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M2 macrophages and tumor cells engage in a metabolic feedback loop to drive HCC progression.
Hepatocellular carcinoma (HCC) represents a significant global health challenge due to its molecular heterogeneity and the immunosuppressive nature of its tumor microenvironment (TME). Tumor-associated macrophages (TAMs), particularly the M2-polarized subset, are crucial in HCC progression, contributing to immune evasion, angiogenesis, and metastasis. However, the mechanisms driving TAM reprogramming and their metabolic interactions within the TME remain poorly understood. In this study, we applied single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to identify a novel metabolic-immune axis in HCC. Our findings revealed increased oxidative phosphorylation (OXPHOS) in tumor cells, which correlated with enhanced ribosomal activity in M2 macrophages, suggesting a metabolic coupling between the two. Tumor cells, under elevated OXPHOS, secrete factors that recruit macrophages and promote M2 polarization, thereby reinforcing immune suppression. Further analysis of The Cancer Genome Atlas (TCGA) dataset led to the development of a prognostic model based on OXPHOS-related genes, showing strong predictive power across multiple cohorts. These results suggest a novel OXPHOS-ribosome-M2 polarization axis and offer potential therapeutic targets for immunometabolic therapies in HCC.
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Single-cell RNA sequencing reveals important role of monocytes and macrophages during mucopolysaccharidosis treatment.
Mucopolysaccharidosis (MPS) encompasses a heterogeneous group of lysosomal storage diseases resulting from mutations in genes encoding lysosomal enzymes responsible for the degradation of mucopolysaccharides, also known as glycosaminoglycans (GAGs). Current therapeutic strategies for MPS include hematopoietic stem cell transplantation (HSCT), enzyme replacement therapy (ERT), and symptomatic therapy. This study investigated dynamic changes in MPS type II (MPS-II) through genomic and single-cell sequencing in a patient undergoing ERT. Analysis of peripheral blood mononuclear cells (PBMCs) from one MPS-II patient of 10 year old at different disease stages through scRNA-seq identified various immune cell types, including natural killer (NK) cells, NKT cells, CD4 + and CD8 + T cells, CD14 + and CD16 + monocytes, and B cells. Monocytes and macrophages were significantly reduced during the severe stage of MPS-II but increased during the recovery stage following ERT. Notably, monocyte subtype mono3 was exclusively expressed in the severe stage, while mono1_2, a subtype of mono1, was absent during the severe stage and exhibited distinct biological functions. These findings suggest that monocytes and macrophages play critical roles in the pathogenesis of MPS-II and in the response to ERT. Pseudotime, Gene Ontology, and cell-communication analyses revealed unique functions for the different cellular subtypes. Notably, key molecules mediating cellular interactions during ERT in MPS-II included CXCR3, PF4, APP, and C5AR1 in macrophages, RPS19 in T cells, HLA-DPB1 in B cells, ADRB2 in NK cells, and IL1B, C5AR1, RPS19, and TNFSF13B in monocytes. Overall, integrative analysis delineated the expression dynamics of various cell types and identified mutations in MPS-II, providing a comprehensive atlas of transcriptional programs, cellular characterizations, and genomic variation profiles in MPS-II. This dataset, along with advanced integrative analysis, represents a valuable resource for the discovery of drug targets and the improvement of therapeutic strategies for MPS-II.
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Role of FSCN1 in the tumor microenvironment of lung squamous cell carcinoma.
Tumor microenvironment (TME) regulated the development of the Lung squamous cell carcinoma (LUSC). To know more about the LUSC, this study tried to figure the role of fscin actin-bundling protein 1 (FSCN1) in the TME. We identified the FSCN1 as the hub immune gene in LUSC, with the use of weighted gene co-expression network analysis (WGCNA) and the Human Protein Atlas. Furthermore, we verified the higher expression of FSCN1 in LUSC compared with the normal tissues by quantitative reverse transcription PCR (qRT-PCR) and immunohistochemistry. We then explored the associations among FSCN1, immune infiltrations, and inflammatory factors with the use of Gene Expression Profiling Interactive Analysis (GEPIA) and Tumor IMmune Estimation Resource (TIMER). As a result, the expressions of FSCN1 was negatively related to the immune infiltrations, and positively related to the expressions of IL1A, IL1B, TGFB1 and TGFA. Moreover, we used the single-cell RNA-sequencing (scRNA-seq) data of LUSC to figure out the expressions level of FSCN1, IL1A, IL1B, TGFB1 and TGFA in the different cell type's of the TME. Finally, through the cytological experiments, we found that FSCN1 affected by TGFB1 contributes to the proliferation, anti-apoptotic effect, migration and invasion of the LUSC cells. In summary, this study Identified FSCN1 as the potential therapeutic target of LUSC, and reveals a complicated immune and inflammatory net in the TME.
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MS4A7 based metabolic gene signature as a prognostic predictor in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) represents the most common form of lung cancer, contributing to significant global mortality. Metabolic reprogramming in tumor cells has been increasingly recognized as a hallmark of tumorigenesis, contributing to an immunosuppressive microenvironment. Given the promising prediction value of metabolism-related genes in LUAD, this study aims to explore the role of MS4A7, a member of the MS4A gene family, in LUAD prognosis and immune microenvironment dynamics. METHODS: A prognostic signature for LUAD was developed using the LASSO-Cox regression algorithm with RNA-seq data from 500 LUAD patients in The Cancer Genome Atlas database. Genes with differential expression linked to metabolic pathways were identified, and 20 genes were included to develop a risk signature. Further functional enrichment analysis was conducted to compare the biological pathways activated in high-risk versus low-risk groups. Single-cell RNA sequencing was employed to identify the expression profile and role of MS4A7 in different macrophage populations within the LUAD. RESULTS: The constructed prognostic model displayed high predictive accuracy, outperforming single gene-based predictions. High-risk patients exhibited significantly poorer survival outcomes. Pathway enrichment analysis revealed dysregulated metabolic pathways in high-risk patients, including activation of glycolysis, mTORC1 signaling, and ROS production. Single-cell RNA sequencing revealed that MS4A7 expression was predominantly found in macrophage populations, with high expression localized in MS4A7+ macrophages. These macrophages exhibited distinct metabolic reprogramming and key immune functions, particularly in crosstalk with T cells and neutrophils. CONCLUSION: The MS4A7 gene plays a critical role in LUAD prognosis, particularly through its involvement in immune modulation within the TME. MS4A7+ macrophages, characterized by distinct metabolic reprogramming and immune interactions, are pivotal in shaping LUAD progression and immune response. The findings highlight the potential of MS4A7 as a novel prognostic biomarker and therapeutic target for LUAD. Further investigation into the metabolic and immune regulatory mechanisms of MS4A7+ macrophages could offer new insights into LUAD treatment strategies.
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Identification of a migrasome-related lncRNA signature and its prognostic and immunological role in bladder cancer.
BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous malignancy with considerable variability in clinical outcomes. Reliable biomarkers for prognosis and treatment guidance remain urgently needed. Migrasomes, recently identified cellular organelles involved in intercellular communication, have been implicated in cancer progression, yet their associated long non-coding RNAs (lncRNAs) and prognostic relevance in BLCA remain unclear. METHODS: We integrated transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) to identify migrasome-related lncRNAs through co-expression analysis. A prognostic signature was constructed using univariate Cox, LASSO, and multivariate regression analyses. Patients were stratified into high- and low-risk groups across training, validation, and entire cohorts. Kaplan-Meier survival, ROC curves, PCA, and Cox analyses were used for validation. Functional annotation was conducted via GO, KEGG, and GSEA. Immune infiltration and tumor microenvironment features were evaluated using ESTIMATE and CIBERSORT, while tumor mutation burden (TMB), immune escape potential, and drug sensitivity were also analyzed. Single-cell RNA sequencing (scRNA-seq) analysis delineated cellular heterogeneity and intercellular communication patterns related to the risk signature in the BLCA microenvironment. Finally, experimental validation of SEC24B-AS1, a key lncRNA within the model, was performed through qRT-PCR, CCK-8, colony formation, and migration assays to confirm its biological relevance. RESULTS: A robust prognostic signature comprising migrasome-related lncRNAs was established, effectively distinguishing patients into high- and low-risk groups with significantly different survival outcomes. The signature remained an independent prognostic factor after multivariate adjustment. Functional enrichment analyses revealed marked biological distinctions between the two groups. Immune profiling showed higher immune and stromal scores in high-risk patients, together with distinct immune cell composition and function. High-risk tumors also exhibited elevated TMB and increased immune evasion potential. Drug sensitivity analysis identified compounds with differential responses between risk subgroups. Single-cell transcriptomic analysis indicated that the signature reflects cellular heterogeneity and intercellular communication patterns within the BLCA microenvironment. Experimental validation of SEC24B-AS1, a key lncRNA in the model, demonstrated its impact on tumor cell proliferation and migration, supporting the biological relevance of the prognostic signature in BLCA. CONCLUSION: This study presents a novel migrasome-related lncRNA signature that independently predicts survival in bladder cancer and reflects the tumor immune landscape.
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A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution.
Caenorhabditis elegans is an animal with few cells but a wide diversity of cell types. In this study, we characterize the molecular basis for their specification by profiling the transcriptomes of 86,024 single embryonic cells. We identify 502 terminal and preterminal cell types, mapping most single-cell transcriptomes to their exact position in C. elegans' invariant lineage. Using these annotations, we find that (i) the correlation between a cell's lineage and its transcriptome increases from middle to late gastrulation, then falls substantially as cells in the nervous system and pharynx adopt their terminal fates; (ii) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and (iii) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state.
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Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.
INTRODUCTION: The development of high-throughput sequencing technologies and targeted therapeutic strategies has significantly improved the prognosis of lung adenocarcinoma (LUAD) patients with sensitive gene mutations. However, patients harboring rare or no actionable mutations were rarely benefit from these targeted therapies. This study aimed to identify novel molecular subtypes and construct a prognostic signature to enhance the stratification of LUAD prognosis. MATERIALS AND METHODS: Novel molecular subtypes of LUAD patients were identified by applying 10 distinct clustering algorithms on multi-omics data. Single-cell RNA-sequencing (scRNA-seq) data were integrated to characterize subtype-specific immune microenvironments. A multi-omics and machine learning-driven prognostic signature (MO-MLPS) was constructed in The Cancer Genome Atlas (TCGA) LUAD dataset using ten machine learning algorithms and subsequently validated across six independent datasets from the Gene Expression Omnibus (GEO) database. The robustness of the model was assessed using the concordance index (C-index), Kaplan-Meier survival analyses, receiver operating characteristic (ROC) curves, and both univariate and multivariate Cox regression analyses. We further confirmed the effects of ANLN knockdown and the expression of a domain-negative anillin protein (dnANLN) via western blotting, cell proliferation assays, flow cytometry, and transwell migration assays in vitro. RESULTS: Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. The MO-MLPS was successfully established and validated across TCGA-LUAD cohorts, six independent GEO datasets, and their composite meta-cohort. Higher risk scores from the MO-MLPS correlated with poorer prognosis in LUAD, with AUC values exceeding 0.5 at 1, 3, and 5 years across various cohorts. The signature outperformed 49 previously published prognostic signatures. Furthermore, patients classified as high risk exhibited significantly worse overall and progression-free survival than those classified as low risk. Notably, ANLN knockdown and dnANLN expression significantly inhibited cell proliferation and migration in vitro and enhanced the efficacy of docetaxel. CONCLUSION: A comprehensive analysis of multi-omics data redefines the molecular subtype of LUAD patients. The MO-MLPS derived from subtype characteristics has the potential to serve as a clinically valuable prognostic tool. Furthermore, ANLN emerges as a promising novel therapeutic target in the treatment of LUAD.
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Single-cell transcriptomics reveals systemic immune dysregulation in non-segmental vitiligo.
BACKGROUND: Non-segmental vitiligo (NSV) is an autoimmune disorder characterized by irregular depigmented skin patches due to melanocyte loss, which causes considerable psychosocial burden. Although localized mechanisms underlying vitiligo pathogenesis have been studied extensively, investigations into peripheral blood mononuclear cells (PBMCs), key mediators of autoimmune diseases, remain limited. METHODS: To address this gap, we performed single-cell RNA sequencing (scRNA-seq) on peripheral blood samples from 3 untreated patients with generalized, progressive non-segmental vitiligo (GP-NSV) and 3 healthy controls. Findings were validated using flow cytometry in an additional cohort of 7 GP-NSV patients and 30 controls. Computational analyses, including pseudotime trajectory reconstruction and pathway enrichment, were employed to characterize immune cell subsets and their functional states. RESULTS: Vitiligo patients exhibited striking heterogeneity in PBMC subsets. KLRC2+ NK cells were markedly reduced and enriched in tumor necrosis factor (TNF) and apoptotic signaling pathways, a finding further confirmed by flow cytometry. Pseudotime analysis indicated that NK cells underwent negative regulation of DNA metabolic processes alongside activation of granzyme-mediated programmed cell death. In addition, the frequency of FCGR3A+ Cytotoxic CD8+T cell was reduced, with enrichment in T cell activation and differentiation signatures. STAM+ regulatory T cells (Tregs) were increased, whereas EGR1+ B cells were decreased, both subsets showing enrichment in pathways linked to osteoclast differentiation and calcium ion metabolism, suggesting a potential role of calcium homeostasis dysregulation in disease pathogenesis. CONCLUSIONS: This study provides the single-cell atlas of PBMCs in GP-NSV, uncovering profound transcriptional and compositional alterations across multiple immune cell subsets in active vitiligo. These findings offer novel insights into systemic immune dysregulation in GP-NSV and pave the way for novel targeted therapeutic strategies.
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LINC01871 is exclusively expressed in T and NK cells and is highly induced upon CD4+ T cell activation.
Long intergenic noncoding RNAs (lincRNAs) regulate biological processes in health and disease. Recent findings highlight the importance of lincRNAs in regulating T cell development and function. Here, we identified a lincRNA, LINC01871, which is highly induced upon CD4+ T cell activation and is predominantly located in the cytoplasm. The anti-inflammatory cytokine TGF-β was found to suppress its expression. Silencing LINC01871 led to a modest decrease in IL-2 secretion. Notably, LINC01871 expression was highly specific to NK cells and T cells in several cross-tissue single cell RNA-seq atlases. These data suggest that LINC01871 is specifically expressed in T and NK cells and may contribute to T cell-mediated immunity in humans.
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HypoMap-a unified single-cell gene expression atlas of the murine hypothalamus.
The hypothalamus plays a key role in coordinating fundamental body functions. Despite recent progress in single-cell technologies, a unified catalog and molecular characterization of the heterogeneous cell types and, specifically, neuronal subtypes in this brain region are still lacking. Here, we present an integrated reference atlas, 'HypoMap,' of the murine hypothalamus, consisting of 384,925 cells, with the ability to incorporate new additional experiments. We validate HypoMap by comparing data collected from Smart-Seq+Fluidigm C1 and bulk RNA sequencing of selected neuronal cell types with different degrees of cellular heterogeneity. Finally, via HypoMap, we identify classes of neurons expressing glucagon-like peptide-1 receptor (Glp1r) and prepronociceptin (Pnoc), and validate them using single-molecule in situ hybridization. Collectively, HypoMap provides a unified framework for the systematic functional annotation of murine hypothalamic cell types, and it can serve as an important platform to unravel the functional organization of hypothalamic neurocircuits and to identify druggable targets for treating metabolic disorders.