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      B cell depletion therapy does not resolve chronic active multiple sclerosis lesions

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          Summary

          Background

          Chronic active lesions (CAL) in multiple sclerosis (MS) have been observed even in patients taking high-efficacy disease-modifying therapy, including B-cell depletion. Given that CAL are a major determinant of clinical progression, including progression independent of relapse activity (PIRA), understanding the predicted activity and real-world effects of targeting specific lymphocyte populations is critical for designing next-generation treatments to mitigate chronic inflammation in MS.

          Methods

          We analyzed published lymphocyte single-cell transcriptomes from MS lesions and bioinformatically predicted the effects of depleting lymphocyte subpopulations (including CD20 B-cells) from CAL via gene-regulatory-network machine-learning analysis. Motivated by the results, we performed in vivo MRI assessment of PRL changes in 72 adults with MS, 46 treated with anti-CD20 antibodies and 26 untreated, over ∼2 years.

          Findings

          Although only 4.3% of lymphocytes in CAL were CD20 B-cells, their depletion is predicted to affect microglial genes involved in iron/heme metabolism, hypoxia, and antigen presentation. In vivo, tracking 202 PRL (150 treated) and 175 non-PRL (124 treated), none of the treated paramagnetic rims disappeared at follow-up, nor was there a treatment effect on PRL for lesion volume, magnetic susceptibility, or T1 time. PIRA occurred in 20% of treated patients, more frequently in those with ≥4 PRL (p = 0.027).

          Interpretation

          Despite predicted effects on microglia-mediated inflammatory networks in CAL and iron metabolism, anti-CD20 therapies do not fully resolve PRL after 2-year MRI follow up. Limited tissue turnover of B-cells, inefficient passage of anti-CD20 antibodies across the blood–brain-barrier, and a paucity of B-cells in CAL could explain our findings.

          Funding

          Intramural Research Program of NINDS, NIH; NINDS grants R01NS082347 and R01NS082347; doi 10.13039/100005984, Dr. Miriam and Sheldon G. Adelson Medical Research Foundation; ; doi 10.13039/501100002803, Cariplo Foundation; (grant #1677), FRRB Early Career Award (grant #1750327); Fund for Scientific Research (FNRS).

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          Most cited references56

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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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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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              Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool

              Background System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. Results Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. Conclusions Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Contributors
                Journal
                eBioMedicine
                EBioMedicine
                eBioMedicine
                Elsevier
                2352-3964
                10 July 2023
                August 2023
                10 July 2023
                : 94
                : 104701
                Affiliations
                [a ]Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
                [b ]Neuroinflammation Imaging Lab (NIL), Université Catholique de Louvain, Brussels, Belgium
                [c ]Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland
                [d ]Institute of Experimental Neurology, Division of Neuroscience, Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital, Milan, Italy
                [e ]Plateforme Technologique de Support en Méthodologie et Calcul Statistique, Université Catholique de Louvain, Brussels, Belgium
                [f ]Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA
                [g ]Hôpital Erasme, Université Libre de Bruxelles, Bruxelles, Belgium
                [h ]Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                Author notes
                []Corresponding author. Cliniques Universitaires Saint-Luc, Av. Hippocrate 10, 1200, Brussels, Belgium. pietro.maggi@ 123456uclouvain.be
                [∗∗ ]Corresponding author. Vita-Salute San Raffaele University and IRCCS San Raffaele Hospital, Via Olgettina, 60, 20132, Milan, Italy. absinta.martina@ 123456hsr.it
                Article
                S2352-3964(23)00266-9 104701
                10.1016/j.ebiom.2023.104701
                10436266
                37437310
                42d9ad25-b7f6-4bc5-852d-171be8a3c861
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 23 January 2023
                : 23 June 2023
                : 23 June 2023
                Categories
                Articles

                susceptibility-based mri,paramagnetic rims,anti-cd20 treatment,machine learning,single cell rna sequencing

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