Search Results (49)

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by irreversible cognitive decline and synaptic dysfunction and represents the most prevalent etiology of dementia, accounting for an estimated 60–70% of all clinically diagnosed cases worldwide. The growing focus on microglia–neuron interactions in AD research highlights their diverse, region-specific responses, which are driven by the functional and pathological heterogeneity across different brain regions. Therefore, investigating the interactions between microglia and neurons is of crucial importance. To explore the regional heterogeneity of microglia–neuron crosstalk in AD, we integrated human single-nucleus RNA sequencing data from the prefrontal cortex (PFC), hippocampus (HPC), and occipital lobe (OL) provided by the ssREAD database. Our study delineated four microglial subtypes and uncovered a pseudotime trajectory activation trajectory leading to the disease-associated microglia (DAM) phenotype. The transition along this trajectory is driven and stabilized by a key molecular switch: the coordinated downregulation of inhibitory factors (e.g., LINGO1) and upregulation of immune-effector and antigen-presentation programs, which collectively establish the pro-inflammatory DAM state. Furthermore, we observed that each brain region displayed unique microglia–neuron communication patterns in response to AD pathology. The PFC and OL engage a THY1-ITGAX/ITGB2 signaling axis; the HPC predominantly utilizes the PTPRM pathway. Notably, THY1 dysregulation strongly correlates with pathology in the PFC, HPC, and OL, suggesting that microglia–neuron crosstalk in AD possesses both heterogeneity and commonality. The main contribution of this study is the systematic characterization of region-specific microglia-neuron interactions and the identification of THY1 as a potential mediator that may be targeted therapeutically to modulate microglial function in affected brain regions. Full article

Background and Objective: Colorectal cancer (CRC) liver metastasis (CRLM) represents a major clinical challenge, and acquired resistance to radiotherapy (RT) significantly limits therapeutic efficacy. A deep and comprehensive understanding of the cellular and molecular mechanisms driving RT resistance is urgently required to develop effective combination strategies. Here, we aimed to dissect the dynamic cellular landscape of the tumor microenvironment (TME) and identify key epigenetic regulators mediating radioresistance in CRLM by integrating cutting-edge single-cell and spatial omics technologies. Methods and Results: We performed integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on matched pre- and post-radiotherapy tumor tissues collected from three distinct CRLM patients. Employing a robust machine-learning framework on the multi-omics data, we successfully identified MORF4L1 (Mortality Factor 4 Like 1), an epigenetic reader, as a critical epigenetic mediator of acquired radioresistance. High-resolution scRNA-seq analysis of the tumor cell compartment revealed that the MORF4L1-high subpopulation exhibited significant enrichment in DNA damage repair (DDR) pathways, heightened activity of multiple pro-survival metabolic pathways, and robust signatures of immune evasion. Pseudotime trajectory analysis further confirmed that RT exposure drives tumor cells toward a highly resistant state, marked by a distinct increase in MORF4L1 expression. Furthermore, cell–cell communication inference demonstrated a pronounced, systemic upregulation of various immunosuppressive signaling axes within the TME following RT. Crucially, high-resolution ST confirmed these molecular and cellular interactions in their native context, revealing a significant spatial co-localization of MORF4L1-expressing tumor foci with multiple immunosuppressive immune cell types, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs), thereby underscoring its role in TME-mediated resistance. Conclusions: Our comprehensive spatial and single-cell profiling establishes MORF4L1 as a pivotal epigenetic regulator underlying acquired radioresistance in CRLM. These findings provide a compelling mechanistic rationale for combining radiotherapy with the targeted inhibition of MORF4L1, presenting a promising new therapeutic avenue to overcome treatment failure and improve patient outcomes in CRLM. Full article

Erythro-myeloid progenitors (EMPs) originate from the haemogenic endothelium in the yolk sac via an endothelial-to-haematopoietic transition (EHT) to generate blood and immune cells that support embryo development. Yet, the transitory nature of EHT and the limited availability of molecular markers have constrained our understanding of the origin, identity, and differentiation dynamics of EMPs. Here, we have refined the annotation of yolk sac haemato-vascular populations in publicly available single-cell RNA sequencing (scRNAseq) datasets from mouse embryos to identify novel molecular markers of haemogenic endothelium and EMPs. By sub-clustering key cell populations followed by pseudotime analysis, we refined cluster annotations and then reconstructed differentiation trajectories. Subsequent differential gene expression analysis between clusters identified novel cell surface markers for haemogenic endothelial cells (Fxyd5 and Scarf1) and EMPs (Fcer1g, Tyrobp, and Mctp1). Further, we have identified candidate signalling and metabolic pathways that may regulate yolk sac haematopoietic emergence and differentiation. The specificity of FXYD5, SCARF1, and FCER1G for haemogenic endothelium and EMPs was validated by immunostaining of the mouse yolk sac. These insights into the transcriptional dynamics in the yolk sac should support future investigation of EHT and haematopoietic differentiation during early mammalian development. Full article

Background: Lung adenocarcinoma (LUAD) exhibits pronounced cellular and molecular heterogeneity that shapes tumor progression and therapeutic response. Although nucleotide metabolism is essential for sustaining tumor proliferation and coordinating immune interactions, its single-cell heterogeneity and clinical implications remain incompletely defined. Methods: We integrated a publicly available scRNA-seq dataset derived from independent LUAD patients to construct a comprehensive LUAD cellular atlas, identified malignant epithelial cells using inferCNV, and reconstructed differentiation trajectories via Monocle2. Cell–cell communication patterns under distinct nucleotide metabolic states were assessed using CellChat. A nucleotide metabolism-related signature (NMRS) was subsequently developed across TCGA-LUAD and multiple GEO cohorts using 101 combinations of machine learning algorithms. Its prognostic and immunological predictive value was systematically evaluated. The functional relevance of the key gene ENO1 was further verified through pan-cancer analyses and in vitro experiments. Results: We identified substantial nucleotide metabolic heterogeneity within malignant epithelial cells, closely linked to elevated proliferative activity, glycolytic activation, and increased CNV burden. Pseudotime analysis showed that epithelial cells gradually acquire enhanced immune-modulatory and complement-related functions along their differentiation continuum. High-metabolism epithelial cells exhibited stronger outgoing communication—particularly via MIF, CDH5, and MHC-II pathways—highlighting their potential role in shaping an immunosuppressive microenvironment. The NMRS built from metabolism-related genes provided robust prognostic stratification across multiple cohorts and surpassed conventional clinical parameters. Immune profiling revealed that high-NMRS tumors displayed increased T-cell dysfunction, stronger exclusion, higher TIDE scores, and lower IPS, suggesting poorer responses to immune checkpoint blockade. ENO1, markedly upregulated in high-NMRS tumors and functioning as a risk factor in several cancer types, was experimentally shown to promote invasion in LUAD cell lines. Conclusions: This study delineates the profound impact of nucleotide metabolic reprogramming on epithelial cell states, immune ecology, and malignant evolution in LUAD. The NMRS provides a robust predictor of prognosis and immunotherapy response across cohorts, while ENO1 emerges as a pivotal metabolic–immune mediator and promising therapeutic target. Full article

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease worldwide. However, the inflammatory mediators that causally drive disease progression remain incompletely defined. In this study, we used a multi-omics approach that combined single-cell RNA sequencing, spatial transcriptomics, pseudotime trajectory analysis, cell-to-cell communication analysis, and Mendelian randomization (MR) to investigate the role of tumor necrosis factor receptor superfamily member 1A (TNFRSF1A) in DKD development. Findings were further validated in zebrafish embryos depleted of pdx1, an established model of DKD. Spatial transcriptomic analysis showed that TNFRSF1A is enriched in cortical kidney regions. Pseudotime analysis revealed progressive immune reprogramming, with an early predominance of T and NK cells and gradual shift to myeloid infiltration and B-cell expansion. Cell-to-cell communication analysis highlighted IL-1β and related signaling pathways that increase NF-κB activity. Mendelian Randomization analysis, complemented by PPI network mapping, identified TNFRSF1A (OR = 1.78, 95% CI: 1.17–2.71, p = 0.007) as a gene with genetic evidence supporting a causal association. Consistent with the human data, experiments in zebrafish showed that TNFRSF1A expression increases significantly following pdx1 knockdown (p = 0.0025). Together, these findings support a role for TNFRSF1A in immune microenvironment reprogramming in DKD, while not excluding the involvement of additional regulatory pathways. Full article

Excessive oxidative stress drives pathological ventricular remodeling after acute myocardial infarction (AMI), yet adaptive cardiomyocyte mechanisms are poorly understood. We analyzed 64,510 human cardiomyocytes from five integrated single-cell datasets to delineate oxidative stress heterogeneity. Using quartile thresholds of a composite oxidative stress score, cells were stratified into three distinct subpopulations: high oxidative stress (HOX, score > 2.608), dynamic transient oxidative stress (DTOX), and low oxidative stress (LOX, score < 2.061). Paradoxically, HOX cells exhibited severe oxidative stress alongside significantly higher cellular plasticity than DTOX and LOX cells (p < 0.001), as confirmed by CytoTRACE and pseudotime trajectory analyses. This subpopulation demonstrated a unique “metabolic activation–immune suppression” signature and served as a central communication hub. An integrative machine-learning framework incorporating six distinct algorithms and independent cohort validation identified five core marker genes (TRIM63, ETFDH, TXNIP, CKMT2, and PDK4). These genes demonstrated stable diagnostic capability for AMI in independent validation cohorts (AUCs 0.688–0.721, all p < 0.001) and were specifically enriched in HOX cells. Our work reveals a previously unrecognized adaptive state in post-infarction cardiomyocytes, offering promising new targets for precision diagnosis and intervention. Full article

Background: Antibody-drug conjugates (ADCs) have ushered in a new era of precision oncology by combining the targeting specificity of monoclonal antibodies with the potent cytotoxicity of chemotherapeutic drugs. However, the cellular and molecular mechanisms underlying their dose-limiting ocular toxicity remain unclear. Elahere™, the first FDA-approved ADC targeting folate receptor α (FRα), demonstrates remarkable efficacy in platinum-resistant ovarian cancer but causes keratitis and other ocular toxicities in some patients. Notably, FRα is not expressed in the corneal epithelium—the primary site of damage—highlighting the urgent need to elucidate its underlying mechanisms. The aim of this study was to identify the cell-type-specific molecular mechanisms underlying Elahere-induced ocular toxicity. Methods: Sprague-Dawley rats were treated with intravenous Elahere (20 mg/kg) or vehicle weekly for five weeks. Ocular toxicity was determined by clinical examination and histopathology. Corneal single-cell suspensions were analyzed using the BD Rhapsody single-cell RNA sequencing (scRNA-seq) platform. Bioinformatic analyses to characterize changes in corneal cell populations, gene expression, and signaling pathways included cell clustering, differential gene expression, pseudotime trajectory inference, and cell-cell interaction modeling. Results: scRNA-seq profiling of 47,606 corneal cells revealed significant damage to the ocular surface and corneal epithelia in the Elahere group. Twenty distinct cell types were identified. Elahere depleted myeloid immune cells; in particular, homeostatic gene expression was suppressed in phagocytic macrophages. Progenitor populations (limbal stem cells and basal cells) accumulated (e.g., a ~2.6-fold expansion of limbal stem cells), while terminally differentiated cells decreased in corneal epithelium, indicating differentiation blockade. Endothelial cells exhibited signs of injury and inflammation, including reduced angiogenic subtypes and heightened stress responses. Folate receptor alpha, the target of Elahere, was expressed in endothelial and stromal cells, potentially driving stromal cells toward a pro-fibrotic phenotype. Fc receptor genes were predominantly expressed in myeloid cells, suggesting a potential mechanism underlying their depletion. Conclusions: Elahere induces complex, multi-compartmental ocular toxicity characterized by initial perturbations in vascular endothelial and immune cell populations followed by the arrest of epithelial differentiation and stromal remodeling. These findings reveal a cascade of cellular disruptions and provide mechanistic insights into mitigating Elahere-associated ocular side effects. Full article

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. However, the spatial and temporal dynamics underlying its development remain poorly characterized. This study employs spatial transcriptomics (ST) to investigate the progression of intestinal tumors in APC ^Min/+ mice across multiple time points. We identified distinct transcriptional profiles between tumor and normal tissues, resolving six major cell types through integrated dimensionality reduction and pathological annotation. Pseudo-time trajectory analysis revealed increased expression of MMP11 and MYL9 in later stages of tumor progression. Analysis of human CRC cohorts from the TCGA database further confirmed that high expression of these genes is associated with advanced clinical stages and promotes tumor proliferation and invasion. Temporal gene expression dynamics indicated enrichment of cancer-related pathways concurrent with suppression of lipid and amino acid metabolism. Notably, genes in the DEFA family were significantly upregulated in normal tissues compared to tumor tissues. Functional validation showed that DEFA3 inhibits colon cancer cell migration and proliferation in vitro. These demonstrate the value of ST in resolving spatiotemporal heterogeneity in CRC and identify both MMP11/MYL9 and DEFA3 as potential biomarkers and therapeutic targets. Full article

Background and objective: Periodontitis, a chronic inflammatory disease driven by host immune dysregulation, leads to progressive destruction of periodontal tissues. This study employed an integrative approach combining single-cell transcriptomics, hierarchical weighted gene co-expression network analysis (hdWGCNA), and deep learning algorithms to identify key biomarkers associated with neutrophil degranulation in periodontitis, aiming to establish diagnostic models for early detection and precision interventions. Methods: We integrated single-cell RNA sequencing (scRNA-seq) data from human gingival tissues with bulk transcriptomic datasets. Pathogenic neutrophil subsets were characterized via pseudotime trajectory and cell–cell communication analyses. Hierarchical weighted gene co-expression network analysis (hdWGCNA) identified functional modules linked to degranulation. Machine learning and a convolutional neural network (CNN) model combining gene expression and immune cell profiles were developed for diagnosis. Results: scRNA-seq revealed a neutrophil subpopulation significantly increased infiltration in periodontitis, with cell–cell communication and pseudotime trajectory analyses demonstrating amplified inflammatory crosstalk. hdWGCNA identified the turquoise module enriched in PD-KEY-Neutrophils, containing hub genes linked to neutrophil degranulation and complement activation. Immune infiltration and non-negative matrix factorization linked high-degranulation neutrophil signatures to the periodontal immunity microenvironment. Machine learning demonstrated that the neutrophil degranulation-associated genes effectively distinguish diseased gingival tissue, suggesting their potential to predict periodontitis. Finally, integrating transcriptomic and immunological data, we developed a gene-immune CNN deep learning model accurately diagnosed periodontitis in diverse cohorts (AUC = 0.922). Conclusions: Our study identified a pathogenic neutrophil subpopulation driving periodontitis through degranulation and inflammation. The neutrophil degranulation genes serve as critical biomarkers, offering new insights for clinical diagnosis and treatment of periodontitis. Full article

Clear cell renal cell carcinoma (ccRCC), the most common RCC subtype, displays significant intratumoral heterogeneity driven by metabolic reprogramming, which complicates our understanding of disease progression and limits treatment efficacy. This study aimed to construct a comprehensive cellular and transcriptional landscape of ccRCC, with emphasis on gene expression dynamics during malignant progression. An integrated analysis of 90 scRNA-seq samples comprising 534,227 cells revealed a progressive downregulation of sodium ion transport-related genes, particularly CHP1 (calcineurin B homologous protein isoform 1), which is predominantly expressed in epithelial cells. Reduced CHP1 expression was confirmed at both mRNA and protein levels using bulk RNA-seq, CPTAC proteomics, immunohistochemistry, and ccRCC cell lines. Survival analysis showed that high CHP1 expression correlated with improved prognosis. Functional analyses, including pseudotime trajectory, Mfuzz clustering, and cell–cell communication modeling, indicated that CHP1⁺ epithelial cells engage in immune interaction via PPIA–BSG signaling. Transcriptomic profiling and molecular docking suggested that CHP1 modulates amino acid transport through SLC38A1. ZNF460 was identified as a potential transcription factor of CHP1. Virtual screening identified arbutin and imatinib mesylate as candidate CHP1-targeting compounds. These findings establish CHP1 downregulation as a novel molecular feature of ccRCC progression and support its utility as a prognostic biomarker. Full article

Background: Tumor-associated macrophages (TAMs) are critical regulators of the hepatocellular carcinoma (HCC) microenvironment, yet their epigenetic heterogeneity and regulatory programs remain poorly defined. Methods: We performed integrative analysis on single-cell RNA-seq and ATAC-seq profiling of HCC patients to dissect TAM subtypes at high resolution. By correlating chromatin accessibility with gene expression, we identified cell-type-specific candidate cis-regulatory elements (CREs). TAM subsets with prognostic significance were determined through integration with HCC clinical cohorts. Pseudotime and multi-regional analyses were used to uncover regulatory trajectories underlying macrophage phenotypic transitions. The identification framework of a super-enhancer (SE) was constructed, and potential therapeutic targets were prioritized using drug–gene interaction data. Results: We delineated the regulatory landscape of TAMs in HCC, revealing cell-type-specific chromatin accessibility patterns underlying TAM heterogeneity. The 65,342 CREs linked to gene expression were identified, with distal CREs contributing most to cell-type-specific regulation. Notably, SPP1⁺ TAMs were found to be enriched in tumor cores and associated with poor prognosis in HCC. Liver-resident Kupffer cells showed progressive loss of the core transcription factors SPIC and MAFB, suggesting a potential transition into SPP1⁺ TAMs under tumor pressure. We identified 133 SPP1⁺ TAM-specific SEs and constructed a TF–SE–target gene regulatory network. Notably, 13 target genes showed higher drug–gene interaction effects, highlighting their therapeutic potential. Conclusions: This study provides the chromatin accessibility map of TAMs in HCC and reveals how distal CRE-driven transcriptional programs shape TAM states. Our findings lay the foundation for understanding the epigenetic regulation of TAM heterogeneity and nominate potential targets for TAM-directed immunotherapy in HCC. Full article

Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease with significant therapeutic challenges. Recent studies suggest that dihydroartemisinin (DHA), a traditional Chinese medicine known for its anti-malarial properties, may be beneficial for SLE treatment, although its precise mechanism remains unclear. This study aimed to investigate the effects of DHA on the cellular composition and molecular events of splenic T cells and B cells in MRL/lpr mice, a widely used SLE model. Methods: T cells and B cells isolated from the spleens of three DHA-treated mice and three control mice underwent single-cell RNA sequencing (scRNA-seq) using the 10× Genomics Chromium system. Comprehensive analyses included cell clustering, signaling pathway enrichment, pseudotime trajectory analysis, and cellular communication assessment using unbiased computational methods. Results: DHA treatment significantly reduced kidney inflammation and altered the proportions of splenic T cells and B cells, particularly decreasing plasma cells. Molecular profiling of effector CD4+ T cells showed a significant reduction in several inflammation-related signaling pathways in DHA-treated mice. Cellular communication analysis indicated altered interactions between effector CD4+ T cells and B cells in MRL/lpr mice after DHA treatment. Conclusions: Our findings reveal changes in cellular composition and signaling pathways in splenic T cells and B cells of MRL/lpr mice following DHA treatment. DHA may inhibit B-cell differentiation into plasma cells by modulating effector CD4+ T cells, potentially through the regulation of HIF1α and ligand–receptor interactions, enhancing our understanding of DHA’s mechanisms in SLE treatment. Full article

Chronic migraine (CM) is a debilitating neurological disorder, yet the glial-specific mechanisms underlying its pathophysiology in the trigeminal nucleus caudalis (TNC)—a critical hub for craniofacial pain processing—remain poorly understood. Here, we employed single-nucleus RNA sequencing (snRNA-seq) to resolve cell-type-specific transcriptional landscapes in a nitroglycerin (NTG)-induced CM rat model, with a particular focus on microglia and astrocytes. We identified 19 transcriptional clusters representing nine major cell types, among which reactive microglia (NTG-Mic) and astrocytes (NTG-Asts) were markedly expanded. The NTG-Mic displayed a glycolysis-dominant, complement-enriched state, whereas the NTG-Asts exhibited concurrent activation of amino acid transport and cytokine signaling pathways. Pseudotime trajectory analysis revealed bifurcated glial activation paths, with NTG driving both cell types toward terminal reactive states. Intercellular communication inference uncovered suppressed homeostatic interactions (e.g., CSF1-CSF1R) alongside enhanced proinflammatory signaling (e.g., FGF1-FGFR2, PTN-SDC4), particularly affecting neuron–glia and glia–glia crosstalk. Together, these findings define a high-resolution atlas of glial reprogramming in CM, implicating state-specific metabolic–immune transitions and dysregulated glial communication as potential targets for therapeutic intervention. Full article

Background: Membranous nephropathy (MN), a prevalent glomerular disorder, remains poorly understood in terms of its association with mitochondrial dynamics (MD). This study investigated the mechanistic involvement of mitochondrial dynamics-related genes (MDGs) in the pathogenesis of MN. Methods: Comprehensive bioinformatics analyses—encompassing Mendelian randomization, machine-learning algorithms, and single-cell RNA sequencing (scRNA-seq)—were employed to interrogate transcriptomic datasets (GSE200828, GSE73953, and GSE241302). Core MDGs were further validated using reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Results: Four key MDGs—RTTN, MYO9A, USP40, and NFKBIZ—emerged as critical determinants, predominantly enriched in olfactory transduction pathways. A nomogram model exhibited exceptional diagnostic performance (area under the curve [AUC] = 1). Seventeen immune cell subsets, including regulatory T cells and activated dendritic cells, demonstrated significant differential infiltration in MN. Regulatory network analyses revealed ATF2 co-regulation mediated by RTTN and MYO9A, along with RTTN-driven modulation of ELOA-AS1 via hsa-mir-431-5p. scRNA-seq analysis identified mesenchymal–epithelial transitioning cells as key contributors, with pseudotime trajectory mapping indicating distinct temporal expression profiles: NFKBIZ (initial upregulation followed by decline), USP40 (gradual fluctuation), and RTTN (persistently low expression). RT-qPCR results corroborated a significant downregulation of all four genes in MN samples compared to controls (p < 0.05). Conclusions: These findings elucidate the molecular underpinnings of MDG-mediated mechanisms in MN, revealing novel diagnostic biomarkers and therapeutic targets. The data underscore the interplay between mitochondrial dynamics and immune dysregulation in MN progression, providing a foundation for precision medicine strategies. Full article

Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Multiple separate tools are used downstream of Seurat and Scanpy cell annotation to study cell differentiation and communication, including cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell–cell communication analysis. To automate the integrative cell differentiation and communication analyses of single-cell RNA-seq data, we developed a single-cell RNA-seq downstream analysis pipeline called “scDown”. This R package includes cell proportion difference analysis, cell–cell communication analysis, pseudotime analysis, and RNA velocity analysis. Both Seurat and Scanpy annotated single-cell RNA-seq data are accepted in this pipeline. We applied scDown to a published dataset and identified a unique, previously undiscovered signature of neuronal inflammatory signaling associated with a rare genetic neurodevelopmental disorder. These findings were not identified with a simple implementation of Seurat differential gene expression analysis, illustrating the value of our pipeline in biological discovery. scDown can be broadly utilized in downstream analyses of scRNA-seq data, particularly in rare diseases. Full article