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      Rituximab-resistant splenic memory B cells and newly engaged naive B cells fuel relapses in patients with immune thrombocytopenia

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          Abstract

          Rituximab (RTX), an antibody targeting CD20, is widely used as a first-line therapeutic strategy in B cell–mediated autoimmune diseases. However, a large proportion of patients either do not respond to the treatment or relapse during B cell reconstitution. Here, we characterize the cellular basis responsible for disease relapse in secondary lymphoid organs in humans, taking advantage of the opportunity offered by therapeutic splenectomy in patients with relapsing immune thrombocytopenia. By analyzing the B and plasma cell immunoglobulin gene repertoire at bulk and antigen-specific single-cell level, we demonstrate that relapses are associated with two responses coexisting in germinal centers and involving preexisting mutated memory B cells that survived RTX treatment and naive B cells generated upon reconstitution of the B cell compartment. To identify distinctive characteristics of the memory B cells that escaped RTX-mediated depletion, we analyzed RTX refractory patients who did not respond to treatment at the time of B cell depletion. We identified, by single-cell RNA sequencing (scRNA-seq) analysis, a population of quiescent splenic memory B cells that present a unique, yet reversible, RTX-shaped phenotype characterized by down-modulation of B cell–specific factors and expression of prosurvival genes. Our results clearly demonstrate that these RTX-resistant autoreactive memory B cells reactivate as RTX is cleared and give rise to plasma cells and further germinal center reactions. Their continued surface expression of CD19 makes them efficient targets for current anti-CD19 therapies. This study thus identifies a pathogenic contributor to autoimmune diseases that can be targeted by available therapeutic agents.

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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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            Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

            Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.
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              Is Open Access

              A Single-Cell Transcriptome Atlas of the Human Pancreas

              Summary To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.
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                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                April 14 2021
                April 14 2021
                April 14 2021
                April 14 2021
                : 13
                : 589
                : eabc3961
                Affiliations
                [1 ]Institut Necker-Enfants Malades, INSERM U1151/CNRS UMS8253, Université Paris Descartes, Sorbonne Paris Cité, 75993 Paris Cedex 14, France.
                [2 ]Service de Médecine Interne, Centre national de référence des cytopénies auto-immunes de l’adulte, Hôpital Henri Mondor, Assistance Publique Hôpitaux de Paris (AP-HP), Université Paris Est Créteil, 94000 Créteil, France.
                [3 ]Inovarion, 75005 Paris, France.
                [4 ]INSERM U955, Université Paris Est Créteil (UPEC), 94000 Créteil, France.
                [5 ]Service d’immunologie clinique, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université Paris Diderot, Sorbonne Paris Cité, 75010 Paris, France.
                [6 ]Service d’anatomopathologie, Hôpital Saint-Louis (AP-HP), 75010 Paris, France.
                [7 ]Service de médecine interne, Hôpital Haut-Lévêque, 33604 Pessac, France.
                [8 ]Service de médecine interne, Hôpital du Bocage, 21000 Dijon, France.
                Article
                10.1126/scitranslmed.abc3961
                33853929
                64cb80ef-d0ae-41a7-b873-27ce7243c82e
                © 2021

                https://www.sciencemag.org/about/science-licenses-journal-article-reuse

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