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Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.
OBJECTIVE: Leaflet thickening, fibrosis, and hardening are early pathological features of calcific aortic valve disease (CAVD). An inadequate understanding of the resident aortic valve cells involved in the pathological process may compromise the development of therapeutic strategies. We aim to construct a pattern of the human aortic valve cell atlas in healthy and CAVD clinical specimens, providing insight into the cellular origins of CAVD and the complex cytopathological differentiation process. Approach and Results: We used unbiased single-cell RNA sequencing for the high-throughput evaluation of cell heterogeneity in 34 632 cells isolated from 6 different human aortic valve leaflets. Cellular experiments, in situ localization, and bulk sequencing were performed to verify the differences between normal, healthy valves and those with CAVD. By comparing healthy and CAVD specimens, we identified 14 cell subtypes, including 3 heterogeneous subpopulations of resident valve interstitial cells, 3 types of immune-derived cells, 2 types of valve endothelial cells, and 6 novel valve-derived stromal cells found particularly in CAVD leaflets. Combining additional verification experiments with single-cell transcriptome profiling provided evidence of endothelial to mesenchymal transition involved in lesion thickening of the aortic valve leaflet. CONCLUSIONS: Our findings deconstructed the aortic valve cell atlas and suggested novel functional interactions among resident cell subpopulations. Our findings may provide insight into future targeted therapies to prevent CAVD.
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Integrated single-cell and transcriptomic analysis of CD8+CD101+TIM3+ T cells in hepatocellular carcinoma: implications for tumor microenvironment and prognostic modeling.
BACKGROUND: Hepatocellular carcinoma (HCC) ranks as a leading global cause of cancer-related mortality. Our study investigates the functional role between CD8+CD101+TIM3+ T cells (CCT) and CD8+CD101-TIM3+ T cells in HCC, with a focus on their implications for immunotherapy. METHODS: We utilized single-cell RNA sequencing (scRNA-seq) to delineate the transcriptional landscapes and intercellular crosstalk of CCT and CD8+CD101-TIM3+ T cells in HCC tissues. Clinical characteristics and scRNA-seq data were curated from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. For the differentially expressed genes (DEGs), Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway-enrichment were performed. Prognostic model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression model, validated through Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves. A corresponding nomogram was also established to predict the prognosis of HCC patients. RESULTS: Our results indicated that T cell re-clustering revealed four functionally distinct subsets: CD8+ T cells, CD8+CD101-TIM3+ T cells, CCT, and CD4+ T cells. Pseudotime trajectory analysis demonstrated that CD8+CD101-TIM3+ T cells progressively differentiated into CCT, a process mediated by MIF-CD74/CXCR4 and SPP1-CD44 signaling axes. Enrichment analysis identified 1,281 CCT-specific genes enriched in mitochondrial electron transport and oxidative phosphorylation pathways. The prognostic model showed strong discriminatory power for overall survival (OS), achieving areas under the curve of 0.825, 0.873, and 0.865 at 1, 3, and 5 years in the training cohort, compared to 0.654, 0.636, and 0.644 in the validation cohort. The developed nomogram effectively predicts OS for HCC patients. CONCLUSIONS: Our findings elucidate the functional alterations of CCT and precursor cells within the context of HCC. The novel prognostic framework provides actionable insights for stratifying patients likely to benefit from combinatorial immunotherapy and chemotherapy.
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Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing.
BACKGROUND: Prostate cancer is a significant clinical issue, particularly for high Gleason score (GS) malignancy patients. Our study aimed to engineer and validate a risk model based on the profiles of high-GS PCa patients for early identification and the prediction of prognosis. METHODS: We conducted differential gene expression analysis on patient samples from The Cancer Genome Atlas (TCGA) and enriched our understanding of gene functions. Using the least absolute selection and shrinkage operator (LASSO) regression, we established a risk model and validated it using an independent dataset from the International Cancer Genome Consortium (ICGC). Clinical variables were incorporated into a nomogram to predict overall survival (OS), and machine learning was used to explore the risk factor characteristics' impact on PCa prognosis. Our prognostic model was confirmed using various databases, including single-cell RNA-sequencing datasets (scRNA-seq), the Cancer Cell Line Encyclopedia (CCLE), PCa cell lines, and tumor tissues. RESULTS: We identified 83 differentially expressed genes (DEGs). Furthermore, WASIR1, KRTAP5-1, TLX1, KIF4A, and IQGAP3 were determined to be significant risk factors for OS and progression-free survival (PFS). Based on these five risk factors, we developed a risk model and nomogram for predicting OS and PFS, with a C-index of 0.823 (95% CI, 0.766-0.881) and a 10-year area under the curve (AUC) value of 0.788 (95% CI, 0.633-0.943). Additionally, the 3-year AUC was 0.759 when validating using ICGC. KRTAP5-1 and WASIR1 were found to be the most influential prognosis factors when using the optimized machine learning model. Finally, the established model was interrelated with immune cell infiltration, and the signals were found to be differentially expressed in PCa cells when using scRNA-seq datasets and tissues. CONCLUSIONS: We engineered an original and novel prognostic model based on five gene signatures through TCGA and machine learning, providing new insights into the risk of scarification and survival prediction for PCa patients in clinical practice.
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Single-cell transcriptomic and cell‑type‑specific regulatory networks in Polima temperature-sensitive cytoplasmic male sterility of Brassica napus L.
BACKGROUND: Thermosensitive male sterility (TMS) is a heritable agronomic trait influenced by the interaction between genotype and environment. The anthers of plants are composed of various specialized cells, each of which plays different roles in plant reproduction. In rapeseed (Brassica napus L.), Polima (pol) temperature-sensitive cytoplasmic male sterility (TCMS) is widely used in two-line breeding because its fertility can be partially restored at certain temperatures. The pol-TCMS line exhibits abnormal anther development and pollen abortion at high (restrictive) temperatures (HT, 25 °C) compared to at low (permissive) temperatures (LT, 16 °C). However, the response of different anther cell types to HT and the dynamic regulation of genes under such conditions remain largely unknown. RESULTS: We present the first single-cell transcriptomic atlas of Brassica napus early developing flower bud tissues in response to HT. We identified 8 cell types and 17 transcriptionally distinct cell clusters via known marker genes under LT and HT treatment conditions. Under HT conditions, changes in the gene expression patterns of different cell clusters were observed, with the number of down-regulated genes in various cell types exceeding that of up-regulated genes. Pseudotime trajectory analysis revealed that HT strongly affected the development of early stamen/anther tissue cells. In combination with the snRNA-seq, WGCNA, and bulk RNA-seq results, we found that many transcription factors play crucial roles in the response to HT, especially heat response family genes. CONCLUSIONS: Our study revealed the transcriptional regulatory network of floral bud tissue in the pol-TCMS line under HT/LT conditions and increased our understanding of high-temperature-induced anther developmental abnormalities, which may help researchers utilize TCMS in the two-line breeding of Brassica plants.
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Single-Cell RNA Sequencing Reveals Cellular Heterogeneity and Stage Transition under Temperature Stress in Synchronized Plasmodium falciparum Cells.
The malaria parasite has a complex life cycle exhibiting phenotypic and morphogenic variations in two different hosts by existing in heterogeneous developmental states. To investigate this cellular heterogeneity of the parasite within the human host, we performed single-cell RNA sequencing of synchronized Plasmodium cells under control and temperature treatment conditions. Using the Malaria Cell Atlas (https://www.sanger.ac.uk/science/tools/mca) as a guide, we identified 9 subtypes of the parasite distributed across known intraerythrocytic stages. Interestingly, temperature treatment results in the upregulation of the AP2-G gene, the master regulator of sexual development in a small subpopulation of the parasites. Moreover, we identified a heterogeneous stress-responsive subpopulation (clusters 5, 6, and 7 [∼10% of the total population]) that exhibits upregulation of stress response pathways under normal growth conditions. We also developed an online exploratory tool that will provide new insights into gene function under normal and temperature stress conditions. Thus, our study reveals important insights into cell-to-cell heterogeneity in the parasite population under temperature treatment that will be instrumental toward a mechanistic understanding of cellular adaptation and population dynamics in Plasmodium falciparum. IMPORTANCE The malaria parasite has a complex life cycle exhibiting phenotypic variations in two different hosts accompanied by cell-to-cell variability that is important for stress tolerance, immune evasion, and drug resistance. To investigate cellular heterogeneity determined by gene expression, we performed single-cell RNA sequencing (scRNA-seq) of about 12,000 synchronized Plasmodium cells under physiologically relevant normal (37°C) and temperature stress (40°C) conditions phenocopying the cyclic bouts of fever experienced during malarial infection. In this study, we found that parasites exhibit transcriptional heterogeneity in an otherwise morphologically synchronized culture. Also, a subset of parasites is continually committed to gametocytogenesis and stress-responsive pathways. These observations have important implications for understanding the mechanisms of drug resistance generation and vaccine development against the malaria parasite.
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APOBEC3C is a novel target for the immune treatment of lower-grade gliomas.
BACKGROUND: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C (A3C) has been identified as a cancer molecular biomarker in the past decade. However, the practical role of A3C in lower-grade gliomas (LGGs) in improving the clinical outcome remains unclear. This study aims to discuss the function of A3C in immunotherapy in LGGs. METHODS: The RNA-Sequencing (RNA-seq) and corresponding clinical data were extracted from UCSC Xena and the results were verified in the Chinese Glioma Genome Atlas (CGGA). Weighted gene co-expression network analysis (WGCNA) was used for screening A3C-related genes. Comprehensive bioinformation analyses were performed and multiple levels of expression, survival rate, and biological functions were assessed to explore the functions of A3C. RESULTS: A3C expression was significantly higher in LGGs than in normal tissues but lower than in glioblastoma (GBM), indicating its role as an independent prognosis predictor for LGGs. Twenty-eight A3C-related genes were found with WGCNA for unsupervised clustering analysis and three modification patterns with different outcomes and immune cell infiltration were identified. A3C and the A3C score were also correlated with immune cell infiltration and the expression of immune checkpoints. In addition, the A3C score was correlated with increased sensitivity to chemotherapy. Single-cell RNA (scRNA) analysis indicated that A3C most probably expresses on immune cells, such as T cells, B cells and macrophage. CONCLUSIONS: A3C is an immune-related prognostic biomarker in LGGs. Developing drugs to block A3C could enhance the efficiency of immunotherapy and improve disease survival.Abbreviation: A3C: Apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) type 3C; LGGs: lower-grade gliomas; CGGA: Chinese Glioma Genome Atlas; WGCNA: Weighted gene co-expression network analysis; scRNA: Single-cell RNA; HGG: higher-grade glioma; OS: overall survival; TME: tumor microenvironment; KM: Kaplan-Meier; PFI: progression-free interval; IDH: isocitrate dehydrogenase; ROC: receiver operating characteristic; GS: gene significance; MM: module membership; TIMER: Tumor IMmune Estimation Resource; GSVA: gene set variation analysis; ssGSEA: single-sample gene-set enrichment analysis; PCA: principal component analysis; AUC: area under ROC curve; HAVCR2: hepatitis A virus cellular receptor 2; PDCD1: programmed cell death 1; PDCD1LG2: PDCD1 ligand 2; PTPRC: protein tyrosine phosphatase receptor type C; ACC: Adrenocortical carcinoma; BLCA: Bladder Urothelial Carcinoma;BRCA: Breast invasive carcinoma; CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOLCholangiocarcinoma; COADColon adenocarcinoma; DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA: Esophageal carcinoma; GBM: Glioblastoma multiforme; HNSC: Head and Neck squamous cell carcinoma; KICH: Kidney Chromophobe; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LAML: Acute Myeloid Leukemia; LGG: Brain Lower Grade Glioma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; MESO: Mesothelioma; OV: Ovarian serous cystadenocarcinoma; PAAD: Pancreatic adenocarcinoma; PCPG: Pheochromocytoma and Paraganglioma; PRAD: Prostate adenocarcinoma; READ: Rectum adenocarcinoma; SARC: Sarcoma; SKCM: Skin Cutaneous Melanoma; STAD: Stomach adenocarcinoma; TGCT: Testicular Germ Cell Tumors; THCA: Thyroid carcinoma; THYM: Thymoma; UCEC: Uterine Corpus Endometrial Carcinoma; UCS: Uterine Carcinosarcoma; UVM: Uveal Melanoma.
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Revealing the vectors of cellular identity with single-cell genomics.
Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.
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A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys.
Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples.
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Identifying candidate genes for spermatogenic failure and predicting ICSI outcomes using single-cell RNA sequencing and protein-protein interaction networks.
STUDY QUESTION: How can integrating updated single-cell transcriptomics and protein-protein interactions (PPIs) with machine learning algorithms improve gene prioritization for spermatogenic failure and predict ICSI outcomes? SUMMARY ANSWER: A machine learning framework integrating single-cell RNA sequencing (scRNA-seq) and PPI networks efficiently identified 320 candidate genes for spermatogenic failure and achieved high precision in predicting ICSI outcomes (precision-recall (PRC)-AUC=0.96, 95% CI: 0.89-1.00; receiver operating characteristic (ROC)-AUC = 0.82, 95% CI: 0.63-0.97). WHAT IS KNOWN ALREADY: Over 100 genes are implicated in spermatogenic failure, yet patients with distinct genetic backgrounds exhibit highly variable ICSI outcomes. While machine learning-based gene prioritization offers potential for novel gene discovery, the existing methods rely on bulk RNA sequencing or lack multi-omics integration, limiting their ability to leverage single-cell resolution or predict clinical outcomes. STUDY DESIGN, SIZE, DURATION: This study combined scRNA-seq data (capturing cell type- and developmental stage-specific expression) from healthy human tissues with PPI networks to train predictive models. Validation included 5-fold cross-validation, functional enrichment analyses, and clinical data from whole-exome sequencing (WES) and ICSI outcomes in 34 patients with spermatogenic failure subtypes (azoospermia, asthenozoospermia, teratozoospermia). PARTICIPANTS/MATERIALS, SETTING, METHODS: Public datasets (Human Protein Atlas, STRING, Gene Expression Omnibus) provided scRNA-seq and PPI data. Node2Vec-derived PPI network embeddings and cell type- and developmental stage-specific expression features were used to train random forest classifiers. Gene Ontology, Mammalian Phenotype Ontology enrichment analyses, and WES of patient blood samples validated candidate genes and ICSI outcomes. MAIN RESULTS AND THE ROLE OF CHANCE: Our models demonstrated robust performance in spermatogenic failure gene prediction (PRC-AUC = 0.88, 95% CI: 0.83-0.93; ROC-AUC = 0.98, 95% CI: 0.96-0.99), subtype classification (e.g. teratozoospermia, PRC-AUC = 0.96, 95% CI: 0.91-0.99; ROC-AUC = 0.94, 95% CI: 0.87-0.98), and ICSI outcome prediction (PRC-AUC = 0.96, 95% CI: 0.89-1.00; ROC-AUC = 0.82, 95% CI: 0.63-0.97). WES of patient samples revealed an increased detection rate of likely causative variants among a subset of model-predicted genes, rising from 11.8% to 29.4%, with clinical outcomes aligning with model predictions. LIMITATIONS, REASONS FOR CAUTION: Model limitations include training on literature-curated or database-annotated gene labels, which may introduce misclassification or annotation bias. Additionally, the absence of experimental validation and the limited size and diversity of external cohorts necessitate further verification. WIDER IMPLICATIONS OF THE FINDINGS: This integrative machine learning framework provides a powerful tool for uncovering genetic contributors to male infertility and predicting treatment outcomes, paving the way for improved diagnostic strategies and more informed clinical decision-making in reproductive medicine. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the National Natural Science Foundation of China (32370719, 32170667), the Shanghai Municipal Science and Technology Major Project (2017SHZDZX01), and the National Key Research and Development Program of China (2021YFC2301503). The authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.
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Single-Cell Transcriptome Atlas of Murine Endothelial Cells.
The heterogeneity of endothelial cells (ECs) across tissues remains incompletely inventoried. We constructed an atlas of >32,000 single-EC transcriptomes from 11 mouse tissues and identified 78 EC subclusters, including Aqp7+ intestinal capillaries and angiogenic ECs in healthy tissues. ECs from brain/testis, liver/spleen, small intestine/colon, and skeletal muscle/heart pairwise expressed partially overlapping marker genes. Arterial, venous, and lymphatic ECs shared more markers in more tissues than did heterogeneous capillary ECs. ECs from different vascular beds (arteries, capillaries, veins, lymphatics) exhibited transcriptome similarity across tissues, but the tissue (rather than the vessel) type contributed to the EC heterogeneity. Metabolic transcriptome analysis revealed a similar tissue-grouping phenomenon of ECs and heterogeneous metabolic gene signatures in ECs between tissues and between vascular beds within a single tissue in a tissue-type-dependent pattern. The EC atlas taxonomy enabled identification of EC subclusters in public scRNA-seq datasets and provides a powerful discovery tool and resource value.