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      Characterization of altered molecular mechanisms in Parkinson’s disease through cell type–resolved multiomics analyses

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          Abstract

          Parkinson’s disease (PD) is a progressive neurodegenerative disorder. However, cell type–dependent transcriptional regulatory programs responsible for PD pathogenesis remain elusive. Here, we establish transcriptomic and epigenomic landscapes of the substantia nigra by profiling 113,207 nuclei obtained from healthy controls and patients with PD. Our multiomics data integration provides cell type annotation of 128,724 cis-regulatory elements (cREs) and uncovers cell type–specific dysregulations in cREs with a strong transcriptional influence on genes implicated in PD. The establishment of high-resolution three-dimensional chromatin contact maps identifies 656 target genes of dysregulated cREs and genetic risk loci, uncovering both potential and known PD risk genes. Notably, these candidate genes exhibit modular gene expression patterns with unique molecular signatures in distinct cell types, highlighting altered molecular mechanisms in dopaminergic neurons and glial cells including oligodendrocytes and microglia. Together, our single-cell transcriptome and epigenome reveal cell type–specific disruption in transcriptional regulations related to PD.

          Abstract

          Single-nucleus transcriptome and epigenome uncover cell type–specific gene dysregulation in Parkinson’s disease.

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: ResourcesRole: VisualizationRole: Writing - review & editing
                Role: Formal analysisRole: InvestigationRole: SoftwareRole: Validation
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Software
                Role: InvestigationRole: Validation
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draft
                Role: Investigation
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: SoftwareRole: Writing - review & editing
                Role: ConceptualizationRole: Project administrationRole: Resources
                Role: ConceptualizationRole: ResourcesRole: SupervisionRole: Writing - review & editing
                Role: ConceptualizationRole: Resources
                Role: InvestigationRole: MethodologyRole: Writing - review & editing
                Role: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: Supervision
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Journal
                Sci Adv
                Sci Adv
                sciadv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                April 2023
                14 April 2023
                : 9
                : 15
                : eabo2467
                Affiliations
                [ 1 ]Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
                [ 2 ]Molecular Neuropathology Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
                [ 3 ]School of Biological Sciences and Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826, Republic of Korea.
                [ 4 ]Department of Biomedical Sciences, Department of Medicine, Neuroscience Research Institute, Convergence Research Center for Dementia, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.
                [ 5 ]Neuramedy Co. Ltd., Seoul 04796, Republic of Korea.
                [ 6 ]Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
                [ 7 ]Department Neurosciences, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
                Author notes
                [†]

                These authors contributed equally to this work.

                [* ]Corresponding author. Email: ijung@ 123456kaist.ac.kr (I.J.); eliezer.masliah@ 123456nih.gov (E.M.)
                Author information
                https://orcid.org/0000-0001-9917-8834
                https://orcid.org/0000-0001-9936-8195
                https://orcid.org/0000-0001-9612-0020
                https://orcid.org/0000-0002-5637-6029
                https://orcid.org/0000-0002-5373-9082
                https://orcid.org/0000-0003-2311-5794
                https://orcid.org/0000-0001-5317-5531
                https://orcid.org/0000-0001-5301-6776
                https://orcid.org/0000-0002-5155-5335
                https://orcid.org/0000-0001-9245-8278
                https://orcid.org/0000-0001-5894-7537
                https://orcid.org/0000-0002-2117-5569
                https://orcid.org/0000-0002-5885-2754
                Article
                abo2467
                10.1126/sciadv.abo2467
                10104466
                37058563
                2eef7197-fb35-4c4a-b479-43d9bfecdab8
                Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 22 January 2022
                : 09 March 2023
                Funding
                Funded by: Suh Kyungbae Foundation;
                Funded by: Korean Ministry of Health and Welfare;
                Award ID: HI19C0256
                Funded by: Ministry of Science and ICT through the National Research Foundation in the Republic of Korea;
                Award ID: NRF-2020R1A2C4001464
                Categories
                Research Article
                Neuroscience
                SciAdv r-articles
                Diseases and Disorders
                Molecular Biology
                Diseases and Disorders
                Custom metadata
                Jeanelle Ebreo

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