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Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma.
PURPOSE: To systematically investigate the characterization of tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), we performed a comprehensive analysis incorporating genomic alterations, cellular interactions, infiltrating immune cells, and risk signature. PATIENTS AND METHODS: Multi-omics data including RNA-seq, single-nucleotide variant (SNV) data, copy number variation (CNV) data, miRNA, and corresponding prognostic data were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The CIBERSORT algorithm was utilized to identify prognostic TME subclusters, and TMEscore was further quantified. Moreover, the mutational landscape of TCGA-KIRC was explored. Lastly, TIDE resource was applied to assess the significance of TMEscore in predicting immunotherapeutic benefits. RESULTS: We analyzed the TME infiltration patterns from 621 ccRCC patients and identified 5 specific TME subclusters associated with clinical outcomes. Then, we found that TMEcluster5 was significantly related to favorable prognosis and enriched memory B-cell infiltration. Accordingly, we depicted the clustering landscape of TMEclusters, TMEscore levels, tumor mutation burden (TMB), tumor grades, purity, and ploidy in all patients. Lastly, TIDE was used to assess the efficiency of immune checkpoint blockers (ICBs) and found that the TMEscore has superior predictive significance to TMB, making it an essential independent prognostic biomarker and drug indicator for clinical use. CONCLUSIONS: Our study depicted the clustering landscape of TMEclusters, TMEscore levels, TMB, tumor grades, purity, and ploidy in total ccRCC patients. The TMEscore was proved to have promising significance for predicting prognosis and ICB responses, in accordance with the goal of developing rationally individualized therapeutic interventions.
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A multiomics atlas of the Arabidopsis nuclear envelope.
The nuclear envelope (NE) is a critical regulatory interface that preserves genome integrity and mediates nucleocytoplasmic transport, yet its compositional and functional landscape in plants remains poorly defined. Here, we present a multiomics atlas of the Arabidopsis NE. By integrating proximity-labeling proteomics with single-cell/single-nucleus transcriptomics, we uncover distinct NE coexpression networks that dynamically shape NE composition and function in cell type-specific manners, enabling adaptation to diverse physiological and environmental demands. Notably, this analysis revealed that nuclear pore complex (NPC) biogenesis is transcriptionally restricted primarily to proliferative cells, whereas the protein complex exhibits remarkable stability and persists throughout cellular differentiation despite the absence of sustained high-level transcription. This supports an "assembly-and-maintenance" model in which NPCs are predominantly assembled during cell division and subsequently maintained as exceptionally long-lived structures throughout developmental progression. Together, this atlas provides a high-resolution framework for plant nuclear organization and establishes the NE as a spatiotemporally regulated hub central to plant growth, development, and responses to stimuli.
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PRISM-xQTL: Pleiotropic Relationships Integrated with System-level Multiomic QTL analysis for causal genes and molecular mediators in Alzheimer's disease.
INTRODUCTION: Most Alzheimer's disease (AD) risk variants identified by genome-wide association studies (GWAS) reside in noncoding regions, complicating causal interpretation. METHODS: We developed PRISM-xQTL (Pleiotropic Relationships Integrated with System-level Multiomic QTL), integrating pleiotropy, co-localization, Mendelian randomization, and mediation analyses across multiomic quantitative trait loci (QTLs; expression QTL [eQTL], methylation QTL [meQTL], splicing QTL [sQTL], proteomic [pQTL], single-cell [sc-eQTL]). Seventy pleiotropic single-nucleotide polymorphisms (SNPs) shared between AD and 11 immune disorders were analyzed. AlphaGenome assessed functional regulation, and The Alzheimer's Cell Atlas (TACA) database was used for drug-target relevance of FCER1G. RESULTS: Across 54 Genotype-Tissue Expression (GTEx) tissues, 410 SNP-gene-tissue co-localizations were identified. SNP rs4233366 co-localized with eQTL/meQTL signals at FCER1G, whereas TSPAN14, ISYNA1, and ELL showed splicing and single-cell associations. Mendelian randomization and mediation analyses indicated cytosine-phosphate-Guanine (CpG) sites might effect on AD risk mediated through FCER1G. AlphaGenome and TACA validated FCER1G's the regulatory function and therapeutic relevance of FCER1G. DISCUSSION: PRISM-xQTL refines causal inference for noncoding risk variants, highlighting immune regulatory mechanisms and prioritizing therapeutic targets in AD. HIGHLIGHTS: We developed PRISM-xQTL (Pleiotropic Relationships Integrated with System-level Multiomic QTL), a multiomic framework integrating pleiotropy, co-localization, Mendelian randomization, and mediation analyses to dissect genetic regulation in Alzheimer's disease (AD) and immune-mediated diseases. Identified 410 single-nucleotide polymorphism (SNP)-gene-tissue co-localizations across 54 Genotype-Tissue Expression (GTEx) tissues, including 28 multi-gene/multi-tissue signals, 35 methylation quantitative trait locus (meQTL) pairs in blood, with rs4233366 and rs479486 overlapping expression QTLs (eQTLs). Multi-trait co-localization, Mendelian randomization and mediation analyses at rs4233366 and rs479486 revealed convergent regulatory effects across genes and tissues. AlphaGenome profiling showed that rs4233366 drives allele-specific transcriptional and splicing regulation at FCER1G, linking immune signaling to AD risk.
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An atlas of tissue-specific conserved coexpression for functional annotation and disease gene prediction.
Gene coexpression relationships that are phylogenetically conserved between human and mouse have been shown to provide important clues about gene function that can be efficiently used to identify promising candidate genes for human hereditary disorders. In the past, such approaches have considered mostly generic gene expression profiles that cover multiple tissues and organs. The individual genes of multicellular organisms, however, can participate in different transcriptional programs, operating at scales as different as single-cell types, tissues, organs, body regions or the entire organism. Therefore, systematic analysis of tissue-specific coexpression could be, in principle, a very powerful strategy to dissect those functional relationships among genes that emerge only in particular tissues or organs. In this report, we show that, in fact, conserved coexpression as determined from tissue-specific and condition-specific data sets can predict many functional relationships that are not detected by analyzing heterogeneous microarray data sets. More importantly, we find that, when combined with disease networks, the simultaneous use of both generic (multi-tissue) and tissue-specific conserved coexpression allows a more efficient prediction of human disease genes than the use of generic conserved coexpression alone. Using this strategy, we were able to identify high-probability candidates for 238 orphan disease loci. We provide proof of concept that this combined use of generic and tissue-specific conserved coexpression can be very useful to prioritize the mutational candidates obtained from deep-sequencing projects, even in the case of genetic disorders as heterogeneous as XLMR.
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A candidate gene identification strategy utilizing mouse to human big-data mining: "3R-tenet" in COPD genetic research.
BACKGROUND: Early life impairments leading to lower lung function by adulthood are considered as risk factors for chronic obstructive pulmonary disease (COPD). Recently, we compared the lung transcriptomic profile between two mouse strains with extreme total lung capacities to identify plausible pulmonary function determining genes using microarray analysis (GSE80078). Advancement of high-throughput techniques like deep sequencing (eg. RNA-seq) and microarray have resulted in an explosion of genomic data in the online public repositories which however remains under-exploited. Strategic curation of publicly available genomic data with a mouse-human translational approach can effectively implement "3R- Tenet" by reducing screening experiments with animals and performing mechanistic studies using physiologically relevant in vitro model systems. Therefore, we sought to analyze the association of functional variations within human orthologs of mouse lung function candidate genes in a publicly available COPD lung RNA-seq data-set. METHODS: Association of missense single nucleotide polymorphisms, insertions, deletions, and splice junction variants were analyzed for susceptibility to COPD using RNA-seq data of a Korean population (GSE57148). Expression of the associated genes were studied using the Gene Paint (mouse embryo) and Human Protein Atlas (normal adult human lung) databases. The genes were also assessed for replication of the associations and expression in COPD-/mouse cigarette smoke exposed lung tissues using other datasets. RESULTS: Significant association (p < 0.05) of variations in 20 genes to higher COPD susceptibility have been detected within the investigated cohort. Association of HJURP, MCRS1 and TLR8 are novel in relation to COPD. The associated ADAM19 and KIT loci have been reported earlier. The remaining 15 genes have also been previously associated to COPD. Differential transcript expression levels of the associated genes in COPD- and/ or mouse emphysematous lung tissues have been detected. CONCLUSION: Our findings suggest strategic mouse-human datamining approaches can identify novel COPD candidate genes using existing datasets in the online repositories. The candidates can be further evaluated for mechanistic role through in vitro studies using appropriate primary cells/cell lines. Functional studies can be limited to transgenic animal models of only well supported candidate genes. This approach will lead to a significant reduction of animal experimentation in respiratory research.
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Single-cell transcriptomes reveal spatiotemporal heat stress response in pearl millet leaves.
With the intensification of global warming, there is an urgent need to develop crops with enhanced heat tolerance. Pearl millet, as a typical C4 heat-tolerant crop, has mechanisms of heat tolerance at the cellular level which remain unclear. Constructed single-cell transcriptomic landscape of pearl millet leaves under heat stress and normal conditions, comprising 20 589 high-quality cells classified into five cell types. Vascular tissue cells were identified as the most critical cell type under heat stress, characterized by the highest number of differentially expressed genes and heat stress memory genes. Through single-cell WGCNA analysis combined with phenotypic and physiological analysis of heat stress memory gene UGT73C3 mutants and overexpression lines, we revealed the important role of heat stress memory genes in enhancing heat tolerance by promoting the clearance of reactive oxygen species accumulation. Our study provides a heat-tolerant crop leaf atlas revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.
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Integration of spatial and single-nucleus transcriptomics to map gene expression in the developing mouse kidney.
The kidney is a complex organ requiring tightly coordinated interactions between epithelial, endothelial and mesenchymal cells during development. Congenital kidney defects can result in kidney disease and renal failure, highlighting the importance of understanding kidney formation mechanisms. Advances in RNA sequencing have revealed remarkable cellular heterogeneity, especially in the kidney stroma, although relationships between stromal, epithelial and endothelial cells remain unclear. This study presents a comprehensive gene expression atlas of embryonic and postnatal kidneys, integrating single-nucleus and in situ RNA sequencing data. We developed the Kidney Spatial Transcriptome Analysis Tool (KSTAT), enabling researchers to identify cell locations, predict cell-cell communication and map gene pathway activity. Using KSTAT, we were able to uncover significant heterogeneity among embryonic kidney pericytes, providing an important resource for hypothesis generation and advancing knowledge of kidney development and disease.
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Multi‑omics identification of a signature based on malignant cell-associated ligand-receptor genes for lung adenocarcinoma.
PURPOSE: Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. METHODS: We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. RESULTS: We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. CONCLUSION: We have identified malignant cell-associated ligand-receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.
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A multi-omics atlas reveals spatially resolved sphingolipid metabolic reprogramming after spinal cord injury.
Secondary injury cascades following spinal cord injury (SCI) are closely associated with local metabolic dysregulation and persistent neuroinflammation. However, the spatiotemporal patterns of alterations centered on sphingolipid metabolism within the injury microenvironment, as well as their specific regulatory mechanisms in relation to neuroimmune inflammation, remain poorly understood at a systematic level. In this study, we integrated high-throughput lipidomics, spatial transcriptomics, and single-cell RNA sequencing (scRNA-seq) of multiple SCI models, to systematically construct a multidimensional spatiotemporal atlas of sphingolipid metabolism following injury. Lipidomics analysis revealed remodeling of sphingolipid metabolism, characterized by the progressive accumulation of neurotoxic sphingolipid metabolites and the depletion of myelin-associated lipids. Spatial transcriptomics demonstrated that regions of sphingolipid metabolism were strikingly confined to the 'inflammatory infiltration core' and the 'scar interface'. At the single-cell level, microglia were identified as the central cellular mediators of this metabolic reprogramming. Notably, we discovered a distinct microglial subpopulation characterized by high expression of the peroxisomal enzyme Hsd17b4 and the disease-associated marker Spp1, termed the 'high-sphingolipid metabolism' subtype. Pseudotime trajectory analysis indicated that sustained elevation of sphingolipid metabolic activity constitutes a potential determinant that prevents microglia from returning to homeostasis and drives their maladaptive polarization toward a chronic pathological phenotype. Immunofluorescence staining confirmed the specific enrichment of Hsd17b4+/Spp1+ microglia within the injury core. Our findings demonstrate that sphingolipid metabolism reprogramming in microglia represents a key mechanistic step in the propagation of secondary injury following SCI. These results provide novel theoretical foundations and potential therapeutic targets for modulating the immuno-metabolic microenvironment to promote functional recovery after spinal cord injury.
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Integrative Mendelian Randomization and Single-Cell Pseudotime Analysis Reveal DKK3 as a PI3K-AKT-Modulated Driver of Esophageal Squamous Cell Carcinoma.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) remains a highly lethal malignancy, and the molecular drivers of its progression are not fully defined. Dickkopf-3 (DKK3), a context-dependent modulator of oncogenic signaling, has been implicated in several solid tumors, but its role in ESCC is unclear. METHODS: We integrated Mendelian randomization based on cis-expression quantitative trait loci (cis-eQTLs) from 31,684 individuals with The Cancer Genome Atlas (TCGA) ESCC transcriptomic data (|log2FC| > 1 and p < 0.05) , single-cell RNA sequencing, and in vitro/in vivo functional assays to identify causal ESCC-associated genes. DKK3 expression was validated by Western blotting, its effects on ESCC cell proliferation, migration, and clonogenicity were tested experimentally, and DKK3-related pathways were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. RESULTS: Integrative differential expression and MR analyses identified 22 ESCC-associated genes. Single-cell epithelial profiling further prioritized three candidates (DKK3, NINJ2, and SAPCD2), among which only DKK3 showed high and progressively increasing expression along the malignant epithelial trajectory and was therefore selected for mechanistic validation. DKK3 knockdown significantly inhibited ESCC cell proliferation, migration, and clonogenic capacity in vitro and suppressed tumor growth in nude mice. DKK3 silencing reduced p-PI3K and p-AKT levels, and enrichment analyses supported that DKK3 promotes ESCC progression at least partly through activation of the PI3K-AKT signaling pathway. CONCLUSION: By linking germline regulatory variation with single-cell tumor profiling and functional validation, this study identifies DKK3 as a causally relevant oncogenic regulator in ESCC that drives epithelial tumor growth and migration via PI3K-AKT pathway activation, supporting DKK3 as a potential biomarker and therapeutic targe.