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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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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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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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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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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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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.
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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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Cardiac Development at a Single-Cell Resolution.
Mammalian cardiac development is a complex, multistage process. Though traditional lineage tracing studies have characterized the broad trajectories of cardiac progenitors, the advent and rapid optimization of single-cell RNA sequencing methods have yielded an ever-expanding toolkit for characterizing heterogeneous cell populations in the developing heart. Importantly, they have allowed for a robust profiling of the spatiotemporal transcriptomic landscape of the human and mouse heart, revealing the diversity of cardiac cells-myocyte and non-myocyte-over the course of development. These studies have yielded insights into novel cardiac progenitor populations, chamber-specific developmental signatures, the gene regulatory networks governing cardiac development, and, thus, the etiologies of congenital heart diseases. Furthermore, single-cell RNA sequencing has allowed for the exquisite characterization of distinct cardiac populations such as the hard-to-capture cardiac conduction system and the intracardiac immune population. Therefore, single-cell profiling has also resulted in new insights into the regulation of cardiac regeneration and injury repair. Single-cell multiomics approaches combining transcriptomics, genomics, and epigenomics may uncover an even more comprehensive atlas of human cardiac biology. Single-cell analyses of the developing and adult mammalian heart offer an unprecedented look into the fundamental mechanisms of cardiac development and the complex diseases that may arise from it.
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A single-cell rice atlas integrates multi-species data to reveal cis-regulatory evolution.
Cis-regulatory elements (CREs) are essential for regulating gene expression, yet their evolutionary dynamics in plants remain elusive. Here we constructed a single-cell chromatin accessibility atlas for Oryza sativa from 103,911 nuclei representing 126 cell states across nine organs. Comparative genomics between O. sativa and 57,552 nuclei from four additional grass species (Zea mays, Sorghum bicolor, Panicum miliaceum and Urochloa fusca) revealed that chromatin accessibility conservation varies with cell-type specificity. Epidermal accessible chromatin regions in the leaf were less conserved compared to other cell types, indicating accelerated regulatory evolution in the L1-derived epidermal layer of O. sativa relative to other species. Conserved accessible chromatin regions overlapping the repressive histone modification H3K27me3 were identified as potentially silencer-like CREs, as deleting these regions led to up-regulation of gene expression. This study provides a comprehensive epigenomic resource for the rice community, demonstrating the utility of a comparative genomics approach that highlights the dynamics of plant cell-type-specific CRE evolution.
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New hypotheses of cell type diversity and novelty from orthology-driven comparative single cell and nuclei transcriptomics in echinoderms.
Cell types are the building blocks of metazoan biodiversity and offer a powerful perspective for inferring evolutionary phenomena. With the development of single-cell transcriptomic techniques, new definitions of cell types are emerging. This allows a conceptual reassessment of traditional definitions of novel cell types and their evolution. Research in echinoderms, particularly sea star and sea urchin embryos has contributed significantly to understanding the evolution of novel cell types, through the examination of skeletogenic mesenchyme and pigment cells, which are found in sea urchin larvae, but not sea star larvae. This paper outlines the development of a gene expression atlas for the bat sea star, Patiria miniata, using single nuclear RNA sequencing (snRNA-seq) of embryonic stages. The atlas revealed 23 cell clusters covering all expected cell types from the endoderm, mesoderm, and ectoderm germ layers. In particular, four distinct neural clusters, an immune-like cluster, and distinct right and left coelom clusters were revealed as distinct cell states. A comparison with Strongylocentrotus purpuratus embryo single-cell transcriptomes was performed using 1:1 orthologs to anchor and then compare gene expression patterns. The equivalent of S. purpuratus piwil3+ Cells were not detected in P. miniata, while the Left Coelom of P. miniata has no equivalent cell cluster in S. purpuratus. These differences may reflect changes in developmental timing between these species. While considered novel morphologically, the Pigment Cells of S. purpuratus map to clusters containing Immune-like Mesenchyme and Neural cells of P. miniata, while the Skeletogenic Mesenchyme of S. purpuratus are revealed as orthologous to the Right Coelom cluster of P. miniata. These results suggest a new interpretation of the evolution of these well-studied cell types and a reflection on the definition of novel cell types.