Mechanical activity enables patterning and discrimination at the immune synapse

TL;DR

This work develops a minimal continuum model coupling receptor binding kinetics, membrane deformation, and cytoskeletal forces, with elastohydrodynamic flow in the synaptic cleft, and highlights how immune cells exploit cytoskeletal remodeling to robustly regulate antigen recognition through synaptic patterning.

Abstract

Immune cells recognize and discriminate antigens through immunological synapses - dynamic intercellular junctions exhibiting highly organized receptor-ligand patterns. While much work has focused on molecular kinetics and passive mechanisms of pattern formation, the role of active mechanical control in patterning and discrimination remains underexplored. We develop a minimal continuum model coupling receptor binding kinetics, membrane deformation, and cytoskeletal forces, with elastohydrodynamic flow in the synaptic cleft. Numerical simulations and scaling analysis reveal that contractile cortical flows arrest coarsening and stabilize long-lived multifocal clusters, whereas active pulling accelerates cluster dissolution and elevates background receptor binding. Nonequilibrium mechanical forces enable adaptive control over the speed, sensitivity, and dynamic range of affinity discrimination in a pattern-dependent manner. Our results highlight how immune cells exploit cytoskeletal remodeling to robustly regulate antigen recognition through synaptic patterning.

Figures (4)

• Figure 1: The immune synapse is dynamically patterned and mechanically active. (A) T cells display a centralized bull's-eye like pattern of TCRs surrounded by adhesion molecules that coarsens over time (adapted from Ref. hartman2009cluster). (B) Naive B cells display a similar centralized cluster of antigen-bound BCRs, but affinity maturating B cells form a distinct pattern of localized puncta that enable antigen extraction using cytoskeletal forces (adapted from Ref. nowosad2016germinal). (C) Model schematic showing how receptor-antigen kinetics, integrin exclusion, membrane deformation ($h$), cytoskeletal forces ($\boldsymbol{\sigma}^{\rm a}_\parallel$, $\sigma^a_{\perp}$), and fluid flow (velocity ${\bf v}_f$) are coupled.
• Figure 2: Activity induces transition in synaptic patterns. (A) State diagram showing representative patterns of normalized bound receptor fraction ($\rho/\rho_{\rm max}$, with $\rho=c_R/c^0_R$) at a fixed time ($t = 300\tau_k$) for varying $\mathsf{Pe}$ and $A_\zeta$. A multifocal pattern with localized puncta emerges when $\mathsf{Pe} \gtrsim 4.25$ (dashed red line), becoming more pronounced for larger $\mathsf{Pe}$. Lower $\mathsf{Pe}$ corresponds to the active coarsening regime. Top inset shows puncta as spikes in $\rho$ near the periphery of the contact zone, with larger domains occupying the center. (B-C) Quantifying receptor patterns in (A) through the spatial average $\langle \rho \rangle=(1/\pi R^2)\int\mathrm{d}{\bf x}\;\rho$ (B) and relative fluctuation $\sqrt{\langle \delta\rho^2\rangle}/\langle \rho \rangle$, where $\delta\rho=\rho-\langle\rho\rangle$ (C). (B) Larger $\mathsf{Pe}$ decreases $\langle\rho\rangle$ by creating more, but smaller puncta, while increasing $A_\zeta$ raises $\langle\rho\rangle$ as the background density increases from balanced pushing that overcomes any decrease in $\rho$ due to direct pulling on clusters. (C) The coefficient of variation captures spatial heterogeneity in the pattern, but shows no significant trend with activity. (D) Active coarsening dynamics ($\mathsf{Pe} = 4.5,\,A_\zeta = 0.75$ corresponding to blue circle in A; bottom) proceeds faster than in the passive case ($\mathsf{Pe} = A_\zeta =0$; top) as active contractility breaks apart large domains while pulling forces accelerate dissolution of small clusters and raise the background density of bound receptors. (E) Viscous fluid flow slows pattern formation. Equal time ($t=350\tau_k$) snapshots for the passive ($\mathsf{Pe}=A_\zeta=0$; top) and active ($\mathsf{Pe}=4.5,\,A_\zeta = 0.25$ corresponding to red square in A; bottom) cases show similarity of patterns at small $\mathsf{Eh}$ in short time and large $\mathsf{Eh}$ in long time.
• Figure 3: Dynamics of active patterns. (A) Time evolution of cluster number and size distinguishes active multifocal patterning from passive coarsening. Left panels show temporal trajectories of cluster number and median size in passive (green dashed) and active (red solid; $\mathsf{Pe}=4.5,A_\zeta=0.25$) scenarios, with sample patterns at labeled time points for passive (a-c) and active (d-f) cases shown on the right. In the active case, the median cluster size at long time plateaus at the typical size $\sim 1.01L_c^2$ of the puncta. Our simulations resolve individual puncta using a mesh size of at most one third of the puncta radius. (B) Time evolution of the bound fraction of receptors in the background (left) versus within clusters (right), per unit domain area for: (i) passive coarsening ($\mathsf{Pe} = A_\zeta = 0$; green), (ii) active coarsening ($\mathsf{Pe} = 4.5$, $A_\zeta = 0.75$; blue), and (iii) active multifocal patterns ($\mathsf{Pe} = 5$ and varying $A_\zeta$; red). Note, $\langle\rho\rangle_{\Omega}(t)=(1/\pi R^2)\int_{\rm \Omega}\mathrm{d}{\bf x}\;\rho({\bf x},t)$, where $\Omega=\{\rm cluster,\,background\}$, so the spatial average $\langle\rho\rangle=\langle\rho\rangle_{\rm cluster}+\langle\rho\rangle_{\rm background}$. See Fig. S3 for the time trace of the total bound fraction $\langle \rho \rangle$.
• Figure 4: Active patterning allows sensitive discrimination over a wide affinity range. Top: The bound fraction of receptors in clusters $\langle \rho \rangle_{\rm cluster}$ as a function of $K_\mathrm{eq}^R$ in passive (green) and active (red) systems at $t=100 \,\tau_k$ (open symbols) and $t=600 \, \tau_k$ (filled symbols). Lines are quadratic fits with $95\%$ confidence interval shown by the shading. Bottom: Logarithmic cluster sensitivity computed as $\partial\ln\langle \rho \rangle_{\rm cluster}/\partial\ln K_\mathrm{eq}^R$ obtained from the fitted curves in the top panel. In the active case, $\mathsf{Pe}=4.5$ and $A_\zeta=0.25$ are fixed; as $K_\mathrm{eq}^R$ increases, the system transitions from active coarsening to multifocal clustering around $K_\mathrm{eq}^R\simeq 5$.