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A Single-Nucleus Transcriptomic Atlas of the Mouse Lumbar Spinal Cord: Functional Implications of Non-Coding RNAs.
The adult lumbar spinal cord plays a critical role in locomotor control and somatosensory integration, whose transcriptional architecture under physiological conditions has been characterized in various studies with restricted numbers of individuals (up to four). Here, we present an integrative single-nucleus RNA sequencing (snRNA-seq) atlas of the healthy adult mouse lumbar spinal cord, assembled from over 86,000 nuclei from 16 samples across five public datasets. Using a harmonized computational pipeline, we identify all major spinal cell lineages and resolve 17 transcriptionally distinct neuronal subtypes. A central novelty of our approach is the systematic inclusion of non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and pseudogenes. By comparing transcriptomic analyses based on coding-only, non-coding-only, and combined gene sets, we show that ncRNAs, despite accounting to a 10% of the recorded information of each cell, contribute to cell type-specific signatures. This resource offers a high-resolution, ncRNA-inclusive reference for the adult spinal cord and provides a foundation for future studies on spinal plasticity, injury, and regeneration.
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PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets.
Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications.
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Single-cell sequencing of the vermiform appendix during development identifies transcriptional relationships with appendicitis in preschool children.
BACKGROUND: The development of the human vermiform appendix at the cellular level, as well as its function, is not well understood. Appendicitis in preschool children, although uncommon, is associated with a high perforation rate and increased morbidity. METHODS: We performed single-cell RNA sequencing (scRNA-seq) on the human appendix during fetal and pediatric stages as well as preschool-age inflammatory appendices. Transcriptional features of each cell compartment were discussed in the developing appendix. Cellular interactions and differentiation trajectories were also investigated. We compared scRNA-seq profiles from preschool appendicitis to those of matched healthy controls to reveal disease-associated changes. Bulk transcriptomic data, immunohistochemistry, and real-time quantitative PCR were used to validate the findings. RESULTS: Our analysis identified 76 cell types in total and described the cellular atlas of the developing appendix. We discovered the potential role of the BMP signaling pathway in appendiceal epithelium development and identified HOXC8 and PITX2 as the specific regulons of appendix goblet cells. Higher pericyte coverage, endothelial angiogenesis, and goblet mucus scores together with lower epithelial and endothelial tight junction scores were found in the preschool appendix, which possibly contribute to the clinical features of preschool appendicitis. Preschool appendicitis scRNA-seq profiles revealed that the interleukin-17 signaling pathway may participate in the inflammation process. CONCLUSIONS: Our study provides new insights into the development of the appendix and deepens the understanding of appendicitis in preschool children.
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snRNA-seq reveals key transcription factors in the inflammatory response of microglia after intracerebral hemorrhage.
Intracerebral hemorrhage (ICH) is a severe neurological disease with limited treatment options. Microglia, the primary immune cells of the brain, contribute significantly to secondary injury following ICH, exhibiting heterogeneity in response across different stages of ICH. However, the exact mechanisms regulating this process remain incompletely elucidated. In this study, we utilized single-nucleus RNA sequencing (snRNA-seq) in conjunction with the ICH model of mice to establish a comprehensive single-nucleus transcriptomic atlas for ICH and further elucidate its pathogenesis to identify potential therapeutic targets. We identified five principal brain clusters with cell-type specific gene expression patterns. Focused on microglia and inflammation-associated subgroup, we identified 8 distinct microglia subtypes and the key transcription factors (TFs) involved in the inflammatory response of microglia following ICH, including Stat2, Stat1, Irf7, Nfkb1, Etv6, Cebpb, Batf, and Bach1. Notably, Stat2 and Stat1 were identified as the most central TFs. In vitro experiments revealed that inhibiting Stat2 and Stat1 can attenuate the inflammatory response in microglia after ICH. Overall, our findings enhance the understanding of cell responses in ICH and highlight potential therapeutic targets, specifically suggesting that inhibiting STAT1 and STAT2 could mitigate microglial-mediated inflammation in ICH.
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The integration of single-cell and bulk RNA-seq atlas reveals ERS-mediated acinar cell damage in acute pancreatitis.
BACKGROUND: Acute pancreatitis (AP) is a clinically common acute abdominal disease, whose pathogenesis remains unclear. The severe patients usually have multiple complications and lack specific drugs, leading to a high mortality and poor outcome. Acinar cells are recognized as the initial site of AP. However, there are no precise single-cell transcriptomic profiles to decipher the landscape of acinar cells during AP, which are the missing pieces of jigsaw we aimed to complete in this study. METHODS: A single-cell sequencing dataset was used to identify the cell types in pancreas of AP mice and to depict the transcriptomic maps in acinar cells. The pathways' activities were evaluated by gene sets enrichment analysis (GSEA) and single-cell gene sets variation analysis (GSVA). Pseudotime analysis was performed to describe the development trajectories of acinar cells. We also constructed the protein-protein interaction (PPI) network and identified the hub genes. Another independent single-cell sequencing dataset of pancreas samples from AP mice and a bulk RNA sequencing dataset of peripheral blood samples from AP patients were also analyzed. RESULTS: In this study, we identified genetic markers of each cell type in the pancreas of AP mice based on single-cell sequencing datasets and analyzed the transcription changes in acinar cells. We found that acinar cells featured acinar-ductal metaplasia (ADM), as well as increased endocytosis and vesicle transport activity during AP. Notably, the endoplasmic reticulum stress (ERS) and ER-associated degradation (ERAD) pathways activated by accumulation of unfolded/misfolded proteins in acinar cells could be pivotal for the development of AP. CONCLUSION: We deciphered the distinct roadmap of acinar cells in the early stage of AP at single-cell level. ERS and ERAD pathways are crucially important for acinar homeostasis and the pathogenesis of AP.
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A spatiotemporal atlas of mouse liver homeostasis and regeneration.
The mechanism by which mammalian liver cell responses are coordinated during tissue homeostasis and perturbation is poorly understood, representing a major obstacle in our understanding of many diseases. This knowledge gap is caused by the difficulty involved with studying multiple cell types in different states and locations, particularly when these are transient. We have combined Stereo-seq (spatiotemporal enhanced resolution omics-sequencing) with single-cell transcriptomic profiling of 473,290 cells to generate a high-definition spatiotemporal atlas of mouse liver homeostasis and regeneration at the whole-lobe scale. Our integrative study dissects in detail the molecular gradients controlling liver cell function, systematically defining how gene networks are dynamically modulated through intercellular communication to promote regeneration. Among other important regulators, we identified the transcriptional cofactor TBL1XR1 as a rheostat linking inflammation to Wnt/β-catenin signaling for facilitating hepatocyte proliferation. Our data and analytical pipelines lay the foundation for future high-definition tissue-scale atlases of organ physiology and malfunction.
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The lncRNA myocardial infarction associated transcript-centric competing endogenous RNA network in non-small-cell lung cancer.
BACKGROUND: The leading cause of death for cancer is lung cancer, of which the majority subtype is non-small cell lung cancer (NSCLC). Recent studies have shown long non-coding RNAs are transcribed and contribute to cancer. Previous study has shown that a few single nucleotide polymorphisms (SNPs) in myocardial infarction associated transcript (MIAT) were associated with some diseases or function as competing endogenous RNA (ceRNA) in some cancer. PATIENTS AND METHODS: We performed bioinformatic methods for analyzing RNA-seq and miRNA-seq data of NSCLC from The Cancer Genome Atlas database. 1352 NSCLC patients and 1320 cancer-free controls for genotyping, and dual luciferase reporter assay, real-time PCR are performed in A549 and H1975 lung cancer cell lines. Results are analyzed by SPSS v16.0. RESULTS: In the present study, we focus on the role of over-expression MIAT in NSCLC. We confirmed that rs1061451 T>C (allele odds ratio = 0.22; P < 0.01) was associated with NSCLC. Furthermore, we constructed MIAT-centric ceRNA network, and three mRNAs (MYO1B, SGK1 and WNT9A) was identified as targets by MIAT via miR-133a-5p. CONCLUSION: C-containing genotypes of MIAT rs1061451 were protective factor of NSCLC, and MIAT, which may act as ceRNA via miR-133a-5p, modulated MYO1B, SGK1 and WNT9A expression level.
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A scalable framework for single-cell eQTL mapping uncovers genetic regulators of meat production traits in pigs.
BACKGROUND: Genetic determinants that regulate molecular phenotypes and complex traits often act in a highly context-dependent manner, and the underlying cell-type-specific regulatory mechanisms remain incompletely understood. RESULTS: In this study, we analyzed 42 single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) datasets from pig skeletal muscle. Through systematic benchmarking, we optimized a robust workflow for SNP calling and genotype imputation tailored to sc/snRNA-seq data, achieving high accuracy and computational efficiency. We constructed a comprehensive single-cell atlas of skeletal muscle that delineates cellular components and developmental trajectories, and identified 5,020 significant single-cell expression quantitative trait loci (eQTLs). By integrating phenotypic data from the PigGTEx project and performing phenome-wide association studies (PheWAS), we further pinpointed three candidate loci significantly associated with meat quality and growth traits whose effects were dependent on cell type. CONCLUSIONS: This work offers a scalable computational framework for single-cell eQTL mapping and characterizes cell-type-specific associations between genetic variation and gene expression relevant to economically important traits in livestock, thereby providing functional context in particular for non-coding variants implicated by GWAS and helping to inform genomic selection and precision genome editing.
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Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice.
Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.
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Single-Soma Deep RNA sequencing of Human DRG Neurons Reveals Novel Molecular and Cellular Mechanisms Underlying Somatosensation.
The versatility of somatosensation arises from heterogeneous dorsal root ganglion (DRG) neurons. However, soma transcriptomes of individual human DRG (hDRG) neurons-critical in-formation to decipher their functions-are lacking due to technical difficulties. Here, we developed a novel approach to isolate individual hDRG neuron somas for deep RNA sequencing (RNA-seq). On average, >9,000 unique genes per neuron were detected, and 16 neuronal types were identified. Cross-species analyses revealed remarkable divergence among pain-sensing neurons and the existence of human-specific nociceptor types. Our deep RNA-seq dataset was especially powerful for providing insight into the molecular mechanisms underlying human somatosensation and identifying high potential novel drug targets. Our dataset also guided the selection of molecular markers to visualize different types of human afferents and the discovery of novel functional properties using single-cell in vivo electrophysiological recordings. In summary, by employing a novel soma sequencing method, we generated an unprecedented hDRG neuron atlas, providing new insights into human somatosensation, establishing a critical foundation for translational work, and clarifying human species-species properties.