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A single-cell transcriptome atlas of Lueyang black-bone chicken skin.
As the largest organ of the body, the skin participates in various physiological activities, such as barrier function, sensory function, and temperature regulation, thereby maintaining the balance between the body and the natural environment. To date, compositional and transcriptional profiles in chicken skin cells have not been reported. Here, we report detailed transcriptome analyses of cell populations present in the skin of a black-feather chicken and a white-feather chicken using single-cell RNA sequencing (scRNA-seq). By analyzing cluster-specific gene expression profiles, we identified 12 cell clusters, and their corresponding cell types were also characterized. Subsequently, we characterized the subpopulations of keratinocytes, myocytes, mesenchymal cells, fibroblasts, and melanocytes. It is worth noting that we have identified a subpopulation of keratinocytes involved in pigment granule capture and a subpopulation of melanocytes involved in pigment granule deposition, both of which have a higher cell abundance in black-feather chicken compared to white-feather chicken. Meanwhile, we also compared the cellular heterogeneity features of Lueyang black-bone chicken skin with different feather colors. In addition, we also screened out 12 genes those could be potential markers of melanocytes. Finally, we validated the specific expression of SGK1, WNT5A, CTSC, TYR, and LAPTM5 in black-feather chicken, which may be the key candidate genes determining the feather color differentiation of Lueyang black-bone chicken. In summary, this study first revealed the transcriptome characteristics of chicken skin cells via scRNA-seq technology. These datasets provide valuable information for the study of avian skin characteristics and have important implications for future poultry breeding.
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Time-Varying Gene Expression Network Analysis Reveals Conserved Transition States in Hematopoietic Differentiation between Human and Mouse.
(1) Background: analyses of gene networks can elucidate hematopoietic differentiation from single-cell gene expression data, but most algorithms generate only a single, static network. Because gene interactions change over time, it is biologically meaningful to examine time-varying structures and to capture dynamic, even transient states, and cell-cell relationships. (2) Methods: a transcriptomic atlas of hematopoietic stem and progenitor cells was used for network analysis. After pseudo-time ordering with Monocle 2, LOGGLE was used to infer time-varying networks and to explore changes of differentiation gene networks over time. A range of network analysis tools were used to examine properties and genes in the inferred networks. (3) Results: shared characteristics of attributes during the evolution of differentiation gene networks showed a "U" shape of network density over time for all three branches for human and mouse. Differentiation appeared as a continuous process, originating from stem cells, through a brief transition state marked by fewer gene interactions, before stabilizing in a progenitor state. Human and mouse shared hub genes in evolutionary networks. (4) Conclusions: the conservation of network dynamics in the hematopoietic systems of mouse and human was reflected by shared hub genes and network topological changes during differentiation.
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Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk.
Inflammatory Bowel Disease ( IBD ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response. Genome-wide association studies ( GWAS ) have identified 418 independent genetic risk loci contributing to IBD, yet the majority are noncoding and their mechanisms of action are difficult to decipher. If causal, they likely alter transcription factor ( TF ) binding and downstream gene expression in particular cell types and contexts. To bridge this knowledge gap, we built a novel resource: multiome-seq (tandem single-nuclei ( sn )RNA-seq and chromatin accessibility ( snATAC )-seq) of intestinal tissue from pediatric IBD patients, where anti-TNF response was defined by endoscopic healing. From the snATAC-seq data, we generated a first-time atlas of chromatin accessibility (putative regulatory elements) for diverse intestinal cell types in the context of IBD. For cell types/contexts mediating genetic risk, we reasoned that accessible chromatin will co-localize with genetic disease risk loci. We systematically tested for significant co-localization of our chromatin accessibility maps and risk variants for 758 GWAS traits. Globally, genetic risk variants for IBD, autoimmune and inflammatory diseases are enriched in accessible chromatin of immune populations, while other traits (e.g., colorectal cancer, metabolic) are enriched in epithelial and stromal populations. This resource opens new avenues to uncover the complex molecular and cellular mechanisms mediating genetic disease risk.
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Single cell atlas of canine natural killer cells identifies distinct circulating and tissue resident gene profiles.
INTRODUCTION: Natural killer (NK) cells in mice and humans are key effectors of the innate immune system with complex immunoregulatory functions, and diverse subsets have been identified with distinct characteristics and roles. Companion dogs with spontaneous cancer have been validated as models of human disease, including cancer immunology and immunotherapy, and greater understanding of NK cell heterogeneity in dogs can inform NK biology across species and optimize NK immunotherapy for both dogs and people. METHODS: Here, we assessed canine NK cell populations by single-cell RNA sequencing (scRNAseq) across blood, lung, liver, spleen, and placenta with comparison to human NK cells from blood and the same tissues to better characterize the differential gene expression of canine and human NK cells regarding ontogeny, heterogeneity, patterns of activation, inhibition, and tissue residence. RESULTS: Overall, we observed tissue-specific NK cell signatures consistent with immature NK cells in the placenta, mature and activated NK cells in the lung, and NK cells with a mixed activated and inhibited signature in the liver with significant cross-species homology. DISCUSSION: Together, our results point to heterogeneous canine NK populations highly comparable to human NK cells, and we provide a comprehensive atlas of canine NK cells across organs which will inform future cross-species NK studies and further substantiate the spontaneous canine model to optimize NK immunotherapy across species.
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Benchmarking deep learning methods for biologically conserved single-cell integration.
BACKGROUND: Advancements in single-cell RNA sequencing have enabled the analysis of millions of cells, but integrating such data across samples and methods while mitigating batch effects remains challenging. Deep learning approaches address this by learning biologically conserved gene expression representations, yet systematic benchmarking of loss functions and integration performance is lacking. RESULTS: We evaluate 16 integration methods using a unified variational autoencoder framework, incorporating batch and cell-type information. Results reveal limitations in the single-cell integration benchmarking index (scIB) for preserving intra-cell-type information. To address this, we introduce a correlation-based loss function and enhance benchmarking metrics to better capture biological conservation. Using cell annotations from lung and breast atlases, our approach improves biological signal preservation. We propose a refined integration framework, scIB-E, and metrics that provide deeper insights into the integration process and offer guidance for advanced developments in integrating increasingly complex single-cell data. CONCLUSIONS: This benchmark highlights the potential of deep learning-based approaches for single-cell data integration, emphasizing the importance of biologically informed metrics and improved benchmarking strategies.
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A single-cell atlas and lineage analysis of the adult Drosophila ovary.
The Drosophila ovary is a widely used model for germ cell and somatic tissue biology. Here we use single-cell RNA-sequencing (scRNA-seq) to build a comprehensive cell atlas of the adult Drosophila ovary that contains transcriptional profiles for every major cell type in the ovary, including the germline stem cells and their niche cells, follicle stem cells, and previously undescribed subpopulations of escort cells. In addition, we identify Gal4 lines with specific expression patterns and perform lineage tracing of subpopulations of escort cells and follicle cells. We discover that a distinct subpopulation of escort cells is able to convert to follicle stem cells in response to starvation or upon genetic manipulation, including knockdown of escargot, or overactivation of mTor or Toll signalling.
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A comprehensive prognostic signature for glioblastoma patients based on transcriptomics and single cell sequencing.
PURPOSE: Glioblastoma (GBM) is the most common and deadly brain tumor. We aimed to reveal potential prognostic GBM marker genes, elaborate their functions, and build an effective a prognostic model for GBM patients. METHODS: Through data mining of The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), we screened for significantly differentially expressed genes (DEGs) to calculate risk scores for individual patients. Published data of somatic mutation and copy number variation profiles were analyzed for distinct genomic alterations associated with risk scores. In addition, single-cell sequencing was used to explore the biological functions of the identified prognostic marker genes. By combining risk scores and other clinical features, we built a comprehensive prognostic GBM model. RESULTS: Seven DEGs (CLEC5A, HOXC6, HOXA5, CCL2, GPRASP1, BSCL2 and PTX3) were identified as being prognostic for GBM. Expression of these genes was confirmed in different GBM cell lines using real-time PCR. Risk scores calculated from the seven DEGs revealed prognostic value irrespective of other clinical factors, including IDH mutation status, and were negatively correlated with TP53 expression. The prognostic genes were found to be associated with tumor proliferation and progression based on pseudo-time analysis in neoplastic cells. A final prognostic model was developed and validated with a good performance, especially in geriatric GBM patients. CONCLUSIONS: Using genetic profiles, age, IDH mutation status, and chemotherapy and radiotherapy, we constructed a comprehensive prognostic model for GBM patients. The model has a good performance, especially in geriatric GBM patients.
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Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas.
The use of single-cell RNA sequencing in clinical and translational research is limited by the challenge of identifying cell-type-specific, targetable molecular changes in individual patients and cross-species differences. Here we created an integrated single-cell kidney atlas including over 1 million cells from 140 samples, defining more than 70 conserved cell states in human and rodent models. We developed CellSpectra, a computational tool that quantifies changes in gene expression coordination across cellular functions, which we applied to kidney and lung cancer data. This tool powers our patient-level single-cell functional profiling report, which highlights cell-type-specific changes in the coordination of pathway gene expression in individuals. Our cross-species atlas facilitates the selection of a rodent model that closely reflects the cellular and pathway-level signatures observed in patient samples, advancing the application of single-cell methodologies in clinical precision medicine. Finally, using experimental models, we demonstrate how our informatics approach can be applied for the potential selection of suitable therapeutics.
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Sex-dependent molecular landscape of Alzheimer's disease revealed by large-scale single-cell transcriptomics.
INTRODUCTION: Alzheimer's disease (AD) shows significant sex differences in prevalence and clinical manifestations, but the underlying molecular mechanisms remain unclear. METHODS: This study used a large-scale, single-cell transcriptomic atlas of the human prefrontal cortex to investigate sex-dependent molecular changes in AD. Our approach combined cell type-specific and sex-specific differential gene expression analysis, pathway enrichment, gene regulatory network construction, and cell-cell communication analysis to identify sex-dependent changes. RESULTS: We found significant sex-specific gene expression patterns and pathway alterations in AD. Male astrocytes showed changes in cell death pathways, with RPTOR as a key regulator, while female astrocytes had alterations in Wnt signaling and cell cycle regulation. Cell-cell communication analysis uncovered sex-specific intercellular signaling patterns, with male-specific impacts on apoptosis-related signaling and female-specific alterations in Wnt and calcium signaling. DISCUSSION: This study reveals sex-dependent gene expression patterns, pathway alterations, and intercellular communication changes, suggesting the need for sex-specific approaches in AD research. HIGHLIGHTS: Single-cell transcriptomics reveals significant sex-specific molecular signatures in Alzheimer's disease (AD). Male astrocytes show enhanced modulation of apoptotic and cell death pathways in AD; female astrocytes exhibit distinct alterations in Wnt signaling and cell-cycle regulation. Sex-dimorphic changes in mitochondrial gene expression are observed in excitatory neurons, suggesting divergent energy metabolism profiles may contribute to AD sex differences. RPTOR is identified as a key regulator of male-specific changes in astrocytes, implicating the mechanistic target of rapamycin pathway in sex-dependent AD pathology. New cell-cell communication analyses reveal sex-specific patterns of intercellular signaling, providing insights into how cellular interactions may differentially contribute to AD pathology in males and females.
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Genetic and Molecular Regulation of Neuronal Excitability in Anesthesia and Pain.
Genetic and molecular studies increasingly explain anesthetic and analgesic responses at the level of defined conductances, neuronal populations, and injury states. Their clinical implications depend on distinguishing target engagement from changes in firing and, ultimately, from a measurable therapeutic effect. This review examines recent evidence linking sodium, potassium, calcium, and sensory transduction channels to pain and inhibitory receptor composition, chloride regulation, and arousal circuits to anesthesia. Selective NaV1.8 inhibition provides an unusually complete translational example, spanning channel pharmacology, recordings from human sensory neurons, and randomized postoperative trials. Nevertheless, residual firing during substantial NaV1.8 inhibition and divergent explanations of NaV1.7-null analgesia show why the effects of a drug cannot be inferred directly from a genetic phenotype. Structural studies now resolve anesthetic binding and gating mechanisms in GABAA, NMDA, and HCN1 channels, while circuit experiments distinguish emergence, immobility, and antinociception. Neuroimmune signaling extends beyond cytokine-driven excitation to include inhibitory synapse removal, sex-dependent cellular pathways, and active pain resolution. Chromatin regulation, RNA modification, and neuronal senescence can stabilize altered excitability after injury. Human ganglion and spinal cord atlases, Patch-seq, and sensory assembloids provide more relevant models for connecting these mechanisms across scales, although none substitute for clinical validation. We emphasize research published from 2021 onward, retaining foundational studies where they establish a causal mechanism. The resulting evidence supports cell- and state-specific intervention rather than a single molecular explanation of anesthesia or chronic pain and identifies practical requirements for translating genetic discovery into perioperative treatment.