Similar Datasets
Project description:Single-cell and spatial mapping identify cell types and signaling networks in the human ureter
| PRJNA799264 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-03-23 | GSE105115 | GEO
Project description:The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types. We combined single-cell RNA-seq (scRNA-seq) with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of ?13,000 habenular cells with 4× cellular coverage identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a reference atlas created a resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions.
| S-EPMC6042852 | biostudies-literature
Project description:If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant cell types. However, it is unclear which of the many marks are most useful in defining cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically cell type specific (P < 1 × 10(-6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(-5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T cells (P = 1 × 10(-4)), 67 SNPs for type 2 diabetes in pancreatic islet cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how cell type-specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation.
| S-EPMC3826950 | biostudies-literature
Project description:Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.
| S-EPMC6063279 | biostudies-literature
Project description:Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.
| S-EPMC5120284 | biostudies-literature
Project description:Previous lesion behavior studies primarily used univariate lesion behavior mapping techniques to map the anatomical basis of spatial neglect after right brain damage. These studies led to inconsistent results and lively controversies. Given these inconsistencies, the idea of a wide-spread network that might underlie spatial orientation and neglect has been pushed forward. In such case, univariate lesion behavior mapping methods might have been inherently limited in detecting the presumed network due to limited statistical power. By comparing various univariate analyses with multivariate lesion-mapping based on support vector regression, we aimed to validate the network hypothesis directly in a large sample of 203 newly recruited right brain damaged patients. If the exact same correction factors and parameter combinations (FDR correction and dTLVC for lesion size control) were used, both univariate as well as multivariate approaches uncovered the same complex network pattern underlying spatial neglect. At the cortical level, lesion location dominantly affected the temporal cortex and its borders into inferior parietal and occipital cortices. Beyond, frontal and subcortical gray matter regions as well as white matter tracts connecting these regions were affected. Our findings underline the importance of a right network in spatial exploration and attention and specifically in the emergence of the core symptoms of spatial neglect.
| S-EPMC6765420 | biostudies-literature
Project description:Kinase signaling networks are well-established mediators of cell cycle transitions. However, how kinases interact with the ubiquitin proteasome system (UPS) to elicit protein turnover is not fully understood. We sought a means of identifying kinase-substrate interactions to better understand signaling pathways controlling protein degradation. Our prior studies used a luciferase fusion protein to uncover kinase networks controlling protein turnover. In this study, we utilized a similar approach to identify pathways controlling the cell cycle protein p27(Kip1). We generated a p27(Kip1)-luciferase fusion and expressed it in cells incubated with compounds from a library of pharmacologically active compounds. We then compared the relative effects of the compounds on p27(Kip1)-luciferase fusion stabilization. This was combined with in silico kinome profiling to identify potential kinases inhibited by each compound. This approach effectively uncovered known kinases regulating p27(Kip1) turnover. Collectively, our studies suggest that this parallel screening approach is robust and can be applied to fully understand kinase-ubiquitin pathway interactions.
| S-EPMC4614911 | biostudies-literature
Project description:MotivationRecent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell-cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact.ResultsWe developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells.Availability and implementationMESSI is available at: https://github.com/doraadong/MESSI.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8128476 | biostudies-literature
Project description:The new coronavirus (SARS-CoV-2) outbreak from December 2019 in Wuhan, Hubei, China, has been declared a global public health emergency. Angiotensin I converting enzyme 2 (ACE2), is the host receptor by SARS-CoV-2 to infect human cells. Although ACE2 is reported to be expressed in lung, liver, stomach, ileum, kidney and colon, its expressing levels are rather low, especially in the lung. SARS-CoV-2 may use co-receptors/auxiliary proteins as ACE2 partner to facilitate the virus entry. To identify the potential candidates, we explored the single cell gene expression atlas including 119 cell types of 13 human tissues and analyzed the single cell co-expression spectrum of 51 reported RNA virus receptors and 400 other membrane proteins. Consistent with other recent reports, we confirmed that ACE2 was mainly expressed in lung AT2, liver cholangiocyte, colon colonocytes, esophagus keratinocytes, ileum ECs, rectum ECs, stomach epithelial cells, and kidney proximal tubules. Intriguingly, we found that the candidate co-receptors, manifesting the most similar expression patterns with ACE2 across 13 human tissues, are all peptidases, including ANPEP, DPP4 and ENPEP. Among them, ANPEP and DPP4 are the known receptors for human CoVs, suggesting ENPEP as another potential receptor for human CoVs. We also conducted "CellPhoneDB" analysis to understand the cell crosstalk between CoV-targets and their surrounding cells across different tissues. We found that macrophages frequently communicate with the CoVs targets through chemokine and phagocytosis signaling, highlighting the importance of tissue macrophages in immune defense and immune pathogenesis.
| S-EPMC7156119 | biostudies-literature