogenic studies.\u003c/p\u003e\n\u003cp\u003eHaving defined their progenitor identity, we moved beyond transcriptional profiles and functionally validate their capacity to generate the skeletal lineages relevant to our model, osteogenic and chondrogenic fates. Complementary bulk RNA-Seq and RT-qPCR analyses showed upregulation of canonical osteogenic and chondrogenic markers (Figure 2C-F), demonstrating that USCs can faithfully execute mesodermal lineage-specific programs. This functional validation is important because transcriptional similarity alone does not guarantee actual differentiation potential. Confirming that USCs can produce mature lineage-specific phenotypes strengthens their relevance as a cellular model. Moreover, this predisposition aligns with their renal epithelial origin, which may prime them for mesenchymal-like transitions under appropriate cues. Parietal epithelial cells, the presumed source of USCs, have been shown to undergo epithelial-to-mesenchymal transition and adopt progenitor-like phenotypes in vitro and in vivo\u003csup\u003e65,66\u003c/sup\u003e, suggesting that these cells harbor latent plasticity that may facilitate skeletal lineage reprogramming in our differentiation assays. This context-dependent flexibility likely underlies the ability of USCs to efficiently adopt osteogenic and chondrogenic fates, bridging epithelial and mesenchymal lineages. This indicates that USCs are not only transcriptionally competent but also functionally poised for skeletal differentiation, increasing their reliability for modeling cartilage and bone development, studying disease mechanisms, and ultimately serving as a scalable and patient-specific platform for regenerative applications.\u003c/p\u003e\n\u003cp\u003eAs our results showed a marked bias toward chondrogenesis, we employed complementary 2D and 3D culture systems because they provide different but essential insights. Monolayer cultures allowed analysis of gene expression and cellular morphology during differentiation, while 3D spheroid cultures recapitulated key morphogenetic events such as mesenchymal condensation and extracellular matrix (ECM) deposition. To confirm that the matrix produced in 3D was authentic cartilage, we performed staining for the cartilage‐specific proteoglycan aggrecan, a definitive marker of articular cartilage extracellular matrix structure and function\u003csup\u003e67\u003c/sup\u003e. The strong aggrecan deposition observed validates that our 3D differentiated tissues replicate key compositional features of native cartilage, thereby supporting their relevance for developmental and regenerative applications (Figure 2G). This identifies USCs as a powerful tool for studying cartilage biology. Their intrinsic preference toward chondrogenesis is particularly attractive, as it indicates that these cells naturally favor to recreate cartilage-specific microenvironments, enabling more reliable modeling of cartilage development, disease processes, and potentially regenerative therapies.\u003c/p\u003e\n\u003cp\u003eWhile bulk assays confirmed differentiation potential, we aimed to unravel the precise sequence of cellular events and identify key transitional states during chondrogenesis at single-cell resolution. We therefore performed pseudo time analysis (Figure 3B), which revealed the chondrogenic trajectory emanating from \u003cem\u003eTOP2A\u003c/em\u003e⁺\u0026nbsp;progenitors, converging through an \u003cem\u003eALDH1A2\u003c/em\u003e⁺\u0026nbsp;intermediate enriched for chondrocyte-specific programs (Figure 3C-F). The presence of this intermediate in undifferentiated populations suggests pre-patterning at the metabolic level via retinoic acid signaling\u003csup\u003e68\u003c/sup\u003e, highlighting the nuanced interplay between cellular state and lineage bias. This finding uncovers early regulatory checkpoints that may prime cells for chondrogenic commitment, providing mechanistic insight into how lineage bias is established. By defining these transitional states, we gain the ability to predict, manipulate, or enhance chondrogenic differentiation, thereby improving the fidelity of USCs as a model for cartilage development and disease, and informing strategies for regenerative therapies.\u003c/p\u003e\n\u003cp\u003eA critical question was whether the endpoint of our chondrogenic differentiation resembled genuine chondrocytes. Terminal differentiation yielded discrete chondrocyte-like subpopulations: a \u003cem\u003eTIMP3\u003c/em\u003e⁺, and a \u003cem\u003eCDH1\u003c/em\u003e⁺\u0026nbsp;cluster (Figure 3A-D). TIMP3, a tissue inhibitor of metalloproteinases abundantly expressed in mature cartilage\u003csup\u003e69\u003c/sup\u003e, plays a crucial role in maintaining extracellular matrix integrity and protecting against proteolytic degradation\u003csup\u003e70\u003c/sup\u003e. Its strong induction therefore signifies the acquisition of a regulatory, matrix-stabilizing phenotype characteristic of functional chondrocytes. In parallel, the emergence of CDH1⁺\u0026nbsp;cells reflects activation of intercellular adhesion mechanisms. E-cadherin (\u003cem\u003eCDH1\u003c/em\u003e) has been shown to enhance chondrogenic differentiation in human mesenchymal stem cell aggregates, acting together with N-cadherin to promote mesenchymal condensation and lineage commitment\u003csup\u003e71\u003c/sup\u003e. This underscores the importance of \u003cem\u003eCDH1\u003c/em\u003e in facilitating mesenchymal condensation, an early and indispensable step of chondrogenic differentiation. Together, the coordinated expression of these structural and regulatory markers indicates that USCs undergo a structured, stepwise maturation process akin to endochondral development. This single-cell resolution map reveals not only the hierarchical differentiation of USCs but also actionable molecular targets, such as \u003cem\u003eTIMP3\u003c/em\u003e-mediated matrix regulation and \u003cem\u003eCDH1\u003c/em\u003e-associated adhesion, which may be leveraged to modulate differentiation or tissue formation.\u003c/p\u003e\n\u003cp\u003eSince a primary advantage of USCs is their non-invasive nature, we engineered a xeno-free protocol to make the entire procedure clinically relevant. We first asked whether xeno-free expansion alters the fundamental identity of USCs. HS-expanded USCs modestly increased \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eKLF4\u003c/em\u003e expression compared with FBS, while \u003cem\u003eOCT4\u003c/em\u003e remained unchanged, confirming safety with respect to pluripotency activation (Figure 4A-C). This coordinated, increased, expression of stemness-associated and renal lineage-associated transcription factors is especially advantageous because it allows USCs to expand efficiently while retaining their original renal identity, minimizing the risk of dedifferentiation or acquisition of unintended phenotypes. This makes HS-expanded USCs a safer and more reliable starting material for translational applications, including patient-specific disease modeling and regenerative therapies.\u003c/p\u003e\n\u003cp\u003eWe then rigorously compared the differentiation efficacy of HS- versus FBS-expanded cells to ensure that the clinical suitability did not compromise functional outcomes. During 2D differentiation, HS-expanded cells formed pronounced mesenchymal condensates, indicative of bona fide chondrogenesis\u003csup\u003e59\u003c/sup\u003e, whereas FBS-expanded cells accumulated more bulk glycosaminoglycans (Figure 4D-E), reflecting accelerated ECM deposition. This comparison is beneficial because it highlights that xeno-free conditions do not simply mimic conventional culture but instead preserve a more physiologically relevant developmental program. To resolve this apparent discrepancy and better approximate in vivo cartilage formation, we switched to 3D pellet differentiation and gene expression analysis. In this context, HS-expanded USCs maintained \u003cem\u003eTOP2A\u003c/em\u003e expression while robustly activating chondrogenic programs, corresponding to the late pseudotime chondrocyte-like states identified in our single-cell analyses (Figure 4F-I). This finding is particularly advantageous because it demonstrates that xeno-free expansion strikes an optimal balance, by retaining a pool of proliferative progenitors necessary for long-term differentiation capacity while allowing faithful progression toward mature, functional chondrocytes. This balance ensures that USCs can serve as a reproducible, clinically compatible platform for cartilage modeling and regenerative applications, combining safety, scalability, and developmental fidelity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo conclude, our study successfully establishes urine-derived stem cells (USCs) as a non-invasive, physiologically relevant, and translational model of human cartilage development. By combining single-cell transcriptomic mapping with functional differentiation in 2D, spheroid, and pellet cultures under xeno-free conditions, we delineated a hierarchical structure of USC chondrogenesis, from proliferative \u003cem\u003eMYC\u003c/em\u003e/\u003cem\u003eE2F4\u003c/em\u003e-active \u003cem\u003eTOP2A\u003c/em\u003e⁺\u0026nbsp;progenitors through \u003cem\u003eALDH1A2\u003c/em\u003e⁺\u0026nbsp;intermediates to terminal \u003cem\u003eTIMP3\u003c/em\u003e⁺ chondrocyte-like cells. This framework not only recapitulates key stages of native cartilage formation but also confirms that USCs preserve both lineage fidelity and differentiation potential during expansion. Together, these findings demonstrate that USCs provide a robust, clinically compatible platform for modeling genetic skeletal disorders and advancing personalized regenerative therapies, effectively bridging mechanistic insight with translational application.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were in accordance with the ethical standards of the FAU Erlangen-N\u0026uuml;rnberg (reference number: 180_15 Bc) and the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Friedrich-Alexander-University Erlangen-N\u0026uuml;rnberg under two institutional review board approvals: (i) project title: \u0026ldquo;Identification and functional characterization of novel genetic causes of autosomal-dominant short stature\u0026rdquo;, approval number 180_15 BC, approval date: 14 July 2025; and (ii) project title: \u0026ldquo;Elucidation of genetic causes and pathomechanisms of rare, inherited diseases\u0026rdquo;, approval date: 21 September 2022. Written informed consent for participation in the study and for the use of biological samples was obtained from all participants and/or their parents or legally authorized representatives in the case of minors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable. The manuscript does not contain any identifiable individual patient data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequencing data generated in this study have been deposited in the Gene Expression Omnibus (GEO) database. scRNA-Seq of USCs: accession code GSE307232. Bulk-RNA-Seq of USCs: accession code GSE306729. All the R codes used to generate the results of this study are publicly available on GitHub: https://github.com/alexschulzcell/USC-paper. A preprint of this work has been deposited on bioRxiv (doi: 10.1101/2025.09.26.678723). This preprint has not undergone peer review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) \u0026ndash; [TH896 7-1].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.S: performed the experiments, processed and analyzed the data, and wrote the original draft, E.M.B: processed the raw sequencing data. M.Z: revised figures and contributed to xenofree media development, A.S.B: revised manuscript, S.U: uploaded all sequencing data, A.B.E: assisted with sample collection, M.D: Helped with scRNA-Seq data processing, S.Z: Contributed to scRNA-Seq data gathering, C.T.T: project P.I, acquired funding, supervised research and study design, and revised manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Mohammad Deen Hayatu, as well as Evelyn Galsterer for excellent technical assistance. An AI-based language model (ChatGPT, OpenAI) was used for language editing of this manuscript; all scientific content was provided and verified by the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFormosa, M. M.\u003cem\u003e et al.\u003c/em\u003e A Roadmap to Gene Discoveries and Novel Therapies in Monogenic Low and High Bone Mass Disorders. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 709711, doi:10.3389/fendo.2021.709711 (2021).\u003c/li\u003e\n\u003cli\u003eSharma, L., Rani, D., Kanchan, T. \u0026amp; et al. in \u003cem\u003eStatPearls [Internet]\u003c/em\u003e (StatPearls Publishing, 2025).\u003c/li\u003e\n\u003cli\u003eButler, M. G. Prader-Willi syndrome: current understanding of cause and diagnosis. \u003cem\u003eAm J Med Genet\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 319-332, doi:10.1002/ajmg.1320350306 (1990).\u003c/li\u003e\n\u003cli\u003eClement-Jones, M.\u003cem\u003e et al.\u003c/em\u003e The short stature homeobox gene SHOX is involved in skeletal abnormalities in Turner syndrome. \u003cem\u003eHuman Molecular Genetics\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 695-702, doi:10.1093/hmg/9.5.695 (2000).\u003c/li\u003e\n\u003cli\u003eNoonan, J. A. Noonan syndrome and related disorders: alterations in growth and puberty. \u003cem\u003eRev Endocr Metab Disord\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 251-255, doi:10.1007/s11154-006-9021-1 (2006).\u003c/li\u003e\n\u003cli\u003ePrice, S. M., Stanhope, R., Garrett, C., Preece, M. A. \u0026amp; Trembath, R. C. The spectrum of Silver-Russell syndrome: a clinical and molecular genetic study and new diagnostic criteria. \u003cem\u003eJ Med Genet\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 837-842 (1999).\u003c/li\u003e\n\u003cli\u003eLindsay, R., Feldkamp, M., Harris, D., Robertson, J. \u0026amp; Rallison, M. Utah Growth Study: Growth standards and the prevalence of growth hormone deficiency. \u003cem\u003eThe Journal of Pediatrics\u003c/em\u003e \u003cstrong\u003e125\u003c/strong\u003e, 29-35, doi:https://doi.org/10.1016/S0022-3476(94)70117-2 (1994).\u003c/li\u003e\n\u003cli\u003eKotake, M.\u003cem\u003e et al.\u003c/em\u003e Short stature due to growth hormone deficiency associated with Cushing\u0026apos;s disease and ulcerative colitis. \u003cem\u003eHorm Metab Res\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 565-569, doi:10.1055/s-2007-979854 (1996).\u003c/li\u003e\n\u003cli\u003eAl-Omari, A. F. \u0026amp; Omer, Z. K. The impact of juvenile hypothyroidism on stature. \u003cem\u003eJ Med Life\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1211-1214, doi:10.25122/jml-2022-0303 (2023).\u003c/li\u003e\n\u003cli\u003eSeema, Abbas, S., Ahsan, M. N. \u0026amp; Asghar, M. S. Frequency of skeletal dysplasia in children with short stature presenting to endocrine clinic: An observational study. \u003cem\u003eJ Family Med Prim Care\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 3143-3147, doi:10.4103/jfmpc.jfmpc_2393_21 (2022).\u003c/li\u003e\n\u003cli\u003eSentchordi-Montan\u0026eacute;, L.\u003cem\u003e et al.\u003c/em\u003e High prevalence of variants in skeletal dysplasia associated genes in individuals with short stature and minor skeletal anomalies. \u003cem\u003eEur J Endocrinol\u003c/em\u003e \u003cstrong\u003e185\u003c/strong\u003e, 691-705, doi:10.1530/eje-21-0557 (2021).\u003c/li\u003e\n\u003cli\u003eKyle, U. G., Shekerdemian, L. S. \u0026amp; Coss-Bu, J. A. Growth failure and nutrition considerations in chronic childhood wasting diseases. \u003cem\u003eNutr Clin Pract\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 227-238, doi:10.1177/0884533614555234 (2015).\u003c/li\u003e\n\u003cli\u003eRanke, M. B. Towards a Consensus on the Definition of Idiopathic Short Stature. \u003cem\u003eHormone Research\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 64-66, doi:10.1159/000184851 (2008).\u003c/li\u003e\n\u003cli\u003eWit, J. M.\u003cem\u003e et al.\u003c/em\u003e Idiopathic short stature: definition, epidemiology, and diagnostic evaluation. \u003cem\u003eGrowth Horm IGF Res\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 89-110, doi:10.1016/j.ghir.2007.11.004 (2008).\u003c/li\u003e\n\u003cli\u003eArgente, J., Tatton-Brown, K., Lehwalder, D. \u0026amp; Pf\u0026auml;ffle, R. Genetics of Growth Disorders\u0026mdash;Which Patients Require Genetic Testing? \u003cem\u003eFrontiers in Endocrinology\u003c/em\u003e \u003cstrong\u003eVolume 10 - 2019\u003c/strong\u003e, doi:10.3389/fendo.2019.00602 (2019).\u003c/li\u003e\n\u003cli\u003eVoller, T., Cameron, P., Watson, J. \u0026amp; Phadnis, J. The growth plate: anatomy and disorders. \u003cem\u003eOrthopaedics and Trauma\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 135-140, doi:https://doi.org/10.1016/j.mporth.2020.03.006 (2020).\u003c/li\u003e\n\u003cli\u003ePhornphutkul, C. \u0026amp; Gruppuso, P. A. Disorders of the growth plate. \u003cem\u003eCurrent Opinion in Endocrinology, Diabetes and Obesity\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e (2009).\u003c/li\u003e\n\u003cli\u003eNilsson, O.\u003cem\u003e et al.\u003c/em\u003e Short Stature, Accelerated Bone Maturation, and Early Growth Cessation Due to Heterozygous Aggrecan Mutations. \u003cem\u003eThe Journal of Clinical Endocrinology \u0026amp; Metabolism\u003c/em\u003e \u003cstrong\u003e99\u003c/strong\u003e, E1510-E1518, doi:10.1210/jc.2014-1332 (2014).\u003c/li\u003e\n\u003cli\u003eForeman, P. K.\u003cem\u003e et al.\u003c/em\u003e Birth prevalence of achondroplasia: A systematic literature review and meta-analysis. \u003cem\u003eAmerican Journal of Medical Genetics Part A\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e, 2297-2316, doi:https://doi.org/10.1002/ajmg.a.61787 (2020).\u003c/li\u003e\n\u003cli\u003ePhull, A. R., Eo, S. H., Abbas, Q., Ahmed, M. \u0026amp; Kim, S. J. Applications of Chondrocyte-Based Cartilage Engineering: An Overview. \u003cem\u003eBiomed Res Int\u003c/em\u003e \u003cstrong\u003e2016\u003c/strong\u003e, 1879837, doi:10.1155/2016/1879837 (2016).\u003c/li\u003e\n\u003cli\u003eNeybecker, P.\u003cem\u003e et al.\u003c/em\u003e Respective stemness and chondrogenic potential of mesenchymal stem cells isolated from human bone marrow, synovial membrane, and synovial fluid. \u003cem\u003eStem Cell Res Ther\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 316, doi:10.1186/s13287-020-01786-5 (2020).\u003c/li\u003e\n\u003cli\u003eKangari, P., Talaei-Khozani, T., Razeghian-Jahromi, I. \u0026amp; Razmkhah, M. Mesenchymal stem cells: amazing remedies for bone and cartilage defects. \u003cem\u003eStem Cell Res Ther\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 492, doi:10.1186/s13287-020-02001-1 (2020).\u003c/li\u003e\n\u003cli\u003eMoy, A. B., Kamath, A., Ternes, S. \u0026amp; Kamath, J. The Challenges to Advancing Induced Pluripotent Stem Cell-Dependent Cell Replacement Therapy. \u003cem\u003eMed Res Arch\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, doi:10.18103/mra.v11i11.4784 (2023).\u003c/li\u003e\n\u003cli\u003eDoss, M. X. \u0026amp; Sachinidis, A. Current Challenges of iPSC-Based Disease Modeling and Therapeutic Implications. \u003cem\u003eCells\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, doi:10.3390/cells8050403 (2019).\u003c/li\u003e\n\u003cli\u003eLee, A. S., Tang, C., Rao, M. S., Weissman, I. L. \u0026amp; Wu, J. C. Tumorigenicity as a clinical hurdle for pluripotent stem cell therapies. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 998-1004, doi:10.1038/nm.3267 (2013).\u003c/li\u003e\n\u003cli\u003eZhang, D., Wei, G., Li, P., Zhou, X. \u0026amp; Zhang, Y. Urine-derived stem cells: A novel and versatile progenitor source for cell-based therapy and regenerative medicine. \u003cem\u003eGenes Dis\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 8-17, doi:10.1016/j.gendis.2014.07.001 (2014).\u003c/li\u003e\n\u003cli\u003eWan, S.\u003cem\u003e et al.\u003c/em\u003e Extracellular Vesicles from Hypoxic Pretreated Urine-Derived Stem Cells Enhance the Proliferation and Migration of Chondrocytes by Delivering miR-26a-5p. \u003cem\u003eCartilage\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 19476035221077401, doi:10.1177/19476035221077401 (2022).\u003c/li\u003e\n\u003cli\u003eSun, J.\u003cem\u003e et al.\u003c/em\u003e Comparison of chondrogenesis-related biological behaviors between human urine-derived stem cells and human bone marrow mesenchymal stem cells from the same individual. \u003cem\u003eStem Cell Research \u0026amp; Therapy\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 366, doi:10.1186/s13287-021-02370-1 (2021).\u003c/li\u003e\n\u003cli\u003ePei, M.\u003cem\u003e et al.\u003c/em\u003e Matrix from urine stem cells boosts tissue-specific stem cell mediated functional cartilage reconstruction. \u003cem\u003eBioactive Materials\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 353-367, doi:https://doi.org/10.1016/j.bioactmat.2022.11.012 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, Y.\u003cem\u003e et al.\u003c/em\u003e Urine derived cells are a potential source for urological tissue reconstruction. \u003cem\u003eJ Urol\u003c/em\u003e \u003cstrong\u003e180\u003c/strong\u003e, 2226-2233, doi:10.1016/j.juro.2008.07.023 (2008).\u003c/li\u003e\n\u003cli\u003eBui, H. T. H., Nguyen, L. T. \u0026amp; Than, U. T. T. Influences of Xeno-Free Media on Mesenchymal Stem Cell Expansion for Clinical Application. \u003cem\u003eTissue Eng Regen Med\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 15-23, doi:10.1007/s13770-020-00306-z (2021).\u003c/li\u003e\n\u003cli\u003eCulenova, M.\u003cem\u003e et al.\u003c/em\u003e Isolation, Culture and Comprehensive Characterization of Biological Properties of Human Urine-Derived Stem Cells. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, doi:10.3390/ijms222212503 (2021).\u003c/li\u003e\n\u003cli\u003eZhang, C.\u003cem\u003e et al.\u003c/em\u003e Reno-protection of Urine-derived Stem Cells in A Chronic Kidney Disease Rat Model Induced by Renal Ischemia and Nephrotoxicity. \u003cem\u003eInternational Journal of Biological Sciences\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 435-446, doi:10.7150/ijbs.37550 (2020).\u003c/li\u003e\n\u003cli\u003eHao, Y.\u003cem\u003e et al.\u003c/em\u003e Dictionary learning for integrative, multimodal and scalable single-cell analysis. \u003cem\u003eNature Biotechnology\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 293-304, doi:10.1038/s41587-023-01767-y (2024).\u003c/li\u003e\n\u003cli\u003eValero-Mora, P. M. ggplot2: Elegant Graphics for Data Analysis. \u003cem\u003eJournal of Statistical Software, Book Reviews\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1 - 3, doi:10.18637/jss.v035.b01 (2010).\u003c/li\u003e\n\u003cli\u003eSlowikowski, K. ggrepel: Automatically Position Non-Overlapping Text Labels with \u0026apos;ggplot2\u0026apos;. (2024).\u003c/li\u003e\n\u003cli\u003eGarnier, S.\u003cem\u003e et al.\u003c/em\u003e (Zenodo, 2023).\u003c/li\u003e\n\u003cli\u003eRColorBrewer: ColorBrewer Palettes v. 1.1-3 (R package, 2022).\u003c/li\u003e\n\u003cli\u003eHu, C.\u003cem\u003e et al.\u003c/em\u003e CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, D870-D876, doi:10.1093/nar/gkac947 (2022).\u003c/li\u003e\n\u003cli\u003eAibar, S.\u003cem\u003e et al.\u003c/em\u003e SCENIC: single-cell regulatory network inference and clustering. \u003cem\u003eNature Methods\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1083-1086, doi:10.1038/nmeth.4463 (2017).\u003c/li\u003e\n\u003cli\u003eTisch, A.\u003cem\u003e et al.\u003c/em\u003e Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, doi:10.1016/j.heliyon.2024.e38567 (2024).\u003c/li\u003e\n\u003cli\u003eKang, M.\u003cem\u003e et al.\u003c/em\u003e Mapping single-cell developmental potential in health and disease with interpretable deep learning. \u003cem\u003ebioRxiv\u003c/em\u003e, 2024.2003.2019.585637, doi:10.1101/2024.03.19.585637 (2024).\u003c/li\u003e\n\u003cli\u003eAlvarez, M. J.\u003cem\u003e et al.\u003c/em\u003e Functional characterization of somatic mutations in cancer using network-based inference of protein activity. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 838-847, doi:10.1038/ng.3593 (2016).\u003c/li\u003e\n\u003cli\u003eGarcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. \u0026amp; Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. \u003cem\u003eGenome Res\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1363-1375, doi:10.1101/gr.240663.118 (2019).\u003c/li\u003e\n\u003cli\u003eDanecek, P.\u003cem\u003e et al.\u003c/em\u003e Twelve years of SAMtools and BCFtools. \u003cem\u003eGigascience\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, doi:10.1093/gigascience/giab008 (2021).\u003c/li\u003e\n\u003cli\u003eMcKenna, A.\u003cem\u003e et al.\u003c/em\u003e The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. \u003cem\u003eGenome Res\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1297-1303, doi:10.1101/gr.107524.110 (2010).\u003c/li\u003e\n\u003cli\u003eDyer, S. C.\u003cem\u003e et al.\u003c/em\u003e Ensembl 2025. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, D948-d957, doi:10.1093/nar/gkae1071 (2025).\u003c/li\u003e\n\u003cli\u003eDobin, A.\u003cem\u003e et al.\u003c/em\u003e STAR: ultrafast universal RNA-seq aligner. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 15-21, doi:10.1093/bioinformatics/bts635 (2013).\u003c/li\u003e\n\u003cli\u003eLiao, Y., Smyth, G. K. \u0026amp; Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 923-930, doi:10.1093/bioinformatics/btt656 (2014).\u003c/li\u003e\n\u003cli\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biology\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 550, doi:10.1186/s13059-014-0550-8 (2014).\u003c/li\u003e\n\u003cli\u003eCarlson, M. org.Hs.eg.db: Genome wide annotation for Human. (2024).\u003c/li\u003e\n\u003cli\u003eSievert, C. Interactive Web-Based Data Visualization with R, plotly, and shiny. (2020).\u003c/li\u003e\n\u003cli\u003eVaidyanathan, R.\u003cem\u003e et al.\u003c/em\u003e htmlwidgets: HTML Widgets for R. \u003cstrong\u003e1.6.4\u003c/strong\u003e (2023).\u003c/li\u003e\n\u003cli\u003eSchindelin, J.\u003cem\u003e et al.\u003c/em\u003e Fiji: an open-source platform for biological-image analysis. \u003cem\u003eNature Methods\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 676-682, doi:10.1038/nmeth.2019 (2012).\u003c/li\u003e\n\u003cli\u003eHuang, C.\u003cem\u003e et al.\u003c/em\u003e Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis. \u003cem\u003eGenes \u0026amp; Diseases\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 101241, doi:https://doi.org/10.1016/j.gendis.2024.101241 (2025).\u003c/li\u003e\n\u003cli\u003eStreet, K.\u003cem\u003e et al.\u003c/em\u003e Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. \u003cem\u003eBMC Genomics\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 477, doi:10.1186/s12864-018-4772-0 (2018).\u003c/li\u003e\n\u003cli\u003eYu, G., Wang, L.-G., Han, Y. \u0026amp; He, Q.-Y. clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. \u003cem\u003eOMICS: A Journal of Integrative Biology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 284-287, doi:10.1089/omi.2011.0118 (2012).\u003c/li\u003e\n\u003cli\u003eBharadwaj, S.\u003cem\u003e et al.\u003c/em\u003e Multipotential differentiation of human urine-derived stem cells: Potential for therapeutic applications in urology. \u003cem\u003eStem Cells\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1840-1856, doi:10.1002/stem.1424 (2013).\u003c/li\u003e\n\u003cli\u003eTacchetti, C.\u003cem\u003e et al.\u003c/em\u003e Cell condensation in chondrogenic differentiation. \u003cem\u003eExperimental Cell Research\u003c/em\u003e \u003cstrong\u003e200\u003c/strong\u003e, 26-33, doi:https://doi.org/10.1016/S0014-4827(05)80067-9 (1992).\u003c/li\u003e\n\u003cli\u003eRoyall, L. N. \u0026amp; Jessberger, S. How stem cells remember their past. \u003cem\u003eCurrent Opinion in Cell Biology\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 17-22, doi:https://doi.org/10.1016/j.ceb.2020.12.008 (2021).\u003c/li\u003e\n\u003cli\u003eDhanasekaran, R.\u003cem\u003e et al.\u003c/em\u003e The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. \u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 23-36, doi:10.1038/s41571-021-00549-2 (2022).\u003c/li\u003e\n\u003cli\u003eJha, R. K., Kouzine, F. \u0026amp; Levens, D. MYC function and regulation in physiological perspective. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1268275, doi:10.3389/fcell.2023.1268275 (2023).\u003c/li\u003e\n\u003cli\u003eTrimarchi, J. M. \u0026amp; Lees, J. A. Sibling rivalry in the E2F family. \u003cem\u003eNature Reviews Molecular Cell Biology\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 11-20, doi:10.1038/nrm714 (2002).\u003c/li\u003e\n\u003cli\u003eHsu, J.\u003cem\u003e et al.\u003c/em\u003e E2F4 regulates transcriptional activation in mouse embryonic stem cells independently of the RB family. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 2939, doi:10.1038/s41467-019-10901-x (2019).\u003c/li\u003e\n\u003cli\u003eSwetha, G., Chandra, V., Phadnis, S. \u0026amp; Bhonde, R. Glomerular parietal epithelial cells of adult murine kidney undergo EMT to generate cells with traits of renal progenitors. \u003cem\u003eJ Cell Mol Med\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 396-413, doi:10.1111/j.1582-4934.2009.00937.x (2011).\u003c/li\u003e\n\u003cli\u003eNaito, S., Pippin, J. W. \u0026amp; Shankland, S. J. The glomerular parietal epithelial cell\u0026rsquo;s responses are influenced by SM22 alpha levels. \u003cem\u003eBMC Nephrology\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 174, doi:10.1186/1471-2369-15-174 (2014).\u003c/li\u003e\n\u003cli\u003eAlberton, P.\u003cem\u003e et al.\u003c/em\u003e Aggrecan Hypomorphism Compromises Articular Cartilage Biomechanical Properties and Is Associated with Increased Incidence of Spontaneous Osteoarthritis. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1008 (2019).\u003c/li\u003e\n\u003cli\u003eZhu, L.\u003cem\u003e et al.\u003c/em\u003e Variants in ALDH1A2 reveal an anti-inflammatory role for retinoic acid and a new class of disease-modifying drugs in osteoarthritis. \u003cem\u003eScience Translational Medicine\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, eabm4054, doi:10.1126/scitranslmed.abm4054.\u003c/li\u003e\n\u003cli\u003eMorris, K. J., Cs-Szabo, G. \u0026amp; Cole, A. A. Characterization of TIMP-3 in human articular talar cartilage. \u003cem\u003eConnect Tissue Res\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 478-490, doi:10.3109/03008201003686958 (2010).\u003c/li\u003e\n\u003cli\u003eGreen, J.\u003cem\u003e et al.\u003c/em\u003e Suramin analogues protect cartilage against osteoarthritic breakdown by increasing levels of tissue inhibitor of metalloproteinases 3 (TIMP-3) in the tissue. \u003cem\u003eBioorganic \u0026amp; Medicinal Chemistry\u003c/em\u003e \u003cstrong\u003e92\u003c/strong\u003e, 117424, doi:https://doi.org/10.1016/j.bmc.2023.117424 (2023).\u003c/li\u003e\n\u003cli\u003eWang, X., Zhang, Y. \u0026amp; Yang, J. Transcriptome analysis reveals synergistic modulation of E-cadherin/N-cadherin in hMSC aggregates chondrogenesis. \u003cem\u003eGenes \u0026amp; Genomics\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 681-692, doi:10.1007/s13258-022-01362-6 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stem-cell-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scrt","sideBox":"Learn more about [Stem Cell Research \u0026 Therapy](http://stemcellres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/scrt/default.aspx","title":"Stem Cell Research \u0026 Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"single-cell RNA sequencing, urine-derived stem cells, chondrogenesis, xeno-free culture, cartilage biology, skeletal dysplasia modeling, translational regenerative medicine, disease modelling, tissue engineering, adult stem cells","lastPublishedDoi":"10.21203/rs.3.rs-8230463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8230463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eClinically compatible, non-invasively harvested stem cell sources are needed to model human skeletal disorders and advance regenerative strategies. Human urine-derived stem cells (USCs) offer patient-specific accessibility, yet their developmental hierarchy and chondrogenic mechanisms remain poorly defined.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eUSCs were isolated, expanded and profiled in detail using single-cell RNA sequencing with lineage reconstruction to map progenitor states and differentiation trajectories. Chondrogenesis was induced in two-dimensional (2D) and three-dimensional (3D) systems. To support translational use, we established a fully xeno-free expansion and differentiation workflow using autologous human serum and benchmarked it against conventional serum-based conditions.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eSingle-cell analysis resolved a structured USC hierarchy originating from MYC/E2F4-regulated TOP2A⁺ proliferative progenitors, progressing through an ALDH1A2⁺ retinoic-acid–responsive intermediate, and culminating in TIMP3⁺ chondrocyte-like cells exhibiting high transcriptional similarity to native cartilage. This trajectory featured coordinated activation of canonical chondrogenic regulators (SOX9, SOX5, SOX6) and enrichment of extracellular matrix programs associated with cartilage formation. Under xeno-free autologous serum conditions, USCs preserved proliferative capacity, enhanced mesenchymal condensation, and generated matrix‑rich cartilage-like constructs in 2D and 3D with superior maturation signatures compared with standard culture conditions.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eWe provide a mechanistic single-cell atlas of human USC chondrogenesis and establish USCs as a non-invasive, patient-compatible, and fully xeno-free stem cell platform for translational cartilage research, skeletal disease modelling, and personalized regenerative medicine applications.\u003c/p\u003e","manuscriptTitle":"Single-cell atlas of human urine-derived stem cell chondrogenesis enables a non-invasive, xeno-free platform for translational cartilage and skeletal disease research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:29:09","doi":"10.21203/rs.3.rs-8230463/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T19:46:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T08:21:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T14:54:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127301662577582285852041216987011914681","date":"2026-01-15T13:29:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70874992975575537669109847188707072079","date":"2026-01-13T20:59:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T12:57:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T13:11:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-19T03:27:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stem Cell Research \u0026 Therapy","date":"2025-12-18T09:51:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stem-cell-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scrt","sideBox":"Learn more about [Stem Cell Research \u0026 Therapy](http://stemcellres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/scrt/default.aspx","title":"Stem Cell Research \u0026 Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ee0fd0a-5236-4dd6-911f-e0cba68679e0","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T12:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:29:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8230463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8230463","identity":"rs-8230463","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}