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CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.
MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.
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Single-cell RNA sequencing reveals the effects of capsaicin in the treatment of sepsis-induced liver injury.
Sepsis is a difficult-to-treat systemic condition in which liver dysfunction acts as both regulator and target. However, the dynamic response of diverse intrahepatic cells to sepsis remains poorly characterized. Capsaicin (CAP), a multifunctional chemical derived from chilli peppers, has recently been shown to potentially possess anti-inflammatory effects, which is also one of the main approaches for drug discovery against sepsis. We performed single-cell RNA transcriptome sequencing on 86,830 intrahepatic cells isolated from normal mice, cecal ligation and puncture-induced sepsis model mice and CAP-treated mice. The transcriptional atlas of these cells revealed dynamic changes in hepatocytes, macrophages, neutrophils, and endothelial cells in response to sepsis. Among the extensive crosstalk across these major subtypes, KC_Cxcl10 shared strong potential interaction with other cells when responding to sepsis. CAP mitigated the severity of inflammation by partly reversing these pathophysiologic processes. Specific cell subpopulations in the liver act collectively to escalate inflammation, ultimately causing liver dysfunction. CAP displays its health-promoting function by ameliorating liver dysfunction induced by sepsis. Our study provides valuable insights into the pathophysiology of sepsis and suggestions for future therapeutic gain.
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A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens.
Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.
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A Drosophila glial cell atlas reveals a mismatch between transcriptional and morphological diversity.
Morphology is a defining feature of neuronal identity. Like neurons, glia display diverse morphologies, both across and within glial classes, but are also known to be morphologically plastic. Here, we explored the relationship between glial morphology and transcriptional signature using the Drosophila central nervous system (CNS), where glia are categorised into 5 main classes (outer and inner surface glia, cortex glia, ensheathing glia, and astrocytes), which show within-class morphological diversity. We analysed and validated single-cell RNA sequencing data of Drosophila glia in 2 well-characterised tissues from distinct developmental stages, containing distinct circuit types: the embryonic ventral nerve cord (VNC) (motor) and the adult optic lobes (sensory). Our analysis identified a new morphologically and transcriptionally distinct surface glial population in the VNC. However, many glial morphological categories could not be distinguished transcriptionally, and indeed, embryonic and adult astrocytes were transcriptionally analogous despite differences in developmental stage and circuit type. While we did detect extensive within-class transcriptomic diversity for optic lobe glia, this could be explained entirely by glial residence in the most superficial neuropil (lamina) and an associated enrichment for immune-related gene expression. In summary, we generated a single-cell transcriptomic atlas of glia in Drosophila, and our extensive in vivo validation revealed that glia exhibit more diversity at the morphological level than was detectable at the transcriptional level. This atlas will serve as a resource for the community to probe glial diversity and function.
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A Cori cycle-related gene signature predicts prognosis, immune microenvironment, and drug response in breast cancer.
BACKGROUND: Breast cancer remains the most common malignancy among women worldwide. Metabolic reprogramming, particularly involving the Cori cycle, plays a crucial role in tumor progression and therapy resistance. However, the prognostic and immunological implications of Cori cycle-related genes (CCRGs) in breast cancer remain underexplored. This study aimed to integrate multi-omics data and machine learning to construct a CCRG-based prognostic signature and evaluate its predictive performance and clinical relevance in breast cancer. METHODS: We integrated multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A machine learning approach was employed to construct a prognostic signature based on CCRGs. The model was validated across independent cohorts. Immune infiltration, drug sensitivity, somatic mutations, and functional enrichment analyses were performed to elucidate the biological and clinical relevance of the signature. Single-cell and pan-cancer analyses were conducted to assess gene expression and functional associations at cellular and cross-cancer levels. RESULTS: A three-gene signature (HK1, PGK1, PGAM1) was identified and used to stratify patients into high- and low-risk groups with distinct survival outcomes. The risk score (RS) was significantly associated with advanced clinicopathological features, immunosuppressive microenvironments, and altered drug sensitivity. Enrichment analyses revealed activation of glycolysis, cell cycle, and PI3K-AKT pathways in high-risk patients. The signature also correlated with tumor mutational burden (TMB) and specific mutational patterns. Pan-cancer analysis confirmed the broad relevance of CCRGs across multiple cancer types. CONCLUSIONS: We developed and validated a robust CCRG signature that effectively predicts prognosis, immune contexture, and therapeutic response in breast cancer. This signature offers novel insights into metabolic immunosuppression and provides a potential tool for risk stratification and personalized treatment strategies.
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Disturbed flow induces reprogramming of endothelial cells to immune-like and foam cells under hypercholesterolaemia during atherogenesis.
AIMS: Atherosclerosis occurs preferentially in the arteries exposed to disturbed flow (d-flow), while the stable flow (s-flow) regions are protected even under hypercholesterolaemic conditions. We recently showed that d-flow alone initiates flow-induced reprogramming of endothelial cells (FIRE), including the novel concept of partial endothelial-to-immune-cell-like transition (partial EndIT), but it was not validated using a genetic lineage-tracing model. In addition, the combined effect of d-flow and hypercholesterolaemia has not been tested. Here, we tested and validated the two-hit hypothesis that d-flow is an initial instigator of partial FIRE but requires hypercholesterolaemia to induce a full-blown FIRE and atherosclerotic plaque development. METHODS AND RESULTS: Mice were treated with AAV-PCSK9 and a Western diet to induce hypercholesterolaemia and/or partial carotid ligation (PCL) surgery to expose the left common carotid artery (LCA) to d-flow. Single-cell RNA sequencing (scRNA-seq) analysis was performed using single cells obtained from the LCAs and the control right common carotid arteries at 2 and 4 weeks post-PCL. Immunohistochemical staining was performed on EC-specific confetti mice at 4 weeks post-PCL and hypercholesterolaemia to validate endothelial reprogramming. Human aortic endothelial cells (HAECs) exposed to d-flow and hypercholesterolaemic conditions were used to validate FIRE. Atherosclerotic plaques developed by d-flow under hypercholesterolaemia, but not by d-flow or hypercholesterolaemia alone. The scRNA-seq results of 98 553 single cells from 95 mice revealed 25 cell clusters: 5 EC, 3 vascular smooth muscle cell (SMC), 5 macrophage (MΦ), and additional fibroblast, T cell, natural killer cell, dendritic cell, neutrophil, and B-cell clusters. Our scRNA-seq analysis results raised a hypothesis that d-flow under hypercholesterolaemia transitioned healthy ECs to full immune-like (EndIT) and, more surprisingly, foam-like cells (EndFT), in addition to inflammatory and mesenchymal cells (EndMT). Further, ECs with characteristics of foam cells shared remarkably similar transcriptomic profiles with foam cells derived from SMCs and MΦs. Lineage-tracing studies using immunohistochemical staining of canonical protein and lipid markers in the EC-specific confetti mice exposed to d-flow and hypercholesterolaemia demonstrated evidence supporting the novel FIRE hypothesis, including EndIT and EndFT. Moreover, reanalysis of the two publicly available human plaque scRNA-seq datasets and our immunostaining studies suggest that FIRE occurs in human atherosclerotic plaques. Additionally, HAECs exposed to d-flow, high cholesterol, and proinflammatory cytokines (identified in our scRNA-seq data) show the markers of EndIT and EndFT at the mRNA, protein, and functional levels. CONCLUSION: The scRNA-seq study raised a two-hit hypothesis for FIRE, including EndIT and EndFT, which was validated by the lineage-tracing and in vitro HAEC studies. D-flow induces partial reprogramming, including inflammation, EndMT, and partial EndIT. Under hypercholesterolaemia, d-flow fully reprogrammes arterial ECs, including the novel EndIT and EndFT, in addition to inflammation and EndMT, during atherogenesis. This single-cell atlas and FIRE programs provide a crucial roadmap for novel mechanistic understanding and therapeutics targeting flow-sensitive genes, proteins, and pathways of atherosclerosis.
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The emerging landscape of spatial profiling technologies.
Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.
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Artificial-intelligence- and multi-omics-guided predictive and drug repurposing framework construction for colorectal cancer: evidence from succinylation-neutrophil signatures.
BACKGROUND: Succinylation-linked metabolic rewiring and neutrophil-driven inflammation are key drivers of colorectal carcinoma (COAD) progression; however, they have rarely been translated to an end-to-end artificial intelligence (AI) drug-design pipeline connecting target nomination, resistance-relevant tumor microenvironment states, and candidate evaluation. METHODS: We integrated bulk transcriptomes from The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) and Gene Expression Omnibus databases to derive succinylation-neutrophil (SN)-associated genes using the limma package, single-sample gene-set enrichment analysis, weighted gene coexpression network analysis, and protein-protein interaction network analysis; we then constructed a cross-cohort prognostic system by screening and selecting the machine-learning combination of random survival forest and ridge regression as the optimal predictor. To enable precision stratification, we applied deep-learning self-organizing maps (SOMs) to define the SN-driven molecular subtypes and characterized their immune and pathway heterogeneities at both bulk and single-cell resolutions through AI-powered virtual perturbation analysis to interrogate the cell-state shifts. Importantly, we extended AI approaches in drug design beyond risk modeling to a target-to-candidate workflow by nominating a druggable SN hub gene. We also performed co-culture in vitro assays for identifying NOX4 molecular insights into neutrophil patterns and their associations with COAD progression. RESULTS: Our approach identifies SN biology as a clinically actionable axis in COAD, highlights NOX4 as a central SN-associated target with immune relevance, and prioritizes talazoparib as a repurposing candidate supported by integrated in silico evidence. CONCLUSION: The findings of this study provide a precision-oncology blueprint that couples AI stratification with therapeutic nomination and evaluation.
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The role of SEC14L4 in esophageal squamous cell cancer: insights into clinical relevance and molecular pathways.
BACKGROUND: Esophageal squamous cell cancer (ESCC) is the most common type of esophageal cancer. This study aimed to elucidate the role of Saccharomyces cerevisiae-like 4 (SEC14L4) in ESCC. METHODS: To elucidate the role of SEC14L4 in ESCC, this study analyzed the clinical data, gene sequencing data, and other relevant data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The methodology involved several analytical approaches, including nomogram model analysis, co-expression analysis, gene set enrichment and variation analysis, weighted correlation network analysis, drug susceptibility analysis, and single-cell analysis. These methods were employed to evaluate the significance of SEC14L4 in ESCC. The expression of SEC14L4 was evaluated via quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: SEC14L4 expression (P<0.001) was significantly elevated in those with ESCC, especially in patients with locally advanced disease (P=0.005), and indicated a poor prognosis (P=0.045). Findings from the nomogram model analysis identified the contribution of clinical indicators to survival prediction with good efficacy. Subsequently, the single-nucleotide polymorphisms and co-expressed genes of SEC14L4 were identified. Furthermore, pathways associated with SEC14L4, including DNA metabolic process, transcription factor binding, apoptosis, and others, were examined. Notably, SEC14L4 expression was predominantly observed in monocytes. Drug sensitivity analysis indicated the association of SEC14L4 expression with sensitivity of ESCC to the common chemotherapy drugs AICAR, BMS.708163, GNF.2, Nutlin.3a, PD.0325901, and RDEA119. Verification of the high expression of SEC14L4 in KYSE520 and KYSE150 was conducted, thereby confirming the study's findings. CONCLUSIONS: High expression of SEC14L4 is associated with poorer clinical outcomes, highlighting its potential as a therapeutic target and suggesting its involvement in the molecular mechanisms underlying ESCC.
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Single-nucleus transcriptome profiling provides insights into the pathophysiology of OSA-related renal injury.
Obstructive sleep apnea (OSA) has been demonstrated to be associated with renal injury. However, the cellular and molecular mechanisms by which chronic intermittent hypoxia (CIH), a hallmark of OSA, contributes to renal injury remain poorly understood. Twelve male Sprague-Dawley rats were randomized into normoxic control (NC) and CIH groups (n = 6 each), with CIH exposure for 12 weeks. Renal injury was evaluated by Hematoxylin and Eosin staining and a modified Jablonski score. Single-nucleus RNA sequencing (snRNA-seq) was performed. Data analysis included clustering of cells, differential gene expression analysis, and functional enrichment through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Histopathological analysis revealed renal injury with renal tubular epithelial cell edema, necrosis in the CIH group compared to NC. A total of 42,581 cells (19,655 cells from NC group and 22,926 cells from CIH group) were retained after quality control, with 13 distinct renal cell populations identified. CIH exposure resulted in an increased proportion of podocytes, while mesangial cells were reduced compared to the NC group. CIH exposure altered the transcriptomic landscape, with differentially expressed genes (DEGs) observed across multiple cell types. Functional enrichment analysis indicated that CIH-induced DEGs were associated with suppressed metabolic and signaling pathways in proximal tubule and loop of Henle epithelial cells, activation of apoptotic and sodium reabsorption pathways in distal convoluted tubule cells, and enhanced inflammatory and phagocytic signaling in mononuclear phagocytes. Our study provides a high-resolution atlas of CIH-induced transcriptional changes in the rat kidney. The findings reveal cell type-specific responses and pathways potentially contributing to OSA-associated renal injury, offering novel insights into pathogenesis and potential therapeutic targets for preventing renal damage in OSA patients.