Multimodal probing of T-cell recognition with hexapod heterostructures.
Xiaodan HuangLingyuan MengGuoshuai CaoAleksander ProminskiYifei HuChuanwang YangMin ChenJiuyun ShiCharles GallagherThao CaoJiping YueJun HuangBozhi TianPublished in: Nature methods (2024)
Studies using antigen-presenting systems at the single-cell and ensemble levels can provide complementary insights into T-cell signaling and activation. Although crucial for advancing basic immunology and immunotherapy, there is a notable absence of synthetic material toolkits that examine T cells at both levels, and especially those capable of single-molecule-level manipulation. Here we devise a biomimetic antigen-presenting system (bAPS) for single-cell stimulation and ensemble modulation of T-cell recognition. Our bAPS uses hexapod heterostructures composed of a submicrometer cubic hematite core (α-Fe 2 O 3 ) and nanostructured silica branches with diverse surface modifications. At single-molecule resolution, we show T-cell activation by a single agonist peptide-loaded major histocompatibility complex; distinct T-cell receptor (TCR) responses to structurally similar peptides that differ by only one amino acid; and the superior antigen recognition sensitivity of TCRs compared with that of chimeric antigen receptors (CARs). We also demonstrate how the magnetic field-induced rotation of hexapods amplifies the immune responses in suspended T and CAR-T cells. In addition, we establish our bAPS as a precise and scalable method for identifying stimulatory antigen-specific TCRs at the single-cell level. Thus, our multimodal bAPS represents a unique biointerface tool for investigating T-cell recognition, signaling and function.
Keyphrases
- single molecule
- single cell
- rna seq
- atomic force microscopy
- living cells
- amino acid
- immune response
- high throughput
- drug delivery
- pain management
- convolutional neural network
- case report
- room temperature
- dendritic cells
- stem cells
- chronic pain
- bone marrow
- machine learning
- cancer therapy
- drug induced
- binding protein
- neural network
- deep learning
- molecular dynamics simulations
- ionic liquid