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Integrative Single-Cell Transcriptomics and Network Pharmacology Analysis of Xiao Qing Long Tang in Pediatric Cough-Variant Asthma.
BACKGROUND: Pediatric cough-variant asthma is characterized by chronic airway inflammation and epithelial dysfunction driven by complex cellular interactions and molecular regulatory networks. Understanding the cellular heterogeneity and molecular mechanisms underlying pediatric asthma pathogenesis remains challenging. Single-cell RNA sequencing (scRNA-seq) provides unprecedented resolution to dissect the molecular landscape of asthmatic airways at the individual cell level. METHODS: We performed a comprehensive single-cell transcriptomic analysis of airway epithelial tissues from pediatric asthma patients using the 10x Genomics platform. Following stringent quality control, dimensionality reduction analysis, and cell clustering, we identified distinct cell populations and characterized their molecular signatures. Developmental trajectory analysis was performed using the Monocle algorithm, and functional enrichment analysis was conducted to elucidate biological pathways. Additionally, network pharmacology was employed to explore the multitarget mechanisms of the traditional Chinese medicine formula Xiao Qing Long Tang (XQLT) in treating asthma. RESULTS: We successfully constructed a single-cell atlas of pediatric asthma airway epithelium, identifying seven major cell subtypes: eosinophil cells, basal cells, ciliated cells, goblet cells, club cells, ionocyte cells, and deuterosomal cells. High-resolution analysis revealed 23 distinct macrophage subpopulations (M1-M23) with unique transcriptional profiles. Key genes including KRT16, CXCL5, MMP10, ADAM12, and MALAT1 showed cell type-specific expression patterns. Pseudotime trajectory analysis revealed aberrant differentiation pathways from basal cells to specialized epithelial cells. Functional enrichment analysis highlighted inflammatory responses, immune system processes, and tissue remodeling as predominant biological processes in asthmatic airways. The network pharmacology analysis further identified 124 common targets, revealing that XQLT exerts multicomponent, synergistic therapeutic effects through core hubs such as NFE2L2 and NOS2. CONCLUSIONS: This comprehensive single-cell transcriptomic atlas provides novel insights into the cellular heterogeneity and molecular mechanisms of pediatric asthma. The identification of cell type-specific gene expression patterns and developmental trajectories offers potential targets for precision therapeutic interventions.
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Integrated mass spectrometry imaging and single-cell transcriptome atlas strategies provide novel insights into taxoid biosynthesis and transport in Taxus mairei stems.
Taxol, which is a widely used important chemotherapeutic agent, was originally isolated from Taxus stem barks. However, little is known about the precise distribution of taxoids and the transcriptional regulation of taxoid biosynthesis across Taxus stems. Here, we used MALDI-IMS analysis to visualize the taxoid distribution across Taxus mairei stems and single-cell RNA sequencing to generate expression profiles. A single-cell T. mairei stem atlas was created, providing a spatial distribution pattern of Taxus stem cells. Cells were reordered using a main developmental pseudotime trajectory which provided temporal distribution patterns in Taxus stem cells. Most known taxol biosynthesis-related genes were primarily expressed in epidermal, endodermal, and xylem parenchyma cells, which caused an uneven taxoid distribution across T. mairei stems. We developed a single-cell strategy to screen novel transcription factors (TFs) involved in taxol biosynthesis regulation. Several TF genes, such as endodermal cell-specific MYB47 and xylem parenchyma cell-specific NAC2 and bHLH68, were implicated as potential regulators of taxol biosynthesis. Furthermore, an ATP-binding cassette family transporter gene, ABCG2, was proposed as a potential taxoid transporter candidate. In summary, we generated a single-cell Taxus stem metabolic atlas and identified molecular mechanisms underpinning the cell-specific transcriptional regulation of the taxol biosynthesis pathway.
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A dataset of single-cell transcriptomic atlas of Bama pig and potential marker genes across seven tissues.
The use of single-cell sequencing technology for single-cell transcriptomics studies in pigs is expanding progressively. However, the comprehensive classification of cell types across different anatomical tissues and organs of pig in multiple datasets remains relatively limited. This study employs single-cell and single-nucleus sequencing technologies in Bama pig to identify unique marker genes and their corresponding transcriptomic profiles across diverse cell types in various anatomical tissues and organs, including subcutaneous fat, visceral fat, psoas major muscle, liver, spleen, lung, and kidney. Through detailed data analyses, we observed widespread cellular diversity across various anatomical tissues and organs of Bama pig. This work contributes a comprehensive dataset that supports physiological studies and aids in the identification and prediction of potential marker genes through single-cell transcriptomics of these tissues. The methodologies and data employed in this study are designed to improve the accuracy of cell type identification and ensure consistent cell type allocation.
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Novel fatty acid metabolism-related molecular subtyping and prognostic signature for breast cancer.
BACKGROUND: Breast cancer (BRCA) is one of the most prevalent malignant tumors in women worldwide, characterized by significant heterogeneity. Fatty acid metabolism (FAM) plays a crucial biological role in the initiation and progression of cancer. This study aims to identify novel, effective biomarkers related to FAM for improved risk stratification and treatment selection in BRCA patients. METHODS: Gene expression data from 1,217 BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. A comprehensive machine learning approach, incorporating ten different methods, was used to develop a FAM-related gene prognostic model (FAMGM). The Kaplan-Meier method and correlation analysis were employed to assess differences in overall survival (OS) and immune characteristics between high- and low-risk groups. External validation was performed using independent datasets. Single-cell RNA sequencing (scRNA-seq) data from 26 BRCA patients were analyzed, and the potential functions and mechanisms of the model genes were investigated using single-sample gene set enrichment analysis (ssGSEA), CellChat, and other algorithms. Finally, spatial transcriptomics (ST) analysis was conducted to examine the expression of model genes in the malignant regions of tumors. RESULTS: The FAMGM, developed using CoxBoost and random survival forest (RSF) methods, was identified as the optimal prognostic model. FAMGM demonstrated stable and robust performance in predicting clinical outcomes for BRCA. The high-risk group showed poor survival prognosis, typically associated with advanced clinical stages, reduced immune cell infiltration, and increased tumor mutational burden (TMB). Model genes were predominantly enriched in macrophages and appeared to influence tumor progression through the upregulation of multiple signaling pathways. Additionally, these model genes exhibited higher expression in malignant tumor regions. CONCLUSIONS: FAMGM holds significant potential as a prognostic marker and could be used in the subsequent diagnosis, treatment, prognostic prediction, and mechanistic research of BRCA.
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Single-Cell Transcriptomics Characterizes Cell Types in the Subventricular Zone and Uncovers Molecular Defects Impairing Adult Neurogenesis.
Neural stem cells (NSCs) contribute to plasticity and repair of the adult brain. Niches harboring NSCs regulate stem cell self-renewal and differentiation. We used comprehensive and untargeted single-cell RNA profiling to generate a molecular cell atlas of the largest germinal region of the adult mouse brain, the subventricular zone (SVZ). We characterized >20 neural and non-neural cell types and gained insights into the dynamics of neurogenesis by predicting future cell states based on computational analysis of RNA kinetics. Furthermore, we applied our single-cell approach to document decreased numbers of NSCs, reduced proliferation activity of progenitors, and perturbations in Wnt and BMP signaling pathways in mice lacking LRP2, an endocytic receptor required for SVZ maintenance. Our data provide a valuable resource to study adult neurogenesis and a proof of principle for the power of single-cell RNA sequencing to elucidate neural cell-type-specific alterations in loss-of-function models.
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Integrative analyses of single-cell and bulk RNA sequencing to construct the tumor-associated macrophage-related prognostic signature in lung adenocarcinoma.
BACKGROUND: Tumor-associated macrophages (TAMs) are a type of tumor-infiltrating immune cell that play a crucial role in tumor progression. However, the roles of TAMs and their regulatory mechanisms in lung adenocarcinoma (LUAD) remain poorly understood. Therefore, we aimed to develop a novel TAM-related prognostic signature to predict survival outcomes and constructed a lncRNA-miRNA-mRNA network based on these genes. METHODS: Transcriptomic data, clinical data, and single-cell RNA-sequencing (scRNA-seq) data were obtained from LUAD patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed-lncRNAs (DE-lncRNAs), miRNAs (DE-miRNAs), and mRNAs (DEGs) were identified in LUAD. Differentially expressed TAM-related genes (DE-TAMGs) were selected and used to construct prognostic signatures. A TAM-related risk score was calculated, and patients were stratified into high- and low-risk groups based on the median risk score. Then, biological functions, immune characteristics, and responses to immunotherapy and chemotherapy were assessed across the risk groups. A TAM-related lncRNA-miRNA-mRNA network was constructed based on DE-lncRNAs, DE-miRNAs, and TAM-related signatures. Quantitative polymerase chain reaction (qPCR) was used to validate the expression of TAM-related genes, and scRNA-seq analysis was used to examine cell-type-specific expression of these genes. RESULTS: A total of 316 DE-lncRNAs, 162 DE-miRNAs, and 2,601 DEGs were screened in LUAD. Among these, 147 DE-TAMGs were selected. KLF4, GAPDH PDGFB, TIMP1, CD74, and CCL20 were identified as the key prognostic markers in LUAD. Patients were divided into high- and low-risk groups based on the median risk score. Enrichment analysis revealed several cancer-related pathways associated with the high-risk group, and significant differences in terms of immune cell infiltration, HLA-related gene expression, immune checkpoints expression, and therapeutic responses were observed between high- and low-risk groups. We also constructed a lncRNA-miRNA-mRNA network, which included 36 DE-lncRNAs, 23 shared miRNA, and four TAMGs (PDGFB, CD74, KLF4, and CCL20). The qPCR results indicated the increased expression of PDGFB, CD74, and KLF4 but decreased expression of CCL20 in LUAD tumor tissues compared with adjacent normal tissues. scRNA-seq analysis revealed that CD74, KLF4, CCL20, PDGFB were specifically expressed in macrophages. CONCLUSIONS: In conclusion, we identify the TAM-related prognostic signature that predicts the survival outcome in patients with LUAD. This signature may offer a novel effective therapeutic strategy for LUAD patients.
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Single-cell transcriptomic analysis of bloodstream Trypanosoma brucei reconstructs cell cycle progression and developmental quorum sensing.
Developmental steps in the trypanosome life-cycle involve transition between replicative and non-replicative forms specialised for survival in, and transmission between, mammalian and tsetse fly hosts. Here, using oligopeptide-induced differentiation in vitro, we model the progressive development of replicative 'slender' to transmissible 'stumpy' bloodstream form Trypanosoma brucei and capture the transcriptomes of 8,599 parasites using single cell transcriptomics (scRNA-seq). Using this framework, we detail the relative order of biological events during asynchronous development, profile dynamic gene expression patterns and identify putative regulators. We additionally map the cell cycle of proliferating parasites and position stumpy cell-cycle exit at early G1 before progression to a distinct G0 state. A null mutant for one transiently elevated developmental regulator, ZC3H20 is further analysed by scRNA-seq, identifying its point of failure in the developmental atlas. This approach provides a paradigm for the dissection of differentiation events in parasites, relevant to diverse transitions in pathogen biology.
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Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA.
Pathway analysis is a crucial analytical phase in disease research on single-cell RNA sequencing (scRNA-seq) data, offering biological interpretations based on prior knowledge. However, currently available tools for generating cell-level pathway activity scores (PAS) exhibit computational inefficacy in large-scale scRNA-seq datasets. Additionally, disease-related pathways are often identified through cross-condition comparisons within specific cell types, overlooking potential patterns that involve multiple cell types. Here, we present single-cell pathway activity factor analysis (scPAFA), a Python library designed for large-scale single-cell datasets allowing rapid PAS computation and uncovering biologically interpretable disease-related multicellular pathway modules, which are low-dimensional representations of disease-related PAS alterations in multiple cell types. Application on colorectal cancer (CRC) datasets and large-scale lupus atlas over 1.2 million cells demonstrated that scPAFA can achieve over 40-fold reductions in the runtime of PAS computation and further identified reliable and interpretable multicellular pathway modules that capture the heterogeneity of CRC and transcriptional abnormalities in lupus patients, respectively. Overall, scPAFA presents a valuable addition to existing research tools in disease research, with the potential to reveal complex disease mechanisms and support biomarker discovery at the pathway level.
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Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer.
Colorectal cancer (CRC) is a prevalent malignant tumor characterized by high global incidence and mortality rates. Furthermore, it is imperative to comprehend the molecular mechanisms underlying its development and to identify effective prognostic markers. These efforts are crucial for pinpointing potential therapeutic targets and enhancing patient survival rates. Therefore, we develop a novel prognostic model aimed at providing new theoretical support for clinical prognosis evaluation and treatment. We downloaded data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, we performed single-cell analysis and developed a prognostic model associated with colorectal cancer. We divided the scRNA-seq dataset (GSE221575) into 19 cell clusters and classified these clusters into 11 distinct cell types using marker genes. Using univariate Cox regression and LASSO (Least Absolute Shrinkage and Selection Operator) analyses, we developed a prognostic model consisting of 9 genes. Based on our 9-gene model, we divided patients into high-risk and low-risk groups using the median risk score. The high-risk group demonstrated significant positive correlations with M0 macrophages, CD8+ T cells, and M2 macrophages. The enrichment analyses indicate significant enrichment of immune-related pathways in the high-risk group, including HEDGEHOG_SIGNALING, Wnt signaling pathway, and cell adhesion molecules. Drug sensitivity analysis revealed that the low-risk group was sensitive to 5 chemotherapeutic drugs, while the high-risk group was sensitive to only 1. Additionally, we developed a highly reliable nomogram for clinical application. This suggests that the risk score derived from our modeling analysis is highly effective for stratifying colorectal cancer samples. This study comprehensively applied bioinformatics methods to construct a risk score model. The model showed good predictive performance, offering potential guidance for individualized treatment of colorectal cancer patients. Furthermore, it may provide valuable insights into the disease's pathogenesis and identify potential therapeutic targets for further research.
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Discrete and cell-specific hypoxic responses in Arabidopsis roots resolved by single-nuclei transcriptomics.
With the intensification of unpredictable flooding events because of global warming, there is a need to understand how root cells perceive and respond to oxygen deprivation. The use of high-throughput single-nuclei RNA-sequencing (snRNA-Seq) allows the examination of gene expression profiles in discrete cell types. Root tip segments of Arabidopsis seedlings subjected to normoxic (21% oxygen) or hypoxic (4% oxygen) treatments for 4 h were analyzed by snRNA-Seq. Hypoxia induces a major transcriptome rewiring, most prominent in the meristematic root cells, with the exclusion of the quiescent center (QC), which is vulnerable to low oxygen. Changes in carbon and nitrogen metabolism by low oxygen were centered around increasing demand for carbohydrate to drive glycolytic fermentation, reduction of nitrate to sustain energetic processes, and the bypass of the TCA cycle via aspartate aminotransferase upregulation. The reduction of nitrate and nitrite in hypoxic cells contributes to amino acid metabolism and the utilization of NO in the phytoglobin-nitric oxide cycle to decrease the redox level and sustain energy production. In conclusion, the use of snRNA-Seq provides a high-resolution atlas of gene expression events defining root cell-specific responses to low oxygen.