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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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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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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-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.
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A high-resolution transcriptomic atlas depicting nitrogen fixation and nodule development in soybean.
Although root nodules are essential for biological nitrogen fixation in legumes, the cell types and molecular regulatory mechanisms contributing to nodule development and nitrogen fixation in determinate nodule legumes, such as soybean (Glycine max), remain incompletely understood. Here, we generated a single-nucleus resolution transcriptomic atlas of soybean roots and nodules at 14 days post inoculation (dpi) and annotated 17 major cell types, including six that are specific to nodules. We identified the specific cell types responsible for each step in the ureides synthesis pathway, which enables spatial compartmentalization of biochemical reactions during soybean nitrogen fixation. By utilizing RNA velocity analysis, we reconstructed the differentiation dynamics of soybean nodules, which differs from those of indeterminate nodules in Medicago truncatula. Moreover, we identified several putative regulators of soybean nodulation and two of these genes, GmbHLH93 and GmSCL1, were as-yet uncharacterized in soybean. Overexpression of each gene in soybean hairy root systems validated their respective roles in nodulation. Notably, enrichment for cytokinin-related genes in soybean nodules led to identification of the cytokinin receptor, GmCRE1, as a prominent component of the nodulation pathway. GmCRE1 knockout in soybean resulted in a striking nodule phenotype with decreased nitrogen fixation zone and depletion of leghemoglobins, accompanied by downregulation of nodule-specific gene expression, as well as almost complete abrogation of biological nitrogen fixation. In summary, this study provides a comprehensive perspective of the cellular landscape during soybean nodulation, shedding light on the underlying metabolic and developmental mechanisms of soybean nodule formation.
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Expression of the foraging gene in adult Drosophila melanogaster.
The foraging gene in Drosophila melanogaster, which encodes a cGMP-dependent protein kinase, is a highly conserved, complex gene with multiple pleiotropic behavioral and physiological functions in both the larval and adult fly. Adult foraging expression is less well characterized than in the larva. We characterized foraging expression in the brain, gastric system, and reproductive systems using a T2A-Gal4 gene-trap allele. In the brain, foraging expression appears to be restricted to multiple sub-types of glia. This glial-specific cellular localization of foraging was supported by single-cell transcriptomic atlases of the adult brain. foraging is extensively expressed in most cell types in the gastric and reproductive systems. We then mapped multiple cis-regulatory elements responsible for parts of the observed expression patterns by a nested cloned promoter-Gal4 analysis. The mapped cis-regulatory elements were consistently modular when comparing the larval and adult expression patterns. These new data using the T2A-Gal4 gene-trap and cloned foraging promoter fusion GAL4's are discussed with respect to previous work using an anti-FOR antibody, which we show here to be non-specific. Future studies of foraging's function will consider roles for glial subtypes and peripheral tissues (gastric and reproductive systems) in foraging's pleiotropic behavioral and physiological effects.
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Single-cell RNA sequencing for the study of development, physiology and disease.
An ongoing technological revolution is continually improving our ability to carry out very high-resolution studies of gene expression patterns. Current technology enables the global gene expression profiles of single cells to be defined, facilitating dissection of heterogeneity in cell populations that was previously hidden. In contrast to gene expression studies that use bulk RNA samples and provide only a virtual average of the diverse constituent cells, single-cell studies enable the molecular distinction of all cell types within a complex population mix, such as a tumour or developing organ. For instance, single-cell gene expression profiling has contributed to improved understanding of how histologically identical, adjacent cells make different differentiation decisions during development. Beyond development, single-cell gene expression studies have enabled the characteristics of previously known cell types to be more fully defined and facilitated the identification of novel categories of cells, contributing to improvements in our understanding of both normal and disease-related physiological processes and leading to the identification of new treatment approaches. Although limitations remain to be overcome, technology for the analysis of single-cell gene expression patterns is improving rapidly and beginning to provide a detailed atlas of the gene expression patterns of all cell types in the human body.