Publikationen

Human liver ductal epithelium is morphologically, functionally, and transcriptionally heterogeneous. Under- standing the impact of this heterogeneity has been challenging due to the absence of systems that recapit- ulate this heterogeneity in vitro. Here, we found that human liver cholangiocyte organoids do not retain the complex cellular heterogeneity of the native ductal epithelium. Inspired by the knowledge of the cellular niche, we refined our previous organoid medium to fully capture the in vivo cellular heterogeneity. We em- ployed this refined system to analyze the relationships between human biliary epithelial cell states. In our refined model, cholangiocytes transition toward hepatocyte-like states through a bipotent state. Additionally, inhibiting WNT signaling enhances the differentiation capacity of the cells toward hepatocyte-like states. By capturing the in vivo cholangiocyte heterogeneity, our improved organoid model represents a platform to investigate the impact of the different liver ductal cell states in cell plasticity, regeneration, and disease.

Over the past decade, advances in organoid culturing methods have enabled the growth of three-dimensional cellular cultures in vitro with increasing fidelity with respect to the cellular composition, architecture and function of in vivo organs. The increased accessibility and ability to manipulate organoids as an in vitro system have led to a shift in the landscape of experimental biology. Whether derived from stem cells or tissue-resident cells, organoids are now routinely used in studies of development, homeostasis, regeneration and disease modelling, including viral infection and cancer. These applications of organoids are highly relevant for gastrointestinal tissues, including the liver and pancreas. In this Review, we explore the current and emerging advances in liver and pancreas organoid technologies for both discovery and clinical translation research and provide an outlook on the challenges ahead.

Microbial alcohol production from waste gases is a game changer for sustainable carbon cycling and remediation. While the biotechnological process using Clostridium autoethanogenum to transform syngas (H2, CO2 and CO) is blooming, scientific debates remain on the ethanol biosynthesis pathway. Here, we experimentally validated that ethanol production is initiated through a tungsten-dependent aldehyde:ferredoxin oxidoreductase (AFOR), which reduces acetate to acetaldehyde. The reaction, thermodynamically unfavorable under standard conditions, has been considered by many as unsuitable in vivo but is rather approved by metabolic modeling. To answer this riddle, we demonstrated that the thermodynamic coupling of CO oxidation and ethanol synthesis allows acetate reduction. The experiments, performed with native CO dehydrogenase and AFOR, highlighted the key role of ferredoxin in stimulating the activity of both metalloenzymes and electron shuttling. The crystal structure of holo AFOR, refined to 1.59-Å resolution, and its biochemical characterization provide new insights into the cofactor chemistry and the specificities of this enzyme, fundamental to sustainable biofuel production.

DNA-based Point Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) is a powerful variant of single-molecule localization microscopy (SMLM) that overcomes the limitations of photobleaching, offers flexible fluorophore selection, and enables fine control of imaging parameters through tunable on- and off-binding kinetics. Its most distinctive feature is its capacity for multiplexing, typically implemented through sequential imaging of targets using an Exchange-PAINT. This technique involves assigning orthogonal DNA strands to different targets within a sample and then sequentially adding and removing complementary imager strands that are specific to only one target at a time. However, manual Exchange-PAINT workflows are often inefficient, prone to drift and variability, and lack reproducibility. Here, we introduce a custom compressed-air-driven microfluidics system specifically designed for multiplexed SMLM. Featuring a stackable and modular design that is, in principle, not limited by the number of channels, the system ensures robust, reproducible, and material-efficient buffer exchange with minimal dead volume. It operates in both manual and automated modes and can be readily adapted to a wide range of commercial and custom microscopes, including wide-field, confocal, STED, MINFLUX and other platforms. We demonstrate robust 5-plex Exchange-PAINT imaging in cancerous U2OS cells, and importantly, we establish multiplexed nanoscale imaging in fragile primary cardiomyocytes. These applications demonstrate that the platform enables reliable super-resolution multiplexing in physiologically relevant systems and supports detailed nanoscale analysis in complex primary cells.

This paper illustrates a further application of topological data analysis to the study of self-organising models for chemical and biological systems. In particular, we investigate whether topological summaries can capture the parameter dependence of pattern topology in reaction diffusion systems, by examining the homology of sublevel sets of solutions to Turing reaction diffusion systems for a range of parameters. We demonstrate that a topological clustering algorithm can reveal how pattern topology depends on parameters, using the chlorite-iodide-malonic acid system, and the prototypical Schnakenberg system for illustration. In addition, we discuss the prospective application of such clustering, for instance in refining priors for detailed parameter estimation for self-organising systems.

Rhodamine dyes conjugated to targeting ligands can yield exceptionally bright fluorescent probes for live-cell imaging. However, the limited permeability of such rhodamine derivatives restricts their broader applications, particularly in vivo. Here, we present Rho-tag and SiR-tag, engineered protein tags derived from bacterial multidrug-resistant proteins that bind unsubstituted (silicon) rhodamines with nanomolar affinity. Unsubstituted (silicon) rhodamines readily cross membranes and enable rapid, reversible, and fluorogenic labeling of the tags in mammalian cells within seconds. The labeling of Rho-tag and SiR-tag is compatible with various super-resolution imaging methods and allows their use alongside self-labeling tags, such as HaloTag7 and SNAP-tag. The high affinity and specificity of both tags, combined with the permeability and outstanding spectroscopic properties of rhodamines, make them particularly attractive for in vivo bioimaging, as demonstrated by efficient fluorescent labeling inC. elegansembryos and zebrafish larvae.

Lumen formation in organ epithelia involves processes such as polarization, secretion, exocytosis and contractility, but what controls lumen shape remains unclear. Here we study how lumina develop spherical or complex structures using pancreatic organoids. Combining computational phase-field modelling and experiments, we found that lumen morphology depends on the balance between cell cycle duration and lumen pressure, low pressure and high proliferation produce complex shapes. Manipulating proliferation and lumen pressure can alter or reverse lumen development both in silico and in vitro. Increasing epithelial permeability reduces lumen pressure, converting from spherical to complex lumina. During pancreas development, the epithelium is initially permeable and becomes sealed, experimentally increasing permeability at late stages impairs ductal morphogenesis. Overall, our work underscores how proliferation, pressure and permeability orchestrate lumen shape, offering insights for tissue engineering and cystic disease treatment.

The effect of the presence of the BAF-binding LEM-domain and LaminA Ig-fold on the stability of the BAF dimer was studied qualitatively using non-equilibrium pull simulations and quantitatively through the calculation of the potential of mean force profile along BAF-BAF separation distance. We find that hydrophobicity plays a significant role in stabilizing the BAF dimer when LEM-domain and LaminA are bound. The role of LEM-domain and LaminA in stabilizing the BAF dimer is explored by quantifying the strength of interaction between them, which are critical components of the nuclear lamina.

The development of complex multicellular human in vitro systems holds great promise for modelling disease and advancing drug discovery and tissue engineering1. In the liver, despite the identification of key signalling pathways involved in hepatic regeneration2,3, in vitro expansion of human hepatocytes directly from fresh patient tissue has not yet been achieved, limiting the possibility of modelling liver composite structures in vitro. Here we first developed human hepatocyte organoids (h-HepOrgs) from 28 different patients. Patient-derived hepatocyte organoids sustained long-term expansion of hepatocytes in vitro and maintained patient-specific gene expression and bile canaliculus features and function of the in vivo tissue. After transplantation, expanded h-HepOrgs rescued the phenotype of a mouse model of liver disease. By combining h-HepOrgs with portal mesenchyme and our previously published cholangiocyte organoids4-6, we generated patient-specific periportal liver assembloids that retain the histological arrangement, gene expression and cell interactions of periportal liver tissue, with cholangiocytes and mesenchyme embedded in the hepatocyte parenchyma. We leveraged this platform to model aspects of biliary fibrosis. Our human periportal liver assembloid system represents a novel in vitro platform to investigate human liver pathophysiology, accelerate drug development, enable early diagnosis and advance personalized medicine.

In recent years, there has been growing interest in jointly analyzing a foreground dataset, representing an experimental group, and a background dataset, representing a control group. The goal of such contrastive investigations is to identify salient features in the experimental group relative to the control. Independent component analysis (ICA) is a powerful tool for learning independent patterns in a dataset. We generalize it to contrastive ICA (cICA). For this purpose, we devise a linear algebra-based tensor decomposition algorithm, which is more expressive but just as efficient and identifiable as other linear algebra-based algorithms. We establish the identifiability of cICA and demonstrate its performance in finding patterns and visualizing data, using synthetic, semisynthetic, and real-world datasets, comparing the approach to existing methods.