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Comparative single-nucleus transcriptomics reveals asymmetric evolution of the Drosophila male and female germlines.
Reproductive organs vary widely across species yet share conserved cell types that produce gametes, sustaining species' perpetuation. However, tissue-level comparisons mask critical differences among cell types, obscuring where evolutionary divergence occurs even between closely related species. We quantified expression divergence at cell-type resolution between two sibling species, Drosophila melanogaster and D. simulans, while disentangling adaptive and nonadaptive evolutionary mechanisms. We built a comparative single-nucleus transcriptomic atlas of over 100,000 nuclei from testes and ovaries of both species. Our analysis revealed sharply heterogeneous divergence across testis cell types, contrasting with a broader conservation across ovary cell types. Notably, in both organs, ~40% of genes showing interspecific differences did so in only one cell type. In the testis, spermatogonia were largely conserved, whereas divergence peaked in primary spermatocytes with extensive rewiring of coexpression modules linked to microtubule and mitochondrial functions. In the ovary, expression was largely conserved, except in early germline and late follicle cells, which showed shifts in oogenesis and cell-cycle-related coexpression modules. Divergent cell types in both tissues were enriched for evolutionarily young genes with narrow expression breadth and faster protein evolution rates. Additionally, the ovary exhibited a faster-X effect consistent with adaptive evolution. These findings reveal a fundamental asymmetry in how male and female germlines evolve, with functional constraints relaxed in specific testis cell types but broadly maintained across the ovary. Our work provides an evolutionary framework explaining how core reproductive functions are safeguarded during species diversification while identifying germline cells that drive evolutionary change.
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Quantifying pathological progression from single-cell transcriptomic data with scPSS.
The surge in single-cell data sets and reference atlases has enabled the comparison of cell states across conditions, yet a gap persists in quantifying pathological shifts from healthy cell states. To address this gap, we introduce single-cell Pathological Shift Scoring (scPSS), which provides a statistical measure for how much a "query" cell from a diseased sample has shifted away from a reference group of healthy cells. In scPSS, the distance of a cell to its k-th nearest reference cell is considered as its pathological shift score. Euclidean distances in the top n principal component space of the gene expressions are used to measure distances between cells. The distribution of shift scores of the reference cells forms a null model. This allows a P-value to be assigned to each query cell's shift score, quantifying its statistical significance of being in the reference cell group. This makes our method both simple and statistically rigorous. The key strength of scPSS is its applicability in a "semisupervised" setting, where only healthy reference cells are known and diseased-labeled data are not provided for model training. As existing methods do not support cell-level pathological progression measurement in this setting, we adapt state-of-the-art supervised pathological prediction and contrastive models for benchmarking. Comparative evaluations against these adapted models demonstrate our method's superiority in accuracy and efficiency. Additionally, we show that the aggregation of cell-level pathological scores from scPSS can be used to predict health conditions at the individual level.
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Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq.
Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.
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From Normal Mucosa to Colorectal Cancer in Lynch Syndrome: Single-Cell Dissection of Cellular Heterogeneity and Communication Networks.
OBJECTIVE: To generate a single-cell atlas of colorectal cancer (CRC) development in Lynch syndrome (LS), and to delineate the associated cellular reprogramming and intercellular communication networks. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on matched normal mucosa, adenoma, and carcinoma tissues obtained from patients with LS. Following quality control and batch-effect correction, we used the Uniform Manifold Approximation and Projection (UMAP) clustering and marker gene analyses to annotate cell types, differential expression analyses to identify stage-specific genes, and GSVA analyses to assess pathway activity. Functional assays with patient-derived organoids and transwell experiments were employed to validate the role of DMBT1. Cell-cell communication networks were inferred using Cell Chat. RESULTS: We identified twelve distinct cell clusters and observed marked shifts in cellular composition during CRC progression, including increased T lymphocytes and macrophages and decreased epithelial cells. Trajectory analyses suggested a potential differentiation path from BEST4+ to CEMIP+/EGFR+ epithelial cells. DMBT1 was consistently down-regulated and its depletion activated the WNT/β-catenin pathway, enhancing organoid growth and cell migration. Expansion of CCR8+ regulatory T cells and C1QC+/IDO1+/IL1B+ macrophages indicated an immunosuppressive and pro-tumorigenic microenvironment. CONCLUSIONS: This study provides a comprehensive single-cell resource for LS-associated CRC and highlights DMBT1 loss, epithelial remodelling, and immune dysregulation as potential therapeutic targets.
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Single-cell transcriptomics identifies a candidate osmoregulatory cell population in the mid-intestine of the saline-alkaline fish Gymnocypris przewalskii.
Saline-alkaline fishes maintain internal homeostasis through effective intestinal ion and acid-base regulation. However, the specific intestinal epithelial cell populations mediating these functions remain poorly characterized. Here, we performed single-cell RNA sequencing on 23,909 mid-intestinal cells from juvenile Gymnocypris przewalskii, a fish native to the saline-alkaline Lake Qinghai, to generate a cell atlas of the mid-intestine. Several major cell classes were identified, including epithelial cells (ECs), enteroendocrine cells (EECs), goblet cells (GCs), and leukocytes (LCs), each exhibiting distinct subpopulation heterogeneity. Within the epithelial cell lineage, we identified a distinct cluster characterized by high expression of best4, cftr, and ca2, suggesting a candidate osmoregulatory cell population (referred to as Best4+ cells). To further evaluate this marker set, we compared fish exposed to freshwater with those exposed to artificial lake water and observed significantly elevated gene expression levels of best4, cftr and ca2 in intestinal cells under lake water conditions. Immunofluorescence detection of BEST4 and CA2 in mid-intestinal sections further provided tissue-level support for the epithelial association of these candidate markers. In addition to this candidate osmoregulatory cell population, the atlas also resolved absorptive epithelial cell states, together with heterogeneity within enteroendocrine cell, goblet cell, and leukocyte lineages. Overall, these findings identify a candidate epithelial cell population relevant to intestinal ion and acid-base regulation in G. przewalskii and clarify the cellular basis of intestinal specialization under saline-alkaline stress.
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Consensus Pituitary Atlas, a scalable resource for annotation, novel marker discovery, and analyses in mouse pituitary gland research.
Previous single-cell profiling studies of the pituitary gland have yielded minimally reproducible insights due to their low statistical power and methodological inconsistencies. To address this, we generate the uniformly pre-processed Consensus Pituitary Atlas (CPA) using all 283 existing mouse pituitary single-cell datasets (∼1.3 million high-quality cells). The CPA reveals cell typing and lineage markers, including low-expression transcripts that previous analyses could not detect. Leveraging the scale of the CPA, we develop machine learning models to automate and standardize cell type annotation and doublet identification for future studies. Utilizing the curated metadata, we identify sex-biased and age-dependent gene expression patterns at cell type resolution. To uncover drivers of cell fates, first we determine consensus cell communication patterns. Second, we use RNA sequencing and chromatin accessibility data to identify transcription factors associated with cell fates across modalities. The epitome platform provides a user-friendly interface with the CPA and allows streamlined analyses.
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FOSL2 drives transcriptional activation of super‑enhancer-regulated DYNC1H1 to promote hypoxia‑induced oral squamous cell carcinoma progression.
Hypoxia is a hallmark of oral squamous cell carcinoma (OSCC) and plays a critical role in driving tumor progression. However, the mechanisms by which OSCC cells respond to hypoxic signaling to promote malignant phenotypes remain unclear. The present study aimed to identify the key regulatory factors governing hypoxic adaptation in OSCC. Based on bulk RNA‑sequencing (RNA‑seq) data from The Cancer Genome Atlas (TCGA) database, hypoxia was identified as a prognostic risk factor for OSCC. Hierarchical clustering revealed 'glycolysis' and 'hypoxia' as the most strongly associated cancer hallmarks. Using single‑cell RNA‑seq data from the Gene Expression Omnibus (GEO) database, 104 hypoxia‑ and glycolysis‑associated genes were identified. Integration of bulk RNA‑seq data from TCGA and GEO databases enabled the development of an optimized prognostic model [stepwise Cox (backward) + elastic network (α=0.8)] through machine learning and highlighted four core genes: Heparan sulfate proteoglycan 2, immunoglobulin superfamily member 3, dihydrouridine synthase 1‑like and dynein cytoplasmic 1 heavy chain 1 (DYNC1H1). Further analysis of chromatin immunoprecipitation sequencing data from GEO database identified DYNC1H1 as the sole super‑enhancer (SE)‑regulated gene among these candidates, characterized by hypoxia‑responsive upregulation. DYNC1H1 knockdown significantly abrogated hypoxia‑induced proliferation, invasion, glucose uptake and lactate production in OSCC cells. Hypoxia‑responsive transcription factor FOS‑like 2 (FOSL2) activated DYNC1H1 transcription by binding to enhancer regions. Functional validation established that the FOSL2/DYNC1H1 axis was a pivotal regulatory hub for hypoxia‑driven malignant phenotypes in OSCC. Overall, DYNC1H1 is an SE‑regulated hypoxia‑responsive gene that undergoes transcriptional activation via FOSL2‑specific binding to typical enhancer and SE regions. The FOSL2/DYNC1H1 axis drives the hypoxia‑induced malignant progression of OSCC. The present study therefore establishes a novel target for precision therapies targeting the hypoxic tumor microenvironment in OSCC.
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Integrative multi-omics suggests core gene dysregulation of histidine metabolism in diabetic kidney disease.
AIMS: To investigate the role of histidine metabolism in diabetic kidney disease (DKD), we analysed histidine metabolism-related genes using an integrative multi-omics approach. METHODS: Untargeted metabolomics was performed on serum from DKD persons and healthy controls, as well as on high glucose-treated and normal glucose-treated HK-2 cells, to assess metabolic alterations. We also analysed publicly available datasets, including GEO transcriptomes, Kidney Precision Medicine Project (KPMP), Kidney Interactive Transcriptomics single-cell data, the Human Protein Atlas (HPA) and Nephroseq, to examine the expression and function of histidine metabolism-related genes. RESULTS: Serum metabolomics confirmed alterations in histidine metabolism. Single-cell analysis revealed that five histidine metabolism-related genes were expressed in proximal tubular epithelial cells and significantly downregulated in DKD. Immunohistochemistry data from the HPA further demonstrated that the corresponding proteins are localized in the proximal tubule (PT). High-glucose treatment suppressed histidine metabolism in HK-2 cells, supporting specific metabolic reprogramming. Clinical correlation analyses using Nephroseq linked the decreased expression of these genes to renal function decline. Collectively, these findings indicate that histidine metabolic disruption is associated with tubular cell dysfunction, suggesting a mechanistic contribution to DKD progression. CONCLUSION: By integrating multi-omics analyses, we suggest that histidine metabolic reprogramming represents a PT pathological event in DKD, providing novel mechanistic insights and potential therapeutic targets.
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Single-cell profiling defines cellular ecosystems and malignant cell state coupling across pan-cancer tumor microenvironments.
Tumors are embedded within complex microenvironments composed of malignant, immune and stromal cells, yet how coordinated cellular organization shapes tumor biology across cancers remains incompletely understood. Here we integrate single-cell transcriptomic profiles from over two million cells spanning 19 human epithelial cancers to construct a comprehensive pan-cancer atlas of tumor microenvironment (TME) organization. We identify cell subsets selectively enriched in tumors compared with normal tissues and show that their relative abundance is associated with clinical outcomes. By clustering tumors according to their cellular composition, we define eight recurrent cellular ecosystem subtypes that capture coordinated variation in immune and stromal architectures across cancer types. Comparative analyses reveal that these ecosystem states are associated with distinct tumor-intrinsic molecular programs, including differences in signaling pathway activity, transcriptional regulation and copy number alterations. We further uncover subtype-specific patterns of ligand-receptor interactions linking malignant cells with immune and stromal compartments and provide independent spatial support for these interactions using spatial transcriptomic data. Together, this work provides a unified framework for tumor ecosystem stratification and highlights cellular organization as a key dimension of tumor heterogeneity with potential implications for precision cancer therapy.
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A Comparative Single-Cell Atlas Reveals Pre-injury Signatures of Superior Healing in Oral Mucosa and Informing a New Paradigm for Wound Management.
Poor surgical wound healing after major surgery could cause serious consequences. Compared to the interventions after complications occur, preoperative assessment and management of tissue healing capacity could optimize outcomes and alleviate patient distress. Notably, oral mucosa heals faster with reduced scarring after injury compared to skin, making it a natural model of superior healing potential. The cellular and molecular networks underlying these divergent healing capacities were valuable to guide the preoperative assessment and management of tissue healing potential. However, these mechanisms have not been fully elucidated due to a lack of comparative studies on healthy homeostatic tissues and the limitation of bulk-RNA sequencing and in vitro experiments in unveiling complex in vivo microenvironments. Herein, single-cell sequencing datasets from healthy oral mucosa and skin were integrated and subjected to comparative analysis as well as in vivo validation to construct a comparative transcriptional atlas. Our results revealed that oral fibroblasts demonstrate higher activation of transcription factors (FOSB, EGR1, FOS, JUNB, JUN, and CEBPB) and transmit distinctive ligand-receptor interactions involving extracellular matrix components and secretory signals that target vascular endothelial cells (ANGPTL, ncWNT, IGF, PTN, MK, LAMININ, COLLAGEN), basal keratinocytes (IGF, MPZ, TENASCIN, MK, COLLAGEN), and immune cells (CXCL, CCL, TGFB). These features are associated with stronger extracellular matrix synthesis capacity and greater capacities of oral fibroblasts in promoting angiogenesis, epithelial regeneration, and creating a pro-healing immune microenvironment. These insights inform a conceptual framework for preoperative assessment and management of tissue’s inherent healing potential to reduce severe complications after major surgeries, potentially shifting the surgical wound management paradigm from post-complication intervention to proactive management.