Conformational States Control Lck Switching between Free and Confined Diffusion Modes in T Cells

Investigating Diffusion Dynamics and Interactions with Scanning Fluorescence Correlation Spectroscopy (sFCS)

Activation of immune cells and formation of immunological synapses (IS) rely critically on the reorganization of the plasma membrane. These highly orchestrated processes are driven by diffusion and oligomerization dynamics, as well as by single molecule interactions. While slow macro- and meso-scale changes in organization can be observed with conventional imaging, fast nano-scale dynamics are often missed with traditional approaches, but resolving them is, nonetheless, essential to understand the underlying biological mechanisms at play. Here, we describe the use of scanning fluorescence correlation spectroscopy (sFCS) and scanning fluorescence cross-correlation spectroscopy (sFCCS) to study reorganization and changes in molecular diffusion dynamics and interactions during IS formation and in other biological settings. We focus on the practical aspects of the measurements including calibration and alignment of the optical setup, present a comprehensive protocol to perform the measurements, and provide data analysis pipelines and strategies. Finally, we show an exemplary application of the technology to studying Lck diffusion during T-cell signaling.

Fyn nanoclustering requires switching to an open conformation and is enhanced by FTLD-Tau biomolecular condensates

Fyn is a Src kinase that controls critical signalling cascades and has been implicated in learning and memory. Postsynaptic enrichment of Fyn underpins synaptotoxicity in dementias such as Alzheimer's disease and frontotemporal lobar degeneration with Tau pathology (FTLD-Tau). The FLTD P301L mutant Tau is associated with a higher propensity to undergo liquid-liquid phase separation (LLPS) and form biomolecular condensates. Expression of P301L mutant Tau promotes aberrant trapping of Fyn in nanoclusters within hippocampal dendrites by an unknown mechanism. Here, we used single-particle tracking photoactivated localisation microscopy to demonstrate that the opening of Fyn into its primed conformation promotes its nanoclustering in dendrites leading to increased Fyn/ERK/S6 downstream signalling. Preventing the auto-inhibitory closed conformation of Fyn through phospho-inhibition or through perturbation of its SH3 domain increased Fyn's nanoscale trapping, whereas inhibition of the catalytic domain had no impact. By combining pharmacological and genetic approaches, we demonstrate that P301L Tau enhanced both Fyn nanoclustering and Fyn/ERK/S6 signalling via its ability to form biomolecular condensates. Together, our findings demonstrate that Fyn alternates between a closed and an open conformation, the latter being enzymatically active and clustered. Furthermore, pathogenic immobilisation of Fyn relies on the ability of P301L Tau to form biomolecular condensates, thus highlighting the critical importance of LLPS in controlling nanoclustering and downstream intracellular signalling events.

Molecular dynamics simulations reveal membrane lipid interactions of the full-length lymphocyte specific kinase (Lck)

The membrane-bound lymphocyte-specific protein-tyrosine kinase (Lck) triggers T cell antigen receptor signalling to initiate adaptive immune responses. Despite many structure-function studies, the mode of action of Lck and the potential role of plasma membrane lipids in regulating Lck's activity remains elusive. Advances in molecular dynamics simulations of membrane proteins in complex lipid bilayers have opened a new perspective in gathering such information. Here, we have modelled the full-length Lck open and closed conformations  using data available from different crystalographic studies and simulated its interaction with the inner leaflet of the T cell plasma membrane. In both conformations, we found that the unstructured unique domain and the structured domains including the kinase interacted with the membrane with a preference for PIP lipids. Interestingly, our simulations suggest that the Lck-SH2 domain interacts with lipids differently in the open and closed Lck conformations, demonstrating that lipid interaction can potentially regulate Lck's conformation and in turn modulate T cell signalling. Additionally, the Lck-SH2 and kinase domain residues that significantly contacted PIP lipids are found to be conserved among the Src family of kinases, thereby potentially representing similar PIP interactions within the family.

The kinase occupancy of T cell coreceptors reconsidered

The sensitivity of the αβ T cell receptor (TCR) is enhanced by the coreceptors CD4 and CD8αβ, which are expressed primarily by cells of the helper and cytotoxic T cell lineages, respectively. The coreceptors bind to major histocompatibility complex (MHC) molecules and associate intracellularly with the Src-family kinase Lck, which catalyzes TCR phosphorylation during receptor triggering. Although coreceptor/kinase occupancy was initially believed to be high, a recent study suggested that most coreceptors exist in an Lck-free state, and that this low occupancy helps to effect TCR antigen discrimination. Here, using the same method, we found instead that the CD4/Lck interaction was stoichiometric (~100%) and that the CD8αβ/Lck interaction was substantial (~60%). We confirmed our findings in live cells using fluorescence cross-correlation spectroscopy (FCCS) to measure coreceptor/Lck codiffusion in situ. After introducing structurally guided mutations into the intracellular domain of CD4, we used FCCS to also show that stoichiometric coupling to Lck required an amphipathic α-helix present in CD4 but not CD8α. In double-positive cells expressing equal numbers of both coreceptors, but limiting amounts of kinase, CD4 outcompeted CD8αβ for Lck. In T cells, TCR signaling induced CD4/Lck oligomerization but did not affect the high levels of CD4/Lck occupancy. These findings help settle the question of kinase occupancy and suggest that the binding advantages that CD4 has over CD8 could be important when Lck levels are limiting.

Joint Estimation of Trajectory and Model Parameters for Single Particle Tracking of 3D Confined Diffusion Using the Double-Helix Point Spread Function

Single particle tracking plays a significant role in biophysics through its ability to reveal dynamic mechanisms and physical properties of biological macromolecules inside living cells. The motion of these molecules can often be modeled as a confined diffusion. The standard paradigm in the biophysics community is to first estimate the trajectory of a particle and then use a technique such as the Mean Square Displacement or the Maximum Likelihood Estimation (MLE) to determine model parameters. These approaches, however, ignore the fact that localization and parameter estimation problems are coupled. We have previously introduced a framework based on optimal estimation theory to simultaneously do localization and parameter estimation. Here we build upon that work by expanding it to include a recent advance in imaging three dimensional motion, namely the Double-Helix (DH) engineered Point Spread Function (PSF). The DH-PSF encodes the axial position of the particle directly into the 2D image acquired by the camera mounted to the microscope. Our approach uses Expectation Maximization (EM) and Sequential Monte Carlo (SMC) to handle the nonlinearities in the observation and motion models. In this paper, we also improve upon the computational complexity of this scheme, using a Gaussian Particle Filter and Backward Simulation Particle Smoother in the SMC elements of the algorithm. We compare our scheme through simulation to state of the art methods based on localization using Gaussian fitting followed by MLE of the model parameters. These results show that our method outperforms GF-MLE at the low signal intensity levels common to biophysical experiments.

Three-dimensional localization refinement and motion model parameter estimation for confined single particle tracking under low-light conditions

Confined diffusion is an important model for describing the motion of biological macromolecules moving in the crowded, three-dimensional environment of the cell. In this work we build upon the technique known as sequential Monte Carlo - expectation maximization (SMC-EM) to simultaneously localize the particle and estimate the motion model parameters from single particle tracking data. We extend SMC-EM to handle the double-helix point spread function (DH-PSF) for encoding the three-dimensional position of the particle in the two-dimensional image plane of the camera. SMC-EM can handle a wide range of camera models and here we assume the data was acquired using a scientific CMOS (sCMOS) camera. The sensitivity and speed of these cameras make them well suited for SPT, though the pixel-dependent nature of the camera noise presents a challenge for analysis. We focus on the low signal setting and compare our method through simulation to more standard approaches that use the paradigm of localize-then-estimate. To localize the particle under the standard paradigm, we use both a Gaussian fit and a maximum likelihood estimator (MLE) that accounts for both the DH-PSF and the pixel-dependent noise of the camera. Model estimation is then carried out either by fitting the model to the mean squared displacement (MSD) curve, or through an optimal estimation approach. Our results indicate that in the low signal regime, the SMC-EM approach outperforms the other methods while at higher signal-to-background levels, SMC-EM and the MLE-based methods perform equally well and both are significantly better than fitting to the MSD. In addition our results indicate that at smaller confinement lengths where the nonlinearities dominate the motion model, the SMC-EM approach is superior to the alternative approaches.