11, eaax0904 (2019). Distinct effector B cells induced by unregulated Toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Numbers indicate percentages of parent population. We used an adaptation of LIBRA-seq68 to identify antigen-specific cells in our sequencing data. Gene expression data and TotalSeq surface proteome data were integrated separately. The authors declare no competing interests. Upon encounter with cognate antigens, lymphocytes are endowed with the capacity to form memory cells1,2. SARS-CoV-2 infection generates tissue-localized immunological memory in humans. Comparison of V heavy and light chain usage within S+ Bm cell subsets in the scRNA-seq data from SARS-CoV-2-recovered individuals (months 6 and 12 post-infection) revealed very similar chain usage in S+ CD21+ resting (CD21+CD27+ and CD21+CD27 combined), CD21CD27+CD71+ activated and CD21CD27FcRL5+ Bm cells (Extended Data Fig. ## [67] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 Cutting edge: B cellintrinsic T-bet expression is required to control chronic viral infection. Bioinformatics 31, 33563358 (2015). Most functions now take an assay parameter, but you can set a Default Assay to avoid repetitive statements. 63). Another cohort (Extended Data Fig. The cohort size was based on sample availability. Atypical B cells up-regulate costimulatory molecules during malaria and secrete antibodies with T follicular helper cell support. after integration I subsetted my cells of interest and did SCTransform on the RNA assay for clustering, but for DE I used the RNA assay, as it is officially recommended (from what I understand, the batch effects are still there). | WhichCells(object = object, ident.remove = "ident.remove") | WhichCells(object = object, idents = "ident.remove", invert = TRUE) | # HoverLocator replaces the former `do.hover` argument It can also show extra data throught the `information` argument, # designed to work smoothly with FetchData, # FeatureLocator replaces the former `do.identify`, # Run analyses by specifying the assay to use, # Pull feature expression from both assays by using keys, # Plot data from multiple assays using keys, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats, Set font sizes for various elements of a plot. ## other attached packages: To learn more, see our tips on writing great answers. By clicking Sign up for GitHub, you agree to our terms of service and "~/Downloads/GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz", # To make life a bit easier going forward, we're going to discard all but the top 100 most highly expressed mouse genes, and remove the "HUMAN_" from the CITE-seq prefix, "~/Downloads/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz". The flow cytometry data further showed that S+ CD21CD27 Bm cells were enriched in IgG3+ compared with CD21+CD27+ resting Bm cells (Extended Data Fig. We obtained paired tonsil and peripheral blood mononuclear cell and serum samples. For example, In FeaturePlot, one can specify multiple genes and also split.by to further split to multiple the conditions in the meta.data. Setliff, I. et al. 1a and Supplementary Table 1). Generic Doubly-Linked-Lists C implementation. It would be nice if Satija lab could give more clear instruction on how to proceed in case of high versus low heterogeneity after subsettting. g, UMAPs represent Monocle 3 analysis of all Bm cells (left) and S+ Bm cells (right). Gene set variation analysis with the package gsva (v1.42.0) was used to estimate gene set enrichments for more than two groups61. rev2023.4.21.43403. Differential gene expression identified higher expression of CR2, CD44, CCR6 and CD69 in tonsillar SWT+ Bm cells compared with blood SWT+ Bm cells, whereas the activation-related genes FGR and CD52 were higher in blood SWT+ Bm cells compared with their tonsillar counterparts (Extended Data Fig. T-bet+ B cells are induced by human viral infections and dominate the HIV gp140 response. ## [4] igraph_1.4.1 lazyeval_0.2.2 sp_1.6-0 ## [16] memoise_2.0.1 tensor_1.5 cluster_2.1.3 On what basis are pardoning decisions made by presidents or governors when exercising their pardoning power? All plotting functions will return a ggplot2 plot by default, allowing easy customization with ggplot2. Antigen-specific cells per sample were sorted with 1,5002,000 nonspecific B cells, as shown in Extended Data Figs. 2a). *P<0.05, **P<0.01. Studies in patients with SLE or HIV infection have suggested that CD21CD27 Bm cells differentiate through an extrafollicular pathway16,17. However i do not believe this is the correct approach to do integration so i did not choose this method. column name in object@meta.data, etc. We also introduce simple functions for common tasks, like subsetting and merging, that mirror standard R functions. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). ## [43] future.apply_1.10.0 BiocGenerics_0.44.0 abind_1.4-5 SubsetData( ## [37] survival_3.3-1 zoo_1.8-11 glue_1.6.2 After sorting, cell suspensions were pelleted at 400g for 10min at 4C, resuspended and loaded into the Chromium Chip following the manufacturers instructions. Samples in f were compared using a Kruskal-Wallis test with Dunns multiple comparison correction, with adjusted P values shown. 2e), which correlated with an improved binding breadth, as measured by variant-binding ability of SWT+ Bm cells (Fig. | object@scale.data | GetAssayData(object = object, slot = "scale.data") | J. Immunol. Seurat (version 3.1.4) Single-cell RNA sequencing (scRNA-seq) indicated that single Bm cell clones adopted different fates upon antigen reexposure. ## [13] bmcite.SeuratData_0.3.0 SeuratData_0.2.2 & Shlomchik, M. J. Germinal center and extrafollicular B cell responses in vaccination, immunity, and autoimmunity. Creates a Seurat object containing only a subset of the cells in the To visualize the two conditions side-by-side, we can use the split.by argument to show each condition colored by cluster. # When adding multimodal data to Seurat, it's okay to have duplicate feature names. ## [13] htmltools_0.5.4 fansi_1.0.4 magrittr_2.0.3 Time-resolved analysis identified a peak in the frequency of S+ Bm cells in the first days post-vaccination, reaching 3% of total B cells on average, followed by a slow decrease in frequency over day 150 post-vaccination (Fig. c, Frequency of S+ Bm cells in total B cells was measured by flow cytometry at acute infection (n=59) and months 6 (n=61) and 12 post-infection (n=17). How about saving the world? Sci. 6a and Extended Data Fig. BCR and IFN- signaling appears to be a defining feature of CD21CD27 Bm cells, and probably induces and governs the T-bet-dependent transcriptional program in these cells32. How to merge clusters and what steps needed after merging in SCTransform workflow? 4d). I hope it is useful. and JavaScript. FindAllMarkers and FindMarkers functions were executed with logfc.thresholds set to 0.25 (0.1 for comparing resting Bm cells at month 6 versus month 12) and a min.pct cutoff at 0.1. c, Pie chart show the percentage of SWT binders that also bind RBD in scRNA-seq dataset. When comparing dataset quality, we noticed a markedly lower median gene detection and unique molecular identifier count per cell in one of our datasets of the SARS-CoV-2 Infection Cohort. Hi all, I'm also interested in this issue, and wonder what is the best way to subset and reclustering data starting from an integrating dataset? ## Matrix products: default Med. Thank you! Gene set enrichments for individual cells were summarized to patient pseudobulks by calculating mean enrichment values of cells belonging to the same patient. 6h). As an internal reference for SHM counts in nave B cells, we co-sorted nave B cells in one experiment of the SARS-CoV-2 Infection Cohort. 1 Answer Sorted by: 1 With a little bit of workaround: i) Add a new column to the data slot (only because your original subset () call does so but it can be raw counts or any other data matrix in your Seurat object). After discussing with colleagues and reading other articles I decided to go for option b). and O.B. seurat_subset <- SubsetData (seurat_object, subset.name = neuron_ids [1], accept.low = 0.1) However, I want to subset on multiple genes. e, SHM counts of S+ Bm cells were derived at preVac (n=634 cells), month 12 nonvaccinated (nonVac; n=197 cells), and early (less than 24days; n=838 cell) and late (more than 84days; n=1,116 cells) postVac. Samples were acquired on a Cytek Aurora cytometer using the SpectroFlo software. Hao, Y. et al. In the meantime, to ensure continued support, we are displaying the site without styles 31,32). Nat. S+ CD21CD27+ activated Bm cells peaked in the first days post-vaccination, followed by a rapid decline over the subsequent 100days (Fig. From my understanding, including all genes into the "Feature.to.integrate" functions will give you a gene matrix of all genes altered based on the integration, but the PCA analysis and subsequent non-linear dimensionality reduction and clustering will still be calculated based on the 2000 features found in the "Find.Integration.anchors" functions (unless otherwise stated), which change depending on the original data used, ie subsetted or whole. a, WNNUMAP was derived from scRNA-seq dataset at months 6 and 12 post-infection (n=9) and colored by indicated Bm cell subsets (top) and S+ and S separated by month 6 preVac, month 12 nonVac and month 12 postVac (bottom). This function performs differential gene expression testing for each dataset/group and combines the p-values using meta-analysis methods from the MetaDE R package. Immunity 51, 398410.e5 (2019). 2c), and S+ Bm cells underwent strong proliferation during the acute phase (Fig. 2b,c). object, b, Paired comparison of S+ Bm cells frequencies (n=10) is shown at month 6 post-second dose and 11-14 days post-third dose. Genes such as CD3D and GNLY are canonical cell type markers (for T cells and NK/CD8 T cells) that are virtually unaffected by interferon stimulation and display similar gene expression patterns in the control and stimulated group. Ogega, C. O. et al. Flow cytometry data were analyzed with FlowJo (version 10.8.0), with gating strategies shown in Extended Data Figs. Independent datasets were then integrated using Seurats anchoring-based integration method. Internet Explorer). & Cancro, M. P. Age-associated B cells: key mediators of both protective and autoreactive humoral responses. Hi Team Seurat, Robbiani, D. F. et al. Immunol. You can subset from the counts matrix, below I use pbmc_small dataset from the package, and I get cells that are CD14+ and CD14-: library (Seurat) CD14_expression = GetAssayData (object = pbmc_small, assay = "RNA", slot = "data") ["CD14",] This vector contains the counts for CD14 and also the names of the cells: Can I general this code to draw a regular polyhedron? ## [7] splines_4.2.0 listenv_0.9.0 scattermore_0.8 Markers were scaled with arcsinh transformation (cofactor 6,000), samples were subsetted to maximally 25 S+ Bm cells per sample. ## [118] data.table_1.14.8 irlba_2.3.5.1 httpuv_1.6.9 What was the actual cockpit layout and crew of the Mi-24A? Peer reviewer reports are available. https://doi.org/10.1038/s41590-023-01497-y, DOI: https://doi.org/10.1038/s41590-023-01497-y. d, Frequency of S+ Bm cells was measured by flow cytometry and separated by mild (acute, n=40; month 6, n=39; month 12, n=11) and severe COVID-19 (acute, n=19; month 6, n=22; month 12, n=6). CD21CD27 Bm cells depend on the transcription factor T-bet for their development30, are CD11chi and express inhibitory coreceptors, such as Fc receptor-like protein 5 (FcRL5) (refs. Lines connect samples of same individual. J. Exp. Cells were sorted on a FACS Aria III 4L sorter using the FACS Diva software. d, Contour plots show CD21 and CD27 expression on blood and tonsillar S+ Bm cells of patient CoV-T2 (left) and frequencies of indicated Bm cell subsets (right). ## [25] spatstat.sparse_3.0-0 colorspace_2.1-0 rappdirs_0.3.3 37, 521546 (2019). Why typically people don't use biases in attention mechanism? This is in line with previous reports that SARS-CoV-2 infection and mRNA vaccination led to lasting Bm cell maturation through an ongoing GC reaction26,44,45,46. Many thanks in advance. 1b and Supplementary Table 3). Nature 604, 141145 (2022). Hello, The interrelatedness between these Bm cell subsets remains unknown. Shared transcriptional profiles of atypical B cells suggest common drivers of expansion and function in malaria, HIV, and autoimmunity. Severe deficiency of switched memory B cells (CD27+IgMIgD) in subgroups of patients with common variable immunodeficiency: a new approach to classify a heterogeneous disease. c, UMAP as in a was colored by normalized expression of indicated markers. control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE) to ## [9] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2 ## [52] metap_1.8 viridisLite_0.4.1 xtable_1.8-4 Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Thanks for contributing an answer to Stack Overflow! Andreas E. Moor or Onur Boyman. Since Seurat v3.0, weve made improvements to the Seurat object, and added new methods for user interaction. Counts of SHM in S+ Bm cells remained high at month 12 (post-vaccination) compared with month 6 post-infection (pre-vaccination) (Fig. # To pull data from an assay that isn't the default, you can specify a key that's linked to an assay for feature pulling. dg, Stacked bar graphs display tissue (d) and isotype distribution (e) in Bm cell clusters, and isotype (f) and cluster distribution (g) in SWT+ Bm cells in tonsils and blood. 2e, as are preVac and nonVac SHM counts. 4e). # split the dataset into a list of two seurat objects (stim and CTRL), # normalize and identify variable features for each dataset independently, # select features that are repeatedly variable across datasets for integration, # this command creates an 'integrated' data assay, # specify that we will perform downstream analysis on the corrected data note that the, # original unmodified data still resides in the 'RNA' assay, # Run the standard workflow for visualization and clustering, # For performing differential expression after integration, we switch back to the original, ## CTRL_p_val CTRL_avg_log2FC CTRL_pct.1 CTRL_pct.2 CTRL_p_val_adj, ## GNLY 0 6.006173 0.944 0.045 0, ## FGFBP2 0 3.243588 0.505 0.020 0, ## CLIC3 0 3.461957 0.597 0.024 0, ## PRF1 0 2.650548 0.422 0.017 0, ## CTSW 0 2.987507 0.531 0.029 0, ## KLRD1 0 2.777231 0.495 0.019 0, ## STIM_p_val STIM_avg_log2FC STIM_pct.1 STIM_pct.2 STIM_p_val_adj, ## GNLY 0.000000e+00 5.858634 0.954 0.059 0.000000e+00, ## FGFBP2 3.408448e-165 2.191113 0.261 0.015 4.789892e-161, ## CLIC3 0.000000e+00 3.536367 0.623 0.030 0.000000e+00, ## PRF1 0.000000e+00 4.094579 0.862 0.057 0.000000e+00, ## CTSW 0.000000e+00 3.128054 0.592 0.035 0.000000e+00, ## KLRD1 0.000000e+00 2.863797 0.552 0.027 0.000000e+00, ## p_val avg_log2FC pct.1 pct.2 p_val_adj, ## ISG15 1.212995e-155 4.5997247 0.998 0.239 1.704622e-151, ## IFIT3 4.743486e-151 4.5017769 0.964 0.052 6.666020e-147, ## IFI6 1.680324e-150 4.2361116 0.969 0.080 2.361359e-146, ## ISG20 1.595574e-146 2.9452675 1.000 0.671 2.242260e-142, ## IFIT1 3.499460e-137 4.1278656 0.910 0.032 4.917791e-133, ## MX1 8.571983e-121 3.2876616 0.904 0.115 1.204621e-116, ## LY6E 1.359842e-117 3.1251242 0.895 0.152 1.910986e-113, ## TNFSF10 4.454596e-110 3.7816677 0.790 0.025 6.260044e-106, ## IFIT2 1.290640e-106 3.6584511 0.787 0.035 1.813736e-102, ## B2M 2.019314e-95 0.6073495 1.000 1.000 2.837741e-91, ## PLSCR1 1.464429e-93 2.8195675 0.794 0.117 2.057961e-89, ## IRF7 3.893097e-92 2.5867694 0.837 0.190 5.470969e-88, ## CXCL10 1.624151e-82 5.2608266 0.640 0.010 2.282419e-78, ## UBE2L6 2.482113e-81 2.1450306 0.852 0.299 3.488114e-77, ## PSMB9 5.977328e-77 1.6457686 0.940 0.571 8.399938e-73, ## Platform: x86_64-pc-linux-gnu (64-bit), ## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3, ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3, ## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C, ## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8, ## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8, ## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C, ## [9] LC_ADDRESS=C LC_TELEPHONE=C, ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C, ## [1] stats graphics grDevices utils datasets methods base, ## [1] cowplot_1.1.1 ggplot2_3.4.1, ## [3] patchwork_1.1.2 thp1.eccite.SeuratData_3.1.5, ## [5] stxBrain.SeuratData_0.1.1 ssHippo.SeuratData_3.1.4, ## [7] pbmcsca.SeuratData_3.0.0 pbmcMultiome.SeuratData_0.1.2, ## [9] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2, ## [11] ifnb.SeuratData_3.1.0 hcabm40k.SeuratData_3.0.0, ## [13] bmcite.SeuratData_0.3.0 SeuratData_0.2.2, ## [15] SeuratObject_4.1.3 Seurat_4.3.0. Seurat is great for scRNAseq analysis and it provides many easy-to-use ggplot2 wrappers for visualization. The expression changes in CD21 and CD27 on S+ Bm cells between acute infection and months 6 and 12 post-infection could also be reproduced by manual gating (Fig. The most common way is using the objects Idents: Idents (skin) <- "predicted_cell_type" skin_subset <- subset (skin, idents = "0:CD8 T cell") For the code you provided, I believe using quotations around the column name will work: SCT_integrated <- IntegrateData(anchorset = SCT_Integrated.anchors, normalization.method = "SCT", features.to.integrate = rownames(SCT_Integrated)) Immunol. Red dashed lines indicate minimal and maximal cumulative enrichment values. ## [10] qqconf_1.3.1 TH.data_1.1-1 digest_0.6.31 Sign in Samples in a and cf were compared using a Kruskal-Wallis test with Dunns multiple comparison correction. | SubsetData(object = object, subset.name = "name", low.threshold = low, high.threshold = high) | subset(x = object, subset = name > low & name < high) | Knox, J. J. et al. Human memory B cells show plasticity and adopt multiple fates upon recall response to SARS-CoV-2. But reading a few posts and issues here, it's not the way to go and I would like to understand why and to know how to do it properly. I have a conceptual question about the batch-correction (integration) model developed by Seurat (the one from the most recent vignette for integration with SCTransform - Compiled: 2019-07-16). Is there a generic term for these trajectories? Gene sets involved in antigen presentation and integrin-mediated signaling, as well as B cell activation, BCR and IFN- signaling were enriched in CD21CD27FcRL5+ Bm cells compared with other Bm cell subsets (Fig. How can I find help page about "%in%"? Nature 584, 437442 (2020). GSEA was performed on this preranked list using the R package fgsea (v.1.2). ISSN 1529-2916 (online) Article | object@cell.names | colnames(x = object) | 131, e145516 (2021). 3d). Multi-Assay Features With Seurat, you can easily switch between different assays at the single cell level (such as ADT counts from CITE-seq, or integrated/batch-corrected data). e, Circos plots of all persistent S+ Bm cell clones (left) and those adopting multiple Bm cell fates (right) are shown, with arrows connecting cells of months 6 with 12 and colored according to Bm cell phenotype at month 12. f, SHM counts were calculated in indicated S+ Bm cell subsets (unswitched, n=53; CD27lo resting, n=122; CD27hi resting, n=535; activated, n=713; CD21CD27FcRL5+, n=531). In other words, is this workflow valid: All tests were performed two-sided. 5a,b and Extended Data Fig. The DotPlot() function with the split.by parameter can be useful for viewing conserved cell type markers across conditions, showing both the expression level and the percentage of cells in a cluster expressing any given gene.