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Bioinformatics Analysis Reveals Prognostic Significance of the Macrophage Marker Gene Signature in Gastric Adenocarcinoma.
BACKGROUND: Gastric adenocarcinoma (GAC) is a malignant tumor with the highest incidence in the digestive system. Macrophages have been proven to play important roles in tumor microenvironment. METHODS: Herein, single-cell RNA sequencing (scRNA-seq) profiles from the Gene Expression Omnibus (GEO) and bulk RNA-seq data from the Cancer Genome Atlas (TCGA) database were utilized to construct a macrophage marker gene signature (MMGS) to predict the prognosis of GAC patients. Subsequently, a risk score model based on the MMGS was built to predict the prognosis of GAC patients; further, this was validated in the GEO cohort. The risk score categorized patients into the high- and low-risk groups. A nomogram model based on the risk score and clinic-pathological characteristics was developed. RESULTS: Seven genes, ABCA1, CTHRC1, GADD45B, NPC2, PLTP, PRSS23, and RNASE1, were included in the risk score model. Patients with a low-risk score showed a better prognosis. The MMGS had good sensitivity and specificity for predicting the prognosis inGAC patients. The risk score was an independent prognostic factor. The constructed nomogram exhibited favorable predictability and reliability for predicting GAC prognosis. CONCLUSION: In conclusion, the risk score model based on the seven MMGSs performed well in the predicting prognosis of GAC patients. Our study may provide new insights into clinical decision-making for the personalized treatment of patients with gastric cancer (GC).
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Single-Cell Deconvolution Reveals Phenotype-Associated Cellular States in the Silk Glands of Bombyx mori and Its Wild Ancestor.
Silk production is a classic example of a domestication trait, yet the cell-type-specific driver of its enhancement in the silkworm Bombyx mori remains unresolved. To address this, we integrated extensive bulk RNA-seq data with a single-nucleus RNA-seq atlas of silk glands (SGs) from domestic B. mori and wild B. mandarina for deconvolution analysis. This identified phenotype-associated cell subpopulations (Scissor+ and Scissor- cells) that enrich in B. mori and B. mandarina, respectively. Transcriptomic characterization revealed that B. mori SG cells exhibit a pervasive "pro-synthesis" transcriptional state, with concerted upregulation of silk protein genes and metabolic pathways. Conversely, B. mandarina cells maintained a "protective-adaptive" state, enriched for stress response and xenobiotic metabolism genes. Pseudotime analysis further delineated the cell state transitions, pinpointing key dynamic gene expression linked to high silk yield. Our findings demonstrate that domestication reshaped the silk gland cellular landscape, promoting a systemic shift toward a synthesis-optimized cell state. This study offers a new framework at the cellular level to elucidate the evolution of complex traits under selection.
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Combining single-cell RNA sequencing with spatial transcriptome analysis reveals dynamic molecular maps of cambium differentiation in the primary and secondary growth of trees.
Primary and secondary growth of the tree stem are responsible for corresponding increases in trunk height and diameter. However, our molecular understanding of the biological processes that underlie these two types of growth is incomplete. In this study, we used single-cell RNA sequencing and spatial transcriptome sequencing to reveal the transcriptional landscapes of primary and secondary growth tissues in the Populus stem. Comparison between the cell atlas and differentiation trajectory of primary and secondary growth revealed different regulatory networks involved in cell differentiation from cambium to xylem precursors and phloem precursors. These regulatory networks may be controlled by auxin accumulation and distribution. Analysis of cell differentiation trajectories suggested that vessel and fiber development followed a sequential pattern of progressive transcriptional regulation. This research provides new insights into the processes of cell identity and differentiation that occur throughout primary and secondary growth of tree stems, increasing our understanding of the cellular differentiation dynamics that occur during stem growth in trees.
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Single-cell transcriptomic atlas of primate cardiopulmonary aging.
Aging is a major risk factor for many diseases, especially in highly prevalent cardiopulmonary comorbidities and infectious diseases including Coronavirus Disease 2019 (COVID-19). Resolving cellular and molecular mechanisms associated with aging in higher mammals is therefore urgently needed. Here, we created young and old non-human primate single-nucleus/cell transcriptomic atlases of lung, heart and artery, the top tissues targeted by SARS-CoV-2. Analysis of cell type-specific aging-associated transcriptional changes revealed increased systemic inflammation and compromised virus defense as a hallmark of cardiopulmonary aging. With age, expression of the SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) was increased in the pulmonary alveolar epithelial barrier, cardiomyocytes, and vascular endothelial cells. We found that interleukin 7 (IL7) accumulated in aged cardiopulmonary tissues and induced ACE2 expression in human vascular endothelial cells in an NF-κB-dependent manner. Furthermore, treatment with vitamin C blocked IL7-induced ACE2 expression. Altogether, our findings depict the first transcriptomic atlas of the aged primate cardiopulmonary system and provide vital insights into age-linked susceptibility to SARS-CoV-2, suggesting that geroprotective strategies may reduce COVID-19 severity in the elderly.
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Restoring gluconeogenesis by TEF inhibited proliferation and promoted apoptosis and immune surveillance in kidney renal clear cell carcinoma.
BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the major histological subtype of kidney tumor which covers approximately 80% of the cases. Although various therapies have been developed, the clinical outcome remains unsatisfactory. Metabolic dysregulation is a key feature of KIRC, which impacts progression and prognosis of the disease. Therefore, understanding of the metabolic changes in KIRC is of great significance in improving the treatment outcomes. METHODS: The glycolysis/gluconeogenesis genes were analyzed in the KIRC transcriptome from the Cancer Genome Atlas (TCGA) by the different expression genes (DEGs) test and survival analysis. The gluconeogenesis-related miRNAs were identified by ImmuLncRNA. The expression levels of indicated genes and miRNAs were validated in KIRC tumor and adjunct tissues by QPCR. The effects of miR-4477b and PCK1 on cell proliferation and apoptosis were examined using the cell viability assay, cell apoptosis assay, and clone information. The interaction of miR-4477b with TEF was tested by the luciferase report gene assay. The different gluconeogenesis statuses of tumor cells and related signatures were investigated by single-cell RNA sequencing (scRNA-seq) analysis. RESULTS: The 11 gluconeogenesis genes were found to be suppressed in KIRC (referring as PGNGs), and the less suppression of PGNGs indicated better survival outcomes. Among the 11 PGNGs, we validated four rate-limiting enzyme genes in clinical tumor patients. Moreover, restoring gluconeogenesis by overexpressing PCK1 or TEF through miR-4477b inhibition significantly inhibited tumor cell proliferation, colony formation, and induced cell apoptosis in vitro. Independent single-cell RNA sequencing (scRNA-seq) data analysis revealed that the tumor cells had high levels of PGNG expression (PGNG + tumor cells) represented a phenotype of early stage of neoplasia and prompted immune surveillance. CONCLUSIONS: Our study suggests that the deficiency of gluconeogenesis is a key metabolic feature of KIRC, and restoring gluconeogenesis could effectively inhibit the proliferation and progression of KIRC cells.
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Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq.
OBJECTIVE: Ovarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels. METHODS: The omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram. RESULTS: A total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score. CONCLUSION: We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.
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An Arabidopsis single-nucleus atlas decodes leaf senescence and nutrient allocation.
With rapid advancements in single-cell RNA sequencing (scRNA-seq) technologies, exploration of the systemic coordination of critical physiological processes has entered a new era. Here, we generated a comprehensive Arabidopsis single-nucleus transcriptomic atlas using over 1 million nuclei from 20 tissues encompassing multiple developmental stages. Our analyses identified cell types that have not been characterized in previous single-protoplast studies and revealed cell-type conservation and specificity across different organs. Through time-resolved sampling, we revealed highly coordinated onset and progression of senescence among the major leaf cell types. We originally formulated two molecular indexes to quantify the aging state of leaf cells at single-cell resolution. Additionally, facilitated by weighted gene co-expression network analysis, we identified hundreds of promising hub genes that may integratively regulate leaf senescence. Inspired by the functional validation of identified hub genes, we built a systemic scenario of carbon and nitrogen allocation among different cell types from source leaves to sink organs.
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Identification of the CD8+ T-cell exhaustion signature of hepatocellular carcinoma for the prediction of prognosis and immune microenvironment by integrated analysis of bulk- and single-cell RNA sequencing data.
BACKGROUND: Hepatocellular carcinoma (HCC) is a prevalent type of cancer with high incidence and mortality rates. It is the third most common cause of cancer-related deaths. CD8+ T cell exhaustion (TEX) is a progressive decline in T cell function due to sustained T cell receptor stimulation from continuous antigen exposure. Studies have shown that CD8+ TEX plays an important role in the anti-tumor immune process and is significantly correlated with patient prognosis. The aim of the research is to establish a reliable CD8+ TEX-based signature using single-cell RNA sequencing (scRNA-seq) and high-throughput RNA sequencing (RNA-seq), providing a new approach to evaluate HCC patient prognosis and immune microenvironment. METHODS: The RNA-seq data of HCC patients were download from three different databases: The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC). HCC's 10× scRNA data were acquired from GSE149614. Based on single-cell sequencing data, CD8+ TEX-related genes were identified using uniform manifold approximation and projection (UMAP) algorithm, singleR, and marker gene methods. Afterwards, we proceeded to construct CD8+ TEX signature using differential gene analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis. We also validated the CD8+ TEX signature in GEO and ICGC external cohorts and investigated clinical characteristics, chemotherapy sensitivity, mutation landscape, functional analysis, and immune cell infiltration in different risk groups. RESULTS: The CD8+ TEX signature, consisting of 13 genes (HSPD1, UBB, DNAJB4, CALM1, LGALS3, BATF, COMMD3, IL7R, FDPS, DRAP1, RPS27L, PAPOLA, GPR171), was found to have a strong predictive effect on the prognosis of HCC. The Kaplan-Meier (KM) analysis showed that the overall survival (OS) rate of patients in the low-risk group was higher than that of patients in the high-risk group across different datasets and specific populations. The research findings suggested that the risk score was an independent predictor of HCC prognosis. The model based on clinical features and risk score has a strong predictive effect. We observed significant differences among various risk groups in terms of clinical characteristics, functional analysis, mutation landscape, chemotherapy sensitivity, and immune cell infiltration. CONCLUSIONS: We constructed a CD8+ TEX signature to predict the survival probability of patients with HCC. We also found that the model could predict the sensitivity of targeted drugs and immune cell infiltration, and the risk score was negatively correlated with CD8+ T cell infiltration. In summary, the CD8+ TEX signature of HCC was constructed for the prediction of prognosis and immune microenvironment by integrated analysis of bulk and scRNA-seq data.
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Multicellular network analysis of melanoma heterogeneity by scRNA-seq.
OBJECTIVE: Melanoma neoplasia is a complicated process. Not only melanocytes are involved, stromal cells and immune cells would also regulate cancer development. However, the cell type composition and tumor immune microenvironment of melanoma are poorly understood. METHOD: Here we report a map of the cellular landscape of human melanoma via analyzing published single-cell RNA sequencing (scRNA-seq) dataset. Transcriptional profiles of 4645 cells obtained from 19 melanoma tissues were dissected. RESULT: Using gene expression patterns and flow cytometry, 8 discrete cell populations were identified including endothelial cells (ECs), cancer related fibroblasts (CAFs), macrophages, B cells, T cells (NK cells), memory T cells (MTCs), melanocytes and podocytes. By constructing the cell-specific network (CSN) for each cell population, scRNA-seq data can be used for clustering and pseudo-trajectory analyzing from network perspective. In addition, the differentially expressed genes (DEGs) between malignant and non-malignant melanocytes were identified and analyzed together with clinical data from The Cancer Genome Atlas (TCGA). CONCLUSION: This study shows a comprehensive view of melanoma at the single cell resolution which outlines the characteristics of resident cells in tumor. Particular, it provides an immune microenvironment map of melanoma.
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Molecular taxonomy of human ocular outflow tissues defined by single-cell transcriptomics.
The conventional outflow pathway is a complex tissue responsible for maintaining intraocular pressure (IOP) homeostasis. The coordinated effort of multiple cells with differing responsibilities ensures healthy outflow function and IOP maintenance. Dysfunction of one or more resident cell types results in ocular hypertension and risk for glaucoma, a leading cause of blindness. In this study, single-cell RNA sequencing was performed to generate a comprehensive cell atlas of human conventional outflow tissues. We obtained expression profiles of 17,757 genes from 8,758 cells from eight eyes of human donors representing the outflow cell transcriptome. Upon clustering analysis, 12 distinct cell types were identified, and region-specific expression of candidate genes was mapped in human tissues. Significantly, we identified two distinct expression patterns (myofibroblast- and fibroblast-like) from cells located in the trabecular meshwork (TM), the primary structural component of the conventional outflow pathway. We also located Schwann cell and macrophage signatures in the TM. The second primary component structure, Schlemm's canal, displayed a unique combination of lymphatic/blood vascular gene expression. Other expression clusters corresponded to cells from neighboring tissues, predominantly in the ciliary muscle/scleral spur, which together correspond to the uveoscleral outflow pathway. Importantly, the utility of our atlas was demonstrated by mapping glaucoma-relevant genes to outflow cell clusters. Our study provides a comprehensive molecular and cellular classification of conventional and unconventional outflow pathway structures responsible for IOP homeostasis.