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Spatial, transcriptomic and epigenomic analyses link dorsal horn neurons to chronic pain genetic predisposition.
Key mechanisms underlying chronic pain occur within the neural circuitry of the dorsal horn. Recent genome-wide association studies (GWAS) have identified genetic variants associated with the predisposition to chronic pain. However, most of these variants lie in regulatory non-coding regions that have so far not been linked to spinal cord function. Here, we take a multi-species approach to determine whether chronic pain variants impact regulatory elements of dorsal horn neurons. We first built a more comprehensive single cell atlas; filling gaps by generating a high-quality Rhesus macaque atlas and integrating it with human and mouse. With cellular-resolution spatial transcriptomics, we mapped the laminar distributions of the resulting species-conserved neuron subtypes, uncovering an unexpected organization. Lastly, we generated a mouse single-nucleus open chromatin atlas to partition the heritability of chronic pain traits. From this, we identified strong, selective associations between specific, conserved neuron subtypes and major forms of chronic pain.
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Cascading effects of hypobaric hypoxia on the testis: insights from a single-cell RNA sequencing analysis.
Most mammals tolerate exposure to hypobaric hypoxia poorly as it may affect multiple regulatory mechanisms and inhibit cell proliferation, promote apoptosis, limit tissue vascularization, and disrupt the acid-base equilibrium. Here, we quantified the functional state of germ cell development and demonstrated the interaction between the germ and somatic cells via single-cell RNA sequencing (scRNA-seq). The present study elucidated the regulatory effects of hypobaric hypoxia exposure on germ cell formation and sperm differentiation by applying enrichment analysis to genomic regions. Hypobaric hypoxia downregulates the genes controlling granule secretion and organic matter biosynthesis, upregulates tektin 1 (TEKT1) and kinesin family member 2C (KIF2C), and downregulates 60S ribosomal protein 11 (RPL11) and cilia- and flagella-associated protein 206 (CFAP206). Our research indicated that prosaposin-G protein-coupled receptor 37 (PSAP-GPR37) ligands mediate the damage to supporting cells caused by hypobaric hypoxic exposure. The present work revealed that hypoxia injures peritubular myoid (PTM) cells and spermatocytes in the S phase. It also showed that elongating spermatids promote maturation toward the G2 phase and increase their functional reserve for sperm-egg binding. The results of this study provide a theoretical basis for future investigations on prophylactic and therapeutic approaches toward protecting the reproductive system against the harmful effects of hypobaric hypoxic exposure.
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Single-Cell RNA Sequencing Reveals Cellular Heterogeneity and Microenvironmental Remodeling in Human Ureteral Scar Stricture Tissue.
PURPOSE: This study aimed to construct a comprehensive single-cell transcriptomic atlas of human ureteral scar stricture tissue using single-cell RNA sequencing (scRNA-seq), to uncover cellular heterogeneity, subpopulation dynamics, and intercellular communication networks. METHODS: Ureteral tissues were collected from three normal controls (CTR) and three patients with ureteral scar stricture (US). Single-cell suspensions were prepared using the MobiNova-100 platform and sequenced on the Illumina NovaSeq 6000 system. Data were analyzed using Seurat, Harmony, Monocle2 (for pseudotime trajectory analysis), CellChat (for cell-cell communication), and SCP (for GO/KEGG enrichment). Key findings were validated by multiplex immunofluorescence (IF) and immunohistochemistry (IHC). RESULTS: Eleven major cell types were identified, including epithelial, stromal, endothelial, and immune cells, each comprising distinct subpopulations. Compared to CTR tissues, US tissues exhibited an increased proportion of S100A8+ and MT1E+ basal epithelial cells with pro-inflammatory characteristics. Fibroblasts displayed substantial heterogeneity, with expansion of inflammatory fibroblasts and smooth muscle cell subsets. Endothelial cells (ECs) showed upregulated inflammatory and antigen presentation pathways. Macrophages exhibited mixed M1/M2 polarization, with enrichment of APOE+ and APOBEC3A+ subsets. Additionally, Th17, Treg, and CD8+ T cell populations were elevated. Cell-cell communication analysis revealed enhanced signaling among fibroblasts, ECs, and immune subsets, particularly via PERIOSTIN, collagen, and laminin pathways. CONCLUSION: This study presents the first high-resolution single-cell atlas of ureteral scar stricture tissue, revealing profound cellular heterogeneity and remodeling of the immune-stromal-epithelial landscape. The findings also highlight intensified intercellular communication within the fibrotic microenvironment, offering novel insights into disease pathogenesis and potential therapeutic targets.
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Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients.
BACKGROUND: Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC. OBJECTIVE: To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients. DESIGN, SETTING, AND PARTICIPANTS: We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples. INTERVENTION: Radical or partial nephrectomy on untreated ccRCC patients. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters. RESULTS AND LIMITATIONS: We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients. CONCLUSIONS: We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management. PATIENT SUMMARY: We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.
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A single-cell transcriptome atlas profiles early organogenesis in human embryos.
The early window of human embryogenesis is largely a black box for developmental biologists. Here we probed the cellular diversity of 4-6 week human embryos when essentially all organs are just laid out. On the basis of over 180,000 single-cell transcriptomes, we generated a comprehensive atlas of 313 clusters in 18 developmental systems, which were annotated with a collection of ontology and markers from 157 publications. Together with spatial transcriptome on embryonic sections, we characterized the molecule and spatial architecture of previously unappreciated cell types. Combined with data from other vertebrates, the rich information shed light on spatial patterning of axes, systemic temporal regulation of developmental progression and potential human-specific regulation. Our study provides a compendium of early progenitor cells of human organs, which can serve as the root of lineage analysis in organogenesis.
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Unveiling Novel Double-Negative Prostate Cancer Subtypes Through Single-Cell RNA Sequencing Analysis.
Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into disease heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising both publicly available cohorts and data generated by our research team, and established the HuPSA (Human Prostate Single cell Atlas) and the MoPSA (Mouse Prostate Single cell Atlas) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression, and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution allowed for the re-classification of human PCa specimens, validating the presence of these novel subtypes. Leveraging these findings, we developed a user-friendly web application, "HuPSA-MoPSA" (https://pcatools.shinyapps.io/HuPSA-MoPSA/), for visualizing gene expression across all newly-established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.
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ChatMDV: reducing technical barriers in bioinformatics analysis using large language models.
BACKGROUND: The rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable of managing complex biological data. Tools such as Multi-Dimensional Viewer (MDV) offer comprehensive interfaces for data exploration but often require advanced computational expertise and manual configuration to generate visualisation outputs, limiting accessibility for many users. RESULTS: We present ChatMDV, a natural language interface integrated with MDV that enables users to generate high-quality, interactive visualisations and analyses through natural language commands. ChatMDV employs a retrieval-augmented generation pipeline in combination with large language models to translate user queries into executable, reproducible Python code and interactive output. This conversational layer facilitates both exploratory and targeted analyses in diverse biological domains. We demonstrate ChatMDV's capabilities using 3 datasets of increasing complexity: the Peripheral Blood Mononuclear Cells 3K single-cell RNA-sequencing (scRNA-seq) dataset, the lung cancer atlas scRNA-seq dataset included in the Human Cell Atlas, and the longitudinal TAURUS study scRNA-seq dataset. Across all use cases, ChatMDV produced high-quality, reproducible visualisations from simple natural language queries, achieving a high semantic success rate between 79% and 97% when visualising the datasets. CONCLUSIONS: By bridging the gap between natural language processing and bioinformatics visualisation, ChatMDV reduces technical barriers, enhances reproducibility, and supports more inclusive scientific inquiry. Its modular design and adherence to Findability, Accessibility, Interoperability, and Reuse (FAIR) principles make it a scalable and adaptable framework for accelerating biological data analysis.
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An atlas of gene regulatory elements in adult mouse cerebrum.
The mammalian cerebrum performs high-level sensory perception, motor control and cognitive functions through highly specialized cortical and subcortical structures1. Recent surveys of mouse and human brains with single-cell transcriptomics2-6 and high-throughput imaging technologies7,8 have uncovered hundreds of neural cell types distributed in different brain regions, but the transcriptional regulatory programs that are responsible for the unique identity and function of each cell type remain unknown. Here we probe the accessible chromatin in more than 800,000 individual nuclei from 45 regions that span the adult mouse isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to map the state of 491,818 candidate cis-regulatory DNA elements in 160 distinct cell types. We find high specificity of spatial distribution for not only excitatory neurons, but also most classes of inhibitory neurons and a subset of glial cell types. We characterize the gene regulatory sequences associated with the regional specificity within these cell types. We further link a considerable fraction of the cis-regulatory elements to putative target genes expressed in diverse cerebral cell types and predict transcriptional regulators that are involved in a broad spectrum of molecular and cellular pathways in different neuronal and glial cell populations. Our results provide a foundation for comprehensive analysis of gene regulatory programs of the mammalian brain and assist in the interpretation of noncoding risk variants associated with various neurological diseases and traits in humans.
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SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing.
BACKGROUND: Single-cell RNA-sequencing is revolutionising the study of cellular and tissue-wide heterogeneity in a large number of biological scenarios, from highly tissue-specific studies of disease to human-wide cell atlases. A central task in single-cell RNA-sequencing analysis design is the calculation of cell type-specific genes in order to study the differential impact of different replicates (e.g. tumour vs. non-tumour environment) on the regulation of those genes and their associated networks. The crucial task is the efficient and reliable calculation of such cell type-specific 'marker' genes. These optimise the ability of the experiment to isolate highly-specific cell phenotypes of interest to the analyser. However, while methods exist that can calculate marker genes from single-cell RNA-sequencing, no such method places emphasise on specific cell phenotypes for downstream study in e.g. differential gene expression or other experimental protocols (spatial transcriptomics protocols for example). Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA-sequencing data which reliably characterise highly-specific and niche populations of cells in numerous different biological data-sets. RESULTS: SMaSH extracts robust and biologically well-motivated marker genes, which characterise a given single-cell RNA-sequencing data-set better than existing computational approaches for general marker gene calculation. We demonstrate the utility of SMaSH through its substantial performance improvement over several existing methods in the field. Furthermore, we evaluate the SMaSH markers on spatial transcriptomics data, demonstrating they identify highly localised compartments of the mouse cortex. CONCLUSION: SMaSH is a new methodology for calculating robust markers genes from large single-cell RNA-sequencing data-sets, and has implications for e.g. effective gene identification for probe design in downstream analyses spatial transcriptomics experiments. SMaSH has been fully-integrated with the ScanPy framework and provides a valuable bioinformatics tool for cell type characterisation and validation in every-growing data-sets spanning over 50 different cell types across hundreds of thousands of cells.
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Single-Nucleus RNA Sequencing Reveals Cellular Heterogeneity and Trajectories of Lineage Differentiation during Chicken Skeletal Muscle Development.
Understanding chicken skeletal muscle development is crucial for meat production, yet its cellular and molecular mechanisms are not fully understood. To address this, we built a single-nucleus RNA sequencing atlas of chicken skeletal muscle across key developmental stages (E4-P30) using 111,328 nuclei. We identified 22 cell populations, including nine myogenic ones, revealing extensive heterogeneity and transcriptional diversity. Myonuclei were classified into three fiber-type-associated subpopulations: FGF13+ type IIA fast-twitch, MYH1F+ type IIB fast-twitch, and MYH7B+ type I slow-twitch. Pseudotime analysis reconstructed the developmental trajectory from myogenic progenitors to mature myofibers and highlighted RBM24 as a pivotal regulator. Functional validation confirmed that RBM24 promotes myoblast differentiation while inhibiting proliferation. We further identified putative fiber-type-specific regulators (e.g., TBX15 for IIA, TNNT3 for IIB, and FGFRL1 for I). This study delineates myogenic lineage development and identifies key regulatory genes, offering valuable insights into chicken skeletal muscle biology.