As important innate immune cells, natural killer (NK) cells play an essential role in resisting pathogen invasion and eliminating transformed cells. However, the hypoxic microenvironment caused by disease conditions is an important physicochemical factor that impairs NK cell function. With the increasing prominence of NK cells in immunotherapy, there has been a surge of interest in developing biological means through which NK cells may overcome the inhibition caused by hypoxia in disease conditions. Although the effects of hypoxic conditions in shaping the functions of NK cells have been increasingly recognized and investigated, reviews have been scantly. A comprehensive understanding of how NK cells adapt to hypoxia can provide valuable insights into how the functional capacity of NK cells may be restored. This review focuses on the functional alterations of NK cells in response to hypoxia. It delineates the mechanisms by which NK cells adapt to hypoxia at the transcriptional, metabolic, translational levels. Furthermore, given the complexity of the hypoxic microenvironment, we also elucidated the effects of key hypoxic metabolites on NK cells. Finally, this review discusses the current clinical therapies derived from targeting hypoxic NK cells. The study of NK cell adaptation to hypoxia has yielded new insights into immunotherapy. These insights may lead to development of novel strategies to improve the treatment of infectious diseases and cancer.

Traditionally considered as homogeneous innate immune cells, neutrophils are now found to exhibit phenotypic and functional heterogeneity. How to determine whether the functional changes of neutrophils are caused by activation or the result of gene reprogramming? Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics, have comprehensively explained the mechanism of neutrophil heterogeneity. At the same time, artificial intelligence, especially machine learning, has promoted the in-depth analysis of multi-omics. Here, we introduce the latest progress in the discovery of neutrophil subsets by omics research. We will further discuss the application of machine learning in analyzing the heterogeneity of neutrophils through omics methods. Our goal is to provide a comprehensive overview of how machine learning and multi-omics are reshaping our understanding of neutrophil biology and pathophysiology.

Global disability from osteoarthritis (OA) remains a staggering burden, yet disease-modifying pharmacological therapies remain limited. Current evidence supports OA as a whole-joint disease in which subchondral bone remodeling acts as an early and active driver of structural progression and pain. Osteoclasts are key regulators of this process, but clinical trials targeting osteoclast-dependent remodeling have yielded inconsistent results. This translational gap suggests that the traditional view of osteoclasts as a homogeneous, terminally differentiated resorptive population is no longer sufficient. Latest breakthroughs in single-cell and spatial multi-omics have begun to redefine osteoclast biology by revealing heterogeneity across precursor origin, lineage state, functional output, and niche-specific adaptation. In OA, these studies have clarified the osteoclastogenic microenvironment more clearly than the terminal taxonomy of mature osteoclast subsets, thereby shifting the field toward a state-spectrum framework. In this review, we synthesize recent high-resolution evidence to examine how osteoclast-lineage heterogeneity is organized across disease stages and osteochondral microenvironments, and how distinct osteoclast-lineage programs contribute to subchondral remodeling, angiogenic coupling, interface instability, and pain-related pathology. We further discuss how this framework may inform patient stratification, mechanism-matched intervention, and the development of program-specific therapies in OA.