Background:Kidney cell lines are widely used to model kidney physiology and disease; however, their gene expression profiles may differ from primary cells due to immortalization, culture conditions, or experimental treatments. Determining whether a cell line resembles its native cell type is critical for interpreting in vitro findings. We developed a transcriptome-based approach that matches bulk RNA-seq data from kidney cell lines, primary cells, or tissues to reference cell types derived from single-cell RNA-seq (scRNA-seq) datasets. Methods:Reference transcriptomic profiles were generated from two human and two murine kidney scRNA-seq datasets by pseudobulk aggregation. Bulk RNA-seq data from microdissected kidney tissue, non-kidney negative controls, and kidney cell lines were matched to these references using three statistical similarity measures (Spearman correlation, Euclidean distance, Poisson distance) and three machine learning classifiers (Random Forest, XGBoost, TabPFN). Each was assessed with global gene expression, curated kidney marker gene lists, and the most variable genes. Matching accuracy was evaluated through a three-step validation strategy: within-dataset matching, cross-reference comparison, and validation against primary kidney tissue and negative controls. Results:Gene expression rank-based Spearman correlation and TabPFN, a foundation model for tabular data, emerged as the most accurate and specific approaches, particularly with curated kidney marker gene lists. Both methods correctly identified microdissected kidney tubule segments and were robust against non-kidney negative controls. Applied to commonly used kidney cell lines, OK cells retained proximal tubule identity, particularly under shear stress, while other proximal tubule lines (HK-2, HKC-8, HKC-11) showed inconsistent matching. Collecting duct-derived mIMCD-3 maintained stable similarity across passages, culture conditions, and genetic modifications. Conclusion:We provide two complementary implementations: CellMatchR, an accessible web-based tool using Spearman correlation for routine use, and comprehensive scripts for TabPFN-based matching (link will be added after peer reviewed publication). Together, these resources enable researchers to make informed decisions about kidney cell culture model selection, interpretation, and stability. Translational Statement:Kidney cell lines are fundamental tools in nephrology research, yet their transcriptomic similarity to native cell types is rarely validated systematically. We demonstrate that combining bulk RNA-seq data with single-cell reference datasets enables robust assessment of cell line identity using gene expression-rank-based correlation and machine learning approaches. By providing a comprehensive evaluation of matching methods, curated kidney marker gene lists, and reference datasets, our study serves as both a practical resource and a methodological framework for the kidney research community, facilitating informed selection of cell culture models, quality control of experimental conditions, developing new experimental cell culture models, and more reliable translation of in vitro findings to kidney physiology and disease.

Autosomal dominant polycystic kidney disease (ADPKD), the leading genetic cause of kidney failure, results from loss-of-function mutations in PKD1, encoding polycystin-1 (PC1). PC1 localizes to the primary cilium. In the absence of PC1, adverse signaling from the primary cilium orchestrates cyst formation, but the biomechanical underpinnings of this cilia-dependent cyst activation (CDCA) remain unclear. Combining tubule-specific orthologous mouse models with a tubule-on-chip platform, we show that PC1 and cilia govern the composition, mechanical properties and shape of the tubular basement membrane (TBM), the principal rigid determinant of tubule geometry. PC1 loss triggers TBM thinning, heparan sulfate enrichment and deformation, leading to distension, preferentially of the distal nephron. These changes are driven by a cilia-dependent transcriptional program, with GLIS2 - a key CDCA effector - participating as a downstream mediator. Reduction of TBM stiffness amplifies Pkd1-/- tubule-on-chip dilation and increases cyst formation in vivo. Conversely, increasing luminal pressure through ureteral obstruction induces disproportionate distension of Pkd1-deficient tubules and triggers an irreversible cystogenic program. Together, these findings establish a TBM-centered biomechanical model of ADPKD in which tubule deformation is governed by both basolateral and luminal mechanical factors, and identify the cilium-TBM axis, operating in part through GLIS2, as a central driver of cystogenesis.

Abstract Autosomal dominant polycystic kidney disease (ADPKD) exhibits substantial interpatient variability in disease course and therapeutic response, but the cellular basis for this variability remains poorly understood. Here, we combine single-nucleus RNA sequencing of human cyst epithelia with machine learning–based histological analysis of >1,800 cysts to resolve three epithelial cyst types—proximal tubule–like, collecting duct–like, and mixed. These cyst types display distinct injury states, metabolic programs, and stromal microenvironments, including a mixed-cyst niche enriched for CCL2-associated inflammatory signaling. Expression of key therapeutic targets was highly cell-type specific with CFTR enriched in proximal-like epithelia, whereas AVPR2 expression was confined to AQP2-positive collecting duct–like cells. Cyst-type composition varied widely across patients and in an orthologous mouse model ( Pkd1 RC/RC ) in which the burden of AQP2-positive cysts correlated with responsiveness to tolvaptan. These findings identify cyst-type heterogeneity as a major determinant of molecular pathway activation and predictability of therapeutic response in ADPKD.

Cell shape is a fundamental determinant of tissue architecture and organ function. In epithelial tissues, cytoskeletal organization and apical junctions regulate cell geometry, shaping functional tissue units. Disruption of these mechanisms is associated with diseases such as autosomal dominant polycystic kidney disease (ADPKD), in which epithelial organization is altered leading to cyst formation. Quantitative analysis of epithelial morphology can provide mechanistic insight, but existing approaches are often manual, low-throughput, and difficult to standardize. Here, we present a fully automated, deep learning-supported image analysis workflow for quantifying epithelial morphology in immunofluorescence images of zonula occludens protein 1 (ZO-1)-stained monolayers. Using a U-Net-based segmentation approach designed to mitigate out-of-focus regions, we extract standard cell shape features together with readouts tailored to the phenotype under study, including the R-index for junctional meandering and a border-based proxy for intercellular force transmission at shared cell-cell interfaces. We apply this workflow to genetically modified Madin-Darby canine kidney (MDCK) cell models of ADPKD and show that it captures genotype-associated differences in junctional organization that are not fully described by conventional shape descriptors alone. The workflow enables standardized, high-throughput phenotyping across large image datasets, reduces observer dependence, and supports analysis of mixed-cell experiments with genotype-resolved shared-border behavior. Together, these results establish a scalable framework for assay-specific quantification of epithelial morphology and junctional organization in defined experimental systems.

Mutations in the co-chaperone DNAJB11 have been shown to cause polycystic kidney disease. The molecular mechanism underlying DNAJB11-related kidney disease involves impaired processing of Polycystin-1 (PC1), the protein most commonly mutated in autosomal dominant polycystic kidney disease (ADPKD). Chaperones are known to form multiprotein complexes to facilitate folding and processing of client proteins. Yet, it is unknown whether DNAJB11 forms complexes with other proteins that are required for PC1 processing. In this study, we perform an unbiased interaction proteomics screen for DNAJB11-interacting proteins. We identify two highly homologous proteins, SDF2 and SDF2L1, as strong interaction partners of DNAJB11. Using newly established knockout cell lines, we demonstrate a reciprocal interdependence of DNAJB11 and SDF2/SDF2L1 protein abundance. Furthermore, we show that concomitant loss of SDF2 and SDF2L1 impairs PC1 processing, mimicking the biochemical phenotype caused by loss of DNAJB11. Using a combination of knockout cell lines and reexpression of the respective members of the DNAJB11 protein complex, we show that SDF2 or SDF2L1 are elementary subunits of the DNAJB11 complex required for normal PC1 processing.

Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene–metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases. Gene-based rare variant aggregation study with the levels of 1,294 plasma and 1,396 urine metabolites from paired specimens of 4,737 participants reveals graded effects of rare, putatively damaging variants on gene function and human traits.

Choline has important physiological functions as a precursor for essential cell components, signaling molecules, phospholipids, and the neurotransmitter acetylcholine. Choline is a water-soluble charged molecule requiring transport proteins to cross biological membranes. Although transporters continue to be identified, membrane transport of choline is incompletely understood and knowledge about choline transport into intracellular organelles such as mitochondria remains limited. Here we show that SLC25A48 imports choline into human mitochondria. Human loss-of-function mutations in SLC25A48 show impaired choline transport into mitochondria and are associated with elevated urine and plasma choline levels. Thus, our studies may have implications for understanding and treating conditions related to choline metabolism.

Transient receptor potential (TRP) channels represent an extensive and diverse protein family fulfilling salient roles as versatile cellular sensors and effectors. The pivotal role of TRP and related ion channels in sensory processes has been well documented. Over the last few years, a new concept has emerged that TRP proteins control an exceptionally broad spectrum of homeostatic physiological functions such as maintenance of body temperature, blood pressure, transmitter release from neurons, mineral and energy homeostasis, and reproduction. This notion is further supported by more than 20 hereditary human diseases in areas as diverse as neurology, cardiology, hematology, pulmonology, nephrology, dermatology, and urology. Most TRP channel-related human disorders impinge on development, metabolism, and other homeostatic functions. The remarkable diversity of pathologies caused by TRP channel dysfunction underscores these proteins' broad spectrum of roles in vivo. Here, we provide a comprehensive overview of our progress in the identification, characterization, and clinical relevance of pharmacological agents targeting mammalian TRP channels. SIGNIFICANCE STATEMENT: Accumulating evidence links transient receptor potential (TRP) channels to various human diseases and highlights TRPs as the most appealing pharmacological targets. The review provides an overview of this quickly developing research area, focusing on identified pharmacological modulators of mammalian TRP channels.

Full understanding of the functions of the polycystin proteins, PC1 and PC2, in renal epithelial cells is obscured by signaling complexity and renal injury that occurs in Autosomal Dominant Polycystic Kidney Disease (ADPKD). The polycystins likely function as a complex in the primary cilium, yet previous work hinted at a critical role for PC1 function outside of the primary cilium (extra-ciliary) during tubule development. Here, we investigate an extra-ciliary role for the polycystins in regulating renal cell and tubular morphology. First, we found acute loss of polycystins significantly increased the circularity of renal epithelial cells and tubuloids grown in 3D culture. Next, we demonstrated that both PC1 and PC2 can immunoprecipitate Ezrin, an ERM protein important for apical compartment shape. In human ADPKD renal cystic tissue, and after acute inducible knockout of Pkd1 or Pkd2, we found that Ezrin protein abundance is significantly reduced, with the remaining Ezrin protein mis-localized. Immunofluorescence in 2D cells and 3D tubuloids suggested acute polycystin loss specifically reduced the active form of Ezrin at the apical surface, leaving inactive Ezrin colocalized with ZO1 in the cell junctions. A specific ERM phosphorylation inhibitor, NSC668394, phenocopied the increased circularity observed in the Pkd1 knockout spheroids, as did inhibition of PKC activity, implicating the polycystin complex in regulating Ezrin phosphorylation. Our data strongly support a role for the polycystin complex in regulating renal cell and tubular shape via interactions with the ERM protein Ezrin, interactions that do not require trafficking to the primary cilium.

Abstract Background and Aims Cultured kidney cell lines are broadly used to model the physiology and pathophysiology of the kidney. Due to immortalization, passaging and culture conditions, a cell line's gene expression profile might differ greatly from the primary cells it was originally derived from. This might be further enhanced by experimental treatments with compounds or genetic modifications. It is therefore a relevant question if a given cell culture system is still the appropriate model for a certain disease or scientific investigations. Reasoning that RNA sequencing (RNA-seq) based transcriptomes are generated as part of many experimental setups and hence may be used to address this question, we developed and tested an RNA-seq based approach, CellMatchR. This approach matches kidney cell lines, primary cells and even tissue specimen to known kidney reference cell types to determine which primary kidney cell type is the most similar and to what degree global gene expression or the expression of selected marker genes differs. Method CellMatchR uses published murine and human kidney single cell- and tubule-level transcriptomic datasets from healthy mice and human donors as references. Single cell datasets were further processed to pseudobulk references for each of the contained cell types. Then RNA-seq data from cell lines or tissues of interest (test data) was compared to the reference (pseudo)bulk data with Spearman correlations of gene counts-per-million values (CPM) or Euclidean distance using log-transformed CPM values. Both approaches were systematically tested for various combinations of test and reference datasets across different species and using global gene expression (i.e. all genes measured in reference and test datasets) and a set of 315 manually curated, kidney cell type marker genes. Results We sequentially used different kidney tissue types, primary cells and cell lines as positive controls and matched them to our reference datasets. Spearman correlations of gene expression rankings showed the highest correlation coefficients with biologically correct reference cell types (examples in Fig. 1A-C). We found Spearman correlations to be superior to Euclidean distances as method of comparison. Analyses based on global gene expression compared to our curated set of 315 kidney cell type marker genes yielded similar results. Notably, correlation coefficients were higher and showed less variation between reference cell types when using the global gene set. Matchings across species, i.e. using murine test and human reference data or vice versa still yielded mostly correct results. However, correlation coefficients were generally lower (i.e. rho = 0.9 vs. 0.6) and varied more across reference datasets if comparisons were performed across species. Using published RNA-seq data for different cell lines of the proximal tubule (e.g. human HK-2 and opossum OKH cells) we observed low similarities with proximal tubule cells (Fig. 1D), whereas two tested cell lines of the collecting duct (mIMCD-3 and mpkCCD cells) plausibly showed the highest similarity to medullary cell types like collecting duct and Loop of Henle cells. The most similar reference cell types did not change in mIMCD-3 cells when kept in 2-dimensional vs. 3-dimensional culture conditions, and in mpkCCD cells when the osmolality of the culture medium was changed from 300 to 600 mosmol/kg. Also, a Pkd1 knockout in mIMCD-3 cells did not change the most similar reference cell type in our analyses (Fig. 1E and F). Conclusion Our CellMatchR approach uses publicly available kidney single cell and bulk RNA-seq datasets and combines these with simple, computationally fast yet effective statistical methods to determine similarities of kidney cell lines and tissue samples to reference cell types of interest. It can easily be implemented by trained users or integrated into online resources for usage with a visual interface. It relies on RNA-seq data from the cell lines of interest and hence presents a feasible complementary method to check the general similarity of cell culture models to kidney cell types and assess their stability across experimental conditions.

Different cell channels and transporters tightly regulate cytoplasmic levels and the intraorganelle distribution of cations. Perturbations in these processes lead to human diseases that are frequently associated with kidney impairment. The family of melastatin-related transient receptor potential (TRPM) channels, which has eight members in mammals (TRPM1–TRPM8), includes ion channels that are highly permeable to divalent cations, such as Ca 2+ , Mg 2+ and Zn 2+ (TRPM1, TRPM3, TRPM6 and TRPM7), non-selective cation channels (TRPM2 and TRPM8) and monovalent cation-selective channels (TRPM4 and TRPM5). Three family members contain an enzymatic protein moiety: TRPM6 and TRPM7 are fused to α-kinase domains, whereas TRPM2 is linked to an ADP-ribose-binding NUDT9 homology domain. TRPM channels also function as crucial cellular sensors involved in many physiological processes, including mineral homeostasis, blood pressure, cardiac rhythm and immunity, as well as photoreception, taste reception and thermoreception. TRPM channels are abundantly expressed in the kidney. Mutations in TRPM genes cause several inherited human diseases, and preclinical studies in animal models of human disease have highlighted TRPM channels as promising new therapeutic targets. Here, we provide an overview of this rapidly evolving research area and delineate the emerging role of TRPM channels in kidney pathophysiology.

Choline has important physiological functions as a precursor for essential cell components and signaling molecules including phospholipids and the neurotransmitter acetylcholine. Choline is a water-soluble charged molecule and therefore requires transport proteins to cross biological membranes. Membrane transport of choline is incompletely understood. Here we show that SLC25A48 is a human mitochondrial choline transporter. Loss-of-function mutations in SLC25A48 are associated with elevated urine and plasma choline levels resulting from impaired choline transport into mitochondria.

Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in PKD1 and PKD2 , encoding polycystin-1 (PC1) and polycystin-2 (PC2), which are required for the regulation of the renal tubular diameter. Loss of polycystin function results in cyst formation. Atypical forms of ADPKD are caused by mutations in genes encoding endoplasmic reticulum (ER)-resident proteins through mechanisms that are not well understood. Here, we investigate the function of DNAJB11, an ER co-chaperone associated with atypical ADPKD. We generated mouse models with constitutive and conditional Dnajb11 inactivation and Dnajb11 -deficient renal epithelial cells to investigate the mechanism underlying autosomal dominant inheritance, the specific cell types driving cyst formation, and molecular mechanisms underlying DNAJB11-dependent polycystic kidney disease. We show that biallelic loss of Dnajb11 causes cystic kidney disease and fibrosis, mirroring human disease characteristics. In contrast to classical ADPKD, cysts predominantly originate from proximal tubules. Cyst formation begins in utero and the timing of Dnajb11 inactivation strongly influences disease severity. Furthermore, we identify impaired PC1 cleavage as a potential mechanism underlying DNAJB11-dependent cyst formation. Proteomic analysis of Dnajb11 - and Pkd1 -deficient cells reveals common and distinct pathways and dysregulated proteins, providing a foundation to better understand phenotypic differences between different forms of ADPKD.

AbstractMutations in the mitochondrial enzyme propionyl-CoA carboxylase (PCC) cause propionic aciduria (PA). Chronic kidney disease (CKD) is a known long-term complication. However, good metabolic control and standard therapy fail to prevent CKD. The pathophysiological mechanisms of CKD are unclear. We investigated the renal phenotype of a hypomorphic murine PA model (Pcca-/-(A138T)) to identify CKD-driving mechanisms. Pcca-/-(A138T) mice show elevated retention parameters and express markers of kidney damage progressing with time. Morphological assessment of the Pcca-/-(A138T) mouse kidneys indicated partial flattening of tubular epithelial cells and focal tubular-cystic dilation. We observed altered renal mitochondrial ultrastructure and mechanisms acting against oxidative stress were active. LC–MS/MS analysis confirmed disease-specific metabolic signatures and revealed disturbances in mitochondrial energy generation via the TCA cycle. Our investigations revealed altered mitochondrial networks shifted towards fission and a marked reduction of mitophagy. We observed a steep reduction of PGC-1-α, the key mediator modulating mitochondrial functions and a counter actor of mitochondrial fission. Our results suggest that impairment of mitochondrial homeostasis and quality control are involved in CKD development in PA. Therapeutic targeting of the identified pathways might help to ameliorate CKD in addition to the current treatment strategies.