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      Repetitive head impacts induce neuronal loss and neuroinflammation in young athletes

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          Abstract

          Repetitive head impacts (RHI) sustained from contact sports are the largest risk factor for chronic traumatic encephalopathy (CTE). Currently, CTE can only be diagnosed after death and the multicellular cascade of events that trigger initial hyperphosphorylated tau (p-tau) deposition remain unclear. Further, the symptoms endorsed by young individuals with early disease are not fully explained by the extent of p-tau deposition, severely hampering development of therapeutic interventions. Here, we show that RHI exposure associates with a multicellular response in young individuals (<51 years old) prior to the onset of CTE p-tau pathology that correlates with number of years of RHI exposure. Leveraging single nucleus RNA sequencing of tissue from 8 control, 9 RHI-exposed, and 11 low stage CTE individuals, we identify SPP1+ inflammatory microglia, angiogenic and inflamed endothelial cell profiles, reactive astrocytes, and altered synaptic gene expression in excitatory and inhibitory neurons in all individuals with exposure to RHI. Surprisingly, we also observe a significant loss of cortical sulcus layer 2/3 neurons in contact sport athletes compared to controls independent of p-tau pathology. These results provide robust evidence that multiple years of RHI exposure is sufficient to induce lasting cellular alterations that may underlie p-tau deposition and help explain the early clinical symptoms observed in young former contact sport athletes. Furthermore, these data identify specific cellular responses to repetitive head impacts that may direct future identification of diagnostic and therapeutic strategies for CTE.

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          Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

          A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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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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              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

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                28 March 2024
                : 2024.03.26.586815
                Affiliations
                [1 ]Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston MA, USA
                [2 ]Boston University Alzheimer’s Disease and CTE Centers, Boston University Chobanian & Avedisian School of Medicine, Boston MA
                [3 ]Section of Computational Biomedicine, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston MA, USA
                [4 ]VA Boston Healthcare System, Jamaica Plain MA, USA
                [5 ]VA Bedford Healthcare System, Bedford MA, USA
                [6 ]Department of Pathology, University of Iowa Health Care, Iowa City IA, USA
                [7 ]National Center for PTSD, VA Boston Healthcare System, Boston MA, USA
                [8 ]Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston MA, USA
                [9 ]Department of Pathology and Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston MA, USA
                Author notes

                Author Contribution

                Article conceptualization and writing performed by MLB and JDC. Experiments were performed by MLB, PY, JCB, RN, LS. Analysis was performed by MLB, NP, YW. Computational support provided by SM, JC. Neuropathologic diagnosis performed by MH, VEA, BH, TDS, ACM.

                [* ]Corresponding Author: Jonathan D Cherry, VA Boston Healthcare System, 150 S. Huntington Ave., Boston MA 02130, Jdcherry@ 123456bu.edu
                Author information
                http://orcid.org/0000-0001-6954-4477
                http://orcid.org/0000-0002-1257-981X
                Article
                10.1101/2024.03.26.586815
                10996668
                38585925
                f7a03469-4d13-4aa5-847e-1f4c10e5d322

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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                Funding
                This work was supported by grant funding from: NINDS (F31NS132407), NIH (U19-AG068753), NIA (AG057902, AG062348), NINDS (U54NS115266), National Institute of Aging Boston University AD Center (P30AG072978); Department of Veterans Affairs Biorepository (BX002466), and the Department of Veterans Affairs Career Development Award (BX004349), BLRD Merit Award (I01BX005933). The views, opinions, and/or findings contained in this article are those of the authors and should not be construed as an official Veterans Affairs or Department of Defense position, policy, or decision, unless so designated by other official documentation. Funders did not have a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
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