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Cross-System Transcriptomics Suggests Enterovirus A71 Exploits Wound Healing Programs to Target Neural Progenitors in Human Spinal Cord Organoids.
INTRODUCTION: The mechanisms of EV-A71 immune evasion and neuroinvasion are poorly understood. Inactivated vaccines provide protective immunity without the dysregulated inflammation and viral persistence caused by active infection. In this bioinformatics-driven study, our objective was to utilize computational transcriptomics to investigate this dichotomy and determine the specific neural cell populations susceptible to enteroviral infection. METHODS: We compared bulk RNA-seq data from THP-1 macrophages infected with active versus inactivated EV-A71 to identify viral hijacking programs. We then applied these systemic findings to the neural environment via an iterative Area Under the Curve (AUC)-based optimization strategy. This process refined the broad bulk-derived candidates into a 10-gene consensus signature, which was projected onto a human spinal cord organoid single-cell RNA-seq atlas to map viral susceptibility across distinct developmental lineages. RESULTS: While active infection with EV-A71 is associated with a wound-healing program defined by the expression of PMEPA1, ALOX5, CCL1, and SPRED3, the administration of an inactivated virus elicits an anti-inflammatory response without activating a similar transcriptional program. In fact, decreased expression of inflammatory factors (ADORA2A, CCR1, CXCL1) marks this wound-healing stage and leads to the development of an immunosuppressive M2 phenotype. By projecting the highly robust 10-gene consensus (comprising ALDH2, SERPINE2, and L1CAM) against the organoid atlas, we have predicted that the cycling neural progenitors could be the most likely reservoir for EV-A71 replication. Using a Wilcoxon rank sum test with Benjamini-Hochberg correction, we observed the only statistically enriched signature in progenitor cells Discussion: Transcriptomic data indicate that EV-A71 might hijack TGF-β/PMEPA1 and ALOX5 signaling pathways to establish EMT-like behavior. The local acquisition of an M2-like immunosuppressive state might help promote early systemic dissemination, immunologic escape, followed by colonization of the proliferative neural niche prior to the development of neurological symptoms. CONCLUSION: Computational identification of a macrophage gene signature provides robust discrimination of EV-A71 infection from vaccine-based protection in vivo. Mapping the signature to the context of human spinal cord organoids indicates that EV-A71 specifically targets proliferative and regenerative programs of neural stem cells.
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Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.
Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus-a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.
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PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens.
Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo-devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web-based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de.
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Integrative Analysis of Glycosylation-Related Genes Reveals Prognostic Subtypes, Immune Evasion, and Therapeutic Vulnerabilities in Lung Adenocarcinoma.
Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and remains a leading cause of cancer-related mortality worldwide. Aberrant glycosylation contributes to tumor progression by regulating receptor signalling, immune evasion, and metastatic. However, the prognostic and therapeutic relevance of glycosylation-related genes (GRGs) in LUAD has not been comprehensively defined. Therefore, this study aimed to comprehensively evaluate GRG-associated molecular subtypes and their clinical and therapeutic relevance in LUAD. Methods: GRGs were curated from multiple public databases and integrated with transcriptomic and clinical data from The Cancer Genome Atlas LUAD cohort (TCGA-LUAD) with validation in Gene Expression Omnibus (GEO) datasets. Consensus clustering, pathway enrichment, and immune profiling were used to identify glycosylation-associated subtypes. A glycosylation activity scoring (Glyco. marker) was developed to quantify glycosylation features. Drug response prediction was analyzed using OncoPredict and the Genomics of Drug Sensitivity in Cancer (GDSC) database. Single-cell RNA sequencing (scRNA-seq) was analyzed to evaluate cell-type-specific GRG expression. Selected proteins were by immunohistochemistry (IHC) in LUAD tissue microarrays. Results: GRG expression stratified 513 LUAD patients into four molecular clusters with distinct clinical and immune characteristics. The Glyco.High group showed elevated expression of MGAT5 (mannosyl (α-1,6)-glycoprotein β-1,6-N-acetylglucosaminyltransferase), ST6GAL1 (β-galactoside α-2,6-sialyltransferase 1), GALNT7 (polypeptide N-acetylgalactosaminyltransferase 7), and FUT8 (fucosyltransferase 8), frequent tumor protein p53 (TP53) mutations, increased immune checkpoint expression, and enrichment of regulatory T cells. The Glyco. marker score predicted overall survival and was associated with stemness signatures. Drug response prediction suggested reduced sensitivity to platinum chemotherapy and epidermal growth factor receptor (EGFR) inhibitors but increased sensitivity to phosphoinositide 3-kinase/protein kinase B/mechanistic target of rapamycin (PI3K/AKT/mTOR) inhibitors. Conclusion: GRG-based molecular stratification identifies clinically distinct LUAD subtypes associated with immune regulation, tumor stemness, and therapeutic response. The Glyco. marker system provides a potential framework for prognostic assessment and precision oncology strategies in LUAD.
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Single-cell spatial transcriptomic profiling defines a pathogenic inflammatory niche in chronic active multiple sclerosis lesions.
Compartmentalized inflammation is a key driver of multiple sclerosis (MS) progression, but the mechanisms sustaining its persistence remain unclear. A hallmark of this persistent and slowly evolving inflammatory process is chronic active MS lesions. We generated a high-resolution, single-cell molecular and spatial atlas of such lesions by combining single-nucleus RNA sequencing (snRNA-seq) with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Within lesion rims, we identified CD8+ T cell niches associated with inflamed microglia displaying an interferon response and upregulated lipid metabolism. To investigate their function, we deleted ATP-binding cassette transporters A1 and G1 (ABCA1/G1) in the microglia of mice with experimental autoimmune encephalomyelitis (EAE), which increased the formation of lipid-storing phagocytes that amplified inflammation. Moreover, pharmacologically targeting sterol metabolism mitigated foam cell formation and inflammatory demyelination in EAE. Thus, our high-resolution map of immune niches in chronic active MS lesions identifies a role for lipid-storing, dysfunctional microglia in persistent neuroinflammation.
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A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma.
Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.
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Leveraging single-cell RNA-seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is a typical highly heterogeneous solid tumor with high morbidity and mortality worldwide, especially in China; however, the immune microenvironment of HCC has not been clarified so far. Here, we employed single-cell RNA sequencing (scRNA-seq) on diethylnitrosamine (DEN)-induced mouse HCC model to dissect the immune cell dynamics during tumorigenesis. Our findings reveal distinct immune profiles in both precancerous and cancerous lesions, indicating early tumor-associated immunological alterations. Notably, specific T and B cell subpopulations are preferentially enriched in the HCC tumor microenvironment (TME). Furthermore, we identified a subpopulation of naïve B cells with high CD83 expression, correlating with improved prognosis in human HCC. These signature genes were validated in The Cancer Genome Atlas HCC RNA-seq dataset. Moreover, cell interaction analysis revealed that subpopulations of B cells in both mouse and human samples are activated and may potentially contribute to oncogenic processes. In summary, our study provides insights into the dynamic immune microenvironment and cellular networks in HCC pathogenesis, with a specific emphasis on naïve B cells. These findings emphasize the significance of targeting TME in HCC patients to prevent HCC pathological progression, which may give a new perspective on the therapeutics for HCC.
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A spatially resolved single-cell atlas identifies blood-brain barrier integrity as a central regulator of macrophages and microglia localization and functional states in gliomas.
Given that glioma-associated macrophages and microglia (GAMs) critically shape tumor evolution, we aimed to define their spatial distribution and functional reprogramming in relation to MRI-defined BBB integrity. Twelve patients underwent MRI-guided sampling, yielding 65 biopsies from 40 enhancing and 25 non-enhancing areas. Single-cell RNA-seq generated a high-resolution cellular atlas, and we analyzed cellular composition, transcriptional programs, macrophage polarization, and transcriptional subtype distribution relative to anatomical region and enhancement status. We profiled 5445 cells and identified 9 major cell populations. Macrophages were enriched in tumoral and enhancing regions, whereas microglia predominated in peritumoral and non-enhancing niches. Tumor cells in enhancing regions exhibited a growth-dominant anabolic state, while non-enhancing and peritumoral tumor cells retained neuronal development and synapse organization programs. GAMs displayed region-specific functional polarization: peritumoral and non-enhancing macrophages were enriched for neural development and tissue remodeling programs, whereas tumoral and enhancing macrophages activated chemotactic, inflammatory, and immunoregulatory pathways. Integration with transcriptional subtypes demonstrated spatial coupling of Mesenchymal/Classical programs with enhancing regions and Proneural features with non-enhancing regions, underscoring substantial intra-tumor spatial mosaicism. MRI-defined BBB permeability spatially organizes a glioma ecosystem where tumor metabolism and macrophage states are tightly coupled, refining understanding of the immune microenvironment and rationale for spatially informed immunotherapy.
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Single-cell transcriptomics reveals lineage trajectory of human scalp hair follicle and informs mechanisms of hair graying.
Hair conditions, such as hair loss and graying, are prevalent human conditions. But they are often poorly controlled due to our insufficient understanding of human scalp hair follicle (hsHF) in health and disease. Here we describe a comprehensive single-cell RNA-seq (scRNA-seq) analysis on highly purified black and early-stage graying hsHFs. Based on these, a concise single-cell atlas for hsHF and its early graying changes is generated and verified using samples from multiple independent individuals. These data reveal the lineage trajectory of hsHF in unprecedented detail and uncover its multiple unexpected features not found in mouse HFs, including the presence of an innerbulge like compartment in the growing phase, lack of a discrete companion layer, and enrichment of EMT features in HF stem cells (HFSCs). Moreover, we demonstrate that besides melanocyte depletion, early-stage human hair graying is also associated with specific depletion of matrix hair progenitors but not HFSCs. The hair progenitors' depletion is accompanied by their P53 pathway activation whose pharmaceutical blockade can ameliorate hair graying in mice, enlightening a promising therapeutic avenue for this prevalent hair condition.
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Mechanosensitive ion channel-related genes in hepatocellular carcinoma: Unraveling prognostic genes and their roles in drug resistance and immune modulation.
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, and its etiology involves a complex interplay of genetic and environmental factors. Despite advancements in our understanding of HCC biology and the development of novel therapeutic strategies, the molecular mechanisms underlying its onset, progression, and resistance to therapy remain largely vague. This study aimed to investigate the role of mechanosensitive ion channel-related genes (MICRGs) in HCC, focusing on their potential as prognostic biomarkers and their involvement in immune modulation and drug resistance. METHODS: A comprehensive analysis was conducted using The Cancer Genome Atlas database to identify MICRGs that are upregulated in HCC. Gene expression profiling, bioinformatics tools, and functional experiments were employed to elucidate the role of these channels. In addition, protein-protein interaction (PPI) network analyses and enrichment analyses were performed to explore the biological significance of these genes. An immune cell infiltration analysis was also conducted to understand MICRG-related immune landscape. Single-cell RNA sequencing (scRNA-seq) data were utilized to identify MICRGs in different cell types within the HCC tissue. Deep-learning neural network analysis across patient cohorts was conducted to identify genes associated with sorafenib resistance. Knockdown experiments, cell viability assays, and apoptosis assays on HCC cell lines were performed to examine the role of Piezo-type mechanosensitive ion channel component 1 (PIEZO1) in sorafenib resistance. RESULTS: The analysis identified a subset of MICRGs, including PIEZO1, that were significantly upregulated in HCC and associated with poor prognosis. The PPI network analysis revealed complex interactions among these genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses proposed the involvement of these genes in calcium signaling pathways. Immune cell infiltration analysis demonstrated distinct associations between MICRGs and various immune subpopulations, highlighting their potential roles in immune modulation. scRNA-seq data indicated the upregulation of MICRGs in various cell types in HCC tissues, particularly in endothelial cells and tumor-associated macrophages. Deep-learning neural network analysis across different patient cohorts identified PIEZO1 as a crucial regulator of sorafenib resistance in HCC, which was further validated by functional assays on HCC cell lines. CONCLUSIONS: This study provides evidence that MICRGs, particularly PIEZO1, take on crucial roles in HCC progression and drug resistance. The upregulation of PIEZO1 in HCC cells is associated with poor prognosis and resistance to sorafenib. These findings indicate that PIEZO1 could serve as a potential therapeutic target for overcoming drug resistance and a prognostic biomarker in HCC. Future studies should focus on validating these findings in larger patient cohorts and exploring the functional implications of targeting PIEZO1 in preclinical models.