The 23 pairs of chromosomes comprising the human genome are intricately folded within the nucleus of each cell in a manner that promotes efficient gene regulation and cell function. Consequently, active gene rich regions are compartmentally segregated from inactive gene poor regions of the genome. To better understand the mechanisms driving compartmentalization we investigated what would occur if this system was disrupted. By digesting the genome to varying sizes and analyzing the fragmented 3D structure over time, our work revealed essential laws governing nuclear compartmentalization. At a finer resolution within compartments, chromatin forms loop structures capable of regulating gene expression. Genome wide association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with the neuropsychiatric disease schizophrenia. When these SNPs are not located within a gene it is difficult to gain insight into disease pathology; however, in some cases chromatin loops may link these noncoding schizophrenia risk variants to their pathological gene targets. By generating 3D genome maps, we identified and analyzed loops of glial cells, neural progenitor cells, and neurons thereby expanding the set of genes conferring schizophrenia risk. The binding of T-cell receptors (TCRs) to foreign peptides on the surface of diseased cells triggers an immune response against the foreign invader. Utilizing available structural information of the TCR antigen interface, we developed computational methods for successful prediction of TCR-antigen binding. As this binding is a prerequisite for immune response, such improvements in binding prediction could lead to important advancements in the fields of autoimmunity and TCR design for cancer therapeutics.

T cell activation and thymic selection are thought to be determined by the binding propensity (avidity or affinity) of the T cell receptor (TCR) to its ligands. However, binding propensity quantified by previous 3D TCR–pMHC kinetics such as using tetramer staining or surface plasmon resonance (SPR) under estimate TCR–pMHC interaction due to neglecting physiological conditions. Recent studies considering membrane contribution in TCR–pMHC interaction reported 2D kinetics and force regulated bond dissociation kinetics have better prediction to biological responses in CD8+ T cells. In this study, we further tested the findings in CD4+ T cells and CD4+ CD8+ (double-positive, DP) thymocytes. We analyzed TCR–pMHC interaction for a well-characterized panel of altered peptide ligands (APLs) on multiple transgenic mouse TCR systems. Using ultrasensitive 2D mechanical assays, in situ 2D kinetic measurements show better sensitivity than the SPR 3D kinetic measurements in gauging the ligand potency and thymic selection. Furthermore, force-regulated bond lifetime of TCR–pMHC interaction amplifies the discrimination in recognition of APLs and thymic selection. When force was applied to TCR–pMHC–CD4/8 bonds, two distinct patterns emerged: agonist/negative selecting ligands formed CD4/8-dependent catch-slip bonds where lifetime first increased, reached a maximum, then decreased with increasing force, whereas antagonist/positive selecting ligands formed slip-only bonds where lifetime monotonically decreases with increasing force. Our results highlight an important role of mechanical force in ligand discrimination and suggest a new mechanism for T cell activation and thymic selection that is distinct from previous models based on 3D measurements.