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      Single-cell molecular profiling of all three components of the HPA axis reveals adrenal ABCB1 as a regulator of stress adaptation

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      1 , 2 , 1 , 2 , 3 , 4 , 5 , 1 , 2 , 6 , 1 , 2 , 7 , 3 , 8 , 8 , 8 , 8 , 9 , 9 , 1 , 1 , 8 , 7 , 7 , 7 , 10 , 10 , 11 , 12 , 12 , 13 , 9 , 8 , 6 , 14 , 7 , 15 , 4 , 5 , 1 , 2 , 16 , *
      Science Advances
      American Association for the Advancement of Science

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          Abstract

          Dissection of the HPA axis using single-cell RNA sequencing uncovers adrenal ABCB1 as an important player of stress adaptation.

          Abstract

          Chronic activation and dysregulation of the neuroendocrine stress response have severe physiological and psychological consequences, including the development of metabolic and stress-related psychiatric disorders. We provide the first unbiased, cell type–specific, molecular characterization of all three components of the hypothalamic-pituitary-adrenal axis, under baseline and chronic stress conditions. Among others, we identified a previously unreported subpopulation of Abcb1b + cells involved in stress adaptation in the adrenal gland. We validated our findings in a mouse stress model, adrenal tissues from patients with Cushing’s syndrome, adrenocortical cell lines, and peripheral cortisol and genotyping data from depressed patients. This extensive dataset provides a valuable resource for researchers and clinicians interested in the organism’s nervous and endocrine responses to stress and the interplay between these tissues. Our findings raise the possibility that modulating ABCB1 function may be important in the development of treatment strategies for patients suffering from metabolic and stress-related psychiatric disorders.

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          Most cited references68

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          A RATING SCALE FOR DEPRESSION

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            SCANPY : large-scale single-cell gene expression data analysis

            Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                January 2021
                27 January 2021
                : 7
                : 5
                : eabe4497
                Affiliations
                [1 ]Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Bavaria 80804, Germany.
                [2 ]The Max Planck Society–Weizmann Institute of Science Laboratory for Experimental Neuropsychiatry and Behavioral Neurogenetics, Rehovot 76100, Israel and Munich, Bavaria 80804, Germany.
                [3 ]International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Bavaria 80804, Germany.
                [4 ]Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE11UL, UK.
                [5 ]Department of Internal Medicine III, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Saxony 01307, Germany.
                [6 ]Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria 85764, Germany.
                [7 ]Department for Endocrinology, Medizinische Klinik und Poliklinik IV, Ludwig-Maximilians-University, Munich, Bavaria 80336, Germany.
                [8 ]Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Bavaria 80804, Germany.
                [9 ]Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Bavaria 80804, Germany.
                [10 ]Department of Internal Medicine I, Division of Endocrinology and Diabetes, University Hospital, University of Würzburg, Würzburg, Bavaria 97080, Germany.
                [11 ]Departments of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                [12 ]Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30329, USA.
                [13 ]Department of Psychiatry, University of Texas at Austin Dell Medical School, Austin, TX 78738, USA.
                [14 ]Department of Mathematics, Technische Universität München, Munich, Bavaria 85748, Germany.
                [15 ]Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitätsspital Zürich, Zurich 8091, Switzerland.
                [16 ]Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel.
                Author notes
                [* ]Corresponding author. Email: alon_chen@ 123456psych.mpg.de
                Author information
                http://orcid.org/0000-0002-5812-4220
                http://orcid.org/0000-0002-6213-0973
                http://orcid.org/0000-0002-1533-5562
                http://orcid.org/0000-0002-2079-5555
                http://orcid.org/0000-0002-7450-0871
                http://orcid.org/0000-0003-2230-2846
                http://orcid.org/0000-0002-6789-6548
                http://orcid.org/0000-0001-8274-7076
                http://orcid.org/0000-0001-5631-5552
                http://orcid.org/0000-0003-3231-7435
                http://orcid.org/0000-0002-6271-5533
                http://orcid.org/0000-0001-6170-6398
                http://orcid.org/0000-0002-1672-2716
                http://orcid.org/0000-0002-4653-0483
                http://orcid.org/0000-0001-7867-1160
                http://orcid.org/0000-0002-3788-2268
                http://orcid.org/0000-0001-7088-6618
                http://orcid.org/0000-0002-2419-1943
                http://orcid.org/0000-0001-7826-3984
                http://orcid.org/0000-0003-4311-5855
                http://orcid.org/0000-0003-3625-8233
                Article
                abe4497
                10.1126/sciadv.abe4497
                7840126
                33571131
                7541b039-56e0-43fe-bb40-b771f8ff6bdc
                Copyright © 2021 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
                : 21 August 2020
                : 09 December 2020
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Diseases and Disorders
                Neuroscience
                Neuroscience
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                Mjoy Azul

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