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Page 180 of 492-
Mapping DNA Methylation Signatures to Identify Epigenetic Variation Across Subcortical Regions of the Human Posttraumatic Stress Disorder Brain.
BACKGROUND: Posttraumatic stress disorder (PTSD) is a mental disorder that may occur in the aftermath of severe psychological trauma. Epigenetic changes in the brain may play a critical role in understanding the neurobiology of PTSD by linking environmental traumatic stress exposure to lasting alterations in gene expression that shape neuronal function. METHODS: We examined 1,065,750 DNA methylation (DNAm) sites from 171 donors including neurotypical controls and PTSD and major depressive disorder (MDD) cases across 6 regions implicated in the fear circuitry of the brain. We performed RNA sequencing (RNA-seq) to examine changes in gene expression and linked these changes to changes in DNAm at nearby sites in a case-control manner. We created a single cell-type atlas of DNAm using a single-nucleus RNA-seq reference panel to map epigenetic changes to specific cell types. Finally, we leveraged a human PTSD ketamine trial to associate blood DNAm biomarkers of ketamine efficacy with specific changes in DNAm in the brain. RESULTS: We found significant differential methylation for PTSD near 195 genes, and to further resolve the changes we observed, we constructed a cell type-specific DNAm atlas defined for changes to the PTSD methylome across 6 cell types. To identify potential therapeutic intersections for PTSD, we found significant methylation levels in the MAD1L1, ELFN1, and WNT5A genes in patients with PTSD who responded to ketamine. Finally, to better understand the unique biology of PTSD, we analyzed matching methylation data for a cohort of donors with MDD with no known history of trauma or PTSD. CONCLUSIONS: Our results implicate DNAm as an epigenetic mechanism underlying the molecular changes associated with the subcortical fear circuitry of the PTSD brain.
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CIPHER: An end-to-end framework for designing optimized aggregated spatial transcriptomics experiments.
MOTIVATION: Most imaging-based spatial transcriptomics methods measure individual genes, which limits scalability and typically requires integration with scRNA-seq to recover full cellular states. Recent approaches such as CISI, FISHnCHIPs, and ATLAS address this limitation by measuring aggregate transcriptional signatures, where multiple genes are pooled into each channel to increase throughput. While aggregate measurements improve scalability, they shift the problem from gene selection to feature design. For effective integration with scRNA-seq, these signatures must be not only discriminative in transcriptional space but also straightforward to measure, with balanced signal, sufficient dynamic range, and robustness to experimental noise. By optimizing decoding accuracy in isolation, existing methods leave substantial performance on the table. RESULTS: We present CIPHER (Cell Identity Projection using Hybridization Encoding Rules), a neural-network framework that jointly optimizes the experimental encoding matrix, i.e., the way that genes are aggregated to signatures, and the downstream cell embedding. CIPHER integrates the physical limits of imaging assays directly into its loss function, shaping the latent space to maximize discriminability while maintaining robustness to measurement noise and signal constraints. Using a large-scale mouse brain scRNA-seq reference, we show that CIPHER-designed encodings yield latent spaces with improved cell-type separability, uniform signal utilization, and greater resilience to hybridization variability, resulting in higher decoding accuracy from both simulated and experimental data. CONCLUSION: CIPHER formulates aggregate signature design as a joint optimization problem over decoding accuracy and experimental measurability. This enables systematic, scRNA-seq-aligned feature design for scalable spatial transcriptomics based on aggregate measurements. AVAILABILITY: Code and documentation are available at https://github.com/wollmanlab/Design/ . AUTHOR SUMMARY: Spatial transcriptomics reveals how cells are organized within tissues by mapping where genes are expressed. To achieve both scale and resolution, many approaches now combine spatial imaging with single-cell RNA-seq references to reconstruct complete transcriptomes in situ . New methods such as CISI, FISHnCHIPs, and ATLAS accelerate this process by measuring combinations of co-expressed genes rather than each gene individually. These aggregate measurements simplify experiments but introduce a new challenge: deciding which genes to combine so that the resulting features are both experimentally reliable and computationally informative for integration with scRNA-seq data. We developed CIPHER, a computational framework that learns how to design and decode these aggregate measurements optimally. By integrating experimental constraints and decoding accuracy into a unified neural-network model, CIPHER provides a principled approach for designing signature-based spatial transcriptomics experiments that enable efficient and accurate reconstruction of cellular transcriptomes.
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A systems biology approach uncovers novel disease mechanisms in age-related macular degeneration.
Age-related macular degeneration (AMD) is a leading cause of blindness, affecting 200 million people worldwide. To identify genes that could be targeted for treatment, we created a molecular atlas at different stages of AMD. Our resource is comprised of RNA sequencing (RNA-seq) and DNA methylation microarrays from bulk macular retinal pigment epithelium (RPE)/choroid of clinically phenotyped normal and AMD donor eyes (n = 85), single-nucleus RNA-seq (164,399 cells), and single-nucleus assay for transposase-accessible chromatin (ATAC)-seq (125,822 cells) from the retina, RPE, and choroid of 6 AMD and 7 control donors. We identified 23 genome-wide significant loci differentially methylated in AMD, over 1,000 differentially expressed genes across different disease stages, and an AMD Müller state distinct from normal or gliosis. Chromatin accessibility peaks in genome-wide association study (GWAS) loci revealed putative causal genes for AMD, including HTRA1 and C6orf223. Our systems biology approach uncovered molecular mechanisms underlying AMD, including regulators of WNT signaling, FRZB and TLE2, as mechanistic players in disease.
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Single-cell transcriptomes reveal spermatogonial stem cells and the dynamic heterogeneity of spermatogenesis in a seasonal breeding teleost.
Seasonal spermatogenesis in fish is driven by spermatogonial stem cells (SSCs), which undergo a complex cellular process to differentiate into mature sperm. In this study, we characterized spermatogenesis in the large yellow croaker (Larimichthys crocea), a marine fish of significant commercial value, based on a high-resolution single-cell RNA-sequencing atlas of testicular cells from three distinct developmental stages: juvenile, adult differentiating and regressed testes. We detailed a continuous developmental trajectory of spermatogenic cells, from spermatogonia to spermatids, elucidating the molecular events involved in spermatogenesis. We uncovered dynamic heterogeneity in cellular compositions throughout the annual reproductive cycle, accompanied by strong molecular signatures within specific testicular cells. Notably, we identified a distinct population of SSCs and observed a critical metabolic transition from glycolysis to oxidative phosphorylation, enhancing our understanding of the biochemical and molecular characteristics of SSCs. Additionally, we elucidated the interactions between somatic cells and spermatogonia, illuminating the mechanisms that regulate SSC development. Overall, this work enhances our understanding of spermatogenesis in seasonal breeding teleosts and provides essential insights for the further conservation and culture of SSCs.
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A Prognostic 9-Gene Signature Linked to Autophagy-Dependent Cell Death in Hepatocellular Carcinoma.
OBJECTIVES: Autophagy-dependent cell death (ADCD) plays a pivotal role in solid tumors, ultimately influencing immunotherapeutic efficacy and cancer prognosis. However, its significance in hepatocellular carcinoma (HCC) remains underexplored. METHODS: Through integrated analysis of single-cell and bulk transcriptomic data, this research systematically identified ADCD-associated genes in LIHC. This was achieved by applying AddModuleScore, ssGSEA, and WGCNA for robust gene screening. A prognostic model was developed for LIHC grounded in The Cancer Genome Atlas (TCGA) dataset. Its validity was confirmed through internal validation with an independent TCGA cohort and external validation using GEO datasets. Immune characteristics were assessed by adopting CIBERSORT and ESTIMATE algorithms. Through LASSO-Cox regression analysis, this research established a 9-gene ADCD signature and derived the ADCD-related risk score system (ADCDRS). RESULTS: The ADCDRS demonstrated superior prognostic performance. Aside from that, this unique system was significantly associated with clinical features, immune infiltration patterns, and the tumor's local environment. To improve clinical applicability, this research constructed a nomogram incorporating the ADCDRS. Additionally, potential therapeutic agents targeting specific risk subgroups were identified. CONCLUSION: This study highlights the prognostic and therapeutic potential of ADCD-related biomarkers in LIHC.
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TAGAP expression influences CD4+ T cell differentiation, immune infiltration, and cytotoxicity in LUAD through the STAT pathway: implications for immunotherapy.
BACKGROUND: T-cell Activation GTPase Activating Protein (TAGAP) plays a role in immune cell regulation. This study aimed to investigate TAGAP's expression and its potential impact on CD4+ T cell function and prognosis in lung adenocarcinoma (LUAD). METHODS: We analyzed TAGAP expression and its correlation with immune infiltration and clinical data in LUAD patients using multiple datasets, including The Cancer Genome Atlas (TCGA-LUAD), Gene Expression Omnibus (GEO), and scRNA-seq datasets. In vitro and in vivo experiments were conducted to explore the role of TAGAP in CD4+ T cell function, chemotaxis, and cytotoxicity. RESULTS: TAGAP expression was significantly lower in LUAD tissues compared to normal tissues, and high TAGAP expression correlated with better prognosis in LUAD patients. TAGAP was positively correlated with immune/stromal/ESTIMATE scores and immune cell infiltration in LUAD. Single-cell RNA sequencing revealed that TAGAP was primarily distributed in CD4+/CD8+ T cells. In vitro experiments showed that TAGAP overexpression enhanced CD4+ T cell cytotoxicity, proliferation, and chemotaxis. Gene Set Enrichment Analysis (GSEA) indicated that TAGAP was enriched in the JAK-STAT signaling pathway. In vivo experiments in a xenograft tumor model demonstrated that TAGAP overexpression suppressed tumor growth and promoted CD4+ T cell cytotoxicity. CONCLUSIONS: TAGAP influences CD4+ T cell differentiation and function in LUAD through the STAT pathway, promoting immune infiltration and cytotoxicity. This study provides a scientific basis for developing novel LUAD immunotherapy strategies and exploring new therapeutic targets.
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Up-regulated DSG2 promotes tumor growth and reduces immune infiltration in cervical cancer.
BACKGROUND: Desmoglein-2 (DSG2) has been reported to play pivotal roles in various diseases. However, its roles in cervical cancer (CC) remain insufficiently elucidated. Here, we aimed to comprehensively explore the functional mechanisms of DSG2 in CC using bioinformatics and experimental methods. METHODS: Several online databases, including Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE, LinkedOmics, MetaScape, Human protein atlas (HPA), OMICS and single-cell RNA sequencing (scRNA-seq) data were used to explore the expression, prognosis, gene mutations, and potential signaling pathway of DSG2 in CC. Quantitative real-time PCR (qRT-PCR) and western blotting were used to measure DSG2 expression in collected samples. Experimental assays were conducted to verify the effects of dysregulated DSG2 on cervical cell lines in vitro. RESULTS: Bioinformatic analyses revealed that DSG2 was significantly up-regulated in CC compared to normal cervical tissues at both mRNA and protein levels. Elevated DSG2 levels were also associated with poor prognosis and clinical parameters (e.g., cancer stages, tumor grade, nodal metastasis status, etc.). DSG2 expression was predominantly observed in epithelial cells, increasing with disease progression on a single-cell resolution. Additionally, up-regulation of DSG2 significantly enhanced tumor purity by reducing the infiltration of immune cells (e.g., B cells, T cells, NK cells, etc.). Over-expression of DSG2 was further validated in collected CC samples at both mRNA and protein levels. Knockdown of DSG2 markedly reduced the proliferation and invasion of CC cell lines in vitro. CONCLUSIONS: In summary, elevated levels of DSG2 were significantly associated with poor prognosis and diminished immune infiltration in CC. Thus, DSG2 may serve as a potential therapeutic and diagnostic biomarker for CC.
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Ferroptosis and low-grade Glioma: The breakthrough potential of NUAK2.
BACKGROUND: Low-grade glioma (LGG) is a primary brain tumor with high cellular heterogeneity and recurrence, leading to poor prognosis. Standard treatments (surgery, radiotherapy, and chemotherapy) have limited efficacy. Ferroptosis, an iron-dependent form of regulated cell death, is a potential therapeutic target, while dysregulated ferroptosis-related genes (FRGs) may drive tumor progression and therapy resistance. METHODS: This study integrated multi-omics data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) to identify FRGs associated with LGG prognosis. Single-cell RNA sequencing (scRNA-seq) and pseudotime trajectory analysis were performed to investigate their functional roles. Key findings were validated through in vitro and in vivo experiments. RESULTS: We identified 345 FRGs associated with LGG prognosis, which are involved in oxidative stress response, cell proliferation, and immune regulation. High-risk patients exhibited an immunosuppressive tumor microenvironment with elevated levels of M2 macrophages and Treg cells but reduced CD8+ T cell infiltration. Pseudotime trajectory analysis highlighted the dynamic roles of macrophages and astrocytes in immune evasion and microenvironment remodeling. Notably, the NUAK2 gene emerged as a key driver of tumor progression and immune suppression. In vitro and in vivo experiments confirmed that targeting NUAK2 significantly reduced tumor cell viability and growth, underscoring its critical regulatory role in LGG. CONCLUSIONS: Our study provides comprehensive insights into the role of FRGs in LGG prognosis and tumor microenvironment regulation, with a particular focus on the NUAK2 gene. As a potential therapeutic target, NUAK2's critical role in tumor progression and immune evasion offers a new direction for LGG treatment. Future research should focus on validating NUAK2's role in larger cohorts and exploring its clinical application as a biomarker and therapeutic target.
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Cross-tissue organization of the fibroblast lineage.
Fibroblasts are non-haematopoietic structural cells that define the architecture of organs, support the homeostasis of tissue-resident cells and have key roles in fibrosis, cancer, autoimmunity and wound healing1. Recent studies have described fibroblast heterogeneity within individual tissues1. However, the field lacks a characterization of fibroblasts at single-cell resolution across tissues in healthy and diseased organs. Here we constructed fibroblast atlases by integrating single-cell transcriptomic data from about 230,000 fibroblasts across 17 tissues, 50 datasets, 11 disease states and 2 species. Mouse fibroblast atlases and a DptIRESCreERT2 knock-in mouse identified two universal fibroblast transcriptional subtypes across tissues. Our analysis suggests that these cells can serve as a reservoir that can yield specialized fibroblasts across a broad range of steady-state tissues and activated fibroblasts in disease. Comparison to an atlas of human fibroblasts from perturbed states showed that fibroblast transcriptional states are conserved between mice and humans, including universal fibroblasts and activated phenotypes associated with pathogenicity in human cancer, fibrosis, arthritis and inflammation. In summary, a cross-species and pan-tissue approach to transcriptomics at single-cell resolution has identified key organizing principles of the fibroblast lineage in health and disease.
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Palmitoylation-related gene expression and its prognostic value in prostate cancer: insights into immune infiltration and therapeutic potential.
BACKGROUND: Prostate cancer (PRAD/PCa) is a leading malignancy in men, with high incidence and mortality rates globally. Although treatments like androgen deprivation therapy (ADT) and chemotherapy have advanced, the prognosis for advanced or metastatic PCa remains unfavorable. Post-translational modifications (PTMs), particularly protein palmitoylation, have emerged as critical regulators of cancer progression and potential therapeutic targets. This study investigates the features of palmitoylation-related genes (PRGs) in PRAD, their links to immune infiltration, and potential therapeutic applications. METHODS: RNA-seq data and clinical information for PRAD were sourced from The Cancer Genome Atlas (TCGA), Deutsches Krebsforschungszentrum (DKFZ), Memorial Sloan Kettering Cancer Center (MSKCC), Gene Expression Omnibus (GEO), ArrayExpress, PCaProfiler, and PCaDB. Palmitoylation scores were calculated using the single-sample gene set enrichment analysis (ssGSEA) method. A prognostic model was developed based on characteristic genes from single-cell RNA sequencing (scRNA-seq) and validated in multiple external datasets. An integrative machine learning approach combining ten algorithms was used to construct a PRG signature. Potential drug sensitivity was analyzed using the oncoPredict R package, and experimental validation was performed on human prostate samples and PRAD cell lines. RESULTS: Significant variations in palmitoylation scores were observed across different PRAD patient types and cell populations. Higher palmitoylation scores were associated with poorer biochemical recurrence (BCR)-free survival in the TCGA dataset. The developed prognostic signature demonstrated significant differences in BCR among patients stratified by risk score. Patients with elevated risk scores demonstrated advanced clinical features such as increased age, advanced tumor stage, lymph node metastasis, elevated prostate-specific antigen (PSA) levels, higher Gleason scores, and BCR. Transcriptomic analysis identified unique gene expression profiles and pathway activities between high- and low-risk groups, with the high-risk group exhibiting heightened activation of oncogenic and immune-related pathways. PRG risk scores significantly impacted the immune landscape, with the high-risk group showing elevated immune checkpoint expression [e.g., programmed death 1 (PD-1) and programmed death ligand-1 (PD-L1)] and a more immunosuppressive microenvironment. Drug response analysis indicated that patients with different risk scores may respond differently to chemotherapy, with significant differences in the half-maximal inhibitory concentration (IC50) values for common chemotherapeutic agents. Functional importance and validation of hub genes, including cholinergic receptor nicotinic alpha 2 subunit (CHRNA2), supported their potential as therapeutic targets. Experimental validation of CHRNA2 knockdown in PRAD cells demonstrated significant inhibition of cell growth, migration, and invasion. CONCLUSIONS: This study underscores the prognostic importance of PRG expression in PRAD, linking it to tumor biology, immune infiltration, and treatment response. Targeting palmitoylation pathways may offer novel therapeutic opportunities for improving patient outcomes in PRAD. Future research should aim to clarify the molecular mechanisms of palmitoylation in PRAD and investigate its therapeutic potential through preclinical and clinical studies.