Advanced Quantification of Receptor–Ligand Interaction Lifetimes via Single-Molecule FRET Microscopy

Author:

Schrangl Lukas12ORCID,Mühlgrabner Vanessa3ORCID,Platzer René3ORCID,Kellner Florian3ORCID,Wieland Josephine4,Obst Reinhard4ORCID,Toca-Herrera José L.1ORCID,Huppa Johannes B.3ORCID,Schütz Gerhard J.2ORCID,Göhring Janett3ORCID

Affiliation:

1. Institute of Biophysics, Department of Bionanosciences, University of Natural Resources and Life Sciences, Muthgasse 11, 1190 Vienna, Austria

2. Institute of Applied Physics, TU Wien, Wiedner Hauptstr. 8-10, 1040 Vienna, Austria

3. Institute for Hygiene and Applied Immunology, Center for Pathophysiology, Infectiology and Immunology, Medical University of Vienna, Lazarettgasse 19, 1090 Vienna, Austria

4. Institute for Immunology, Biomedical Center, Medical Faculty, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany

Abstract

Receptor–ligand interactions at cell interfaces initiate signaling cascades essential for cellular communication and effector functions. Specifically, T cell receptor (TCR) interactions with pathogen-derived peptides presented by the major histocompatibility complex (pMHC) molecules on antigen-presenting cells are crucial for T cell activation. The binding duration, or dwell time, of TCR–pMHC interactions correlates with downstream signaling efficacy, with strong agonists exhibiting longer lifetimes compared to weak agonists. Traditional surface plasmon resonance (SPR) methods quantify 3D affinity but lack cellular context and fail to account for factors like membrane fluctuations. In the recent years, single-molecule Förster resonance energy transfer (smFRET) has been applied to measure 2D binding kinetics of TCR–pMHC interactions in a cellular context. Here, we introduce a rigorous mathematical model based on survival analysis to determine exponentially distributed receptor–ligand interaction lifetimes, verified through simulated data. Additionally, we developed a comprehensive analysis pipeline to extract interaction lifetimes from raw microscopy images, demonstrating the model’s accuracy and robustness across multiple TCR–pMHC pairs. Our new software suite automates data processing to enhance throughput and reduce bias. This methodology provides a refined tool for investigating T cell activation mechanisms, offering insights into immune response modulation.

Funder

the Vienna Science and Technology Fund

FWF

German Research Council

Publisher

MDPI AG

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