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      Transcriptomic atlas of midbrain dopamine neurons uncovers differential vulnerability in a Parkinsonism lesion model

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          Abstract

          Midbrain dopamine (mDA) neurons comprise diverse cells with unique innervation targets and functions. This is illustrated by the selective sensitivity of mDA neurons of the substantia nigra compacta (SNc) in patients with Parkinson’s disease, while those in the ventral tegmental area (VTA) are relatively spared. Here, we used single nuclei RNA sequencing (snRNA-seq) of approximately 70,000 mouse midbrain cells to build a high-resolution atlas of mouse mDA neuron diversity at the molecular level. The results showed that differences between mDA neuron groups could best be understood as a continuum without sharp differences between subtypes. Thus, we assigned mDA neurons to several ‘territories’ and ‘neighborhoods’ within a shifting gene expression landscape where boundaries are gradual rather than discrete. Based on the enriched gene expression patterns of these territories and neighborhoods, we were able to localize them in the adult mouse midbrain. Moreover, because the underlying mechanisms for the variable sensitivities of diverse mDA neurons to pathological insults are not well understood, we analyzed surviving neurons after partial 6-hydroxydopamine (6-OHDA) lesions to unravel gene expression patterns that correlate with mDA neuron vulnerability and resilience. Together, this atlas provides a basis for further studies on the neurophysiological role of mDA neurons in health and disease.

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          Most cited references59

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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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.
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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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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                08 April 2024
                2024
                : 12
                : RP89482
                Affiliations
                [1 ] Department of Cell and Molecular Biology, Karolinska Institutet ( https://ror.org/056d84691) Stockholm Sweden
                [2 ] Department of Neurology, Karolinska University Hospital ( https://ror.org/00m8d6786) Stockholm Sweden
                [3 ] Department of Clinical Neuroscience, Karolinska Institutet ( https://ror.org/056d84691) Stockholm Sweden
                Harvard University ( https://ror.org/03vek6s52) United States
                Brown University ( https://ror.org/05gq02987) United States
                Harvard University United States
                Karolinska Institute Stockholm Sweden
                Karolinska Institutet Stockholm Sweden
                Karolinska Institute Stockholm Sweden
                Karolinska Institute Stockholm Sweden
                Karolinska Institute Stockholm Sweden
                Karolinska Institutet Stockholm Sweden
                Karolinska Institute Stockholm Sweden
                Author information
                https://orcid.org/0000-0002-4221-6243
                https://orcid.org/0000-0003-4821-8036
                Article
                89482
                10.7554/eLife.89482
                11001297
                38587883
                1a322d93-4e2a-4455-bd5b-743445f3e020
                © 2023, Yaghmaeian Salmani et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 26 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: VR 2020-00884
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003792, Hjärnfonden;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007464, Torsten Söderbergs Stiftelse;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004047, Karolinska Institutet;
                Award ID: Postdoctoral grant
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: VR 2016-02506
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Neuroscience
                Custom metadata
                Single nuclei RNA sequencing unmasks 16 variants of midbrain dopamine neurons within a continuum of their gene expression landscape and resolves their differential vulnerability.
                prc

                Life sciences
                substantia nigra,ventral tegmental area,vulnerability,6- hydroxydopamine,single-cell rna sequencing,mouse

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