Advanced Quantification of Receptor-Ligand Interaction Lifetimes via Single-Molecule FRET Microscopy.
Lukas SchranglVanessa MühlgrabnerRené PlatzerFlorian KellnerJosephine WielandReinhard ObstJose Luis Toca-HerreraJohannes B HuppaGerhard J SchützJanett GöhringPublished in: Biomolecules (2024)
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.
Keyphrases
- single molecule
- energy transfer
- regulatory t cells
- living cells
- atomic force microscopy
- immune response
- induced apoptosis
- quantum dots
- binding protein
- optical coherence tomography
- electronic health record
- oxidative stress
- big data
- deep learning
- case report
- single cell
- dna binding
- high resolution
- cell proliferation
- inflammatory response
- high throughput
- cell death
- data analysis
- bone marrow
- toll like receptor
- free survival
- artificial intelligence
- mesenchymal stem cells
- pi k akt