Stimulatory immune receptor NKG2D binds diverse ligands to elicit differential anti-tumor and anti-virus immune responses. Two conflicting degeneracy recognition models based on static crystal structures and in-solution binding affinities have been considered for almost two decades. Whether and how NKG2D recognizes and discriminates diverse ligands still remain unclear. Using live-cellbased single-molecule biomechanical assay, we characterized the in situ binding kinetics of NKG2D interacting with different ligands in the absence or presence of mechanical force. We found that mechanical force application selectively prolonged NKG2D interaction lifetimes with the ligands MICA and MICB, but not with ULBPs, and that force-strengthened binding is much more pronounced for MICA than for other ligands. We also integrated steered molecular dynamics simulations and mutagenesis to reveal force-induced rotational conformational changes of MICA, involving formation of additional hydrogen bonds on its binding interface with NKG2D, impeding MICA dissociation under force. We further provided a kinetic triggering model to reveal that force-dependent affinity determines NKG2D ligand discrimination and its downstream NK cell activation. Together, our results demonstrate that NKG2D has a discrimination power to recognize different ligands, which depends on selective mechanical force-induced ligand conformational changes.

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem’s complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

BackgroundThe early diagnosis of hepatocellular carcinoma (HCC) can greatly improve patients’ 5-year survival rate, and the early efficacy assessment is important for oncologists to harness the anti-programmed cell death protein 1 (PD-1) immunotherapy in patients with advanced HCC. The lack of effective predicting biomarkers not only leads to delayed detection of the disease but also results in ineffective immunotherapy and limited clinical survival benefit.MethodsWe exploited the single-cell approach (cytometry by time of flight (CyTOF)) to analyze peripheral blood mononuclear cells from multicohorts of human samples. Immune signatures for different stages of patients with HCC were systematically profiled and statistically compared. Furthermore, the dynamic changes of peripheral immune compositions for both first-line and second-line patients with HCC after anti-PD-1 monotherapy were also evaluated and systematically compared.ResultsWe identified stage-specific immune signatures for HCC and constructed a logistic AdaBoost-SVM classifier based on these signatures. The classifier provided superior performance in predicting early-stage HCC over the commonly used serum alpha-fetoprotein level. We also revealed the treatment stage-specific immune signatures from peripheral blood and their dynamical changing patterns, all of which were integrated to achieve early discrimination of patients with non-durable benefit for both first-line and second-line anti-PD-1 monotherapies.ConclusionsOur newly identified single-cell peripheral immune signatures provide promising non-invasive biomarkers for early detection of HCC and early assessment for anti-PD-1 immunotherapy efficacy in patients with advanced HCC. These new findings can potentially facilitate early diagnosis and novel immunotherapy for patients with HCC in future practice and further guide the utility of CyTOF in clinical translation of cancer research.Trial registration numbersNCT02576509 and NCT02989922.