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Construction of a Computationally Inferred Single-Cell miRNA Activity Atlas and Analysis of Regulatory Networks in Spinal Cord Injury.
UNLABELLED: Background. Spinal cord injury (SCI) is a debilitating neurological disorder characterized by complex pathological processes involving dynamic responses of multiple cell types and intricate regulatory networks. Although single-cell RNA sequencing (scRNA-seq) has been employed to dissect cellular heterogeneity in SCI, the activity and regulatory functions of microRNAs (miRNAs) at single-cell resolution remain largely unexplored. Methods. This study utilized the GSE189070 dataset, integrating single-cell transcriptomic data from mouse spinal cord tissues at eight time points: uninjured control and 0.5, 1, 3, 7, 14, 60, and 90 days post-injury. Following quality control, batch correction, and cell-type annotation, miRNA activities across 12 major cell types were computationally inferred using a motif enrichment-based approach. Temporal clustering of miRNA activity was performed with Mfuzz, and subsequent analyses included target gene prediction, functional enrichment, and network construction. Inferred miRNA activity patterns were validated using an independent bulk miRNA sequencing dataset (GSE90452). Key findings were experimentally verified in BV-2 microglial cells and C8-D1A astrocytes using miRNA inhibitors and mimics. Results. We successfully constructed a single-cell transcriptomic atlas and a dynamic computationally inferred miRNA activity landscape during SCI progression. Independent dataset validation confirmed that inferred miRNA activity changes (e.g., mmu-miR-488-3p, mmu-miR-132-3p) were consistent with actual miRNA expression alterations. Our analysis revealed that microglia and macrophages exhibited dynamic miRNA activity patterns closely associated with inflammatory pathways, including TNFA_SIGNALING_VIA_NFKB and TOLL-LIKE RECEPTOR signaling. Cross-cell-type comparison identified 38 common differentially active miRNAs shared between microglia and macrophages, with network analysis revealing coordinated regulation of key inflammatory genes (e.g., Lif, Csf1, Il18rap). In astrocytes, specific miRNAs (e.g., miR-7a-5p, miR-124-3p) were found to regulate apoptotic pathways by targeting Casp3 and Bcl2 family genes. Experimental validation confirmed that miR-130a-3p promotes microglial inflammation, while miR-7a-5p inhibits astrocyte apoptosis under oxidative stress. Conclusion. This study presents a computationally inferred, experimentally validated single-cell-resolution map of miRNA activity dynamics during SCI, revealing potential regulatory networks in key cell types. The concordance between computational inference and independent experimental data supports the biological plausibility of our findings and provides a foundation for further therapeutic exploration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12031-026-02528-7.
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Spatiotemporal trajectory of senescence in mesocarp cell clusters of Hylocereus undatus based on single-cell and spatial transcriptomics.
Fruit senescence is a complex physiological process. Single-cell RNA sequencing (scRNA-seq) analysis revealed the differentiation trajectories of 13 cell clusters during the senescence of Hylocereus undatus (H. undatus). The mesocarp of the fruit contained four cell clusters, but their precise localization and functional division remained unclear. This work documented mesocarp phenotypic alterations and elucidated the time courses of mesocarp flavonoid biosynthesis and superoxide anion generation. Additionally, overall ROS changes were observed using fluorescence microscopy. By combining the single-cell atlas with spatial transcriptomics data at resolutions of 0.2 and 0.8, and applying four computational algorithms (SingleR, SciBet, CARD, and RCTD), we accurately mapped the spatial distribution of the four cell populations in the two layers of the mesocarp from outer to inner regions. Furthermore, we identified highly correlated cells with cell-specific functions, which allowed us to perform a detailed analysis of the differentiation trajectories of these four cell clusters. We proposed a hypothesis that these four clusters in the mesocarp participate in the senescence process. Finally, using SCODE, we uncovered the gene regulatory networks of the pericarp's highly correlated cell clusters during fruit senescence. Through single-cell technology, the functional division of the four cell clusters in the mesocarp-responsible for stress responses, signal transduction, material preparation, and cell differentiation trajectories-has been revealed. These findings provide insights from a single-cell dimension and a spatiotemporal perspective, enhancing the understanding of the dynamic process of plant senescence.
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A single-cell atlas of mouse lung development.
Lung organogenesis requires precise timing and coordination to effect spatial organization and function of the parenchymal cells. To provide a systematic broad-based view of the mechanisms governing the dynamic alterations in parenchymal cells over crucial periods of development, we performed a single-cell RNA-sequencing time-series yielding 102,571 epithelial, endothelial and mesenchymal cells across nine time points from embryonic day 12 to postnatal day 14 in mice. Combining computational fate-likelihood prediction with RNA in situ hybridization and immunofluorescence, we explore lineage relationships during the saccular to alveolar stage transition. The utility of this publicly searchable atlas resource (www.sucrelab.org/lungcells) is exemplified by discoveries of the complexity of type 1 pneumocyte function and characterization of mesenchymal Wnt expression patterns during the saccular and alveolar stages - wherein major expansion of the gas-exchange surface occurs. We provide an integrated view of cellular dynamics in epithelial, endothelial and mesenchymal cell populations during lung organogenesis.
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Multi-omics approaches including single-cell analysis and machine learning were used to identify the roles of telomere-related genes in esophageal squamous cell carcinoma.
BACKGROUND: Telomeres, consisting of TTAGGG repeats and six shelterin proteins, have been extensively studied in oncogenesis due to their crucial role in maintaining genomic stability. Despite these advances, the prognostic significance of telomere-related genes (TRGs) in esophageal squamous cell carcinoma (ESCC) remains poorly characterized. METHODS: Transcriptomic data, single-cell RNA sequencing (scRNA-seq) data, and clinical information of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. TRGs were retrieved from the TelNet database. Core TRGs were identified using three machine learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), and random forest (RF). Based on prognosis-related TRGs, unsupervised clustering was performed to classify ESCC patients into two distinct molecular subtypes, and a prognostic risk model was subsequently constructed. Following risk stratification, survival analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted to further evaluate the potential clinical value of the risk model in ESCC. In addition, the expression patterns and intercellular communication of the model genes were examined using single-cell data. Finally, the differential expression of the core genes was validated by quantitative real-time PCR (qRT-PCR). RESULTS: We developed a prognostic risk prediction model using five independent prognostic TRGs. The model demonstrated excellent performance in predicting patient outcomes and effectively revealed the heterogeneity of the tumor immune microenvironment (TIME). Drug sensitivity analysis indicated that ribociclib was more effective in the high-risk group. Single-cell analysis revealed the distribution of the five genes across cellular subpopulations. The qRT-PCR results further validated the differential expression of the core genes. CONCLUSION: Through a systematic analysis of TRGs expression patterns, we successfully developed a prognostic prediction model that effectively captures the heterogeneity in survival outcomes, TIME characteristics, and drug sensitivity among patients with ESCC.
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Integration of single-cell and bulk RNA-sequencing data to construct and validate a signature based on NK cell marker genes to predict immunotherapy response and prognosis in colorectal cancer.
We aimed to create a NK cell marker genes-based signature to predict immunotherapy response and prognosis in colorectal cancer. We integrated scRNA-seq data from four Gene Expression Omnibus (GEO) samples and performed Weighted gene correlation network analysis (WGCNA) based on 587 the Cancer Genome Atlas (TCGA) colorectal cancer samples to uncover NK cell-related genes. We identified 1080 NK cell-related core genes and 276 NK cell-related feature genes based on WGCNA and clustering and annotation of scRNA-seq data, respectively. Six key NK cell-related prognostic signature genes were obtained by univariate and LASSO regression analyses, including ADAM8, CTSD, CCL4, IL2RB, TTC38, and PLEK. Two validation cohorts from the GEO dataset, comprising 124 and 201 samples respectively, were used. The signature was significantly associated with overall survival and correlated with immune cell infiltration, immune and stromal scores, and immune checkpoint genes. Furthermore, the signature was associated with the homologous recombination deficiency (HRD) and T-cell receptor (TCR) scores. In conclusion, our study proposes a new prognostic signature based on NK cell marker genes, which may serve as a potential tool to predict overall survival and immunotherapy response for CRC patients.
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Integrative single-cell and spatial multi-Omics analyses identify NETs-driven colon cancer subtypes with distinct metabolic features and prognostic implications.
BACKGROUND: Colon cancer remains one of the leading causes of cancer-related deaths worldwide and is associated with high rates of recurrence and metastasis despite advances in therapy. The tumor microenvironment (TME), characterized by complex immune and stromal interactions, plays a pivotal role in tumor progression and treatment resistance. Neutrophil extracellular traps (NETs), an emerging component of the TME, have been implicated in promoting tumor invasion, metastasis, and immune evasion. However, their specific role in colon cancer remains unclear. METHODS: Comprehensive transcriptomic analyses were conducted using datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify NETs-associated genes involved in colon cancer. Single-cell RNA sequencing (scRNA-seq) was utilized to assess cellular heterogeneity, and spatial transcriptomics mapped NETs activity and cell-cell interactions within the TME. A prognostic model was constructed using multivariate Cox and LASSO regression analyses based on key NETs-related genes. Model performance was validated using internal and external cohorts. Additionally, colocalization analysis between TUBB2A eQTL signals and colorectal cancer overall survival GWAS effects was performed to investigate potential genetic correlations. RESULTS: Two distinct NETs-related molecular subtypes of colon cancer were identified, differing in immune composition, metabolic activity, and gene expression profiles. NETs-high malignant cells demonstrated metabolic reprogramming involving oxidative phosphorylation and cellular respiration, contributing to immune escape and therapeutic resistance. A four-gene prognostic signature (ARRDC1, TUBB2A, DUSP5, and SLC2A3) was developed and showed moderate prognostic discrimination across internal and external cohorts, indicating potential value for risk stratification but limited predictive accuracy at the current stage. Colocalization analysis revealed a modest negative correlation between TUBB2A expression and overall survival and cancer-specific survival, suggesting that increased TUBB2A expression may be associated with adverse clinical outcomes. CONCLUSIONS: This study reveals that NETs-associated genes play crucial roles in colon cancer progression, immune modulation, and metabolic reprogramming. The identified four-gene signature may serve as a candidate biomarker for risk stratification in colon cancer, although further validation is needed.
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An updated and spatially validated somatic single-cell atlas of Hydractinia symbiolongicarpus.
Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic research, enabling the creation of detailed tissue, organ, and species-level atlases for model organisms. In Hydractinia, a cnidarian model for stem cell and regeneration studies, recent atlases have revealed key insights into cell types and developmental processes. However, these atlases remain limited in cell numbers and transcriptomic depth and cell type assignments were largely made in silico. Here, we present an updated Hydractinia single-cell atlas by integrating new datasets from fixed cells with previously published live-cell data. This expanded atlas captures over 47,000 cells from feeding polyps and stolon tissue, recovering and refining major somatic cell lineages including cnidocytes, neurons, gland cells, epithelial cells, and stem cells (i-cells), as well as identifying a novel population of putative immune cells. We investigated the spatial expression patterns of selected marker genes and validated all major cell types and several cell states. Our analyses uncovered a previously undescribed neural subtype, two spatially distinct gland cell populations, a stolon-specific cell type, and a putative immune cell cluster. Additionally, we recovered and explored a complete Hydractinia cnidocyte trajectory with two distinct endpoints, supported by spatial marker gene expression that reflects the developmental progression of cnidoblasts as they mature and migrate towards the tentacles. Subclustering of somatic i-cells revealed putative progenitor states and a potential population of true stem cells. Together, this atlas significantly advances our understanding of Hydractinia cellular diversity and dynamics, allowing us to generate new hypotheses and provide a valuable resource for the cnidarian research community and beyond.
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Development and validation of a macrophage-related prognostic model for overall survival in ovarian cancer via integrated RNA sequencing analysis.
BACKGROUND: The immunosuppressive tumor microenvironment (TME) poses challenges to effective immunotherapy in ovarian cancer (OC). As a key component of the TME, tumor-associated macrophages (TAMs) are strongly associated with prognosis, yet their precise molecular characteristics and impact on treatment response remain incompletely understood. This study aimed to develop and validate a prognostic model for OC based on TAM-related genes, using integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data, and to explore its implications for the immune landscape and therapeutic sensitivity. METHODS: RNA-seq data and prognostic information were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The R package "Seurat" was used to annotate the cell types and visualize the scRNA-seq data. The differentially expressed genes (DEGs) in the macrophages were identified using the "FindAllMarkers" function and further analyzed using the limma package. The resulting genes, combined with the survival data, underwent univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. Model accuracy was assessed through time-dependent receiver operating characteristic curve analysis, Kaplan-Meier survival analysis in the GEO cohort, independent prognostic evaluation via uni- and multivariate Cox regression, and predictive calibration using a nomogram with calibration curves. RESULTS: Integrating scRNA-seq and bulk RNA-seq data, this study established a prognostic model comprising 19 macrophage-related genes. Validation confirmed the risk score as an independent prognostic factor for overall survival (OS) in patients with OC. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1-, 3-, and 5-year survival were 0.71, 0.68, and 0.73, respectively. The subsequent immune analysis revealed distinct TMEs between the high- and low-risk groups. Compared to the low-risk group, the high-risk group exhibited higher immune infiltration, decreased M1 macrophage infiltration, elevated M2 macrophage infiltration, and decreased sensitivity to immunotherapy but increased sensitivity to anti-angiogenic drugs. CONCLUSIONS: The study analyzed the DEGs related to macrophages in OC and constructed a prognostic model. Moreover, it identified the risk score as a prognostic factor in OC, which shows potential clinical relevance in predicting patient sensitivity to immunotherapy.
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Pan-cancer analysis reveals RIPK2 predicts prognosis and promotes immune therapy resistance via triggering cytotoxic T lymphocytes dysfunction.
BACKGROUND: Receptor-interacting protein kinase 2 (RIPK2, also known as RIP2) was reported to be associated with bacterial infections as well as inflammatory responses. However, the role of RIPK2 in prognosis and immunotherapy response is yet to be elucidated in human pan-cancer. METHODS: In this study, we investigated the expression, gene alteration landscape and prognostic value of RIPK2 in 33 cancers through various databases including Ualcan, cBioportal and Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Then, the correlation between RIPK2 and immune infiltration, immune score, stromal score, and ESTIMATE score was investigated in the Cancer Genome Atlas (TCGA) and tumor immune estimation resource (TIMER) databases. Independent cohorts were utilized to explore the role of RIPK2 in tumor immunotherapy response. Furthermore, Gene set enrichment analysis (GSEA) was conducted to explore the mechanisms by which RIPK2 regulates immune therapy resistance. Single-cell RNA-seq datasets were used to analyze the expression level of RIPK2 on different immune cells. Moreover, CellMiner database was used to explore the relationship between RIPK2 expression with drug response. RESULT: Compared with normal tissue, tumor tissue had a higher expression level of RIPK2 in various cancers. Survival analysis showed that high expression of RIPK2 associated with poor prognosis in numerous cancers. RIPK2 was found to promote a series of immune cell infiltration and B cells, macrophages, and neutrophils were significantly positively correlated with the expression of RIPK2. Moreover, RIPK2 affected immune score, stromal score and ESTIMATE score for a wide range of cancers. In the vast majority of 33 cancers, gene co-expression analysis showed that RIPK2 was positively correlated with the expression of immune checkpoint markers, such as PDCD1 (PD-1), CD274 (PD-L1), CTLA4 and TIGIT. RIPK2 aggravated cytotoxic T lymphocyte (CTL) dysfunction and related to the poor efficacy of immune checkpoint blockade in skin cutaneous melanoma (SKCM) and kidney renal clear cell carcinoma (KIRC). High expression of RIPK2 promoted innate immunotherapy resistance and adaptive immunotherapy resistance through IL-6/JAK/STAT3 signaling, interferon-gamma response, and interferon-alpha response pathway. CONCLUSIONS: These results confirmed that RIPK2 could serve as a prognostic biomarker and promoted immune therapy resistance via triggering cytotoxic T lymphocytes dysfunction.
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Persistent pre-exhausted CD8+ T cells shape the tumor immune microenvironment in anaplastic thyroid cancer.
ABSTRACT: Anaplastic thyroid cancer (ATC) is an aggressive form of cancer with poor prognosis, heavily influenced by its tumor immune microenvironment (TIME). Understanding the cellular and gene expression dynamics within the TIME is crucial for developing targeted therapies. This study analyzes the immune microenvironment of ATC and papillary thyroid cancer (PTC) using single-cell RNA sequencing (scRNA-seq). We performed a comprehensive scRNA-seq analysis on ATC and PTC samples, incorporating cell type annotation, marker gene identification and clustering based on gene expression. A specific focus was on the prevalence and biomarkers of pre-exhausted CD8+ T cells in ATC, utilizing the single-cell tumor immune atlas for immune cell characterization. The scRNA-seq analysis identified distinct immune cell populations and differentially expressed genes in ATC and PTC samples. Notably, pre-exhausted CD8+ T cells were found to be prevalent in ATC datasets. Additional immunofluorescence staining and coculture experiments with the ATC cell line identified GNLY, a member of the saposin-like protein family, as a potential biomarker for pre-exhausted CD8+ T cells in ATC. This study provides valuable insights into the immune landscape of ATC, emphasizing the prevalence of pre-exhausted CD8+ T cells and identifying GNLY as a potential biomarker. Understanding the TIME composition and the role of specific immune cells in cancer progression can inform the development of targeted immunotherapies for ATC. Future research should explore the functional implications of GNLY and other identified biomarkers in modulating the immune response in thyroid cancer. HIGHLIGHTS: A computational pipeline was constructed to identify ATC-specific immune cell populations and differentially expressed genes via multiple independent ATC and PTC single-cell transcriptomes.A total of 221 uniquely differentially expressed genes associated with the adaptive immune response across two ATC datasets were identified.Markedly prevalent pre-exhausted CD8+ T cells in ATC datasets compared with PTC datasets were identified.One hundred fifteen potential biomarker genes of pre-exhausted CD8+ T cells were identified, with GNLY experimentally validated as the top candidate.