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Single-cell transcriptomic profiling of halo nevi and normal nevi reveals CD8+ T cell activation in melanocytic autoimmunity.
Halo nevus is an autoimmune skin disorder characterized by autoreactive CD8+ T cells that target and destroy melanocytes, resulting in a depigmented perilesional halo. Yet the upstream triggers of this activation remain unclear. Here, we performed single-cell RNA sequencing (scRNA-seq) on halo nevi and normal nevi, identifying ten canonical skin cell types and five melanocyte subclusters. Integrated scRNA-seq and immunofluorescence staining revealed that halo nevi, unlike normal nevi, exhibit robust infiltration of activated, type II interferon-responsive CD8+ T cells. Among melanocyte subclusters, one subset (Cluster 3) showed pronounced upregulation of antigen-presenting molecules, interferon-stimulated genes, and chemokines. Comparative analyses further demonstrated that melanocytes in halo nevi increased oxidative phosphorylation, interferon-driven pathways, and antigen processing and presentation. Together, these data provide single-cell transcriptional atlas of halo nevi versus normal nevi, reveal melanocyte-intrinsic and immune-mediated mechanisms underlying autoimmune melanocyte destruction, and highlight pathogenic pathways shared with vitiligo that may inform the development of targeted therapies.
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Exploiting RAG1-deficient chickens to construct a single-cell transcriptional atlas of chicken heterophils during inflammation.
INTRODUCTION: Avian heterophils naturally lack myeloperoxidase (MPO), making chicken a unique evolutionary model for investigating MPO-independent innate immune strategies. However, the molecular programs governing heterophil maturation and activation remain largely unexplored. METHODS: Using single-cell RNA sequencing in a RAG1-deficient chicken model, we mapped heterophil maturation during emergency granulopoiesis and identified eight transcriptionally distinct subsets (H0-H7). We inferred a developmental trajectory spanning transcriptionally distinct heterophil subsets, from early subsets enriched for genes associated with translational activity, oxidative phosphorylation, and survival-related programs to terminal subsets enriched for effector-associated gene signatures. RESULTS AND DISCUSSION: Unlike mammalian neutrophils, heterophils did not sequentially deploy canonical granule gene expression; instead, maturation was marked by progressive MMP9 upregulation. Transcriptional profiling revealed a prominent enrichment of nitric oxide signaling pathway, with basal level of NADPH oxidase expression. Trajectory analysis indicated that cells transitioned from interferon-stimulated gene (ISG)-dominant states to effector profiles capable of degranulation, phagocytosis, and extracellular trap formation. Terminal subsets showed exhibited transcriptional signatures associated with enhanced extracellular matrix remodeling, acute phase responses, and nutritional immunity. Together, these findings provide a detailed single-cell atlas of avian heterophils, offering valuable evolutionary insights into the developmental trajectories and transcriptional heterogeneity of innate immune cells in a naturally MPO-independent species.
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Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.
INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.
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Insights into speckled eggs provided by single-cell transcriptomics in high-yield laying hens.
Eggshell quality is a major concern in the poultry industry. The speckle phenotype in eggshell affects eggshell color, thickness, and Haugh Unit (HU) and significantly affects the hatchability rate. The rate of speckled eggs increases during the late-laying period, which can lead to notable economic losses. Eggshell speckles have medium heritability; however, the genetic architecture of speckled eggshells remains unclear. In this study, the chickens were divided into two groups based on the rate of egg speckling. Uterine tissues were collected and analyzed using 10X Genomics single-cell RNA sequencing to delineate the molecular signatures of the various cell types. A total of 10 cell types were identified: immune (B, T, monocyte macrophages, and plasma cells), endothelial, epithelial, stromal, neuroendocrine, serous, and protoporphyrin IX pigment cells. The trends of cell type distributions were consistent in two groups. Functional enrichment analysis showed that unique biological processes, such as IgA production and Toll-like C-type lectin receptor signaling pathways, were enriched in immune cells, whereas TGF-beta, Wnt, and NOD-like receptor signaling pathways were enriched in epithelial cells. Subsequently, we identified differentially expressed genes in the two groups, among which SPP1 (secreted phosphoprotein 1) was down-regulated in all cell types in the speckled group. The cellular interaction signals of speckled group were significantly enhanced compared to the control group. The cell proliferation and differentiation pathways mediated by WNT, SPP1, and BMP were inhibited and the interaction related to cell migration was activated in the speckled group. This study explored cell heterogeneity, extended prior findings by adding single-cell resolution atlas, and identified cell type specific patterns that can guide future functional studies of speckled eggs.
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Integrated single-cell and transcriptome sequencing data reveal the value of AOC1 in gliomas tumor microenvironment and prognosis.
BACKGROUND: Glioma is a primary brain tumor that is frequently encountered in humans, with malignant gliomas representing an exceptionally aggressive subtype. The role of AOC1 in the progression of various human tumors has been documented; however, its involvement in gliomas (including distinct WHO grades, namely WHO Grade IV glioblastoma multiforme [GBM] and WHO Grades II–III low-grade gliomas [LGG]), remains largely unexplored. METHODS: We analyzed RNA sequencing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases to assess AOC1 expression. We performed transcriptomic analysis and differential gene expression studies based on AOC1 levels in TCGA sequencing data. Functional analyses exploring the signaling pathways involved in AOC1. CIBERSORT, single-sample Gene Set Enrichment Analysis (ssGSEA), and ESTIMATE were employed to estimate the tumor microenvironment in gliomas. Analyzing the single-cell sequencing data further revealed the value of AOC1 played in intercellular communication. Finally, the mechanism of AOC1 involvement in gliomas was verified by cellular and molecular experiments. RESULTS: High tumor expression of AOC1 is associated with lower patient survival. Our analyses revealed that cytokine signaling pathways are significantly suppressed in AOC1-high GBMLGG tissues. Q-PCR results corroborated that AOC1 inhibits IL17, IL6 and IL1β secretion in GBM cells. The CIBERSORT analysis showed a significant increase in the proportion of M2 macrophages in AOC1-high cancer tissues. Cell-cell communication analysis indicated that the MIF signaling network between AOC1-high GBMLGG tumor cells and macrophages is notably weakened. Additionally, AOC1 silencing directly delays the proliferation and migration of GBMLGG cells in vitro. CONCLUSIONS: Tumor AOC1 levels can serve as a molecular marker for diagnosis and molecular subtyping. As a secreted protein, AOC1 holds promise as a new target for non-invasive molecular diagnosis and precision therapy in GBMLGG.
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Characterization of the Prognosis and Tumor Microenvironment of Cellular Senescence-related Genes through scRNA-seq and Bulk RNA-seq Analysis in GC.
BACKGROUND: Cellular senescence (CS) is thought to be the primary cause of cancer development and progression. This study aimed to investigate the prognostic role and molecular subtypes of CS-associated genes in gastric cancer (GC). MATERIALS AND METHODS: The CellAge database was utilized to acquire CS-related genes. Expression data and clinical information of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Patients were then grouped into distinct subtypes using the "Consesus- ClusterPlus" R package based on CS-related genes. An in-depth analysis was conducted to assess the gene expression, molecular function, prognosis, gene mutation, immune infiltration, and drug resistance of each subtype. In addition, a CS-associated risk model was developed based on Cox regression analysis. The nomogram, constructed on the basis of the risk score and clinical factors, was formulated to improve the clinical application of GC patients. Finally, several candidate drugs were screened based on the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset. RESULTS: According to the cluster result, patients were categorized into two molecular subtypes (C1 and C2). The two subtypes revealed distinct expression levels, overall survival (OS) and clinical presentations, mutation profiles, tumor microenvironment (TME), and drug resistance. A risk model was developed by selecting eight genes from the differential expression genes (DEGs) between two molecular subtypes. Patients with GC were categorized into two risk groups, with the high-risk group exhibiting a poor prognosis, a higher TME level, and increased expression of immune checkpoints. Function enrichment results suggested that genes were enriched in DNA repaired pathway in the low-risk group. Moreover, the Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that immunotherapy is likely to be more beneficial for patients in the low-risk group. Drug analysis results revealed that several drugs, including ML210, ML162, dasatinib, idronoxil, and temsirolimus, may contribute to the treatment of GC patients in the high-risk group. Moreover, the risk model genes presented a distinct expression in single-cell levels in the GSE150290 dataset. CONCLUSION: The two molecular subtypes, with their own individual OS rate, expression patterns, and immune infiltration, lay the foundation for further exploration into the GC molecular mechanism. The eight gene signatures could effectively predict the GC prognosis and can serve as reliable markers for GC patients.
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Bone formation niche dysfunction in osteoporosis: insights from single-cell and spatial transcriptomic studies.
Bone formation is spatially organized, yet the molecular architecture of the bone formation niche has only recently become accessible. Osteoporosis is a major skeletal disorder characterized by bone loss, microarchitectural deterioration, and increased fracture risk. Although antiresorptive and anabolic therapies are effective, a key conceptual gap remains: the cellular components of bone formation are increasingly well defined, but how these cells are spatially arranged and coordinated in health and disease is less understood. Recent advances in single-cell RNA sequencing and spatial transcriptomics now allow transcriptome-wide mapping of bone-forming microenvironments in intact human and mouse tissues. This review synthesizes emerging findings into a spatial framework for understanding bone formation and osteoporosis. We trace the conceptual lineage from the bone-remodeling compartment canopy hypothesis to its molecular refinement through recent single-cell and spatial atlases. We integrate evidence for mesenchymal stromal cell heterogeneity and spatial niche organization to propose a three-zone model of the bone formation niche, comprising a canopy or marrow-side stromal zone, a reversal or transition zone, and a bone-surface formation zone. We further propose "spatial de-zonation" as a hypothesis-generating framework for osteoporosis, referring to the potential erosion of spatial gradients that normally separate osteogenic, stromal, vascular, and adipogenic domains. Importantly, the available human spatial atlases were generated predominantly from osteoarthritic femoral heads and therefore provide reference maps rather than direct evidence of osteoporosis-specific niche disorganization. Finally, we consider niche-restorative therapeutic strategies that may move beyond targeting individual cell types toward restoring the architectural integrity of the bone formation niche.
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Identification of mitochondria-related genes in calcific aortic valve disease by integrated analysis of single-cell and bulk transcriptomic atlases.
Calcific aortic valve disease (CAVD) is a highly prevalent heart valve disorder in which mitochondria act as critical regulators of calcification, yet their precise pathogenic mechanisms remain unclear. To elucidate these mechanisms, we integrated single-cell transcriptomic datasets comparing normal and calcified human aortic valves to identify 200 differentially expressed mitochondria-related genes (DE-MRGs), each exhibiting distinct expression patterns across diverse cellular subpopulations. Pseudotime trajectory analysis revealed 18 DE-MRGs with dynamic changes during the endothelial-to-mesenchymal transition, and intercellular communication analysis highlighted enhanced signaling between valve interstitial cells (VICs) and macrophages. Specifically, we hypothesized that distinct mitochondrial hubs may modulate these interactions. Using machine learning and bulk transcriptomic data, we identified microsomal glutathione S-transferase 1 (MGST1), an enzyme located on the outer mitochondrial and endoplasmic reticulum membranes, as a hub gene with high predictive performance. Subsequent validation confirmed that MGST1 is functionally involved in calcification, as its expression was markedly upregulated under calcifying conditions. Molecular docking further predicted that ritlecitinib exhibits the highest binding affinity for MGST1, and this molecule was shown to ameliorate calcification. In conclusion, this study delineates a comprehensive molecular network of MRGs in CAVD pathogenesis and identifies MGST1 as a mitochondria‑related hub gene that is upregulated in CAVD and functionally promotes calcification in vitro.
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Single-nucleus RNA sequencing dataset of diverse tissues from wild-type monkey and Tau-P301L transgenic monkey.
Utilizing non-human primates to study the role of human Tau and its related pathologies is logical and important due to their closer similarity to human brain structure and function. In our earlier research, we generated a transgenic cynomolgus monkey model expressing Tau (P301L) through lentiviral infection of monkey embryos. These monkeys exhibited age-dependent neurodegeneration and motor dysfunction. Single-nucleus RNA sequencing (snRNA-seq) is a powerful and promising technique for elucidating the cellular complexity and pathology across different tissues. However, single-cell data from non-human primate models of Tau pathology are currently nonexistent. In this study, we performed snRNA-seq on the hippocampus, striatum, and spinal cord of Tau (P301L) monkey, providing the first snRNA-seq atlas of multiple tissue regions in a non-human primate model that simulates human tauopathies. This will offer crucial data references for cross-species single-cell level studies of tau and its related pathologies.
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Pareto optimality reveals an atlas of cellular archetypes.
We sought to identify universal organizing principles behind phenotypic variation within cell types. Pareto optimality describes how trade-offs between optimal solutions account for variation, predicting that the boundary points of a data distribution reflect specialized functions. We hypothesized that transcriptomic variation was explained by Pareto optimality across all cell types. We then used the Tabula Sapiens Atlas of single-cell RNA sequencing across cell types and tissues in the human body to test this hypothesis and found that most cell types adhere to this theory. This enabled us to use this principled method to characterize the functions performed by each cell type. These phenotypes are derived from an unbiased approach and do not incorporate ideas from existing biological models or theories, and yet in many cases they recapitulate our understanding of the functions of major cell types. Ultimately, we conclude that multiobjective optimization broadly shapes the observed phenotypic variation within cell types. This finding enables us to write explicit representations of the low-dimensional manifolds on which transcriptomes of single cells reside. This can inform the design of the next generation of virtual cell language models, which aim to statistically learn low-dimensional transcriptomic manifolds.