Biphasic mechanosensitivity of T cell receptor-mediated spreading of lymphocytes.

Mechanosensing by T cells through the T cell receptor (TCR) is at the heart of immune recognition. While the mechanobiology of the TCR at the molecular level is increasingly well documented, its link to cell-scale response is poorly understood. Here we explore T cell spreading response as a function of substrate rigidity and show that remarkably, depending on the surface receptors stimulated, the cellular response may be either biphasic or monotonous. When adhering solely via the TCR complex, T cells respond to environmental stiffness in an unusual fashion, attaining maximal spreading on an optimal substrate stiffness comparable to that of professional antigen-presenting cells. However, in the presence of additional ligands for the integrin LFA-1, this biphasic response is abrogated and the cell spreading increases monotonously with stiffness up to a saturation value. This ligand-specific mechanosensing is effected through an actin-polymerization-dependent mechanism. We construct a mesoscale semianalytical model based on force-dependent bond rupture and show that cell-scale biphasic or monotonous behavior emerges from molecular parameters. As the substrate stiffness is increased, there is a competition between increasing effective stiffness of the bonds, which leads to increased cell spreading and increasing bond breakage, which leads to decreased spreading. We hypothesize that the link between actin and the receptors (TCR or LFA-1), rather than the ligand/receptor linkage, is the site of this mechanosensing.

Ligand Nanocluster Array Enables Artificial-Intelligence-Based Detection of Hidden Features in T-Cell Architecture.

Protein patterning has emerged as a powerful means to interrogate adhering cells. However, the tools to apply a sub-micrometer periodic stimulus and the analysis of the response are still being standardized. We propose a technique combining electron beam lithography and surface functionalization to fabricate nanopatterns compatible with advanced imaging. The repetitive pattern enables a deep-learning algorithm to reveal that T cells organize their membrane and actin network differently depending upon whether the ligands are clustered or homogeneously distributed, an effect invisible to the unassisted human eye even after extensive image analysis. This fabrication and analysis toolbox should be useful, both together and separately, for exploring general correlation between a spatially structured subcellular stimulation and a subtle cellular response.