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      Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state

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          Abstract

          Idiopathic Parkinson’s disease is characterized by a progressive loss of dopaminergic neurons, but the exact disease aetiology remains largely unknown. To date, Parkinson’s disease research has mainly focused on nigral dopaminergic neurons, although recent studies suggest disease-related changes also in non-neuronal cells and in midbrain regions beyond the substantia nigra. While there is some evidence for glial involvement in Parkinson’s disease, the molecular mechanisms remain poorly understood. The aim of this study was to characterize the contribution of all cell types of the midbrain to Parkinson’s disease pathology by single-nuclei RNA sequencing and to assess the cell type-specific risk for Parkinson’s disease using the latest genome-wide association study.

          We profiled >41 000 single-nuclei transcriptomes of post-mortem midbrain from six idiopathic Parkinson’s disease patients and five age-/sex-matched controls. To validate our findings in a spatial context, we utilized immunolabelling of the same tissues. Moreover, we analysed Parkinson’s disease-associated risk enrichment in genes with cell type-specific expression patterns. We discovered a neuronal cell cluster characterized by CADPS2 overexpression and low TH levels, which was exclusively present in idiopathic Parkinson’s disease midbrains. Validation analyses in laser-microdissected neurons suggest that this cluster represents dysfunctional dopaminergic neurons. With regard to glial cells, we observed an increase in nigral microglia in Parkinson’s disease patients. Moreover, nigral idiopathic Parkinson’s disease microglia were more amoeboid, indicating an activated state. We also discovered a reduction in idiopathic Parkinson’s disease oligodendrocyte numbers with the remaining cells being characterized by a stress-induced upregulation of S100B. Parkinson’s disease risk variants were associated with glia- and neuron-specific gene expression patterns in idiopathic Parkinson’s disease cases. Furthermore, astrocytes and microglia presented idiopathic Parkinson’s disease-specific cell proliferation and dysregulation of genes related to unfolded protein response and cytokine signalling. While reactive patient astrocytes showed CD44 overexpression, idiopathic Parkinson’s disease microglia revealed a pro-inflammatory trajectory characterized by elevated levels of IL1B, GPNMB and HSP90AA1.

          Taken together, we generated the first single-nuclei RNA sequencing dataset from the idiopathic Parkinson’s disease midbrain, which highlights a disease-specific neuronal cell cluster as well as ‘pan-glial’ activation as a central mechanism in the pathology of the movement disorder. This finding warrants further research into inflammatory signalling and immunomodulatory treatments in Parkinson’s disease.

          Abstract

          See Lin and Narendra ( https://doi.org/10.1093/brain/awac071) for a scientific commentary on this article.

          To elucidate the contribution of individual cell types to Parkinson’s disease, Smajic et al. apply scRNA-seq to postmortem midbrain tissue. The results reveal a Parkinson’s disease-specific neuronal cluster, as well as changes in abundance, activation and an enrichment of Parkinson’s disease risk variants in glial cells.

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
            Bookmark
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            Fast unfolding of communities in large networks

            Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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              • Record: found
              • Abstract: found
              • Article: not found

              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

                Journal
                Brain
                Brain
                brainj
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                March 2022
                17 December 2021
                17 December 2021
                : 145
                : 3
                : 964-978
                Affiliations
                [1 ] Luxembourg Centre for Systems Biomedicine, University of Luxembourg , L-4362 Esch-sur-Alzette, Luxembourg
                [2 ] Max Planck Institute for Molecular Genetics , D-14195 Berlin, Germany
                [3 ] OrganoTherapeutics SARL-S , L-4362 Esch-sur-Alzette, Luxembourg
                [4 ] Institute of Human Genetics, Kiel University , D-42118 Kiel, Germany
                [5 ] Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University , NE1 7RU Newcastle upon Tyne, UK
                [6 ] Max-Delbrück-Centrum für Molekulare Medizin, Genomics Group , D-13125 Berlin, Germany
                [7 ] Institute of Neurogenetics, University of Lübeck , D-23562 Lübeck, Germany
                [8 ] Institute of Human Genetics, University of Lübeck , D-23562 Lübeck, Germany
                Author notes
                Correspondence to: Malte Spielmann Institute of Human Genetics University of Lübeck Ratzeburger Allee 160 23562 Lübeck, Germany E-mail: malte.spielmann@ 123456uksh.de
                Correspondence may also be addressed to: Anne Grünewald Luxembourg Centre for Systems Biomedicine University of Luxembourg 6 avenue du Swing L-4367 Belvaux, Luxembourg E-mail: anne.gruenewald@ 123456uni.lu
                [†,‡]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-7178-8040
                https://orcid.org/0000-0003-4496-0559
                https://orcid.org/0000-0001-8698-3770
                Article
                awab446
                10.1093/brain/awab446
                9050543
                34919646
                9537af47-c753-4253-b945-5b5211b0f10b
                © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 June 2021
                : 21 September 2021
                : 18 November 2021
                : 14 March 2022
                Page count
                Pages: 15
                Funding
                Funded by: Luxembourg National Research Fund;
                Award ID: PRIDE17/12244779/PARK-QC
                Funded by: Le Foyer Assurances Luxembourg;
                Funded by: National Centre of Excellence in Research on Parkinson’s disease;
                Funded by: ‘MiRisk-Parkinson’s disease’;
                Award ID: C17/BM/11676395
                Funded by: ‘ProtectMove’;
                Award ID: INTER/DFG/19/14429377
                Funded by: Deutsche Forschungsgemeinschaft, doi 10.13039/501100001659;
                Award ID: SP1532/3-1
                Award ID: SP1532/4-1
                Award ID: SP1532/5-1
                Funded by: Deutsches Zentrum für Luft- und Raumfahrt, doi 10.13039/501100002946;
                Funded by: Newcastle Brain Tissue Resource from UK MRC;
                Award ID: MR/L016451/1
                Funded by: National Institutes for Health Research Biomedical Research Centre Newcastle;
                Funded by: Parkinson’s UK, doi 10.13039/501100000304;
                Award ID: 258197
                Award ID: SC037554
                Categories
                Original Article
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                Neurosciences
                parkinson’s disease,midbrain substantia nigra,single-cell sequencing,microglia,neuroinflammation

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