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      Architecture of the subthalamic nucleus

      review-article
      1 , , 2 ,
      Communications Biology
      Nature Publishing Group UK
      Cellular neuroscience, Basal ganglia

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          Abstract

          The subthalamic nucleus (STN) is a major neuromodulation target for the alleviation of neurological and neuropsychiatric symptoms using deep brain stimulation (DBS). STN-DBS is today applied as treatment in Parkinson´s disease, dystonia, essential tremor, and obsessive-compulsive disorder (OCD). STN-DBS also shows promise as a treatment for refractory Tourette syndrome. However, the internal organization of the STN has remained elusive and challenges researchers and clinicians: How can this small brain structure engage in the multitude of functions that renders it a key hub for therapeutic intervention of a variety of brain disorders ranging from motor to affective to cognitive? Based on recent gene expression studies of the STN, a comprehensive view of the anatomical and cellular organization, including revelations of spatio-molecular heterogeneity, is now possible to outline. In this review, we focus attention to the neurobiological architecture of the STN with specific emphasis on molecular patterns discovered within this complex brain area. Studies from human, non-human primate, and rodent brains now reveal anatomically defined distribution of specific molecular markers. Together their spatial patterns indicate a heterogeneous molecular architecture within the STN. Considering the translational capacity of targeting the STN in severe brain disorders, the addition of molecular profiling of the STN will allow for advancement in precision of clinical STN-based interventions.

          Abstract

          The subthalamic nucleus is a highly evolutionary conserved brain region. Emerging studies reveal spatio-molecular heterogeneity leading to improved understanding of the organization of the STN and targeting for specific neurological disorders.

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          Most cited references184

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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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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Single-cell gene expression analyses hold promise for characterizing cellular heterogeneity, but current methods compromise on either the coverage, the sensitivity or the throughput. Here, we introduce Smart-seq2 with improved reverse transcription, template switching and preamplification to increase both yield and length of cDNA libraries generated from individual cells. Smart-seq2 transcriptome libraries have improved detection, coverage, bias and accuracy compared to Smart-seq libraries and are generated with off-the-shelf reagents at lower cost.
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                Author and article information

                Contributors
                asheeta.prasad@sydney.edu.au
                asa.mackenzie@ebc.uu.se
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                10 January 2024
                10 January 2024
                2024
                : 7
                : 78
                Affiliations
                [1 ]University of Sydney, School of Medical Sciences, Faculty of Medicine and Health, ( https://ror.org/0384j8v12) Sydney, NSW Australia
                [2 ]Uppsala University, Department of Organism Biology, ( https://ror.org/048a87296) 756 32 Uppsala, Sweden
                Author information
                http://orcid.org/0000-0001-9752-8175
                http://orcid.org/0000-0002-8713-070X
                Article
                5691
                10.1038/s42003-023-05691-4
                10782020
                38200143
                0899e18f-37a3-4f04-84bf-d0f086840546
                © Crown 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 June 2023
                : 11 December 2023
                Funding
                Funded by: Preparation of this manuscript was supported by grants to A.A.P. from the National Health and Medical Research Council of Australia (APP1160412) and to Å.W-M. from the Swedish Research Council (Vetenskapsrådet), Swedish Brain Foundation (Hjärnfonden), Swedish Parkinson Foundation (Parkinsonfonden), Hållsten Research Foundation and Åhlénstiftelsen.
                Categories
                Review Article
                Custom metadata
                © Springer Nature Limited 2024

                cellular neuroscience,basal ganglia
                cellular neuroscience, basal ganglia

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