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A single-cell and spatially resolved atlas of human breast cancers.
Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2+ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.
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Integrating single-cell and bulk RNA sequencing data unveils antigen presentation and process-related CAFS and establishes a predictive signature in prostate cancer.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are heterogeneous and can influence the progression of prostate cancer in multiple ways; however, their capacity to present and process antigens in PRAD has not been investigated. In this study, antigen presentation and process-related CAFs (APPCAFs) were identified using bioinformatics, and the clinical implications of APPCAF-related signatures in PRAD were investigated. METHODS: SMART technology was used to sequence the transcriptome of primary CAFs isolated from patients undergoing different treatments. Differential expression gene (DEG) screening was conducted. A CD4 + T-cell early activation assay was used to assess the activation degree of CD4 + T cells. The datasets of PRAD were obtained from The Cancer Genome Atlas (TCGA) database and NCBI Gene Expression Omnibus (GEO), and the list of 431 antigen presentation and process-related genes was obtained from the InnateDB database. Subsequently, APP-related CAFs were identified by nonnegative matrix factorization (NMF) based on a single-cell seq (scRNA) matrix. GSVA functional enrichment analyses were performed to depict the biological functions. A risk signature based on APPCAF-related genes (APPCAFRS) was developed by least absolute shrinkage and selection operator (LASSO) regression analysis, and the independence of the risk score as a prognostic factor was evaluated by univariate and multivariate Cox regression analyses. Furthermore, a biochemical recurrence-free survival (BCRFS)-related nomogram was established, and immune-related characteristics were assessed using the ssGSEA function. The immune treatment response in PRAD was further analyzed by the Tumor Immune Dysfunction and Exclusion (TIDE) tool. The expression levels of hub genes in APPCAFRS were verified in cell models. RESULTS: There were 134 upregulated and 147 downregulated genes, totaling 281 differentially expressed genes among the primary CAFs. The functions and pathways of 147 downregulated DEGs were significantly enriched in antigen processing and presentation processes, MHC class II protein complex and transport vesicle, MHC class II protein complex binding, and intestinal immune network for IgA production. Androgen withdrawal diminished the activation effect of CAFs on T cells. NMF clustering of CAFs was performed by APPRGs, and pseudotime analysis yielded the antigen presentation and process-related CAF subtype CTSK + MRC2 + CAF-C1. CTSK + MRC2 + CAF-C1 cells exhibited ligand‒receptor connections with epithelial cells and T cells. Additionally, we found a strong association between CTSK + MRC2 + CAF-C1 cells and inflammatory CAFs. Through differential gene expression analysis of the CTSK + MRC2 + CAF-C1 and NoneAPP-CAF-C2 subgroups, 55 significant DEGs were identified, namely, APPCAFRGs. Based on the expression profiles of APPCAFRGs, we divided the TCGA-PRAD cohort into two clusters using NMF consistent cluster analysis, with the genetic coefficient serving as the evaluation index. Four APPCAFRGs, THBS2, DPT, COL5A1, and MARCKS, were used to develop a prognostic signature capable of predicting BCR occurrence in PRAD patients. Subsequently, a nomogram with stability and accuracy in predicting BCR was constructed based on Gleason grade (p = n.s.), PSA (p < 0.001), T stage (p < 0.05), and risk score (p < 0.01). The analysis of immune infiltration showed a positive correlation between the abundance of resting memory CD4 + T cells, M1 macrophages, resting dendritic cells, and the risk score. In addition, the mRNA expression levels of THBS2, DPT, COL5A1, and MARCKS in the cell models were consistent with the results of the bioinformatics analysis. CONCLUSIONS: APPCAFRS based on four potential APPCAFRGs was developed, and their interaction with the immune microenvironment may play a crucial role in the progression to castration resistance of PRAD. This novel approach provides valuable insights into the pathogenesis of PRAD and offers unexplored targets for future research.
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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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VAMP5 promotes glioma progression through bioinformatics analysis clinical correlation and functional validation.
BACKGROUND: Glioma is the most common and aggressive primary brain tumor, characterized by high heterogeneity, invasive growth, and poor prognosis. Identifying novel molecular drivers is essential for improving diagnosis, prognosis, and treatment. This study aimed to investigate the expression pattern, clinical significance, and functional role of VAMP5 in glioma. METHODS: This study integrated data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and systematically evaluated VAMP5 expression in glioma, with validation performed via Real-time PCR, immunohistochemistry, and Western blot. Meanwhile, we analysed the prognostic value, clinicopathological features, function enrichment analysis, immune cell infiltration relevance of VAMP5. Subsequently, in vitro and in vivo experiments were conducted to further validate the role and mechanism of VAMP5 in glioma. RESULTS: VAMP5 was significantly upregulated in glioma tissues compared to normal brain tissue across multiple datasets and clinical samples. Single-cell RNA sequencing (scRNA-seq) further confirmed distinct distribution heterogeneity of VAMP5 within glioma cells. A protein-protein interaction (PPI) network was constructed to identify key hub genes, combined with pathway enrichment analysis and immune infiltration profiling to decipher VAMP5-mediated mechanisms in glioma progression. Finally, functional assays in GBM cell lines were conducted to illustrate the molecular mechanisms underlying VAMP5-mediated promotion of GBM progression. In vitro and in vivo experiments further validated that VAMP5 promotes the proliferation and migration of glioma. CONCLUSION: VAMP5 promotes glioma progression and is associated with immune modulation and adverse clinical outcomes. It may serve as a novel biomarker for prognosis and a potential therapeutic target in glioma.
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Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in patients with LUAD. The tumor microenvironment (TME) of LUAD BrM remains incompletely characterized, and there is an urgent need to identify biologically relevant biomarkers and potential therapeutic vulnerabilities. METHODS: Single-cell transcriptomic data from LUAD BrM samples were processed through dimensionality reduction, clustering, and cell annotation. BrM-associated core genes were identified from BrM scRNA-seq datasets using inferCNV and hdWGCNA. A prognostic model for LUAD was then constructed using 101 combinatorial approaches derived from 10 machine learning algorithms and validated in LUAD cohorts. We further performed supportive analyses including enrichment, pseudotime trajectory, cell-cell communication, immune infiltration, and single-cell-based drug sensitivity prediction to provide additional biological context. Key findings were validated by immunohistochemistry (IHC) in human tissues and by qRT-PCR and IHC in a mouse BrM model. Functional assays were performed in PC9-BrM cells to assess the roles of SEC61G and CFL1 in LUAD BrM. RESULTS: Our study unveiled a comprehensive single-cell atlas of the BrM TME and established a robust 12-gene BrM-informed prognostic signature for LUAD, validated across multiple LUAD survival cohorts. Functional analysis revealed dysregulated cell cycle control in BrM progression. Immune profiling demonstrated significantly reduced CD4+ and CD8+ T-cell infiltration in high-risk subgroups. Cell-cell interaction analysis highlighted the MIF-CD74/CXCR4 and MIF-CD74/CD44 signaling axes as key regulators of TME remodeling. IHC in human tissues and validation using a mouse BrM model supported the biological relevance of SEC61G and CFL1 in LUAD BrM. qRT-PCR further showed elevated expression of SEC61G and CFL1 mRNA in brain-metastatic cells. Knockdown of SEC61G or CFL1 significantly inhibited PC9-BrM cell viability, proliferation, and migration. CONCLUSIONS: This study identified genes associated with LUAD BrM and established a machine-learning-based prognostic signature for LUAD. Further experimental validation supports SEC61G and CFL1 as candidate biomarkers with potential therapeutic relevance.
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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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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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Multi-omics analysis identifies RARRES1 as a potential biomarker linked to immunosuppressive microenvironment and its radiomics prediction in ovarian cancer.
BACKGROUND: Retinoic acid receptor responder 1 (RARRES1) is aberrantly expressed across multiple cancers, its prognostic and immune associations in ovarian cancer (OV) have been increasingly reported, but an integrated multi-omics interpretation across cellular heterogeneity, mutational landscape, and non-invasive radiomics prediction remains insufficiently established. This study aimed to systematically investigate the multi-omics role of RARRES1 in OV and to develop a noninvasive radiomics approach for predicting its expression. METHODS: Bulk RNA sequencing (RNA-seq) data from The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) cohort were used to assess associations between RARRES1, survival, and tumor microenvironment (TME) features. Single-cell RNA sequencing dissected cellular heterogeneity and macrophage subpopulations, and weighted gene co-expression network analysis identified RARRES1-associated gene modules. Whole-exome sequencing profiled tumor mutation burden and landscapes. RARRES1 and key immune markers were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR)/Western blot. A radiomics-based random forest model was constructed to non-invasively predict RARRES1 expression. RESULTS: High RARRES1 expression was associated with poor overall survival, reduced stromal and immune scores, and increased tumor purity. Functional analyses showed downregulation of antigen presentation, chemokine, interferon, and natural killer (NK) cytotoxicity pathways. Single-cell analysis identified macrophage subpopulations, with M2-like tumor-associated macrophages central and enriched in immunosuppressive and inflammatory pathways. High RARRES1 expression exhibited distinct mutational patterns and weighted gene co-expression network analysis (WGCNA) modules correlated with M1 macrophages and immune activation. Compared with the RARRES1-low group, RARRES1-high tumors showed decreased CD8 and increased CD68 and CD163 expression. The radiomics-based random forest model demonstrated good discrimination and calibration in predicting RARRES1 expression, establishing a direct link between CT-derived imaging features and molecular expression states. CONCLUSIONS: This study establishes a radiogenomic framework for non-invasive prediction of RARRES1 expression in OV. While transcriptomic and immunological findings are consistent with existing evidence, the radiomics-based prediction model offers a clinically applicable strategy to infer tumor immune-molecular states from routine imaging.
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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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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.