Biophysical Society Thematic Meeting | Tutzing 2026

Single-Molecule FRET: The Next 30 Years

Wednesday Speaker Abstracts

QUANTITATIVE IMAGE SPECTROSCOPY FOR PROTEIN-PROTEIN INTERACTIONS IN LIVING CELLS: AN INTEGRATED FRET FRAMEWORK Katherina Hemmen 1 ; Annemarie Greife 3 ; Ruiqi Liu 4 ; Paul S Köhler 1 ; Katrin G Heinze 1 ; Thomas-Otavio Peulen 1,2 ; 1 Julius-Maximilians-University Würzburg, Rudolf-Virchow-Center for Integrative and Translational Bioimaging, Würzburg, Germany 2 Technische Universität Dortmund, Biophysical Chemistry, Department of Chemistry and Chemical Biology, Dortmund, Germany 3 Heinrich Heine University Düsseldorf, Molecular Physical Chemistry, Düsseldorf, Germany 4 Guangzhou University, South China Biodiversity Research Center, School of Life Sciences, Guangzhou, China Quantifying protein assemblies in living cells remains a major challenge due to heterogeneous expression levels, dynamic interactions, and the difficulty of accessing absolute protein concentrations. We present an open-source, standardized framework that integrates heteroFRET (Förster Resonance Energy Transfer), homoFRET (fluorescence anisotropy), and fluorescence lifetime imaging (FLIM) with molecular brightness-based concentration estimation to enable quantitative analysis of protein oligomerization states directly in living cells. The framework extracts inter-fluorophore distance distributions, discriminates monomers, dimers, and higher order oligomers, and determines association constants - all under physiologically relevant conditions. A key feature is the exploitation of natural cell-to-cell variability in protein expression as a built-in concentration series, allowing affinity quantification without exogenous perturbation. Intensity- and fluctuation-based image segmentation further extends the accessible concentration range within individual cells, improving the robustness of affinity analysis across expression regimes. To benchmark the approach, we use natural variants of the melanocortin-4 receptor (MC4R), a vertebrate GPCR, as a well-defined model system for monomeric, dimeric, and oligomeric states. High-content imaging across large cell populations overcomes biological noise, yielding data quality comparable to conventional in vitro biochemical assays. All computational workflows are implemented in open-source software and accompanied by detailed protocols and analysis scripts, supporting reproducibility and straightforward adaptation to new biological systems. Beyond GPCRs, the framework offers a practical and transferable methodology for studying protein-protein interactions, with applications in mechanistic cell biology and cell-based drug discovery.

29