ObjectiveTo understand the single-cell RNA sequencing (scRNA-seq) and its research progress in the tumor microenvironment (TME) of breast cancer, in order to provide new ideas and directions for the research and treatment of breast cancer. MethodThe development of scRNA-seq technology and its related research literature in breast cancer TME at home and abroad in recent years was reviewed. ResultsThe scRNA-seq was a quantum technology in high-throughput sequencing of mRNA at the cellular level, and had become a powerful tool for studying cellular heterogeneity when tissue samples were fewer. While capturing rare cell types, it was expected to accurately describe the complex structure of the TME of breast cancer. ConclusionsAfter decades of development, scRNA-seq has been widely used in tumor research. Breast cancer is a malignant tumor with high heterogeneity. The application of scRNA-seq in breast cancer research can better understand its tumor heterogeneity and TME, and then promote development of personalized diagnosis and treatment.
The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.
ObjectiveTo review the role of chemokine networks in regulating synovial macrophage heterogeneity during osteoarthritis (OA) pathogenesis. Methods A review of recent literature on the developmental origins of OA synovial macrophages, single-cell transcriptomic characteristics, and chemokine signaling pathways was conducted to systematically summarize the functional phenotypes, immunometabolic mechanisms, and regulatory roles of synovial macrophages in OA. Results OA has been established as a low-grade, chronic inflammatory disease affecting the entire joint. Single-cell and spatial transcriptomic studies have confirmed that synovial macrophages are not a single population but rather a dynamic continuum of different functional states, including steady-state barrier-like, inflammatory amplification, fibrosis-related, and lipid-enriched phenotypes. Chemokine networks play a dual crucial role in this process: on one hand, chemokine gradients guide the migration of peripheral monocytes to the synovium and influence their differentiation; on the other hand, synovial macrophages in different states secrete chemokines, mediating transcellular communication between the synovium, subchondral bone, and peripheral nerves. This process reshapes the microenvironment and amplifies local inflammation and pain signals. Current therapeutic strategies targeting macrophage metabolic reprogramming and chemokine axis blockade show potential clinical applications. Conclusion Re-examining the interaction between synovial macrophages and microenvironment and constructing an integrated perspective of “lineage-state-chemokine network” will help to understand the pathological progression mechanism of OA. In the future, it is expected to provide a theoretical framework and intervention targets for the precise immune regulation of OA and the development of new targeted drugs by accurately analyzing the spatiotemporal evolution of macrophage subsets and their interaction with chemokines.
ObjectiveTo analyze the correlation between the molecular biological information of SMARCA4-deficient non-small cell lung cancer (SMARCA4-dNSCLC) and its clinical prognosis, and to explore the spatial features and molecular mechanisms of interactions between cells in the tumor microenvironment (TME) of SMARCA4-dNSCLC. MethodsUsing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), this study conducted functional enrichment analysis on differentially expressed genes (DEGs) in SMARCA4-dNSCLC and depicted its genomic variation landscape. Through weighted gene co-expression network analysis (WGCNA) and a combination of 10 different machine learning algorithms, patients in the training group were divided into a low-risk group and a high-risk group based on a median risk score (RiskScore). A corresponding prognostic prediction model was established, and on this basis, a nomogram was constructed to predict the 1, 3, and 5-year survival rates of patients. K-M survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model. External datasets from GEO further validated the prognostic value of the prediction model. In addition, we also evaluated the immunological characteristics of the TME of the prognostic model. Finally, using single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST), we explored the spatial features of interactions between cells in the TME of SMARCA4-dNSCLC, intercellular communication, and molecular mechanisms. ResultsA total of 56 patients were included in the training group, including 38 males and 18 females, with a median age of 62 (56-70) years. There were 28 patients in both the low-risk and high-risk groups. A total of 474 patients were included in the training group, including 265 males and 209 females, with a median age of 65 (58-70) years. A risk score model composed of 8 prognostic feature genes (ELANE, FSIP2, GFI1B, GPR37, KRT81, RHOV, RP1, SPIC) was established. Compared with patients in the low-risk group, those in the high-risk group showed a more unfavorable prognostic outcome. Immunological feature analysis revealed differences in the infiltration of various immune cells between the low-risk and high-risk groups. ScRNA-seq and ST analyses found that interactions between cells were mainly through macrophage migration inhibitory factor (MIF) signaling pathways (MIF-CD74+CXCR4 and MIF-CD74+CD44) via ligand-receptor pairs, while also describing the niche interactions of the MIF signaling pathway in tissue regions. ConclusionThe 8-gene prognostic model constructed in this study has certain predictive accuracy in predicting the survival of SMARCA4-dNSCLC. Combining the ScRNA-seq and ST analyses, cell-to-cell crosstalk and spatial niche interaction may occur between cells in the TME via the MIF signaling pathway (MIF-CD74+CXCR4 and MIF-CD74+CD44).
Non-small cell lung cancer (NSCLC) is one of the most common types of cancer in the world and is an important cause for cancer death. Although the application of immunotherapy in recent years has greatly improved the prognosis of NSCLC, there are still huge challenges in the treatment of NSCLC. The immune microenvironment plays an important role in the process of NSCLC development, infiltration and metastasis, and they can interact and influence each other, forming a vicious circle. Notably, single-cell RNA sequencing enables high-resolution analysis of individual cells and is of great value in revealing cell types, cell evolution trajectories, molecular mechanisms of cell differentiation, and intercellular regulation within the immune microenvironment. Single-cell RNA sequencing is expected to uncover more promising immunotherapies. This article reviews the important researches and latest achievements of single-cell RNA sequencing in the immune microenvironment of NSCLC, and aims to explore the significance of applying single-cell RNA sequencing to analyze the immune microenvironment of NSCLC.
Non-small cell lung cancer is one of the primary types of cancer that leads to brain metastases. Approximately 10% of patients with non-small cell lung cancer have brain metastases at the time of diagnosis, and 26%-53% of patients develop brain metastases during the progression of their disease. However, the underlying mechanisms of lung cancer brain metastasis have not been fully elucidated. With the continuous development of single-cell and spatial transcriptomics, the genomic and transcriptomic characteristics of lung cancer brain metastasis are gradually being revealed. In February 2025, the journal Nature Medicine published an article titled "Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases". This article aims to provide a brief interpretation of the paper for colleagues in research and clinical practice.
ObjectiveTo explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). MethodsThe Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. Gene ontology, Kyoto encyclopedia of genes and genomes, and gene set enrichment analysis were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox regression, least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients’ 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. ResultsDifferentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poorer prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. The scRNA-seq analysis showed that RORA and KLF10 were mainly expressed in natural killer cells. ConclusionThe prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.
Objective To investigate the heterogeneity of lung epithelial cells, particularly alveolar type II (AT2) cells, in chronic obstructive pulmonary disease (COPD), and to explore their potential interactions with immune cells.Methods Single-cell RNA sequencing (scRNA-seq) was performed to systematically analyze 57,879 lung-derived cells obtained from patients with mild to moderate COPD and age-matched control individuals. Major cell types were identified through cell clustering and annotation. AT2 cells were further subdivided into distinct subpopulations, and differential gene expression analysis, functional enrichment analysis, pseudotime trajectory analysis, and cell–cell communication analysis were conducted to characterize their transcriptional features, differentiation trajectories, and interaction patterns with immune cells.Results Single-cell analysis identified major cell populations, including epithelial, immune, stromal, and endothelial cells. Compared with controls, AT2 cells from COPD patients exhibited pronounced heterogeneity, with a significantly increased proportion of an inflammation-associated AT2 subpopulation characterized by high expression of complement component C3. Functional enrichment analysis revealed that this subpopulation was significantly enriched in complement activation and inflammatory response–related pathways. Cell–cell communication analysis suggested that this inflammatory AT2 subpopulation may aberrantly interact with macrophages via the C3–ITGAX/ITGB2 signaling axis, potentially contributing to the formation and maintenance of the chronic inflammatory microenvironment in COPD.Conclusions At the single-cell transcriptomic level, this study identifies a novel inflammation-associated AT2 cell subpopulation in COPD lung tissue and suggests its potential involvement in immune regulation through complement-mediated AT2–macrophage interactions. These findings provide new insights into the pathogenesis of COPD and offer a theoretical basis for the exploration of potential targeted therapeutic strategies.
ObjectiveTo investigate the expression of neutrophil extracellular traps (NETs) marker proteins citrullinated histone H3 (CitH3) and myeloperoxidase (MPO) in early-stage lung adenocarcinoma, and to analyze the dynamics of neutrophil infiltration, subpopulation heterogeneity, and the relationship between NET activity and tumor progression. MethodsPaired tumor and adjacent normal tissue specimens were collected from patients with pathologically confirmed early-stage lung adenocarcinoma after surgical resection at the Second Affiliated Hospital of Hainan Medical University between October and December 2024. Immunofluorescence staining was performed to detect CitH3 and MPO expression. Image J software was used for quantitative analysis of fluorescence intensity and density of double-positive cells. Single-cell RNA sequencing data of cancerous and matched normal tissues were obtained from patients with early-stage lung adenocarcinoma at the same hospital from October 2024 to April 2025. Patients were stratified into groups with versus without high-grade components, and well-differentiated versus moderately/poorly differentiated groups according to pathological subtypes. Unsupervised clustering was applied to identify cell populations. The ratio of observed to expected values was used to analyze tissue distribution preference. Subset clustering was performed based on neutrophil marker genes, and a NETs gene set score was calculated to assess activity signatures. ResultsPaired tumor and normal tissues from 3 patients with early-stage lung adenocarcinoma (2 males and 1 female; median age 68.0 years, range 60.0-70.0 years) were included for immunofluorescence staining. Another 14 patients with early-stage lung adenocarcinoma were enrolled, including 6 males and 8 females, with a median age of 62.5 years (range 41.0-73.0 years). Three of these 14 patients also provided paired normal tissues. Single-cell RNA sequencing was conducted on tumor tissues from all 14 patients and paired normal tissues from the 3 patients. Among the 14 invasive non-mucinous adenocarcinomas, 7 samples were well-differentiated and 7 were moderately/poorly differentiated; 10 samples had no high-grade components and 4 had high-grade components. Immunofluorescence showed that NETs expression was significantly higher in tumor tissues than in normal tissues (P<0.001). Single-cell sequencing revealed an immunosuppressive tumor microenvironment in early lung adenocarcinoma, characterized by an early depletion of neutrophils followed by a sharp increase in neutrophil abundance as disease progressed. Two neutrophil categories were identified: resident subsets (c0/c4) and tumor-associated subsets (c1/c2/c3/c5). Resident subsets decreased while tumor-associated subsets accumulated with increasing malignancy. Among them, c3-Neut-LGALS3 and c2-Neut-CKLF were associated with progression in the high-grade group. The c0-Neut-AZIN1-AS1 subset was the major source of high NETs expression, and its activity was negatively correlated with tumor malignancy. ConclusionNETs-related proteins (CitH3 and MPO) are highly expressed in early-stage lung adenocarcinoma tissues. The microenvironment of early-stage lung adenocarcinoma exhibits immunosuppressive characteristics, and neutrophil subpopulation composition is remodeled with tumor progression, with the c0-Neut-AZIN1-AS1 subpopulation serving as a key source of high NETs expression.