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Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain.
Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.
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Single-Cell Transcriptome Reveals the Cellular Response to PEG-Induced Stress in Wheat Leaves.
Drought is a major factor limiting the production and yield of wheat bread (Triticum aestivum). Therefore, investigating the wheat drought-related response mechanism is an urgent priority. Here, the single-cell transcriptome of drought-nonsusceptible and susceptible wheat seedlings subjected to PEG-induced stress was systematically analyzed to study the drought-related response at the cellular level. We identified five major cell types using known marker genes and constructed a wheat leaf cell atlas. On this foundation, several potential specific marker genes for each cell type were identified, which provide a reference for further cell type annotation. Moreover, we identified cellular heterogeneity in the drought-related response mechanisms and regulatory networks among cell types. Specifically, the drought response of mesophyll cells was correlated with the photosynthetic pathway. Pseudotime trajectory analysis revealed the transition of epidermal cells from their normal function to a defense response under stress. Moreover, we also characterized the genes associated with the drought response. Notably, we identified two transcription factors (TraesCS1D02G223600 and TraesCS1D02G072900) as master regulators in most cell types. Our study provides detailed insights into the response heterogeneity of cells under PEG-induced stress. The gene resources obtained in our study could be applied to breed better crop plants with improved drought tolerance.
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Single-Cell Multiomics Integration Reveals Cell Trajectories and Regulatory Networks in Breast Cancer Progression.
BACKGROUND: Breast cancer is a molecularly heterogeneous disease encompassing distinct subtypes with divergent prognoses and therapeutic responses. Despite extensive bulk genomic characterization, the single-cell transcriptional trajectories, chromatin regulatory landscapes, and intercellular communication networks that drive breast cancer progression from luminal to mesenchymal states remain incompletely understood. Emerging evidence implicates gut microbiome dysbiosis as a systemic modulator of breast cancer risk, immune regulation, and treatment response; however, its coordinated characterization alongside high-resolution single-cell profiling has not been undertaken. Integrating single-cell RNA sequencing (scRNA-seq) with chromatin accessibility profiling (scATAC-seq), cell-cell communication inference, and gut microbiome 16S rRNA analysis offers a comprehensive multilayered approach to dissecting both the intratumoral regulatory logic and the systemic microbial landscape of breast cancer progression. METHODS: We performed integrated multiomics analysis of 42,037 high-quality single cells from breast cancer patient samples obtained from the GEO database (GSE161529), encompassing tumor and adjacent normal tissue. scRNA-seq data were processed using Seurat v4.3.0 and Scanpy v1.9.1. Dimensionality reduction, Leiden clustering, differential expression analysis, pseudotime trajectory inference (Monocle3, scVelo, Slingshot), SCENIC regulon analysis, chromatin accessibility profiling, cell-cell communication inference (CellChat), and functional enrichment analysis were applied. Key marker genes were validated by quantitative RT-PCR in MCF-7 and MDA-MB-231 cell lines. In parallel, gut microbiome 16S rRNA amplicon sequencing data from 599 matched breast cancer-healthy control pairs (266 genera, four BioProjects) were analyzed for alpha/beta diversity, differential genus abundance (Mann-Whitney U with Benjamini-Hochberg correction), genus co-occurrence networks, and random forest breast cancer classification. RESULTS: Unbiased clustering identified 12 observed cell populations spanning luminal (Luminal A, Luminal B), basal, HER2+, cancer-associated fibroblast (CAF), tumor-associated macrophage (TAM), CD8 T cell, regulatory T cell (Treg), NK cell, B cell, endothelial, and pericyte compartments. Quality control confirmed robust data quality with median 2400-3100 genes per cell and mitochondrial content below 20%. Pseudotime trajectory analysis revealed a continuous epithelial-to-mesenchymal transition (EMT) spectrum with three divergent lineages: EMT, senescence, and immune activation branches. SCENIC identified SNAI1(+), ZEB1(+), and FOXM1(+) as the most differentially activated regulons in basal/mesenchymal cells. ATAC-seq chromatin accessibility profiling confirmed EMT transcription factor access through distal enhancers. CellChat identified TGF-β1-TGFBR1 (CAF → tumor) and PD-L1-PD-1 (tumor → CD8 T cell) as dominant intercellular communication axes. Multiomics integration enhanced cell-type classification AUC from 0.76 (scRNA-seq alone) to 0.91 (full multiomics). EMT-high tumor status was associated with significantly inferior overall survival (HR = 2.14, p = 0.001). qRT-PCR validation confirmed ERBB2 enrichment in HER2+ lines and KRT5/VIM enrichment in basal/TNBC lines (all p < 0.001). Gut microbiome analysis revealed significant dysbiosis in breast cancer patients versus healthy controls, with reduced Shannon diversity (1.94 vs. 2.06, p = 6.1 × 10-4), significant community-level separation (Bray-Curtis PERMANOVA F = 30.2, p = 0.001), and nine differentially abundant genera including enriched Faecalitalea (log2 F C = 1.44), Parasutterella (log2 F C = 1.21), and Bacteroides (log2 F C = 1.19). A random forest microbiome classifier achieved a cross-validated AUC of 0.79 (95% CI: 0.74-0.82), supporting the gut-breast axis as a systemic codeterminant of cancer state. CONCLUSIONS: This integrated single-cell multiomics and gut microbiome atlas of breast cancer progression characterizes the transcriptional regulatory architecture driving cellular trajectory diversification, highlights SNAI1-ZEB1-FOXM1 coregulatory networks as candidate therapeutic targets, suggests that multiomics integration can enhance molecular stratification of breast cancer subtypes with clinical prognostic relevance, and identifies gut microbiome dysbiosis as a potential systemic dimension-characterized by loss of microbial diversity and enrichment of specific genera-as a systemic dimension of breast cancer pathobiology that warrants integration with intratumoral molecular profiling.
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HOX gene expression in the developing human spine.
Positional coding along the anterior-posterior axis is regulated by HOX genes, whose 3' to 5' expression correlates with location along this axis. The precise utilisation of HOX genes in different human cell types is not fully understood. Here, we use single-cell and spatial-transcriptomics, along with in-situ sequencing, to create a developmental atlas of the human fetal spine. We analyse HOX gene expression across cell types during development, finding that neural-crest derivatives unexpectedly retain the anatomical HOX code of their origin while also adopting the code of their destination. This trend is confirmed across multiple organs. In the axial plane of the spinal cord, we find distinct patterns in the ventral and dorsal domains, providing insights into motor pool organisation and loss of collinearity in HOXB genes. Our findings shed new light on HOX gene expression in the developing spine, highlighting a HOX gene 'source code' in neural-crest cell derivatives.
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ESCO2's oncogenic role in human tumors: a pan-cancer analysis and experimental validation.
PURPOSE: Establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the mitotic S-phase adhesins acetylation and is responsible for bridging two sister chromatids. However, present ESCO2 cancer research is limited to a few cancers. No systematic pan-cancer analysis has been conducted to investigate its role in diagnosis, prognosis, and effector function. METHODS: We thoroughly examined the ESCO2 carcinogenesis in pan-cancer by combining public databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), UALCAN and Tumor Immune Single-cell Hub (TISCH). The analysis includes differential expression analysis, survival analysis, cellular effector function, gene mutation, single cell analysis, and tumor immune cell infiltration. Furthermore, we confirmed ESCO2's impacts on clear cell renal cell carcinoma (ccRCC) cells' proliferative and invasive capacities in vitro. RESULTS: In our study, 30 of 33 cancer types exhibited considerably greater levels of ESCO2 expression in tumor tissue using TCGA and GTEx databases, whereas acute myeloid leukemia (LAML) exhibited significantly lower levels. Kaplan-Meier survival analyses in adrenocortical carcinoma (ACC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), and pancreatic adenocarcinoma (PAAD) demonstrated that tumor patients with high ESCO2 expression have short survival periods. However, in thymoma (THYM), colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), ESCO2 was a favorable prognostic factor. Moreover, ESCO2 expression positively correlates with tumor stage and tumor size in several cancers, including LIHC, KIRC, KIRP and LUAD. Function analysis revealed that ESCO2 participates in mitosis, cell cycle, DNA damage repair, and other processes. CDK1 was identified as a downstream gene regulated by ESCO2. Furthermore, ESCO2 might also be implicated in immune cell infiltration. Finally, ESCO2'S knockdown significantly inhibited the A498 and T24 cells' proliferation, invasion, and migration. CONCLUSIONS: In conclusion, ESCO2 is a possible pan-cancer biomarker and oncogene that can reliably predict the prognosis of cancer patients. ESCO2 was also implicated in the cell cycle and proliferation regulation. In a nutshell, ESCO2 is a therapeutically viable and dependable target.
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Designed optogenetic tool for bridging single-neuronal multimodal information in intact animals.
Integrating morphological, functional and molecular information of individual neurons is critical for classifying neuronal cell types and probing circuit mechanisms of brain functions. Despite the emergence of extensive single-neuronal morphology datasets largely via random sparse labeling, it remains challenging to map arbitrarily selected neuron's morphology in vivo, especially in conjunction with its functional and molecular characteristics. Here, we report a genetically encoded Photo-inducible single-cell labeling system (Pisces) that enables simple, rapid and long-term in vivo labeling of the entire morphology of arbitrary neurons, as exemplified in intact larval zebrafish. Pisces allows sequential tracing of multiple neurons within individual animals, facilitating brain-wide projectome mapping. Importantly, combined with in vivo calcium imaging, and fluorescence in situ hybridization or single-cell RNA sequencing, Pisces allows linking individual neurons' morphology characterization with their functional and/or gene expression investigation, respectively. This strategy promises to advance the construction of single-neuronal multimodal atlases and expedite the elucidation of neural circuitries underlying brain functions.
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A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities with adult glioma, it comprises distinct disease entities. In this study, we longitudinally profile a molecularly diverse cohort of 16 pHGG patients through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of neoplastic and microenvironmental features during disease progression and treatment. We define a set of core pHGG neoplastic cell states and observe differential tumor-myeloid interactions between malignant cell phenotypes. We find that essential neuromodulators and the interferon response are upregulated post-therapy, implicating them as malignant cell-intrinsic targets. We observe an increase in oligodendrocytes upon progression and that they coordinate spatial motifs with proneural tumor cells. This multiomic atlas of longitudinal pHGG captures features of therapy response and provides a scalable reference for the study of pediatric brain tumors.
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Multi-omics atlas of ovarian cellular and molecular responses to diabetes.
Diabetes is associated with compromised reproductive health; however, the cellular and molecular mechanisms underlying its impact on ovarian function remain largely unclear. In this study, we integrated single-cell RNA sequencing, DNA methylation profiling, and metabolomic analyses to comprehensively characterize the ovarian cellular landscape, epigenetic alterations, and metabolic reprogramming in diabetic female mice, with a focus on identifying diabetes-induced changes in ovarian cells. Our cell type-specific transcriptomic analysis revealed that dysregulated steroid hormone biosynthesis and impaired fatty acid metabolism are prominent features of diabetic ovarian dysfunction. Notably, key genes including Cyp11a1, Fshr, and Lhcgr exhibited reduced expression accompanied by increased DNA methylation levels in their gene regions within granulosa cells under diabetic conditions. Furthermore, disrupted granulosa cell differentiation was evident, leading to aberrant luteal cell formation and compromised luteal function. In parallel, metabolomic profiling revealed profound metabolic reprogramming in diabetic ovaries, with significant alterations in lipid metabolism pathways, including elevated unsaturated fatty acid and reduced glycerophospholipid metabolism. Taken together, these findings provide novel insights into the molecular pathways underlying ovarian dysfunction in the context of diabetes, thereby enhancing our understanding of folliculogenesis in metabolic disorders.
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Screening of novel biomarkers for breast cancer based on WGCNA and multiple machine learning algorithms.
BACKGROUND: Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis. METHODS: We analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via Protein-Protein Interaction (PPI) networks and multiple machine-learning algorithms. The Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human Protein Atlas (HPA) databases were employed to examine mRNA and protein expression of eight biomarkers. Kaplan-Meier mapper tool assessed their prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, and their relationship with immune infiltration was examined using Tumor Immune Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug prediction was conducted based on the identified biomarkers. RESULTS: We identified 1,673 DEGs and 542 important genes through differential analysis and WGCNA, respectively. Intersection analysis revealed 76 genes, which play significant roles in immune-related viral infection and IL-17 signaling pathways. DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 was the most critical gene for diagnosis. Prospective drugs targeting NEK2 include etoposide and lukasunone. CONCLUSIONS: Our study identified DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and prognosis in clinical settings.
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Single-Cell RNA-Sequencing Atlas Reveals the Tumor Microenvironment of Metastatic High-Grade Serous Ovarian Carcinoma.
Ovarian cancer is the most common and lethal gynecological tumor in women worldwide. High-grade serous ovarian carcinoma (HGSOC) is one of the histological subtypes of epithelial ovarian cancer, accounting for 70%. It often occurs at later stages associated with a more fatal prognosis than endometrioid carcinomas (EC), another subtype of epithelial ovarian cancer. However, the molecular mechanism and biology underlying the metastatic HGSOC (HG_M) immunophenotype remain poorly elusive. Here, we performed single-cell RNA sequencing analyses of primary HGSOC (HG_P) samples, metastatic HGSOC (HG_M) samples, and endometrioid carcinomas (EC) samples. We found that ERBB2 and HOXB-AS3 genes were more amplified in metastasis tumors than in primary tumors. Notably, high-grade serous ovarian cancer metastases are accompanied by dysregulation of multiple pathways. Malignant cells with features of epithelial-mesenchymal transition (EMT) affiliated with poor overall survival were identified. In addition, cancer-associated fibroblasts with EMT-program were enriched in HG_M, participating in angiogenesis and immune regulation, such as IL6/STAT3 pathway activity. Compared with ECs, HGSOCs exhibited higher T cell infiltration. PRDM1 regulators may be involved in T cell exhaustion in ovarian cancer. The CX3CR1_macro subpopulation may play a role in promoting tumor progression in ovarian cancer with high expression of BAG3, IL1B, and VEGFA. The new targets we discovered in this study will be useful in the future, providing guidance on the treatment of ovarian cancer.