Biology subjects

Raedler, J.

Publications and source records attributed to Raedler, J..

4 recordsLinked to original sources

bioRxiv2026

Thermodynamic Model and Experiments Indicate Kinetically Trapped States in Ionisable Lipid Nanoparticles

Lipid nanoparticles (LNPs) are the leading delivery vehicles for RNA-based therapies, with pH-dependent mesophase transitions of their ionizable lipids proposed to play a crucial role in LNP function and endosomal escape. However, a consistent quantitative framework linking molecular lipid properties to the thermodynamics of these transitions is still lacking. Here, we establish such a framework by combining X-ray scattering, fluorescence spectroscopy, all-atom molecular dynamics simulations, and continuum modeling. Using small-angle X-ray scattering (SAXS) and Laurdan fluorescence spectroscopy, we characterize the pH- and temperature-dependent phase behavior of both bulk lipid systems and LNPs containing the ionisable lipids MC3 or ALC-0315. Laurdan fluorescence measurements reveal the same pH- and temperature-dependent transitions in bulk and LNP systems, which closely follow the inverse micellar (LII) - inverse hexagonal (HII) transitions identified by SAXS, establishing Laurdan as a reporter of these mesophase transitions in LNPs. We combine experimental and simulation data to parameterize a continuum model of the LII - HII transition, providing a quantitative description of mesophase stability. The model reveals that geometric frustration arising from packing constraints is a key determinant of mesophase stability. By combining experimental and simulation data with theoretical analysis, we determine the relevant membrane parameters, including mean and Gaussian curvature bending rigidities, and identify the parameter ranges that govern phase stability. Together, these results establish a quantitative continuum theory for pH-dependent mesophase transitions of ionisable lipid and suggest metastable states in response to pH changes as an inherent feature of LNP core phase.

biophysics↗

bioRxiv2024

Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference

Trajectories of motile cells represent a rich source of data that provide insights into the mechanisms of cell migration via mathematical modeling and statistical analysis. However, mechanistic models require cell type dependent parameter estimation, which in case of computational simulation is technically challenging due to the nonlinear and inherently stochastic nature of the models. Here, we employ simulation-based inference (SBI) to estimate cell specific model parameters from cell trajectories based on Bayesian inference. Using automated time-lapse image acquisition and image recognition large sets of 1D single cell trajectories are recorded from cells migrating on microfabricated lanes. A deep neural density estimator is trained via simulated trajectories generated from a previously published mechanical model of cell migration. The trained neural network in turn is used to infer the probability distribution of a limited number of model parameters that correspond to the experimental trajectories. Our results demonstrate the efficacy of SBI in discerning properties specific to non-cancerous breast epithelial cell line MCF-10A and cancerous breast epithelial cell line MDA-MB-231. Moreover, SBI is capable of unveiling the impact of inhibitors Latrunculin A and Y-27632 on the relevant elements in the model without prior knowledge of the effect of inhibitors. The proposed approach of SBI based data analysis combined with a standardized migration platform opens new avenues for the installation of cell motility libraries, including cytoskeleton drug efficacies,and may play a role in the evaluation of refined models. Subject AreasBiological Physics / Interdisciplinary Physics

biophysics↗

bioRxiv2023

Endosome dysfunction leads to gain-of-function TLR7 and human lupus

Hyperactive Toll-like receptor (TLR) 7 signaling has long been appreciated as a driver of autoimmune disease by breaking tolerance to self-nucleic acids in mouse models1-5. Recently, mutations in TLR7 or its associated regulator UNC93B16, 7, were identified as monogenic causes of human lupus; the unifying feature of these mutations being TLR7 gain-of-function. TLR7 is an intracellular transmembrane receptor, sensing RNA breakdown products within late endosomes8, 9. Hence, its function depends on intricate transport mechanisms and membrane interactions within the endomembrane network. Whether perturbations of any of these endosome-related processes can give rise to TLR7 gain-of-function and facilitate self-reactivity has not been investigated. Here, we show that a dysregulated endosomal compartment leads to unrestricted TLR7 signaling and human lupus. The late endosomal BLOC-1-related protein complex (BORC) together with the small Arf1-like GTPase Arl8b controls TLR7 protein levels, and a direct interaction between Arl8b and Unc93b1 is required to regulate TLR7 turnover. We identified an amino acid insertion in UNC93B1 in a patient with childhood-onset lupus, which reduces the interaction with the BORC-Arl8b complex and leads to endosomal TLR7 accumulation. Therefore, a failure to control the proper progression of TLR7 through its endocytic life cycle is sufficient to break immunological tolerance to nucleic acids in humans. Our results highlight the importance of an intact endomembrane system to prevent autoimmune disease. As the cellular mechanisms restricting TLR7 signaling can be manifold, identifying and stratifying lupus patients based on a TLR7-driven pathogenesis could be a viable strategy towards a targeted therapy.

immunology↗

bioRxiv2018

Multi-Experiment Nonlinear Mixed Effect Modeling of Single-Cell Translation Kinetics after Transfection

Single-cell time-lapse studies have advanced the quantitative understanding of cell-to-cell variability. However, as the information content of individual experiments is limited, methods to integrate data collected under different conditions are required.\n\nHere we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.

systems biology↗