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      Single cell transcriptional zonation of human psoriasis skin identifies an alternative immunoregulatory axis conducted by skin resident cells

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          Abstract

          Psoriasis is the most common skin disease in adults. Current experimental and clinical evidences suggested the infiltrating immune cells could target local skin cells and thus induce psoriatic phenotype. However, recent studies indicated the existence of a potential feedback signaling loop from local resident skin cells to infiltrating immune cells. Here, we deconstructed the full-thickness human skins of both healthy donors and patients with psoriasis vulgaris at single cell transcriptional level, and further built a neural-network classifier to evaluate the evolutional conservation of skin cell types between mouse and human. Last, we systematically evaluated the intrinsic and intercellular molecular alterations of each cell type between healthy and psoriatic skin. Cross-checking with psoriasis susceptibility gene loci, cell-type based differential expression, and ligand-receptor communication revealed that the resident psoriatic skin cells including mesenchymal and epidermis cell types, which specifically harbored the target genes of psoriasis susceptibility loci, intensively evoked the expression of major histocompatibility complex (MHC) genes, upregulated interferon (INF), tumor necrosis factor (TNF) signalling and increased cytokine gene expression for primarily aiming the neighboring dendritic cells in psoriasis. The comprehensive exploration and pathological observation of psoriasis patient biopsies proposed an uncovered immunoregulatory axis from skin local resident cells to immune cells, thus provided a novel insight for psoriasis treatment. In addition, we published a user-friendly website to exhibit the transcriptional change of each cell type between healthy and psoriatic human skin.

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          Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

          Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.
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            Molecular Architecture of the Mouse Nervous System

            Summary The mammalian nervous system executes complex behaviors controlled by specialized, precisely positioned, and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse and were grouped by developmental anatomical units and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission, and membrane conductance. We discovered seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system and enables genetic manipulation of specific cell types.
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              DNA methylation-based classification of central nervous system tumours

              Summary Accurate pathological diagnosis is crucial for optimal management of cancer patients. For the ~100 known central nervous system (CNS) tumour entities, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variability in the histopathological diagnosis of many tumour types. We herein present the development of a comprehensive approach for DNA methylation-based CNS tumour classification across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that availability of this method may have substantial impact on diagnostic precision compared with standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility we have designed a free online classifier tool (www.molecularneuropathology.org) requiring no additional onsite data processing. Our results provide a blueprint for the generation of machine learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.
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                Author and article information

                Contributors
                lyzdermatology@163.com
                yizhou.hu@ki.se
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                6 May 2021
                6 May 2021
                May 2021
                : 12
                : 5
                : 450
                Affiliations
                [1 ]GRID grid.412463.6, ISNI 0000 0004 1762 6325, Department of Dermatology, , The Second Affiliated Hospital of Harbin Medical University, ; No.246 Xuefu Road, 150001 Harbin, China
                [2 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Institute of Biotechnology, HILIFE Unit, , University of Helsinki, ; 00014 Helsinki, Finland
                [3 ]GRID grid.411472.5, ISNI 0000 0004 1764 1621, Department of Dermatology, , Peking University First Hospital, ; No.8 XiShiKu Street, 100034 Beijing, China
                [4 ]GRID grid.412463.6, ISNI 0000 0004 1762 6325, Department of Oncology, , The Second Affiliated Hospital of Harbin Medical University, ; No.246 Xuefu Road, 150001 Harbin, China
                [5 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, ; Solnavägen 9, 17165 Solna, Stockholm Sweden
                Author information
                http://orcid.org/0000-0002-2635-0258
                Article
                3724
                10.1038/s41419-021-03724-6
                8102483
                33958582
                09c66aff-b3db-440b-a3a0-a34cde70c7b1
                © The Author(s) 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 November 2020
                : 13 April 2021
                : 14 April 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81972925
                Award ID: 81702464
                Award ID: 81972925
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003748, Svenska Sällskapet för Medicinsk Forskning (Swedish Society for Medical Research);
                Award ID: XDA16020403
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Cell biology
                psoriasis
                Cell biology
                psoriasis

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