Dataset Information

SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.

ABSTRACT: Summary: Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional networks. Targeted perturbation of these signaling hotspots can enable precise control of cell subpopulation phenotypes. SigHotSpotter correctly predicts the signaling hotspots with known experimental validations in different cellular systems. The tool is simple, user-friendly and is available as web-server or as stand-alone software. We believe SigHotSpotter will serve as a general purpose tool for the systematic prediction of signaling hotspots based on single-cell RNA-seq data, and potentiate novel cell rejuvenation strategies in the context of disease and ageing.

Availability and implementation: SigHotSpotter is at https://SigHotSpotter.lcsb.uni.lu as a web tool. Source code, example datasets and other information are available at https://gitlab.com/srikanth.ravichandran/sighotspotter.

Supplementary information: Supplementary data are available at Bioinformatics online.

SUBMITTER: Ravichandran S

PROVIDER: S-EPMC7703776 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.

Ravichandran Srikanth S Hartmann András A Del Sol Antonio A

Bioinformatics (Oxford, England) 20191107

<h4>Summary</h4>Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation pheno ...[more]

PMID: 31697324

Similar Datasets

Project description:BackgroundThe stria vascularis (SV), located in the lateral wall of the cochlea, maintains cochlear fluid homeostasis and mechanoelectrical transduction (MET) activity required for sound wave conduction. The pathogenesis of a number of human inheritable deafness syndromes, age related hearing loss, drug-induced ototoxicity and noise-induced hearing loss results from the morphological changes and functional impairments in the development of the SV. In this study, we investigate the implications of intercellular communication within the SV in the pathogenesis of sensorineural hearing loss (SNHL). We aim to identify commonly regulated signaling pathways using publicly available single-cell transcriptomic sequencing (scRNA-seq) datasets.MethodsWe analyzed scRNA-seq data, which was derived from studying the cochlear SV in mice with SNHL compared to normal adult mice. After quality control and filtering, we obtained the major cellular components of the mouse cochlear SV and integrated the data. Using Seurat's FindAllMarkers and FindMarkers packages, we searched for novel conservative genes and differential genes. We employed KEGG and GSEA to identify molecular pathways that are commonly altered among different types of SNHL. We utilized pySCENIC to discover new specific regulatory factors in SV subpopulation cells. With the help of CellChat, we identified changes in subpopulation cells showing similar trends across different SNHL types and their alterations in intercellular communication pathways.ResultsThrough the analysis of the integrated data, we discovered new conserved genes to SV specific cells and identified common downregulated pathways in three types of SNHL. The enriched genes for these pathways showing similar trends are primarily associated with the Electron Transport Chain, related to mitochondrial energy metabolism. Using the CellChat package, we further found that there are shared pathways in the incoming signaling of specific intermediate cells in SNHL, and these pathways have common upstream regulatory transcription factor of Nfe2l2. Combining the results from pySCENIC and CellChat, we predicted the transcription factor Nfe2l2 as an upstream regulatory factor for multiple shared cellular pathways in IC. Additionally, it serves as an upstream factor for several genes within the Electron Transport Chain.ConclusionOur bioinformatics analysis has revealed that downregulation of the mitochondrial electron transport chain have been observed in various conditions of SNHL. E2f1, Esrrb, Runx1, Yy1, and Gata2 could serve as novel important common TFs regulating the electron transport chain. Adm has emerged as a potential new marker gene for intermediate cells, while Itgb5 and Tesc show promise as potential new marker genes for marginal cells in the SV. These findings offer a new perspective on SV lesions in SNHL and provide additional theoretical evidence for the same drug treatment and prevention of different pathologies of SNHL.

Project description:Pancreatic ductal adenocarcinoma (PDAC), a very aggressive tumour, is currently the third leading cause of cancer-related deaths. Unfortunately, many patients face the issue of inoperability at the diagnostic phase leading to a quite dismal prognosis. The onset of metastatic processes has a crucial role in the elevated mortality rates linked to PDAC. Individuals with metastatic advances receive only palliative therapy and have a grim prognosis. It is essential to carefully analyse the intricacies of the metastatic process to enhance the prognosis for individuals with PDAC. Malignancy development is greatly impacted by the process of macrophage efferocytosis. Our current knowledge about the complete range of macrophage efferocytosis activities in PDAC and their intricate interactions with tumour cells is still restricted. This work aims to resolve communication gaps and pinpoint the essential transcription factor that is vital in the immunological response of macrophage populations. We analysed eight PDAC tissue samples sourced from the gene expression omnibus. We utilized several software packages such as Seurat, DoubletFinder, Harmony, Pi, GSVA, CellChat and Monocle from R software together with pySCENIC from Python, to analyse the single-cell RNA sequencing (scRNA-seq) data collected from the PDAC samples. This study involved the analysis of a comprehensive sample of 22,124 cells, which were classified into distinct cell types. These cell types encompassed endothelial and epithelial cells, PDAC cells, as well as various immune cells, including CD4+ T cells, CD8+ T cells, NK cells, B cells, plasma cells, mast cells, monocytes, DC cells and different subtypes of macrophages, namely C0 macrophage TGM2+, C1 macrophage PFN1+, C2 macrophage GAS6+ and C3 macrophage APOC3+. The differentiation between tumour cells and epithelial cells was achieved by the implementation of CopyKat analysis, resulting in the detection and categorization of 1941 PDAC cells. The amplification/deletion patterns observed in PDAC cells on many chromosomes differ significantly from those observed in epithelial cells. The study of Pseudotime Trajectories demonstrated that the C0 macrophage subtype expressing TGM2+ had the lowest level of differentiation. Additionally, the examination of gene set scores related to efferocytosis suggested that this subtype displayed higher activity during the efferocytosis process compared to other subtypes. The most active transcription factors for each macrophage subtype were identified as BACH1, NFE2, TEAD4 and ARID3A. In conclusion, the examination of human PDAC tissue samples using immunofluorescence analysis demonstrated the co-localization of CD68 and CD11b within regions exhibiting the presence of keratin (KRT) and alpha-smooth muscle actin (α-SMA). This observation implies a spatial association between macrophages, fibroblasts, and epithelial cells. There is variation in the expression of efferocytosis-associated genes between C0 macrophage TGM2+ and other macrophage cell types. This observation implies that the diversity of macrophage cells might potentially influence the metastatic advancement of PDAC. Moreover, the central transcription factor of different macrophage subtypes offers a promising opportunity for targeted immunotherapy in the treatment of PDAC.

Dataset Information

SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.

Publications

SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets