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Loss of wbp11 causes multi-system developmental defects: a zebrafish model of VACTERL association.
INTRODUCTION: VACTERL association is a congenital disorder characterized by the non-random co-occurrence of vertebral, anal, cardiac, tracheo-esophageal, renal, and limb anomalies. WBP11 has been identified as a candidate causative gene; however, existing heterozygous Wbp11 knockout mice recapitulate only a subset of the patient phenotypes, most notably lacking the cardiac defects, which limits a comprehensive understanding of the pathogenic mechanisms. Whether WBP11 loss of function is sufficient to induce the full multi-system spectrum of VACTERL association in a vertebrate model remains unexplored. METHODS: We established a wbp11 knockdown zebrafish model by microinjecting a translation-blocking morpholino (wbp11-MO) at the optimized concentration of 0.25 mM. Knockdown efficiency was validated by Western blot. Bioinformatics analysis of public single cell and expression atlas datasets was combined with whole-mount in situ hybridization to characterize the spatiotemporal expression of wbp11. Phenotypic defects across cardiac, vascular, spinal, fin, and renal systems were assessed by stereomicroscopy, transgenic reporter lines (Tg(myl7:EGFP) and Tg(flila:EGFP)), and whole mount in situ hybridization. Underlying molecular changes were interrogated by transcriptome sequencing (RNA-seq) coupled with differential alternative splicing analysis (rMATS) and quantitative real-time PCR validation. Rescue experiments were performed by co-injection of capped wbp11 mRNA. RESULTS: wbp11 is evolutionarily conserved and maternally expressed, peaking at the gastrula stage with elevated signals in the head, skeletal muscle, and heart. wbp11-MO injection reduced Wbp11 protein levels, induced a 70% overall malformation rate, and caused a general delay in embryonic development. wbp11-deficient larvae exhibited multi-system developmental defects that closely mirror the core clinical features of VACTERL association: cardiac malformations with pericardial edema and impaired heart looping; vascular defects including underdeveloped subintestinal vessels and reduced cranial vasculature; spinal curvature with aberrant notochord structure; hypoplasia of the pectoral, pelvic, anal, and caudal fins; and transcriptional dysregulation of kidney developmental markers. Transcriptomic and alternative splicing analyses revealed significant dysregulation of genes associated with cardiac function, angiogenesis, skeletal patterning, fin development, and kidney development, accompanied by widespread splicing aberrations. DISCUSSION: This study establishes the first wbp11-knockdown zebrafish model that partially recapitulates the key organ phenotypes of human VACTERL association notably including the cardiac defects absent in the mouse model and thereby provides a powerful platform for dissecting the splicing dependent molecular mechanisms underlying this disorder and for exploring potential therapeutic interventions.
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Spatial and functional characterization of IL1RL1+ mast cells reveals immune regulatory roles in renal cancer.
BACKGROUND: Interleukin-1 receptor-like 1 (IL1RL1, also known as ST2) plays a critical role in immune regulation. Pan-cancer analysis has revealed that IL1RL1 is closely associated with cellular immune functions; however, its role in clear cell renal cell carcinoma (ccRCC) and the tumor microenvironment (TME) remains poorly defined. METHODS: We analyzed IL1RL1 expression patterns using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) were employed to investigate the cellular localization and biological functions of IL1RL1 in ccRCC. In addition, IL1RL1 knockout experiments were conducted to explore its potential role in antigen presentation. RESULTS: Compared to adjacent normal tissues, IL1RL1 expression was significantly downregulated in renal cancer tissues. Higher IL1RL1 expression correlated with improved patient prognosis, suggesting its potential as an independent prognostic biomarker. IL1RL1 was predominantly expressed in mast cells, with higher levels observed in adjacent normal tissues than in tumor tissues, implying a regulatory role in tumor immunity. Subset analysis revealed that IL1RL1-high mast cells were enriched in immune-inflammatory functions, including leukocyte activation, chemokine production, and antigen presentation. Cell-cell communication analysis further demonstrated that IL1RL1+ mast cells may enhance CD8+ cytotoxic T cell activation via the MHC-I signaling pathway. Spatial transcriptomics and multiplex immunohistochemistry (mIHC) confirmed the spatial co-localization of IL1RL1+ mast cells and T cells within the TME. Furthermore, IL1RL1 knockout led to a reduction in HLA-A expression, providing functional evidence for its involvement in antigen presentation. CONCLUSION: Our findings highlight the immune-regulatory role of IL1RL1+ mast cells in ccRCC. IL1RL1 may contribute to anti-tumor immunity through the modulation of antigen presentation and CD8+ T cell activation, offering new insights into its potential as a prognostic biomarker and therapeutic target in renal cancer.
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Scalable high-performance single cell data analysis with BPCells.
The growth of single-cell datasets to multi-million cell atlases has uncovered major scalability problems for single-cell analysis software. Here, we present BPCells, a package for high-performance single-cell analysis of RNA-seq and ATAC-seq datasets. BPCells uses disk-backed streaming compute algorithms to reduce memory requirements by nearly 70-fold compared to in-memory workflows with little to no loss of execution speed. BPCells also introduces high-performance compressed formats based on bitpacking compression for ATAC-seq fragment files and single-cell sparse matrices. These novel compression algorithms help to accelerate disk-backed analysis by reducing data transfer from disk, while providing the lowest computational overhead of all compression algorithms tested. Using BPCells, we perform normalization and PCA of a 44 million cell dataset on a laptop, demonstrating that BPCells makes working with the largest contemporary single-cell datasets feasible on modest hardware, while leaving headroom on servers for future datasets an order of magnitude larger.
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Targeting Pan-Cancer Stemness: Core Regulatory lncRNAs as Novel Therapeutic Vulnerabilities.
Tumor stemness represents a key biological process that drives tumor progression and therapeutic resistance across various cancer types. To systematically elucidate the regulatory roles of long non-coding RNAs (lncRNAs) in this process, we integrated bulk transcriptomic data from The Cancer Genome Atlas (TCGA) with publicly available pan-cancer single-cell transcriptomic atlases. Using machine-learning-based stemness metrics, we successfully quantified stemness features and identified unique lncRNA gene sets for each cancer type at the bulk data level. The high-stemness subtype exhibited enhanced proliferation, an immunosuppressive microenvironment, and profound metabolic reprogramming. Based on these findings, we constructed a robust prognostic model with remarkable predictive performance across multiple cancer types. At the single-cell resolution, we reconstructed the dynamic trajectory of stemness evolution, uncovering distinctive metabolic and cell-communication patterns within cancer stem cells (CSCs). This multi-scale analysis consistently nominated a core set of regulatory lncRNAs, including NEAT1 and MALAT1. Our work not only nominates potential targets for stemness-directed therapy but also provides a comprehensive framework for understanding lncRNA-driven mechanisms of cancer aggressiveness and resistance.
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Integrated bulk RNA-seq and scRNA-seq identification of a novel "PET-SPI1-MYL9" transcriptional axis in lung adenocarcinoma driven by polyethylene terephthalate exposure.
BACKGROUND: Airborne microplastic polyethylene terephthalate (PET) accumulates in human lungs and is linked to respiratory pathologies; however, its molecular role in lung adenocarcinoma (LUAD) remains unclear. This study aims to explore the potential carcinogenic mechanisms of PET exposure in LUAD. METHODS: We integrated single-cell RNA sequencing, machine learning algorithms [including Classification and Regression Trees (CART), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM)], molecular docking, survival analysis, and multi-omics data. Through differential expression screening across datasets combined with Venn analysis, we identified seven PET-associated oncogenic targets. Seven PET-associated oncogenic targets were identified via differential gene screening and Venn analysis. RESULTS: MYL9 was validated as a downregulated, LUAD-protective biomarker associated with significant survival benefit [hazard ratio (HR) =0.59, 0.16, 0.23; all P<0.05]. These findings were consistent across the Human Protein Atlas (HPA) database, co-expression networks, and three independent LUAD datasets. SPI1 was identified as a key transcriptional regulator, showing strong co-expression with MYL9 (R=0.556, P<0.05) and concurrent downregulation in LUAD. Molecular docking revealed that PET bound to the DNA-binding pocket of SPI1 (ΔG =-5.30 kcal·mol-1), suggesting its transcriptional inhibition of MYL9. CONCLUSIONS: Our integrated bioinformatics approach supports a novel "PET-SPI1-MYL9" transcriptional axis, revealing a potential non-genotoxic carcinogenic pathway for PET. While the computational evidence is robust, further wet-lab experiments are needed to validate the binding and transcriptional inhibition mechanism. This model provides a framework for understanding airborne microplastic toxicity in LUAD. We propose that PET promotes LUAD by disrupting the SPI1-MYL9 transcriptional axis, highlighting a potential environmental trigger and candidate targets for diagnostic and therapeutic strategies.
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The EBV Gastric Cancer Resource (EBV-GCR): A Suite of Tools for Investigating EBV-Associated Human Gastric Carcinogenesis.
Epstein-Barr virus (EBV) causes lifelong infection in over 90% of the world's population. EBV infection leads to several types of B cell and epithelial cancers due to the viral reprogramming of host-cell growth and gene expression. EBV is associated with 10% of stomach/gastric adenocarcinomas (EBVaGCs), which have distinct molecular, pathological, and immunological characteristics compared to EBV-negative gastric adenocarcinomas (EBVnGCs). Publicly available datasets, such as The Cancer Genome Atlas (TCGA), contain comprehensive transcriptomic, genomic, and epigenomic data for thousands of primary human cancer samples, including EBVaGCs. Additionally, single-cell RNA-sequencing data are becoming available for EBVaGCs. These resources provide a unique opportunity to explore the role of EBV in human carcinogenesis, as well as differences between EBVaGCs and their EBVnGC counterparts. We have constructed a suite of web-based tools called the EBV Gastric Cancer Resource (EBV-GCR), which utilizes TCGA and single-cell RNA-seq data and can be used for research related to EBVaGCs. These web-based tools allow investigators to gain in-depth biological and clinical insights by exploring the effects of EBV on cellular gene expression, associations with patient outcomes, immune landscape features, and differential gene methylation, featuring both whole-tissue and single-cell analyses.
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Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment.
Cancer immunotherapy has revolutionized cancer treatment, and it relies heavily on the comprehensive understanding of the immune landscape of the tumor microenvironment (TME). Here, we obtain a detailed immune cell atlas of esophageal squamous cell carcinoma (ESCC) at single-cell resolution. Exhausted T and NK cells, regulatory T cells (Tregs), alternatively activated macrophages and tolerogenic dendritic cells are dominant in the TME. Transcriptional profiling coupled with T cell receptor (TCR) sequencing reveal lineage connections in T cell populations. CD8 T cells show continuous progression from pre-exhausted to exhausted T cells. While exhausted CD4, CD8 T and NK cells are major proliferative cell components in the TME, the crosstalk between macrophages and Tregs contributes to potential immunosuppression in the TME. Our results indicate several immunosuppressive mechanisms that may be simultaneously responsible for the failure of immuno-surveillance. Specific targeting of these immunosuppressive pathways may reactivate anti-tumor immune responses in ESCC.
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Cell type transcriptome atlas for the planarian Schmidtea mediterranea.
The transcriptome of a cell dictates its unique cell type biology. We used single-cell RNA sequencing to determine the transcriptomes for essentially every cell type of a complete animal: the regenerative planarian Schmidtea mediterranea. Planarians contain a diverse array of cell types, possess lineage progenitors for differentiated cells (including pluripotent stem cells), and constitutively express positional information, making them ideal for this undertaking. We generated data for 66,783 cells, defining transcriptomes for known and many previously unknown planarian cell types and for putative transition states between stem and differentiated cells. We also uncovered regionally expressed genes in muscle, which harbors positional information. Identifying the transcriptomes for potentially all cell types for many organisms should be readily attainable and represents a powerful approach to metazoan biology.
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Morphological diversity of single neurons in molecularly defined cell types.
Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.
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Therapeutic Potential of CLDN Family Proteins in Ovarian Cancer: Emerging Biomarkers and Targets.
BACKGROUND: Claudins (CLDNs), key components of tight junctions, are dysregulated in various cancers. However, the roles and therapeutic potential of specific CLDN family members-particularly CLDN6, CLDN9, and CLDN10-in ovarian cancer (OC) remain incompletely defined. To address this gap, we conducted a comprehensive analysis of the CLDN family to identify novel diagnostic and prognostic biomarkers as well as potential therapeutic targets for OC. METHODS: Gene expression profiles and corresponding clinical data from The Cancer Genome Atlas ovarian cancer cohort (TCGA-OV) and two Gene Expression Omnibus (GEO) datasets (GSE18520, GSE26712) were analyzed. Differential expression of CLDN genes between OC and normal tissues was evaluated using R with appropriate bioinformatics packages (e.g., limma). Logistic regression models were employed to calculate odds ratios (ORs), and receiver operating characteristic (ROC) curves were generated across all datasets to identify consistently dysregulated CLDNs associated with OC. Prognostic hazard ratios (HRs) for these CLDNs were extracted from the Kaplan-Meier Plotter (KM Plotter) database and synthesized using a random-effects model to assess their associations with overall survival. Intersection analysis was performed to identify CLDNs exhibiting both significant differential expression and prognostic significance. Candidate targets underwent comprehensive validation, including single-cell RNA sequencing (scRNA-seq) to characterize cell-type-specific expression patterns. Notably, Key findings regarding CLDN6 were further validated by immunohistochemistry (IHC) on an independent tissue microarray (TMA), as well as functional assays in OC cell lines following siRNA-mediated knockdown. These included transwell invasion, wound healing (scratch) test, and measurements of mitochondrial depolarization, reactive oxygen species (ROS) accumulation, cell cycle arrest, and apoptosis. RESULTS: CLDN6, CLDN9, and CLDN10 were consistently and significantly upregulated in OC compared to normal tissues across all datasets. Single-cell RNA sequencing revealed that CLDN6 and CLDN10 were predominantly expressed in malignant epithelial cell subsets, a pattern associated with aggressive tumor phenotypes. Meta-analysis of HRs showed that HR >1 in CLDN6 and HR <1 in CLDN10. Although CLDN10 is highly expressed in tumor cells, its hazard ratio (HR) is less than 1, and the underlying mechanism of this gene remains unclear. Experiments have confirmed that CLDN6 is closely associated with tumor invasion. Computational analysis, meta-analysis, and single-cell data collectively confirm that only CLDN6 is a clearly defined gene closely associated with tumor progression, a finding subsequently validated by experimental results. Notably, the combined signature comprising CLDN6, CLDN9, and CLDN10 exhibited superior diagnostic performance, with higher area under the curve (AUC) values in ROC analysis, compared to individual CLDNs or established OC biomarkers such as carbohydrate antigen 125 (CA125), human epididymis 4 (HE4), carcinoembryonic antigen (CEA), and alpha-fetoprotein (AFP). The signature also showed enhanced prognostic discrimination, as indicated by time-dependent ROC analysis. Protein overexpression of these targets was validated by IHC and Western blot. Functional assays further demonstrated that siRNA-mediated knockdown of CLDN6 significantly inhibited the proliferation of OC cells, promoted cell apoptosis, increased production of ROS, induced G1 phase arrest, inhibited cell invasion and migration in vitro. Furthermore, western blot analysis identified that knockdown of CLDN6 repressed the Wnt/β-catenin pathway. Nude mice experiments indicated that CLDN6 knockdown in OC cells dramatically suppresses the tumor growth and lung metastasis in vivo. CONCLUSIONS: CLDN6, CLDN9, and CLDN10 are critically involved in the pathogenesis and progression of OC. A biomarker panel combining these three claudins demonstrates superior diagnostic and prognostic performance compared to individual markers and established clinical biomarkers such as CA125 and HE4. Notably, functional evidence indicates that CLDN6 plays a pivotal role in regulating malignant phenotypes, highlighting its potential as a novel therapeutic target. These findings collectively support the clinical utility of the CLDN6/9/10 axis as both a non-invasive biomarker signature and a promising avenue for targeted intervention in ovarian cancer.