RESUMEN
Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.
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Células Ependimogliales , Retina , Análisis de la Célula Individual , Animales , Ratones , Análisis de la Célula Individual/métodos , Retina/metabolismo , Células Ependimogliales/metabolismo , Regeneración/genética , Análisis de Secuencia de ARN/métodos , Degeneración Retiniana/genética , Degeneración Retiniana/terapia , RNA-Seq/métodos , Modelos Animales de EnfermedadRESUMEN
Bisphenol compounds (BPs) have various industrial uses and can enter the environment through various sources. To evaluate the ecotoxicity of BPs and identify potential gene candidates involved in the plant toxicity, Arabidopsis thaliana was exposed to bisphenol A (BPA), BPB, BPE, BPF, and BPS at 1, 3, 10 mg/L for a duration of 14 days, and their growth status were monitored. At day 14, roots and leaves were collected for internal BPs exposure concentration detection, RNA-seq (only roots), and morphological observations. As shown in the results, exposure to BPs significantly disturbed root elongation, exhibiting a trend of stimulation at low concentration and inhibition at high concentration. Additionally, BPs exhibited pronounced generation of reactive oxygen species, while none of the pollutants caused significant changes in root morphology. Internal exposure concentration analysis indicated that BPs tended to accumulate in the roots, with BPS exhibiting the highest level of accumulation. The results of RNA-seq indicated that the shared 211 differently expressed genes (DEGs) of these 5 exposure groups were enriched in defense response, generation of precursor metabolites, response to organic substance, response to oxygen-containing, response to hormone, oxidation-reduction process and so on. Regarding unique DEGs in each group, BPS was mainly associated with the redox pathway, BPB primarily influenced seed germination, and BPA, BPE and BPF were primarily involved in metabolic signaling pathways. Our results provide new insights for BPs induced adverse effects on Arabidopsis thaliana and suggest that the ecological risks associated with BPA alternatives cannot be ignored.
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Arabidopsis , Compuestos de Bencidrilo , Oxidación-Reducción , Fenoles , Raíces de Plantas , Arabidopsis/efectos de los fármacos , Arabidopsis/genética , Fenoles/toxicidad , Compuestos de Bencidrilo/toxicidad , Raíces de Plantas/efectos de los fármacos , Raíces de Plantas/metabolismo , RNA-Seq , Análisis de Secuencia de ARN , Contaminantes del Suelo/toxicidadRESUMEN
The hypothalamic-pituitary-ovarian (HPO) axis represents a central neuroendocrine network essential for reproductive function. Despite its critical role, the intrinsic heterogeneity within the HPO axis across vertebrates and the complex intercellular interactions remain poorly defined. This study provides the first comprehensive, unbiased, cell type-specific molecular profiling of all three components of the HPO axis in adult Lohmann layers and Liangshan Yanying chickens. Within the hypothalamus, pituitary, and ovary, seven, 12, and 13 distinct cell types were identified, respectively. Results indicated that the pituitary adenylate cyclase activating polypeptide (PACAP), follicle-stimulating hormone (FSH), and prolactin (PRL) signaling pathways may modulate the synthesis and secretion of gonadotropin-releasing hormone (GnRH), FSH, and luteinizing hormone (LH) within the hypothalamus and pituitary. In the ovary, interactions between granulosa cells and oocytes involved the KIT, CD99, LIFR, FN1, and ANGPTL signaling pathways, which collectively regulate follicular maturation. The SEMA4 signaling pathway emerged as a critical mediator across all three tissues of the HPO axis. Additionally, gene expression analysis revealed that relaxin 3 (RLN3), gastrin-releasing peptide (GRP), and cocaine- and amphetamine regulated transcripts (CART, also known as CARTPT) may function as novel endocrine hormones, influencing the HPO axis through autocrine, paracrine, and endocrine pathways. Comparative analyses between Lohmann layers and Liangshan Yanying chickens demonstrated higher expression levels of GRP, RLN3, CARTPT, LHCGR, FSHR, and GRPR in the ovaries of Lohmann layers, potentially contributing to their superior reproductive performance. In conclusion, this study provides a detailed molecular characterization of the HPO axis, offering novel insights into the regulatory mechanisms underlying reproductive biology.
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Pollos , Sistema Hipotálamo-Hipofisario , Ovario , Animales , Femenino , Pollos/genética , Pollos/fisiología , Ovario/metabolismo , Sistema Hipotálamo-Hipofisario/metabolismo , Sistema Hipotálamo-Hipofisario/fisiología , RNA-Seq , Regulación de la Expresión Génica , Hipófisis/metabolismo , Transducción de SeñalRESUMEN
In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer.
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RNA-Seq , Análisis de Secuencia de ARN , Análisis de la Célula Individual , Programas Informáticos , Análisis de la Célula Individual/métodos , RNA-Seq/métodos , Análisis de Secuencia de ARN/métodos , Humanos , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , InternetRESUMEN
BACKGROUND: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging. RESULTS: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property. CONCLUSIONS: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .
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Análisis de la Célula Individual , Análisis de la Célula Individual/métodos , Humanos , Análisis de Secuencia de ARN/métodos , Redes Reguladoras de Genes , RNA-Seq/métodos , Algoritmos , Perfilación de la Expresión Génica/métodos , Análisis de Expresión Génica de una Sola CélulaRESUMEN
Single-cell RNA sequencing (scRNA-seq) data, susceptible to noise arising from biological variability and technical errors, can distort gene expression analysis and impact cell similarity assessments, particularly in heterogeneous populations. Current methods, including deep learning approaches, often struggle to accurately characterize cell relationships due to this inherent noise. To address these challenges, we introduce scAMF (Single-cell Analysis via Manifold Fitting), a framework designed to enhance clustering accuracy and data visualization in scRNA-seq studies. At the heart of scAMF lies the manifold fitting module, which effectively denoises scRNA-seq data by unfolding their distribution in the ambient space. This unfolding aligns the gene expression vector of each cell more closely with its underlying structure, bringing it spatially closer to other cells of the same cell type. To comprehensively assess the impact of scAMF, we compile a collection of 25 publicly available scRNA-seq datasets spanning various sequencing platforms, species, and organ types, forming an extensive RNA data bank. In our comparative studies, benchmarking scAMF against existing scRNA-seq analysis algorithms in this data bank, we consistently observe that scAMF outperforms in terms of clustering efficiency and data visualization clarity. Further experimental analysis reveals that this enhanced performance stems from scAMF's ability to improve the spatial distribution of the data and capture class-consistent neighborhoods. These findings underscore the promising application potential of manifold fitting as a tool in scRNA-seq analysis, signaling a significant enhancement in the precision and reliability of data interpretation in this critical field of study.
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Análisis de la Célula Individual , Análisis de la Célula Individual/métodos , Análisis por Conglomerados , Humanos , Análisis de Secuencia de ARN/métodos , Animales , Algoritmos , ARN/genética , Perfilación de la Expresión Génica/métodos , RNA-Seq/métodosRESUMEN
Background: Passion fruit (Passiflora edulis) is loved for its delicious flavor and nutritious juice. Although studies have delved into the cultivation and enhancement of passion fruit varieties, the underlying factors contributing to the fruit's appealing aroma remain unclear. Methods: This study analyzed the full-length transcriptomes of two passion fruit cultivars with different flavor profiles: "Tainong 1" (TN1), known for its superior fruit flavor, and "Guihan 1" (GH1), noted for its strong environmental resilience but lackluster taste. Utilizing PacBio Iso-Seq and Illumina RNA-Seq technologies, we discovered terpene synthase (TPS) genes implicated in fruit ripening that may help explain the flavor disparities. Results: We generated 15,913 isoforms, with N50 lengths of 1,500 and 1,648 bp, and mean lengths of 1,319 and 1,463 bp for TN1 and GH1, respectively. Transcript and isoform lengths ranged from a maximum of 7,779 bp to a minimum of 200 and 209 bp. We identified 14,822 putative coding DNA sequences (CDSs) averaging 1,063 bp, classified 1,007 transcription factors (TFs) into 84 families. Additionally, differential expression analysis of ripening fruit from both cultivars revealed 314 upregulated and 43 downregulated unigenes in TN1 compared to GH1. The top 10 significantly enriched Gene Ontology (GO) terms for the differentially expressed genes (DEGs) indicated that TN1's upregulated genes were primarily involved in nutrient transport, whereas GH1's up-regulated genes were associated with resistance mechanisms. Meanwhile, 17 PeTPS genes were identified in P. edulis and 13 of them were TPS-b members. A comparative analysis when compared PeTPS with AtTPS highlighted an expansion of the PeTPS-b subfamily in P. edulis, suggesting a role in its fruit flavor profile. Conclusion: Our findings explain that the formation of fruit flavor is attributed to the upregulation of essential genes in synthetic pathway, in particular the expansion of TPS-b subfamily involved in terpenoid synthesis. This finding will also provide a foundational genetic basis for understanding the nuanced flavor differences in this species.
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Frutas , Regulación de la Expresión Génica de las Plantas , Passiflora , RNA-Seq , Transcriptoma , Frutas/genética , Frutas/metabolismo , Passiflora/genética , RNA-Seq/métodos , Transcriptoma/genética , Transferasas Alquil y Aril/genética , Transferasas Alquil y Aril/metabolismo , Gusto/genética , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Análisis de Secuencia de ARN/métodos , Perfilación de la Expresión Génica/métodosRESUMEN
R package pathlinkR is designed to aid transcriptomic analyses by streamlining and simplifying the process of analyzing and interpreting differentially expressed genes derived from human RNA-Seq data. It provides an integrated approach to performing pathway enrichment and network-based analyses, while also producing publication-quality figures to summarize these results, allowing users to more efficiently interpret their findings and extract biological meaning from large amounts of data. pathlinkR is available to install from the software repository Bioconductor at https://bioconductor.org/packages/pathlinkR/, with support available through the Bioconductor forums. The code, example, and supporting data is available on the GitHub repository at https://github.com/hancockinformatics/pathlinkR, under the GPL-3.0 license, where users may report problems or make suggestions using GitHub's issue system.
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Biología Computacional , RNA-Seq , Programas Informáticos , Humanos , Biología Computacional/métodos , RNA-Seq/métodos , Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes/genética , Análisis de Secuencia de ARN/métodos , Transcriptoma/genéticaRESUMEN
It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by "Harmony" algorithm. The data was standardized and normalized by using "Seurat" package, and "SingleR" algorithm was used for cell grouping annotation. The "Findmarker" function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.
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RNA-Seq , Ubiquitina , Humanos , Animales , Ratones , Ubiquitina/metabolismo , Ubiquitina/genética , Análisis de la Célula Individual/métodos , Transcriptoma , Fibrosis Pulmonar/genética , Fibrosis Pulmonar/metabolismo , Fibrosis Pulmonar/patología , COVID-19/genética , COVID-19/metabolismo , COVID-19/virología , Perfilación de la Expresión Génica , Mapas de Interacción de Proteínas , Enfermedad Crónica , Fibrosis Pulmonar Idiopática/genética , Fibrosis Pulmonar Idiopática/patología , Fibrosis Pulmonar Idiopática/metabolismo , SARS-CoV-2/genética , Enfermedad Pulmonar Obstructiva Crónica/genética , Enfermedad Pulmonar Obstructiva Crónica/metabolismo , Análisis de Expresión Génica de una Sola CélulaRESUMEN
Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.
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COVID-19 , Células Dendríticas , RNA-Seq , SARS-CoV-2 , Análisis de la Célula Individual , Transcriptoma , Análisis de la Célula Individual/métodos , Humanos , COVID-19/virología , COVID-19/genética , RNA-Seq/métodos , Células Dendríticas/metabolismo , SARS-CoV-2/genética , Biología Computacional/métodos , Perfilación de la Expresión Génica/métodos , Células Asesinas Naturales/metabolismo , Inmunoterapia/métodos , Análisis de Secuencia de ARN/métodos , Análisis de Expresión Génica de una Sola CélulaRESUMEN
BACKGROUND: Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages' prognosis value in PCa. METHODS: Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. RESULTS: A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). CONCLUSIONS: We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.
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Biomarcadores de Tumor , Macrófagos , Neoplasias de la Próstata , Análisis de la Célula Individual , Microambiente Tumoral , Humanos , Masculino , Neoplasias de la Próstata/genética , Neoplasias de la Próstata/inmunología , Neoplasias de la Próstata/patología , Pronóstico , Biomarcadores de Tumor/genética , Biomarcadores de Tumor/metabolismo , Análisis de la Célula Individual/métodos , Microambiente Tumoral/inmunología , Microambiente Tumoral/genética , Macrófagos/metabolismo , Macrófagos/inmunología , RNA-Seq , Macrófagos Asociados a Tumores/inmunología , Macrófagos Asociados a Tumores/metabolismo , Regulación Neoplásica de la Expresión Génica , Perfilación de la Expresión Génica/métodos , Nomogramas , Análisis de Secuencia de ARN , Análisis de Expresión Génica de una Sola CélulaRESUMEN
BACKGROUND: In alfalfa (Medicago sativa), the coexistence of interfertile subspecies (i.e. sativa, falcata and coerulea) characterized by different ploidy levels (diploidy and tetraploidy) and the occurrence of meiotic mutants capable of producing unreduced (2n) gametes, have been efficiently combined for the establishment of new polyploids. The wealth of agronomic data concerning forage quality and yield provides a thorough insight into the practical benefits of polyploidization. However, many of the underlying molecular mechanisms regarding gene expression and regulation remained completely unexplored. In this study, we aimed to address this gap by examining the transcriptome profiles of leaves and reproductive tissues, corresponding to anthers and pistils, sampled at different time points from diploid and tetraploid Medicago sativa individuals belonging to progenies produced by bilateral sexual polyploidization (dBSP and tBSP, respectively) and tetraploid individuals stemmed from unilateral sexual polyploidization (tUSP). RESULTS: Considering the crucial role played by anthers and pistils in the reduced and unreduced gametes formation, we firstly analyzed the transcriptional profiles of the reproductive tissues at different stages, regardless of the ploidy level and the origin of the samples. By using and combining three different analytical methodologies, namely weighted-gene co-expression network analysis (WGCNA), tau (τ) analysis, and differentially expressed genes (DEGs) analysis, we identified a robust set of genes and transcription factors potentially involved in both male sporogenesis and gametogenesis processes, particularly in crossing-over, callose synthesis, and exine formation. Subsequently, we assessed at the same floral stage, the differences attributable to the ploidy level (tBSP vs. dBSP) or the origin (tBSP vs. tUSP) of the samples, leading to the identification of ploidy and parent-specific genes. In this way, we identified, for example, genes that are specifically upregulated and downregulated in flower buds in the comparison between tBSP and dBSP, which could explain the reduced fertility of the former compared to the latter materials. CONCLUSIONS: While this study primarily functions as an extensive investigation at the transcriptomic level, the data provided could represent not only a valuable original asset for the scientific community but also a fully exploitable genomic resource for functional analyses in alfalfa.
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Medicago sativa , RNA-Seq , Medicago sativa/genética , Transcriptoma , Ploidias , Regulación de la Expresión Génica de las Plantas , Genes de Plantas , Reproducción/genética , Flores/genética , Flores/crecimiento & desarrollo , Perfilación de la Expresión GénicaRESUMEN
Background: The mammalian testicular interstitial cells are not well-defined. The present study characterized the interstitial cell types and their turnover dynamics in adult rats. Additionally, the heterogeneity of the mesenchymal population and the effects of Leydig cell elimination on interstitial homeostasis were further analyzed by scRNA-seq datasets and immunocytochemical techniques. Methods: Interstitial cells were defined at the transcriptomic level by scRNA-seq and then confirmed and quantified with protein markers. The dividing activity of the major cell types was determined by continuous EdU labeling of the animals for one week. Some of the rats were also treated with a dose of ethylenedimethylsulfonate (EDS) to examine how the loss of Leydig cells (LCs) could affect interstitial homeostasis for three weeks. Results: Seven interstitial cell types were identified, including cell types (percentage of the whole interstitial population) as follows: Leydig (44.6%), macrophage and dendritic (19.1%), lymphoid (6.2%), vascular endothelial (7.9%), smooth muscle (10.7%), and mesenchymal (11.5%) cells. The EdU experiment indicated that most cell types were dividing at relatively low levels (<9%) except for the mesenchymal cells (MCs, 17.1%). Further analysis of the transcriptome of MCs revealed 4 subgroups with distinct functions, including 1) glutathione metabolism and xenobiotic detoxification, 2) ROS response and AP-1 signaling, 3) extracellular matrix synthesis and binding, and 4) immune response and regulation. Stem LCs (SLCs) are primarily associated with subgroup 3, expressing ARG1 and GAP43. EDS treatment not only eliminated LCs but also increased subgroup 3 and decreased subgroups 1 and 2 of the mesenchymal population. Moreover, EDS treatment increased the division of immune cells by more than tenfold in one week. Conclusion: Seven interstitial cell types were identified and quantified for rat testis. Many may play more diversified roles than previously realized. The elimination of LCs led to significant changes in MCs and immune cells, indicating the importance of LCs in maintaining testicular interstitial homeostasis.
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Células Intersticiales del Testículo , Masculino , Células Intersticiales del Testículo/metabolismo , Células Intersticiales del Testículo/efectos de los fármacos , Animales , Ratas , Inmunohistoquímica , Testículo/metabolismo , Testículo/citología , Ratas Sprague-Dawley , RNA-Seq , Transcriptoma , ARN Citoplasmático Pequeño/metabolismo , ARN Citoplasmático Pequeño/genética , Análisis de Expresión Génica de una Sola CélulaRESUMEN
Breast cancer (BC) is a malignant neoplasm which is classified into various types defined by underlying molecular factors such as estrogen receptor positive (ER+), progesterone receptor positive (PR+), human epidermal growth factor positive (HER2+) and triple negative (TNBC). Early detection of ER+ and TNBC is crucial in the choice of diagnosis and appropriate treatment strategy. Here we report the key genes associated to ER+ and TNBC using RNA-Seq analysis and machine learning models. Three ER+ and TNBC RNA seq datasets comprising 164 patients in-toto were selected for standard NGS hierarchical data processing and data analyses protocols. Enrichment pathway analysis and network analysis was done and finally top hub genes were identified. To come with a reliable classifier which could distinguish the distinct transcriptome patterns associated to ER+ and TNBC, ML models were built employing Naïve Bayes, SVM and kNN. 1730 common DEG's exhibiting significant logFC values with 0.05 p-value threshold were identified. A list of top ten hub genes were screened on the basis of maximal clique centrality (MCC) which included CDC20, CDK1, BUB1, AURKA, CDCA8, RRM2, TTK, CENPF, CEP55 and NDC80.These genes were found to be involved in crucial cell cycle pathways. k-Nearest Neighbor (kNN) model was observed to be best classifier with accuracy 84%, specificity 66% and sensitivity 95% to differentiate between ER+ and TNBC RNA-Seq transcriptomes. Our screened list of 10 hub genes can thus help unearth novel molecular signatures implicated in ER+ and TNBC onset, prognosis and design of novel protocols for breast cancer diagnostics and therapeutics.
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Receptores de Estrógenos , Neoplasias de la Mama Triple Negativas , Humanos , Femenino , Neoplasias de la Mama Triple Negativas/genética , Receptores de Estrógenos/metabolismo , Receptores de Estrógenos/genética , Regulación Neoplásica de la Expresión Génica , RNA-Seq/métodos , Biomarcadores de Tumor/genética , Perfilación de la Expresión Génica/métodos , Transcriptoma/genética , Neoplasias de la Mama/genética , Neoplasias de la Mama/clasificación , Neoplasias de la Mama/patología , Aprendizaje Automático , Redes Reguladoras de GenesRESUMEN
Oncostatin M (OSM) is a member of the interleukin-6 (IL-6) family of cytokines and has been found to have anti-inflammatory and pro-inflammatory properties in various cellular and disease contexts. OSM signals through two receptor complexes, one of which includes OSMRß. Here, we investigated OSM-OSMRß signaling in adult mouse hematopoietic stem cells (HSCs) using the conditional Osmrfl/fl mouse model B6;129-Osmrtm1.1Nat/J. We crossed Osmrfl/fl mice to interferon-inducible Mx1-Cre, which is robustly induced in adult HSCs. From these mice, we isolated HSCs by flow cytometry, stimulated with recombinant OSM or vehicle for 1 hour, and assessed gene expression changes in control versus Osmr knockout HSCs by RNA-seq. This data may be utilized to investigate OSMRß -dependent and -independent OSM signaling as well as the transcriptional effects of an IL-6 family cytokine on mouse HSCs to further define its anti-inflammatory versus pro-inflammatory properties.
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Células Madre Hematopoyéticas , Oncostatina M , Transducción de Señal , Animales , Ratones , Células Madre Hematopoyéticas/metabolismo , Células Madre Hematopoyéticas/efectos de los fármacos , Oncostatina M/farmacología , Subunidad beta del Receptor de Oncostatina M/genética , Análisis de Secuencia de ARN , Receptores de Oncostatina M/genética , RNA-SeqRESUMEN
BACKGROUND: Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. RESULTS: We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as "bubbles" at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. CONCLUSIONS: To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/.
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RNA-Seq , Análisis de la Célula Individual , Programas Informáticos , Análisis de la Célula Individual/métodos , RNA-Seq/métodos , Biología Computacional/métodos , Análisis de Secuencia de ARN/métodos , Humanos , Análisis por Conglomerados , Transcriptoma/genética , AlgoritmosRESUMEN
BACKGROUND: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. RESULTS: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. CONCLUSIONS: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach.
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RNA-Seq , Humanos , RNA-Seq/métodos , Análisis de Supervivencia , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidad , Neoplasias Pulmonares/metabolismo , Neoplasias Pulmonares/patología , Neoplasias/genética , Neoplasias/mortalidad , Neoplasias/metabolismo , Melanoma/genética , Melanoma/mortalidad , Melanoma/metabolismoRESUMEN
Dendritic cells (DCs) serve as key regulators in tumor immunity, with activated DCs potentiating antitumor responses through the secretion of pro-inflammatory cytokines and the expression of co-stimulatory molecules. Most current studies focus on the relationship between DC subgroups and clear-cell renal-cell carcinoma (ccRCC), but there is limited research on the connection between DCs and ccRCC from the perspective of immune activation. In this study, activated DC genes were identified in both bulk and single-cell RNA-seq data. A prognostic model related to activated DCs was constructed using univariate, multivariate Cox regression and LASSO regression. The prognostic model was validated in three external validation sets: GSE167573, ICGC, and E-MTAB-1980. The prognostic model consists of five genes, PLCB2, XCR1, IFNG, HLA-DQB2, and SMIM24. The expression of these genes was validated in tissue samples using qRT-PCR. Stratified analysis revealed that the prognostic model was able to better predict outcomes in advanced ccRCC patients. The risk scores were associated with tumor progression, tumor mutation burden, immune cell infiltration, and adverse outcomes of immunotherapy. Notably, there was a strong correlation between the expression of the five genes and the sensitivity to JQ1, a BET inhibitor. Molecular docking indicated high-affinity binding of the proteins encoded by these genes with JQ1. In conclusion, our study reveals the crucial role of activated DCs in ccRCC, offering new insights into predicting immune response, targeted therapy effectiveness, and prognosis for ccRCC patients.
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Carcinoma de Células Renales , Células Dendríticas , Neoplasias Renales , RNA-Seq , Análisis de la Célula Individual , Carcinoma de Células Renales/genética , Carcinoma de Células Renales/patología , Carcinoma de Células Renales/inmunología , Humanos , Células Dendríticas/metabolismo , Células Dendríticas/inmunología , Neoplasias Renales/genética , Neoplasias Renales/patología , Neoplasias Renales/inmunología , Neoplasias Renales/metabolismo , Pronóstico , Análisis de la Célula Individual/métodos , Regulación Neoplásica de la Expresión Génica , Biomarcadores de Tumor/genética , Masculino , Femenino , Análisis de Expresión Génica de una Sola CélulaRESUMEN
Glehnia littoralis is a perennial herb found in coastal sand dunes throughout East Asia. This herb has been reported to have hepatoprotective, immunomodulatory, antioxidant, antibacterial, antifungal, anti-inflammatory, and anticancer activities. It may be effective against hepatocellular carcinoma (HCC). However, whether this has been proven through gene-level RNA-seq analysis is still being determined. Therefore, we are attempting to identify target genes for the cell death process by analyzing the transcriptome of Hep3B cells among HCC treated with GLE (Glehnia littoralis extract) using RNA-seq. Hep3B was used for the GLE treatment, and the MTT test was performed. Hep3B was then treated with GLE at a set concentration of 300 µg/mL and stored for 24 h, followed by RNA isolation and sequencing. We then used the data to create a plot. As a result of the MTT analysis, cell death was observed when Hep3B cells were treated with GLE, and the IC50 was about 300 µg/mL. As a result of making plots using the RNA-seq data of Hep3B treated with 300 µg/mL GLE, a tendency for the apoptotic process was found. Flow cytometry and annexin V/propidium iodide (PI) staining verified the apoptosis of HEP3B cells treated with GLE. Therefore, an increase or decrease in the DEGs involved in the apoptosis process was confirmed. The top five genes increased were GADD45B, DDIT3, GADD45G, CHAC1, and PPP1R15A. The bottom five genes decreased were SGK1, CX3CL1, ZC3H12A, IER3, and HNF1A. In summary, we investigated the RNA-seq dataset of GLE to identify potential targets that may be involved in the apoptotic process in HCC. These goals may aid in the identification and management of HCC.
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Apoptosis , Carcinoma Hepatocelular , Neoplasias Hepáticas , Extractos Vegetales , RNA-Seq , Humanos , Apoptosis/efectos de los fármacos , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/tratamiento farmacológico , Carcinoma Hepatocelular/patología , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/tratamiento farmacológico , Neoplasias Hepáticas/patología , Extractos Vegetales/farmacología , Línea Celular Tumoral , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Transcriptoma/efectos de los fármacos , Perfilación de la Expresión Génica/métodosRESUMEN
Revealing the heterogeneity among tissues is the greatest advantage of single-cell-sequencing. Marker genes not only act as the key to correctly identify cell types, but also the bio-markers for cell-status under certain experimental imputations. Current analysis methods such as Seurat and Monocle employ algorithms which compares one cluster to all the rest and select markers according to statistical tests. This pattern brings redundant calculations and thus, results in low calculation efficiency, specificity and accuracy. To address these issues, we introduce starTracer, a novel algorithm designed to enhance the efficiency, specificity and accuracy of marker gene identification in single-cell RNA-seq data analysis. starTracer operates as an independent pipeline, which exhibits great flexibility by accepting multiple input file types. The primary output is a marker matrix, where genes are sorted by the potential to function as markers, with those exhibiting the greatest potential positioned at the top. The speed improvement ranges by 2 ~ 3 orders of magnitude compared to Seurat, as observed across three independent datasets with lower false positive rate as observed in a simulated testing dataset with ground-truth. It's worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. These advantages solidify starTracer as an important tool for single-cell RNA-seq data, merging robust accuracy with exceptional speed.