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.
Objective To study the differentially expressed genes (DEG) during the differentiation of human induced pluripotent stem cells (hiPSC) and human embryonic stem cells (hESC) into pericytes and endothelial cells, and to identify key molecules and signaling pathways that may regulate this differentiation process. MethodshiPSC and hESC were selected and expanded using mTeSR medium. A "two-step method" was used to induce the differentiation of hiPSC and hESC into pericytes and endothelial cells. Pericytes were identified using immunofluorescence staining, while endothelial cells were isolated and identified using flow cytometry. Total RNA samples were extracted on days 0, 4, 7, and 10 of differentiation and consistently significant DEGs were screened. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analysis were performed on the screened DEGs. ResultsBoth hiPSCs and hESCs successfully differentiated into pericytes and endothelial cells under induction conditions. Transcriptome sequencing results showed that with the extension of differentiation time, the DEGs in hiPSCs and hESCs were significantly upregulated or downregulated, following a generally consistent trend. During the differentiation process, marker genes for pericytes and endothelial cells were significantly upregulated. A total of 491 persistent DEGs were detected in both hiPSC and hESC, with 164 unique to hiPSCs and 335 to hESCs, while 8 DEGs were co-expressed in both cell lines. Among these, SLC30A3, LCK, TNFRSF8, PRDM14, and GLB1L3 showed sustained downregulation, whereas CLEC18C, CLEC18B, and F2RL2 exhibited sustained upregulation. GO enrichment analysis revealed that DEGs with sustained upregulation were primarily enriched in terms related to neurogenesis, differentiation, and developmental proteins, while DEGs with sustained downregulation were enriched in terms related to membrane structure and phospholipid metabolic processes. KEGG pathway analysis showed that upregulated genes were primarily enriched in cancer-related pathways, pluripotency regulatory pathways, the Wnt signaling pathway, and the Hippo signaling pathway, whereas downregulated genes were predominantly enriched in metabolism-related pathways. ConclusionsDuring the differentiation of hiPSC and hESC into pericytes and endothelial cells, 8 DEGs exhibit sustained specific expression changes. These changes may promote pericyte and endothelial cell differentiation by activating the Wnt and Hippo pathways, inhibiting metabolic pathways, releasing the maintenance of stem cell pluripotency, affecting the cell cycle, and inhibiting cell proliferation.
Retina is composed of a heterogeneous population of cell types, each with a unique biological function. Even if the same type of cells, due to genetic heterogeneity will lead to cell function differences. In the past, traditional molecular biological methods cannot resolve variations in their functional roles that arise from these differences, and some cells are difficult to define due to the lack of specific molecular markers or the scarcity of numbers, which hindered the understanding and research of these cells. With the development of biotechnology, single-cell RNA sequencing can analyze and resolve differences in single-cell transcriptome expression profiles, characterize intracellular population heterogeneity, identify new and rare cell subtypes, and more definitely define the characteristics of each cell type. It clarifies the origin, function, and variations in cell phenotypes. Other attributes include pinpointing both disease-related characteristics of cell subtypes and specific differential gene expression patterns, to deepen our understanding of the causes and progression of diseases, as well as to aid clinical diagnosis and targeted therapy.
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.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.
Diabetic retinopathy (DR) is one of the main causes of vision loss and irreversible blindness in the working-age population, closely regarded as the destruction of the retinal neurovascular unit (NVU). As an important component of the NVU, retinal microglia (RMG) plays a vital role in the progression of DR. In recent years, single-cell RNA sequencing (scRNA-seq) technology has emerged as an important tool in transcriptomic analysis. This latest method reveals the heterogeneity and complexity of RNA transcriptional profiles within individual cells, as well as the composition of different cell types and functions. Utilizing scRNA-seq technology, researchers have further revealed the role of RMG in the occurrence and development of DR, discovering phenotypic heterogeneity, regional heterogeneity, and cell-to-cell communication in RMG. It is anticipated that in the future, more omics technologies and multi-omics correlation analysis methods will be applied to DR and even other ophthalmic diseases, exploring potential diagnostic and therapeutic targets, providing different perspectives for the clinical diagnosis, treatment, and scientific research of DR, and truly promoting clinical translation through technological innovation, thereby benefiting patients with DR diseases.
Objective To explore the role and clinical significance of cell-cycle dependent kinase 1 (CDK1) and its upstream and downstream molecules in the development of malignant peripheral nerve sheath tumor (MPNST) through the analysis of clinical tissue samples. Methods A total of 56 tumor samples from MPNST patients (“Tianjin” dataset) who underwent surgical resection, confirmed by histology and pathology between September 2011 and March 2020, along with 17 normal tissue samples, were selected as the research subjects. MPNST-related hub genes were identified through transcriptome sequencing, bioinformatics analysis, immunohistochemistry staining, and survival analysis, and their expression levels and prognostic associations were analyzed. Results Transcriptome sequencing and bioinformatics analysis revealed that upregulated genes in MPNST were predominantly enriched in cell cycle-related pathways, with CDK1 occupying a central position among all differentially expressed genes. Further differential analysis demonstrated that CDK1 mRNA expression in sarcoma tissues was significantly higher than in normal tissues [based on searching the cancer genome atlas (TCGA) dataset, P<0.05]. In MPNST tissues, CDK1 mRNA expression was not only significantly higher than in normal tissues (based on Tianjin, GSE141438 datasets, P<0.05), but also significantly higher than in neurofibromatosis (NF) and plexiform neurofibromas (PNF) (based on GSE66743 and GSE145064 datasets, P<0.05). Immunohistochemical staining results indicated that the expression rate of CDK1 protein in MPNST tissues was 40.31%. Survival analysis results demonstrated that CDK1 expression was associated with poor prognosis. The survival time of MPNST patients with high CDK1 mRNA expression was significantly lower than that of the low expression group (P<0.05), and the overall survival trend of patients with positive CDK1 protein expression was worse than that of patients with negative CDK1 expression. Additionally, differential analysis of CDK family genes (CDK1-8) revealed that only CDK1 was significantly upregulated in MPNST, NF, and PNF. Conclusion Increased expression of CDK1 is associated with poor prognosis in MPNST patients. Compared to other CDK family members, CDK1 exhibits a unique expression pattern, suggesting its potential as a therapeutic target for MPNST.
With the development of life sciences and informatics, bioinformatics is developing as an interdisciplinary subject. Its main application is the relationship between genes and proteins and their expression. With the help of genomics, proteomics, transcriptomics, and metabolomics, researchers introduce bioinformatics research methods into fundus disease research. A series of gratifying research results have been achieved including the screening of genetic susceptibility genes, the screening of diagnostic markers, and the exploration of pathogenesis. Genomics has the characteristics of high efficiency and accuracy. It has been used to detect new mutation sites in retinoblastoma and retinal pigment degeneration research, which helps to further improve the pathogenesis of retinal genetic diseases. Transcriptomics, proteomics, and metabolomics have high throughput characteristics. They are used to analyze changes in the expression profiles of RNA, proteins, and metabolites in intraocular fluid or isolated cells in disease states, which help to screen biomarkers and further elucidate the pathogenesis. With the advancement of technology, bioinformatics will provide new ideas for the study of ocular fundus diseases.
Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
RNA can be labeled by more than 170 chemical modifications after transcription, and these chemical modifications are collectively referred to as RNA modifications. It opened a new chapter of epigenetic research and became a major research hotspot in recent years. RNA modification regulates the expression of genes from the transcriptome level by regulating the fate of RNA, thus participating in many biological processes and disease occurrence and development. With the deepening of research, the diversity and complexity of RNA modification, as well as its physiological significance and potential as a therapeutic target, can not be ignored.