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With the continuous development of immunology research from single cells and two-dimensional culture systems toward complex tissue microenvironments, traditional immune cell culture models can no longer fully simulate the complex interactions between cells in vivo. In recent years, advanced technologies such as organ-on-a-chip, single-cell sequencing, and spatial transcriptomics have gradually been applied to immunological research.
1 Organ-on-a-Chip
Organ-on-a-Chip is an advanced in vitro model established by integrating microfluidic technology, cell culture, tissue engineering, and biomaterial technology. It can reconstruct parts of tissue structures, cellular compositions, extracellular matrix, tissue interfaces, and dynamic fluid environments within a miniaturized and controllable chip system, thereby more closely resembling physiological and pathological states of human tissues. By designing different microchannels and cell culture regions, multiple cell types, including epithelial cells, vascular endothelial cells, fibroblasts, and immune cells, can be integrated into a controllable microenvironment.
In immunology research, organ-on-a-chip is particularly suitable for studying dynamic interactions between immune cells and tissue cells. For example, vascular-related chips can simulate leukocyte rolling, adhesion, and transendothelial migration under blood flow conditions; tumor chips can be used to study infiltration, activation, and tumor cell killing of T cells, NK cells, and macrophages after entering tumor tissues; lung and intestinal tissue chips can be used to study interactions between immune cells and epithelial barriers as well as inflammatory responses. Compared with traditional static culture systems, organ-on-a-chip provides spatial structures and dynamic stimulation conditions that are closer to those in vivo, and therefore is increasingly used in infection, inflammation, tumor immunity, immune drug evaluation, and immunotherapy research.
Organ-on-a-chip can also combine immune cells with drug treatment, tissue injury, or pathogen stimulation to establish more complex disease models. For example, patient-derived tumor cells and immune cells can be incorporated into tumor chips to observe immune cell infiltration and therapeutic responses; fluid environments and cytokine concentrations can also be controlled to study changes in immune cell functions under different microenvironmental conditions.
2 Single-cell Sequencing
Single-cell sequencing is a class of high-throughput technologies capable of analyzing gene expression and other molecular characteristics at the single-cell level. Traditional bulk RNA sequencing analyzes large numbers of cells together and therefore provides the average molecular characteristics of the entire cell population. When multiple immune cell subsets or different functional states exist in samples, this averaged signal may mask important differences between cells. Single-cell sequencing can separate complex cell populations at the single-cell level, allowing analysis of gene expression characteristics of each individual cell.
For example, CD8⁺ T cells can exhibit different states, including Naïve, Effector, Memory, and Exhausted states; macrophages can also display different inflammatory, immune regulatory, or tissue repair states depending on the tissue environment. Through single-cell sequencing, different immune cell subsets can be identified based on transcriptional expression patterns through clustering and annotation, and their specific genes, activation states, and functional characteristics can be further analyzed.
Single-cell sequencing can also be used to study immune cell differentiation and state transitions. By combining computational methods such as pseudotime analysis, the process of cell transition from one state to another can be inferred at the transcriptional level. For example, it can analyze gene expression changes during the transition of Naïve T cells to Effector T cells or Memory T cells, and study transcriptional regulatory networks involved in the differentiation of monocytes into different macrophage states.
Furthermore, combining scRNA-seq with TCR/BCR sequencing can further analyze T-cell receptor and B-cell receptor clonal composition, diversity, and the relationship between specific clones and cellular functional states. Through single-cell analysis, the cellular composition, functional states, and cell–cell communication within the tumor immune microenvironment (TIME) can be systematically characterized, and changes in immune cell states before and after treatment can be compared, providing molecular evidence for studying immunotherapy mechanisms and resistance mechanisms.
3 Spatial Transcriptomics
Spatial transcriptomics enables analysis of gene expression in different regions of tissues while preserving tissue structure and spatial location information, thereby establishing connections between gene expression, cell types, and spatial positions.
The functions of immune cells depend not only on their own states but also on the tissue microenvironment in which they are located and their interactions with neighboring cells. For example, in tumor tissues, CD8⁺ T cells may be abundant at the tumor edge but rarely enter the tumor core; certain macrophages may be concentrated in hypoxic regions or near blood vessels; dendritic cells, T cells, and B cells may form cell clusters with specific immune functions in certain regions. Spatial transcriptomics can link these spatial distributions with corresponding gene expression characteristics, thereby further understanding why immune cells are located in specific regions and what molecular interactions may occur between different cells.
In tumor immunity, inflammation, and infection research, spatial transcriptomics can be used to analyze immune cell infiltration, immune exclusion, formation of inflammatory regions, and spatial relationships between immune cells and tumor cells. For example, researchers can compare the immune cell composition and gene expression profiles among tumor core, tumor invasive margin, and normal tissue regions, and analyze the functional states of T cells, NK cells, macrophages, and dendritic cells in different regions, thereby gaining a more comprehensive understanding of tissue immune microenvironments.
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