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Designed optogenetic tool for bridging single-neuronal multimodal information in intact animals.
Integrating morphological, functional and molecular information of individual neurons is critical for classifying neuronal cell types and probing circuit mechanisms of brain functions. Despite the emergence of extensive single-neuronal morphology datasets largely via random sparse labeling, it remains challenging to map arbitrarily selected neuron's morphology in vivo, especially in conjunction with its functional and molecular characteristics. Here, we report a genetically encoded Photo-inducible single-cell labeling system (Pisces) that enables simple, rapid and long-term in vivo labeling of the entire morphology of arbitrary neurons, as exemplified in intact larval zebrafish. Pisces allows sequential tracing of multiple neurons within individual animals, facilitating brain-wide projectome mapping. Importantly, combined with in vivo calcium imaging, and fluorescence in situ hybridization or single-cell RNA sequencing, Pisces allows linking individual neurons' morphology characterization with their functional and/or gene expression investigation, respectively. This strategy promises to advance the construction of single-neuronal multimodal atlases and expedite the elucidation of neural circuitries underlying brain functions.
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A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities with adult glioma, it comprises distinct disease entities. In this study, we longitudinally profile a molecularly diverse cohort of 16 pHGG patients through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of neoplastic and microenvironmental features during disease progression and treatment. We define a set of core pHGG neoplastic cell states and observe differential tumor-myeloid interactions between malignant cell phenotypes. We find that essential neuromodulators and the interferon response are upregulated post-therapy, implicating them as malignant cell-intrinsic targets. We observe an increase in oligodendrocytes upon progression and that they coordinate spatial motifs with proneural tumor cells. This multiomic atlas of longitudinal pHGG captures features of therapy response and provides a scalable reference for the study of pediatric brain tumors.
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Single-Cell RNA-Sequencing Atlas Reveals the Tumor Microenvironment of Metastatic High-Grade Serous Ovarian Carcinoma.
Ovarian cancer is the most common and lethal gynecological tumor in women worldwide. High-grade serous ovarian carcinoma (HGSOC) is one of the histological subtypes of epithelial ovarian cancer, accounting for 70%. It often occurs at later stages associated with a more fatal prognosis than endometrioid carcinomas (EC), another subtype of epithelial ovarian cancer. However, the molecular mechanism and biology underlying the metastatic HGSOC (HG_M) immunophenotype remain poorly elusive. Here, we performed single-cell RNA sequencing analyses of primary HGSOC (HG_P) samples, metastatic HGSOC (HG_M) samples, and endometrioid carcinomas (EC) samples. We found that ERBB2 and HOXB-AS3 genes were more amplified in metastasis tumors than in primary tumors. Notably, high-grade serous ovarian cancer metastases are accompanied by dysregulation of multiple pathways. Malignant cells with features of epithelial-mesenchymal transition (EMT) affiliated with poor overall survival were identified. In addition, cancer-associated fibroblasts with EMT-program were enriched in HG_M, participating in angiogenesis and immune regulation, such as IL6/STAT3 pathway activity. Compared with ECs, HGSOCs exhibited higher T cell infiltration. PRDM1 regulators may be involved in T cell exhaustion in ovarian cancer. The CX3CR1_macro subpopulation may play a role in promoting tumor progression in ovarian cancer with high expression of BAG3, IL1B, and VEGFA. The new targets we discovered in this study will be useful in the future, providing guidance on the treatment of ovarian cancer.
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Multi-omics atlas of ovarian cellular and molecular responses to diabetes.
Diabetes is associated with compromised reproductive health; however, the cellular and molecular mechanisms underlying its impact on ovarian function remain largely unclear. In this study, we integrated single-cell RNA sequencing, DNA methylation profiling, and metabolomic analyses to comprehensively characterize the ovarian cellular landscape, epigenetic alterations, and metabolic reprogramming in diabetic female mice, with a focus on identifying diabetes-induced changes in ovarian cells. Our cell type-specific transcriptomic analysis revealed that dysregulated steroid hormone biosynthesis and impaired fatty acid metabolism are prominent features of diabetic ovarian dysfunction. Notably, key genes including Cyp11a1, Fshr, and Lhcgr exhibited reduced expression accompanied by increased DNA methylation levels in their gene regions within granulosa cells under diabetic conditions. Furthermore, disrupted granulosa cell differentiation was evident, leading to aberrant luteal cell formation and compromised luteal function. In parallel, metabolomic profiling revealed profound metabolic reprogramming in diabetic ovaries, with significant alterations in lipid metabolism pathways, including elevated unsaturated fatty acid and reduced glycerophospholipid metabolism. Taken together, these findings provide novel insights into the molecular pathways underlying ovarian dysfunction in the context of diabetes, thereby enhancing our understanding of folliculogenesis in metabolic disorders.
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Pan-cancer analysis of NADPH oxidase 4 identifying its prognostic and immunotherapy predictive value and in vitro experimental verification in glioblastoma.
BACKGROUND: NADPH oxidase 4 (NOX4) plays an important role in metabolic reprogramming, epithelial-mesenchymal transition (EMT), and other cellular processes by strictly regulating intracellular ROS generation. However, there is a lack of analysis on the role of NOX4 in the tumor immune microenvironment and predictive value for prognosis and immunotherapy response in various tumor types. METHODS: This study used data from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Tumor Immune Single-cell Hub (TISCH), Genotype-Tissue Expression (GTEx), cBioPortal, Tumor Immune Estimation Resource (TIMER 2.0), and ROC Plotter databases to analyze the expression, prognosis, biological function, immune cell infiltration, and genetic and epigenetic changes of NOX4 in various tumor types. Meanwhile, in vitro experiments were conducted to verify the role of NOX4 in the migration, invasion, proliferation, and apoptosis of glioblastoma (GBM). RESULTS: The findings indicated that NOX4 is upregulated in various types of cancer, accompanied by gene amplification, mutation, and deletion. The high expression of NOX4 is associated with poor prognosis in various cancers. Functional enrichment analysis showed that NOX4 is mainly enriched in immune and cancer progression pathways, such as angiogenesis, EMT, interferon response, inflammatory response. Immune cell infiltration analysis showed that high expression of NOX4 is associated with increased infiltration of cancer-associated fibroblasts (CAF) and macrophages. In vitro experiments showed that silencing NOX4 inhibits the proliferation, migration, and invasion of GBM and increases cell apoptosis. CONCLUSION: NOX4 can serve as a prognostic and immunotherapy response biomarker for various cancers and targeting NOX4 may be a feasible anti-tumor therapy and may have synergistic anti-tumor effects combined with immunotherapy.
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Screening of novel biomarkers for breast cancer based on WGCNA and multiple machine learning algorithms.
BACKGROUND: Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis. METHODS: We analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via Protein-Protein Interaction (PPI) networks and multiple machine-learning algorithms. The Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human Protein Atlas (HPA) databases were employed to examine mRNA and protein expression of eight biomarkers. Kaplan-Meier mapper tool assessed their prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, and their relationship with immune infiltration was examined using Tumor Immune Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug prediction was conducted based on the identified biomarkers. RESULTS: We identified 1,673 DEGs and 542 important genes through differential analysis and WGCNA, respectively. Intersection analysis revealed 76 genes, which play significant roles in immune-related viral infection and IL-17 signaling pathways. DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 was the most critical gene for diagnosis. Prospective drugs targeting NEK2 include etoposide and lukasunone. CONCLUSIONS: Our study identified DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and prognosis in clinical settings.
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The Myeloid Biomarker MS4A6A Drives an Immunosuppressive Microenvironment in Glioblastoma via Activation of the PGE2 Signaling Axis.
Glioblastoma (GBM) remains one of the most lethal brain tumors, characterized by extensive immune evasion and a macrophage-dominated tumor microenvironment (TME). However, the molecular determinants governing tumor-associated macrophage (TAM) states and their immunoregulatory functions remain poorly understood. We integrated bulk- and single-cell transcriptomic datasets (TCGA, CGGA, Ivy GAP, and Brain Immune Atlas) to systematically characterize the expression, prognostic relevance, and immune contexture of the myeloid biomarker membrane-spanning 4-domain A6A, MS4A6A, in GBM. Differential expression, survival, and pathway enrichment analyses were performed. Single-cell mapping and CellChat modeling delineated MS4A6A-associated TAM subpopulations, intercellular communication networks, and ligand-receptor signaling dynamics. Spatial transcriptomic validation and pharmacogenomic modeling were conducted to assess anatomic enrichment and therapeutic vulnerabilities. High MS4A6A expression predicted unfavorable survival and correlated with increased stromal and immune infiltration. Single-cell analyses localized MS4A6A predominantly to TAMs, especially Regulatory- and Ribo-TAM states enriched for antigen presentation, T-cell regulation, and ribosomal biogenesis pathways. CellChat analysis revealed that MS4A6A-high TAMs exhibited markedly enhanced communication with CD4+ T cells and Tregs through upregulated PGE2-PTGER2/PTGER4, PECAM1-CD38, and THBS1-CD36 signaling axes, implicating MS4A6A in prostaglandin-driven immune suppression. Spatial profiling confirmed preferential localization of MS4A6A within perivascular and angiogenic niches. Pharmacogenomic prediction indicated that MS4A6A-high tumors were more sensitive to ERK, mTOR, and CDK4/6 inhibition. MS4A6A defines a macrophage-centered, immunosuppressive ecosystem in GBM, mediated by the activation of the PGE2 signaling axis. These findings position MS4A6A both as a prognostic biomarker and as a potential therapeutic node linking myeloid reprogramming to actionable pathway vulnerabilities in glioblastoma.
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A Novel Intercellular Communication-Associated Gene Signature for Prognostic Prediction and Clinical Value in Patients With Lung Adenocarcinoma.
BACKGROUND: Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. This study aimed to identify the key intercellular communication-associated genes (ICAGs) in LUAD. METHODS: Eight publicly available datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The prognosis-related ICAGs were identified and a risk score was developed by using survival analysis. Machine learning models were trained to predict LUAD recurrence based on the selected ICAGs and clinical information. Comprehensive analyses on ICAGs and tumor microenvironment were performed. A single-cell RNA-sequencing dataset was assessed to further elucidate aberrant changes in intercellular communication. RESULTS: Eight ICAGs with prognostic potential were identified in the present study, and a risk score was derived accordingly. The best machine-learning model to predict relapse was developed based on clinical information and the expression levels of these eight ICAGs. This model achieved a remarkable area under receiver operator characteristic curves of 0.841. Patients were divided into high- and low-risk groups according to their risk scores. DNA replication and cell cycle were significantly enriched by the differentially expressed genes between the high- and the low-risk groups. Infiltrating immune cells, immune functions were significantly related to ICAGs expressions and risk scores. Additionally, the changes of intercellular communication were modeled by analyzing the single-cell sequencing dataset. CONCLUSION: The present study identified eight key ICAGs in LUAD, which could contribute to patient stratification and act as novel therapeutic targets.
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Single cell transcriptomic analysis reveals molecular mechanisms of chemoresistance and immune microenvironment remodeling in ovarian cancer.
BACKGROUND: Chemotherapy resistance remains a major clinical challenge in ovarian cancer treatment, limiting therapeutic efficacy and patient survival. Understanding the molecular mechanisms underlying chemoresistance at the single-cell level is crucial for developing targeted therapeutic strategies. METHODS: We performed comprehensive single-cell RNA sequencing analysis on primary and chemoresistant ovarian cancer samples to characterize cellular heterogeneity and identify resistance-associated molecular signatures. Cell type identification, differential gene expression analysis, functional enrichment analysis, and immune microenvironment characterization were conducted to elucidate chemoresistance mechanisms. Computational predictions were validated by RT-qPCR analysis of four key resistance genes (BIRC5, ABCB1A, ABCG2, BCL2) in SKOV3 and SKOV3-CDDP cell lines. RESULTS: Our analysis identified nine distinct cell populations within the ovarian cancer microenvironment, revealing extensive transcriptional reprogramming in chemoresistant tumors. Key chemoresistance genes including Abcb1a, Abcg2, Bcl2, Birc5, Nek2, Inhba, Irs1, and Prc1 showed differential expression patterns across cell types. Epithelial cells and proliferating cells exhibited elevated expression of multiple resistance markers, particularly Birc5. Functional analysis revealed enrichment of ribosome biogenesis, protein synthesis machinery, and amoeboid-type cell migration pathways in resistant cells. Macrophage polarization states were significantly altered, and immune checkpoint molecules showed coordinated upregulation, indicating comprehensive immune microenvironment reprogramming. RT-PCR validation confirmed single-cell predictions with significant upregulation in resistant cells: BIRC5 (4.2-fold, p < 0.001), ABCB1A (3.8-fold, p < 0.001), ABCG2 (2.9-fold, p < 0.01), and BCL2 (2.1-fold, p < 0.05). CONCLUSIONS: This study provides a comprehensive single-cell atlas of chemoresistance in ovarian cancer, revealing cell type-specific resistance mechanisms and immune evasion strategies. The identified molecular signatures offer potential therapeutic targets for overcoming chemotherapy resistance and improving patient outcomes.
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Comprehensive analysis of KLF2 as a prognostic biomarker associated with fibrosis and immune infiltration in advanced hepatocellular carcinoma.
PURPOSE: Most Hepatocellular carcinoma (HCC) patients are in advanced or metastatic stage at the time of diagnosis. Prognosis for advanced HCC patients is dismal. This study was based on our previous microarray results, and aimed to explore the promising diagnostic and prognostic markers for advanced HCC by focusing on the important function of KLF2. METHODS: The Cancer Genome Atlas (TCGA), Cancer Genome Consortium database (ICGC), and the Gene Expression Comprehensive Database (GEO) provided the raw data of this study research. The cBioPortal platform, CeDR Atlas platform, and the Human Protein Atlas (HPA) website were applied to analyze the mutational landscape and single-cell sequencing data of KLF2. Basing on the results of single-cell sequencing analyses, we further explored the molecular mechanism of KLF2 regulation in the fibrosis and immune infiltration of HCC. RESULTS: Decreased KLF2 expression was discovered to be mainly regulated by hypermethylation, and indicated a poor prognosis of HCC. Single-cell level expression analyses revealed KLF2 was highly expressed in immune cells and fibroblasts. The function enrichment analysis of KLF2 targets indicated the crucial association between KLF2 and tumor matrix. 33-genes related with cancer associated fibroblasts (CAFs) were collected to identify the significant association of KLF2 with fibrosis. And SPP1 was validated as a promising prognostic and diagnostic marker for advanced HCC patients. CXCR6 CD8+ T cells were noted as a predominant proportion in the immune microenvironment, and T cell receptor CD3D was discovered to be a potential therapeutic biomarker for HCC immunotherapy. CONCLUSION: This study identified that KLF2 is an important factor promoting HCC progression by affecting the fibrosis and immune infiltration, highlighting its great potential as a novel prognostic biomarker for advanced HCC.