13 research outputs found

    Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma

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    Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide; however, the molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and of overlapping expression and splicing quantitative trait loci (e/QTLs and sQTLs) in 49 GTEx tissues and retina prioritizesd causal genes for 60% of loci. These genes awere enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues revealesd that the colocalizing genes and genome-wide POAG and IOP associations awere enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominatesd IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis

    Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

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    Abstract Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types

    Optimal Cost-Based Strengthening of Complex Networks

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    Most real-world complex systems are extremely vulnerable to targeted attacks, making their immunization an important yet challenging task. One of the most effective attack strategies is targeting articulation points specifically. In this study, we first propose a generalized definition of network robustness. Then we address the problem of strengthening network robustness with the minimum cost by exploiting the intuition behind the attack strategy based on articulation points, that is proven to be NP-hard. Accordingly, we propose a heuristic for solving this problem, subject to a cost function whose choice determines the obtained network regime. Experiments on both random and real-world networks show that our algorithm strengthens the robustness by using significantly cheaper edge additions than state-of-the-art methods. Moreover, our algorithm excels against general attack strategies by revealing the essence of strengthening network robustness, that is, increasing the size of the giant connected component optimally in the process of node removal. While considering the realistic problem, our algorithm also provides a reasonable scheme to add edges at a low cost.Peer reviewe

    Spatial organization of the mouse retina at single cell resolution by MERFISH

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    Abstract The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina, little is known about their spatial organization. Here, we generated the single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location

    Additional file 1 of Single-cell multiomics of the human retina reveals hierarchical transcription factor collaboration in mediating cell type-specific effects of genetic variants on gene regulation

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    Additional file 1: Fig. S1. The OCRs identified from snATAC-seq. Fig. S2. Identification of sc-eQTL. Fig. S3. Identification of sc-caQTL. Fig. S4. Identification of sc-caQTL in down-sampled cells. Fig. S5. Cell type enrichment and fine-mapping of GWAS loci. Fig. S6. An example of the fine-mapped GWAS candidate variants with the retinal bulk eQTL signal of its target gene colocalized with GWAS signal. Supplementary Note. Acronyms list
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