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A Tumor-Promoting Inflammatory SPP1+ Macrophage-IL6-CRP Axis Drives Immune Dysfunction in Bladder Cancer.
UNLABELLED: Immune checkpoint blockade (ICB) has revolutionized treatment for urothelial bladder cancer, yet response rates remain limited. Inflammation promotes disease progression and treatment resistance, with macrophages shaping the tumor microenvironment (TME). Although elevated blood C-reactive protein (CRP) is associated with poor clinical outcomes in urothelial bladder cancer, its relationship to the TME remains unclear. In this study, we show that elevated plasma IL6 and CRP associate with increased tumor macrophage infiltration across multiple ICB-treated cohorts. Single-cell RNA sequencing (RNA-seq) of the largest urothelial bladder cancer atlas to date, integrated with bulk RNA-seq, identifies enrichment of immunosuppressive SPP1+ macrophages in TMEs from patients with high plasma IL6. Spatial and functional analyses demonstrate that SPP1+ macrophages suppress T-cell activity partly via IL6 signaling, whereas CXCL9+ macrophages promote T-cell activation. These findings link systemic inflammation to local immune dysfunction and define a macrophage-driven axis associated with ICB resistance and therapeutic targets to improve immunotherapy outcomes in urothelial bladder cancer. SIGNIFICANCE: Single-cell and bulk RNA-seq, spatial analyses, and functional experiments identify opposing SPP1+ and CXCL9+ macrophage programs that regulate T-cell function and ICB therapy response in bladder cancer. Elevated plasma CRP and IL6 mark SPP1+ macrophage-driven immune suppression, defining a targetable IL1β/IL6 axis that contributes to immunotherapy resistance.
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Leveraging a disulfidptosis‑related lncRNAs signature for predicting the prognosis and immunotherapy of glioma.
BACKGROUND: Gliomas, a prevalent form of primary brain tumors, are linked with a high mortality rate and unfavorable prognoses. Disulfidptosis, an innovative form of programmed cell death, has received scant attention concerning disulfidptosis-related lncRNAs (DRLs). The objective of this investigation was to ascertain a prognostic signature utilizing DRLs to forecast the prognosis and treatment targets of glioma patients. METHODS: RNA-seq data were procured from The Cancer Genome Atlas database. Disulfidptosis-related genes were compiled from prior research. An analysis of multivariate Cox regression and the least absolute selection operator was used to construct a risk model using six DRLs. The risk signature's performance was evaluated via Kaplan-Meier survival curves and receiver operating characteristic curves. Additionally, functional analysis was carried out using GO, KEGG, and single-sample GSEA to investigate the biological functions and immune infiltration. The research also evaluated tumor mutational burden, therapeutic drug sensitivity, and consensus cluster analysis. Reverse transcription quantitative PCR was conducted to validate the expression level of DRLs. RESULTS: A prognostic signature comprising six DRLs was developed to predict the prognosis of glioma patients. High-risk patients had significantly shorter overall survival than low-risk patients. The robustness of the risk model was validated by receiver operating characteristic curves and subgroup survival analysis. Risk model was used independently as a prognostic indicator for the glioma patients. Notably, the low-risk patients displayed a substantial decrease in the immune checkpoints, the proportion of immune cells, ESTIMATE and immune score. IC50 values from the different risk groups allowed us to discern three drugs for the treatment of glioma patients. Lastly, the potential clinical significance of six DRLs was determined. CONCLUSIONS: A novel six DRLs signature was developed to predict prognosis and may provide valuable insights for patients with glioma seeking novel immunotherapy and targeted therapy.
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A Molecular, Spatial, and Regulatory Atlas of the Hydra vulgaris Nervous System.
Hydra vulgaris, a cnidarian with a simple nerve net, is an emerging model for developmental, regenerative, and functional neuroscience. Its genetic tractability and capacity for whole-system imaging make it well suited for studying neuron replacement, regeneration, and neural circuit function. Here, we present the most comprehensive molecular and spatial characterization of the H. vulgaris nervous system to date. Using single-cell RNA sequencing, we identified eight neuron types, each defined by distinct neuropeptide expression, and further resolved these into fifteen transcriptionally distinct subtypes with unique spatial distributions and morphologies. To investigate the gene regulatory networks underlying neuronal differentiation, we applied trajectory inference, identified key transcription factors, and performed ATAC-seq on sorted neurons to map chromatin accessibility. All datasets are available through an interactive, user-friendly web portal to support broad use by the research community. Together, these resources provide a foundation for uncovering molecular mechanisms that govern nervous system development, homeostasis, and regeneration in H. vulgaris.
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Immunogenomic Profiling Demonstrate AC003092.1 as an Immune-Related eRNA in Glioblastoma Multiforme.
Enhancer RNAs, a type of long non-coding RNAs (lncRNAs), play a critical role in the occurrence and development of glioma. RNA-seq data from 161 glioblastoma multiforme (GBM) samples were acquired from The Cancer Genome Atlas database. Then, 70 eRNAs were identified as prognosis-related genes, which had significant relations with overall survival (log-rank test, p < 0.05). AC003092.1 was demonstrated as an immune-related eRNA by functional enrichment analysis. We divided samples into two groups based on AC003092.1 expression: AC003092.1 High (AC003092.1_H) and AC003092.1 Low (AC003092.1_L) and systematically analyzed the influence of AC003092.1 on the immune microenvironment by single-sample gene-set enrichment analysis and CIBERSORTx. We quantified AC003092.1 and TFPI2 levels in 11 high-grade gliomas, 5 low-grade gliomas, and 7 GBM cell lines. Our study indicates that AC003092.1 is related to glioma-immunosuppressive microenvironment, and these results offer innovative sights into GBM immune therapy.
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Iterative single-cell multi-omic integration using online learning.
Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than 1 million cells on a standard laptop, integrating large single-cell RNA sequencing and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex.
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Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.
BACKGROUND: Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. METHODS: We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. RESULTS: The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. CONCLUSION: Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.
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Evolutionary convergence and divergence of hippocampal cytoarchitecture between rodents and primates revealed by single-cell spatial transcriptomics.
The hippocampus comprises subregions of distinct cell types critical for memory and cognition, but their gene expression profiles and spatial distribution patterns remain to be clarified. Using single-cell spatial transcriptomic analysis and single-nucleus RNA sequencing, we obtained transcriptome-based atlases for the macaque, marmoset and mouse hippocampus. Cross-species comparison revealed primate- and lamina-specific glutamatergic cell types in the subicular complex, as well as enrichment of VIP-expressing GABAergic cells from mice to primates, including humans. Furthermore, we found reduced transcriptomic differences between CA3 and CA4 subregions and distinct longitudinal distributions of various cell types and expression of ion-channel genes, correlated with differences in electrophysiological properties of CA3, CA4 and CA1 neurons revealed by slice recording from marmosets and mice. Collectively, this cross-species study provides a molecular and cellular basis for understanding the evolution and function of the hippocampus.
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Comparative analysis of human and mouse ovaries across age.
The mouse is a tractable model for human ovarian biology; however, its utility is limited by incomplete understanding of how transcription and signaling differ interspecifically and with age. We compared ovaries between species using three-dimensional imaging, single-cell transcriptomics, and functional studies. In mice, we mapped declining follicle numbers and oocyte competence during aging; in human ovaries, we identified cortical follicle pockets and decreases in density. Oocytes had species-specific gene expression patterns during growth that converged toward maturity. Age-related transcriptional changes were greater in oocytes than in granulosa cells across species, although mature oocytes change more in humans. We identified ovarian sympathetic nerves and glia; axon density increased in aged ovaries and, when ablated in mice, perturbed folliculogenesis. This comparative atlas defines shared and species-specific hallmarks of ovarian biology.
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Gamma-Glutamyl Transferase and Cognitive Outcomes: A Cross-Species Analysis and Hepatic Transcriptomic Signature.
Cognitive impairment is a major public health concern, and the liver-brain axis has attracted growing interest. Gamma-glutamyl transferase (GGT) is a serum marker of liver function, but its association with cognitive outcomes remains uncertain. We conducted a cross-species analysis. In 5xFAD mice (n = 52), we examined serum liver markers and spatial cognition using the Morris water maze. In NHANES 2011-2014 (n = 3181, aged ≥60 years), we evaluated liver markers and low cognitive performance (LCP) using multivariable logistic regression. We also performed virtual hepatic GGT1 knockout in a human liver single-cell atlas and integrated GTEx liver co-expression, transcription factor enrichment, and WGCNA analyses. In mice, GGT-cognition associations largely attenuated after strain adjustment, but a positive association with platform crossings emerged (r = 0.321, p = 0.02). In NHANES, serum GGT was inversely associated with LCP (Q4 vs. Q1 OR = 0.54, p < 0.001), with a nonlinear association and an inflection point near 19 U/L. Virtual hepatic GGT1 knockout identified eight immunoglobulin-related genes associated with SPI1 and oxidative stress-related pathways. These parallel findings are hypothesis-generating. A testable hypothesis is that lower hepatic GGT1 activity or serum GGT is associated with upregulated cholangiocyte immunoglobulin-related programs and poorer cognition.
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Multi-omics Analysis Identifies Hypoxia Subtypes and S100A2 as an Immunosuppressive Factor in Cervical Cancer.
Cervical cancer is a common gynecological oncology. Growing evidence indicates hypoxia plays an important role in tumor progression and immunity. However, no study has examined the hypoxia landscape in cervical cancer. In this study, using hierarchical clustering, we identified three hypoxia subtypes in cervical cancer samples from The Cancer Genome Atlas dataset according to formerly described hypoxia-related genes. The overall survival time, hypoxic features, genomics, and immunological characteristics of these subtypes existed distinct differences. We also created a hypoxia score by principle component analysis for dimension reduction. The hypoxiaScore was an effective prognostic biomarker validated by GSE44001 and was associated with immunotherapy response. Furthermore, combined with single-cell RNA-sequence (scRNA-seq) and experiments, S100A2 was identified as an immunosuppressive factor induced by hypoxia and regulated expression of PD-L1. S100A2 also served as an oncogene promoting the proliferation and migration of cervical cancer cells. These findings depicted a new hypoxia-based classification and identified S100A2 as a potential therapeutic target for cervical cancer, thereby advancing the understanding of immunotherapy resistance mechanisms and cervical cancer genetic markers.