Biology subjects

Westermann, L.

Publications and source records attributed to Westermann, L..

3 recordsLinked to original sources

bioRxiv2026

Transcriptome-based cell type assignment for kidney cell culture models

BackgroundKidney 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. MethodsReference 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. ResultsGene 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. ConclusionWe 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 StatementKidney 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.

bioinformatics↗

bioRxiv2025

Deep learning-based image quantification of epithelial cell shapes and its application to polycystic kidney disease

Cell shape is a fundamental determinant of tissue architecture and organ function. In epithelial tissues, cytoskeletal organization and tight junctions regulate cell geometry, shaping functional tissue units. Disruption of these mechanisms may cause diseases such as autosomal dominant polycystic kidney disease (ADPKD), in which cyst formation is characterized by abnormal regulation of epithelial cell shape. The mechanisms of cystogenesis remain incompletely understood, highlighting the need for robust, high-throughput methods to quantify the morphology of epithelial cells. Here, we present a fully automated, deep learning-based image analysis pipeline to quantify epithelial cell shape and tight junction morphology from immunofluorescence images. Our approach employs a U-Net convolutional neural network for accurate segmentation of fluorescence labeled tight junctions. We introduce novel algorithms to quantify overall cell shape and tight junction morphology, as well as to estimate cytoskeletal traction at shared cell borders. Our analysis pipeline objectively identifies subtle morphogenetic changes associated with disease-related mutations, applied to a genetically modified Madin-Darby Canine Kidney cell model of ADPKD. The method enables high-throughput, standardized analysis, reduces observer bias, and facilitates comparison across experiments. We further demonstrate the pipelines generalizability by applying it to Drosophila egg chamber epithelia. Our results establish a robust and scalable framework for analyzing cell shape and mechanical interactions in epithelial tissues, with broad applications in phenotypic screening, disease modeling, and morphogenesis research. Author SummaryThe shape of epithelial cells is critical for organ function. In the kidney, properly shaped epithelial cells assemble to tubules ensuring efficient waste excretion as well as body electrolyte and water balance. Disruption of cell shape regulation can lead to diseases such as autosomal dominant polycystic kidney disease (ADPKD), characterized by cyst formation and displacement of normal kidney tissue. Traditionally, analysis of epithelial cell morphology has relied on manual, low-throughput methods, which are time-consuming and prone to error. To overcome these limitations, we developed a fully automated, artificial intelligence-based pipeline that rapidly and reliably quantifies cell shape and junctional organization from microscopic images. We validated our approach using a cellular model of ADPKD, demonstrating clear differences in cell shape and junctional structure between normal and mutant cells harboring mutations in PKD-related genes. Our method enables efficient, objective analysis of large datasets and provides a powerful tool for understanding the mechanisms underlying cell shape regulation in health and disease.

bioinformatics↗

bioRxiv2024

The Role of the Co-Chaperone DNAJB11 in Polycystic Kidney Disease: Molecular Mechanisms and Cellular Origin of Cyst Formation

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.

molecular biology↗