Biology subjects

Peulen, T.-O.

Publications and source records attributed to Peulen, T.-O..

4 recordsLinked to original sources

bioRxiv2026

siRNA/mRNA payload of lipid nanoparticles quantified using fluorescence microscopy and correlation spectroscopy

Lipid nanoparticles (LNPs) have emerged as the most promising vectors to deliver nucleic acids to mammalian cells. Advanced delivery strategies involving multiple nucleic acid species require a reliable determination of RNA payload and LNP size. Here, we use total internal reflection fluorescence (TIRF) microscopy and fluorescence correlation spectroscopy (FCS) to determine mRNA payload and siRNA/mRNA codelivered payload. Particularly, FCS proves to be a reliable method for payload determination, independently of fluorophore properties that can be altered during encapsulation. By varying the PEG-lipid fraction relative to the reference ONPATTRO formulation, we find that mRNA payload of enhanced green fluorescent protein (eGFP) coding mRNA increases from [~]10 with LNP size according to a power law with an exponent 1.3. Cryogenic electron microscopy (Cryo-EM) reveals that in this case blebs occur more frequently in small LNPs. For siRNA/mRNA coencapsulation the siRNA fraction within LNPs exceeds that in the initial RNA mixture, suggesting that coencapsulation is competitive and constrained by nucleic acid diffusion. Together, our findings highlight the importance of particle size and encapsulation kinetics in controlling the loading and composition of RNA payloads in LNPs.

biophysics↗

bioRxiv2026

Multidomain Coupling Governs FoxP1 Assembly and Nuclear Compartmentalization

FoxP1 is a multidomain transcription factor implicated in development, immunity, and cancer, widely proposed to function as a dimer. However, the molecular mechanisms governing its assembly and nuclear organization in living cells remain unclear. Here, we combine biochemical assays and live-cell fluorescence lifetime imaging to resolve FoxP1 homotypic interactions. We show that FoxP1 forms heterogeneous complexes whose stability is governed by antagonistic coupling between its leucine-zipper (ZIP) and Forkhead (FKH) domains. The FoxP1 ZIP domain promotes dimerization while suppressing FKH-mediated interactions, revealing a competing interdomain mechanism that tunes complex formation. Pathogenic and deletion variants disrupt this intricate balance, altering interaction stability and promoting the formation of dense nuclear condensates upon loss of DNA binding. Together, our results demonstrate that FoxP1 assembly is encoded by its multidomain architecture. Our findings show how competing interaction domains regulate transcription factor complex formation, nuclear organization, and DNA binding, with implications for disease-associated dysregulation. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/728184v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@bb8cbcorg.highwire.dtl.DTLVardef@1149339org.highwire.dtl.DTLVardef@1cd87d7org.highwire.dtl.DTLVardef@9147a4_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIFoxP1 complexes assemble through two distinct mechanisms mediated by the FKH and ZIP domains. C_LIO_LIInterdomain coupling switches the configuration of the FoxP1 complex. C_LIO_LIFKH-mediated FoxP1 oligomers and other higher-order complexes accumulate at the nuclear periphery, whereas ZIP-mediated dimers remain more uniformly distributed. C_LIO_LIDisruption of FKH-DNA binding drives formation of protein-dense FoxP1 condensates. C_LI

biophysics↗

bioRxiv2026

Bound or unbound: Mapping and monitoring receptor oligomerization using time-resolved fluorescence

Understanding protein oligomerization in living cells is essential for elucidating cellular signaling and regulation, yet quantitative analysis remains challenging due to heterogeneous expression levels, dynamic interactions, and limited access to absolute protein concentrations. Here, we present a standardized, open-source framework for quantifying protein assemblies in living cells by integrating fluorescence lifetime and anisotropy imaging (heteroFRET and homoFRET) with molecular brightness-based concentration estimation and image analysis. Using natural variants of a vertebrate GPCR, the melanocortin-4 receptor (MC4R-A and MC4R-B2), as a model system, we demonstrate how to discriminate monomers, dimers, and higher-order oligomers, extract inter-fluorophore distance distributions, and determine association constants under physiologically relevant conditions in living cells. Standard fluorescent protein tags report on proximity and oligomerization via Homo- and HeteroFRET. Association constants are quantified using the variable protein expression in living cells and the spectroscopy readouts. By high-content imaging we overcome the biological noise and attain data qualities comparable to conventional biochemical in vitro assays. Intensity- and fluctuation-based segmentation further extends the accessible concentration range within individual cells, improving affinity analysis robustness. Our results establish quantitative image spectroscopy on living cells as quantitative tool for investigating protein-protein interactions under physiologically relevant conditions. All computational workflows are implemented in open-source software and are accompanied by detailed protocols and analysis scripts, enabling reproducible application and adaptation. Beyond GPCRs, this framework provides a practical and transferable methodology for quantitative studies on protein-protein interactions, mechanistic studies and drug discovery in complex cellular environments.

biophysics↗

bioRxiv2023

Bayesian Fluorescence Framework for integrative modeling of biomolecules

Fluorescence spectroscopic and imaging techniques, such as fluorescence-correlation spectroscopy, image correlation spectroscopy, time-resolved fluorescence spectroscopy, and intensity-based spectroscopy, can provide sparse time-dependent positional and inter-fluorophore distance information for macromolecules and their complexes in vitro and in living cells. Here, we formulated a Bayesian framework for processing and using the fluorescence data for interpreting by static and dynamic models of biomolecules. We introduce Bayesian Fluorescence Framework (BFF) as part of the open-source Integrative Modeling Platform (IMP) software environment, facilitating the development of modeling protocols based in part on fluorescence data. BFF improves the accuracy, precision, and completeness of the resulting models by formulating the modeling problem as a sampling problem dependent on general and flexible libraries of (i) atomic and coarse-grained molecular representations of single-state models, multi-state models, and dynamic processes, (ii) Bayesian data likelihoods and priors, as well as (iii) sampling schemes. To illustrate the framework, we apply it to a sample synthetic single-molecule FRET dataset of the human transglutaminase 2. We show how to integrate time-resolved fluorescence intensities, fluorescence correlation spectroscopy curves, and fluorescence anisotropies to simultaneously resolve dynamic structures, state populations, and molecular kinetics. As BFF is part of IMP, fluorescence data can be easily integrated with other data types to solve challenging modeling problems. Statement of SignificanceBayesian Framework for Fluorescence (BFF) is software that implements a probabilistic framework for processing experimental fluorescence data to provide input information for Bayesian integrative structure modeling. BFF facilitates constructing integrative modeling protocols based in part on fluorescence data by reducing the required fluorescence spectroscopy and microscopy domain knowledge. In addition, it improves the precision and accuracy of the resulting models.

biophysics↗