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      Single-cell RNA sequencing of CSF reveals neuroprotective RAC1 + NK cells in Parkinson’s disease

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          Abstract

          Brain infiltration of the natural killer (NK) cells has been observed in several neurodegenerative disorders, including Parkinson’s disease (PD). In a mouse model of α-synucleinopathy, it has been shown that NK cells help in clearing α-synuclein (α-syn) aggregates. This study aimed to investigate the molecular mechanisms underlying the brain infiltration of NK cells in PD. Immunofluorescence assay was performed using the anti-NKp46 antibody to detect NK cells in the brain of PD model mice. Next, we analyzed the publicly available single-cell RNA sequencing (scRNA-seq) data (GSE141578) of the cerebrospinal fluid (CSF) from patients with PD to characterize the CSF immune landscape in PD. Results showed that NK cells infiltrate the substantia nigra (SN) of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD model mice and colocalize with dopaminergic neurons and α-syn. Moreover, the ratio of NK cells was found to be increased in the CSF of PD patients. Analysis of the scRNA-seq data revealed that Rac family small GTPase 1 (RAC1) was the most significantly upregulated gene in NK cells from PD patients. Furthermore, genes involved in regulating SN development were enriched in RAC1 + NK cells and these cells showed increased brain infiltration in MPTP-induced PD mice. In conclusion, NK cells actively home to the SN of PD model mice and RAC1 might be involved in regulating this process. Moreover, RAC1 + NK cells play a neuroprotective role in PD.

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          Most cited references31

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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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            clusterProfiler 4.0: A universal enrichment tool for interpreting omics data

            Summary Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
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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
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                21 September 2022
                2022
                : 13
                : 992505
                Affiliations
                [1] 1 Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [2] 2 Department of Public Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [3] 3 Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [4] 4 Department of General Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                [5] 5 Department of Pathophysiology, Key Laboratory of Ministry of Education for Neurological Disorders, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China
                Author notes

                Edited by: Uday Kishore, Brunel University London, United Kingdom

                Reviewed by: Abhishek Shastri, Central and North West London NHS Foundation Trust, United Kingdom; Liu-lin Xiong, Sichuan University, China

                *Correspondence: Liming Cheng, chengliming2015@ 123456163.com ; Xiong Wang, wangxiong@ 123456tjh.tjmu.edu.cn

                †These authors have equally contributed to this work

                This article was submitted to Molecular Innate Immunity, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.992505
                9532252
                36211372
                317950ca-b31b-4936-8763-af7f9d09c8ca
                Copyright © 2022 Guan, Liu, Mu, Hu, Xie, Cheng and Wang

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 July 2022
                : 05 September 2022
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 31, Pages: 10, Words: 3725
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81500925 , 81901713
                Categories
                Immunology
                Original Research

                Immunology
                parkinson’s disease,nk cells,single-cell rna sequencing,rac1,brain infiltration
                Immunology
                parkinson’s disease, nk cells, single-cell rna sequencing, rac1, brain infiltration

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