Viral infection engenders bona fide and bystander lung memory B cell subsets through permissive selection
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Abstract
Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. scRNA-seq analysis and confocal imaging revealed that two main transcriptionally distinct subsets of MBCs colonize the lung peribronchial niche after infection. These subsets arise from different progenitors and are both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire unveiled a highly permissive selection process that segregates these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.
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SciScore for 10.1101/2021.12.14.472614: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IACUC: Experimental procedures were conducted in accordance with French and European guidelines for animal care under the permission number 16708-2018091116493528 following review and approval by the local animal ethics committee in Marseille. Sex as a biological variable Mice were used at the age of 8 to 12 weeks and littermates (males or females) were randomly assigned to experimental groups. Randomization Mice were used at the age of 8 to 12 weeks and littermates (males or females) were randomly assigned to experimental groups. Blinding not detected. Power Analysis not detected. Cell Line Authentication not detected. Table 2: Resources
Antibodies Sentences Resources For in vivo labelling of immune cells … SciScore for 10.1101/2021.12.14.472614: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IACUC: Experimental procedures were conducted in accordance with French and European guidelines for animal care under the permission number 16708-2018091116493528 following review and approval by the local animal ethics committee in Marseille. Sex as a biological variable Mice were used at the age of 8 to 12 weeks and littermates (males or females) were randomly assigned to experimental groups. Randomization Mice were used at the age of 8 to 12 weeks and littermates (males or females) were randomly assigned to experimental groups. Blinding not detected. Power Analysis not detected. Cell Line Authentication not detected. Table 2: Resources
Antibodies Sentences Resources For in vivo labelling of immune cells in circulation, 3 μg of anti-CD45 antibody was administered i.v 5 minutes before sacrifice. anti-CD45suggested: NoneFor labeling of surface markers, cells were stained for 20 minutes on ice with the indicated anti-mouse antibodies and fluorescently-labelled influenza hemagglutinin, nucleoprotein or SARS-CoV-2 spike protein (Sino biological). anti-mousesuggested: NoneSARS-CoV-2 spike protein ( Sino biological) .suggested: NoneELISA: To measure influenza-specific antibodies, ELISA (enzyme-linked immunosorbent assay) plates were coated overnight at 4°C with 1μg/ml of nucleoprotein or hemagglutinin (Sino Biological) diluted in PBS. hemagglutininsuggested: NoneAfter cell surface staining with the mix of antibodies used for gating MBCs, single-cell suspensions from each organ of each individual mice were independently stained with a distinct barcoded anti-mouse CD45 antibody (in-house conjugated) in PBS 2%FCS 2mM EDTA for 30 min on ice, then washed and resuspended in PBS. anti-mouse CD45suggested: NoneExperimental Models: Cell Lines Sentences Resources Influenza virus was amplified on MDCK cells. MDCKsuggested: CLS Cat# 602280/p823_MDCK_(NBL-2, RRID:CVCL_0422)Infectious stocks were grown by inoculating Vero E6 cells and collecting supernatant upon observation of cytopathic effect; debris were removed by centrifugation and passage through a 0.22-μm filter. Vero E6suggested: NoneExperimental Models: Organisms/Strains Sentences Resources Mice: 8-week old wild-type C57BL/6 mice were obtained from Janvier Labs. C57BL/6suggested: NoneAicda-CreERT2 mice were obtained from Claude-Agnès Reynaud and Jean Claude Weill, Institut Necker Enfants Malades, France. Rosa26-EYFP, CCR6-/- and K18-hACE2 mice were obtained from Jackson Laboratories, USA. μMT mice were obtained from Stéphane Mancini, Centre de Recherche en Cancérologie de Marseille, France. CXCR3-/- and Fcmr-/- bone marrow were obtained from Jacqueline Marvel, Centre International de Recherche en Infectiologie, France and Tak Mak, University of Toronto, Canada, respectively. Aicda-CreERT2suggested: NoneRosa26-EYFPsuggested: NoneK18-hACE2suggested: RRID:IMSR_GPT:T037657)Aicda-CreERT2 mice were further crossed with Rosa26-EYFP and CCR6-/- mice. CCR6-/-suggested: NoneFB5P-seq library preparation: Single-cell suspensions from lungs of three previously infected Aid-EYFP mice were prepared as described above with enzymatic digestion, and stained with a panel of antibodies for identifying subsets of antigen-specific MBCs (GL7-PerCP-Cy5.5, HA-PE, Ccr6-PE-Dazzle594, CD38-PE-Cy7, NP-APC, CD19-APC-Cy7, Cxcr3-BV421 Aid-EYFPsuggested: NoneSoftware and Algorithms Sentences Resources This software uses the 4 files exported by the ImageJ macro and allows scatterplot gating (cross, polygon or lasso) to filter and define cell populations interactively with the COI overlaid on the image. ImageJsuggested: (ImageJ, RRID:SCR_003070)Libraries were tagged with a plate-specific i7 index and were pooled for sequencing on an Illumina NextSeq2000 platform, with P2 flow cells, targeting 2.5×105 reads per cell in paired-end single-index mode (Read 1: 103 cycles, Read i7: 8 cycles, Read 2: 16 cycles). scRNA-seq analysis: Preprocessing and analysis of data were done through the usage of standard tools and custom R and Python scripts. Pythonsuggested: (IPython, RRID:SCR_001658)All codes and data are available on Github and Zenodo. Pre-processing of FB5P-seq dataset: We used a custom bioinformatics pipeline to process fastq files and generate single-cell gene expression matrices and BCR sequence files as previously described (Attaf et al., 2020). Zenodosuggested: (ZENODO, RRID:SCR_004129)Index-sorting FCS files were visualized in FlowJo software and compensated parameters values were exported in CSV tables for further processing. FlowJosuggested: (FlowJo, RRID:SCR_008520)Filtered contigs were aligned to reference constant region sequences using Blastn. Blastnsuggested: (BLASTN, RRID:SCR_001598)Pre-processing of 10x 5’ datasets: Raw fastq files from gene expression libraries were processed using Cell Ranger software, with alignment on the mm10 reference genome. Cell Rangersuggested: (Cell Ranger , RRID:SCR_017344)BCR-seq raw fastq files were processed with the FB5P-seq pipeline (Attaf et al., 2020) as described above for FB5P-seq datasets, omitting the part of the pipeline related to gene expression analysis, and using the list of cell-associated 10x barcodes from CellRanger analysis as inputs for splitting bam files upstream Trinity assembly of BCR contigs. Trinitysuggested: (Trinity, RRID:SCR_013048)UMAP embeddings colored by sample metadata or clusters were generated by Seurat DimPlot, those colored by single gene expression or module scores were generated by Seurat FeaturePlot, those colored by BCR sequence metadata were generated with ggplot2 ggplot. ggplot2suggested: (ggplot2, RRID:SCR_014601)Marker genes between clusters were identified using the FindAllMarkers method of the Seurat package using the Wilcoxon Rank Sum test on genes expressed at least in 10% of the cells, a logFC threshold of 0.25 and a FDR threshold of 0.001. Seuratsuggested: (SEURAT, RRID:SCR_007322)Heatmap of gene expression along tissue clusters was done performing a mean of the expression of the genes of interest over the clusters, using the pheatmap package (version 1.0.12) for the plot. pheatmapsuggested: (pheatmap, RRID:SCR_016418)We used Clustal Omega from msa R package (10.1093/bioinformatics/btv494) to evaluate the sequence proximity of clonotypes and verify if full BCR sequences in clonotype were consistent. Clustal Omegasuggested: (Clustal Omega, RRID:SCR_001591)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 41, 42 and 37. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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