The cellular compartments in the lymph node form dynamic networks, enabling coordinated innate and adaptive immunological responses. This compartmentalization of the lymph node into subcompartments, such as the T and B zones, has been proven to be beneficial. The study of lymph node microarchitecture has yielded new insights into a range of fields, including anatomy, pathology and biological processes. This review focuses on three-dimensional (3D) and four-dimensional (4D) investigations of human lymph nodes, with a particular emphasis on comparisons with data obtained from mice. It will discuss the findings of 3D/4D investigations of human lymph nodes. The investigation of the immune system in 3D space and time offers numerous advantages over the analysis of thin tissue sections. It provides data that is not visible in two-dimensional (2D) representations. A comparison of volumes, surfaces, cell speeds, cell contact numbers, contact duration times, morphologies and other variables can be made in the context of immune responses and lymphomas. The evaluation of data, the application of statistics and the use of machine learning have all been demonstrated to be valuable. In conditions of reactivity and neoplasia, T cells are the fastest-moving cells. In contrast, B cells show slower movement and higher turning angles in reactive lymphoid tissue and lymphomas. Even slower than B cells are reticulum cells, like follicular dendritic reticulum cells (FDC) of the B zones and macrophages. Fast T cells are especially found in Hodgkin lymphomas and mantle cell lymphomas. Contact times between T and B cells differ between different lymphoma types and may prove useful in defining lymphomas. 4D technologies, which evaluate living tissue slices, are suitable for use in testing checkpoint blockers (such as nivolumab) and other therapeutic drugs or cells. Following incubation with nivolumab, the duration of contacts between CD4-positive T cells and CD30-positive Hodgkin-Reed-Sternberg cells was documented. The preliminary data indicate that 3D and 4D experiments in hematopathology may facilitate new insights into diagnostics, biology, and clinical applications, including the development of new lymphoma classifications.
Keywords: 3D; 4D; Dynamic network; Lymph node; Lymphomas; Machine learning.
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