What was the actual cockpit layout and crew of the Mi-24A? d, Stacked bar graphs represent isotype and subtype distribution in scRNA-seq dataset on all B cells (left), all S+ Bm cells (middle) and indicated S+ Bm cell subsets (right). The text was updated successfully, but these errors were encountered: @attal-kush I hope its okay to piggyback of your question. The alternative would be to subset() the population of interest and run the complete preprocessing including integration only on those cells again. Blood 99, 15441551 (2002). We found that the various S+ Bm cell subsets contained comparable amounts of SHM, suggesting that CD21CD27 Bm cells originated either from the GC or from a GC-derived progenitor Bm cell upon antigen rechallenge. c, Violin plots represent geometric mean fluorescence intensities (gMFI) or percentages of indicated markers in S+ Bm cells at acute infection (n=23), and months 6 (n=52) and 12 post-infection (n=16), compared with S Bm cells at acute infection (n=23). Several of these differences, such as T-bet, and CD11c, were confirmed at the protein level (Fig. If split.by is not NULL, the ncol is ignored so you can not arrange the grid. As far as heterogeneity goes, if you keep sub-sampling till you reach 2 cells you will find differences between even them. Levine, J. H. et al. 59). Commun. 2 and 5. Immunol. Most functions now take an assay parameter, but you can set a Default Assay to avoid repetitive statements. I just do not want to do manual subsetting on 10 genes, then manually getting @data matrix from each subset, and recreating seurat object afterwards. c, Stacked bar plots (mean + SD) show isotypes of S+ Bm cells at week 2 (n=10) and month 6 (n=11) post-second dose and at week 2 post-third dose (n=10). Sci. This process consists of data normalization and variable feature selection, data scaling, a PCA on variable features, construction of a shared-nearest-neighbors graph, and clustering using a modularity optimizer. Lines connect paired samples. J. Exp. 6f). First, we focused on samples from nonvaccinated individuals at acute infection (n=59, day 14 on average after symptom onset), month 6 (n=61, day 202 after symptom onset) and month 12 (n=17, day 374) (Fig. Tan, H. X. et al. designed and performed scRNA-seq experiments, and analyzed and interpreted data. J. Clin. 25,26,27,28,29). 11, eaax0904 (2019). SCT_integrated <- FindClusters(SCT_integrated), control_subset <- subset(SCT_integrated, orig.ident = 'Chow') g, Comparison of somatic hypermutation (SHM) counts are provided in SWT+ Bm cells at indicated timepoints (week 2 post-second dose, n=174 cells; month 6 post-second dose, n=271 cells; week 2 post-third dose, n=698 cells). I know that I can do subsetting on just one gene in Seurat: However, I want to subset on multiple genes. accept.value = NULL, Preprocessing of raw scRNA-seq data was done as described51. Elsner, R. A. Austin, J. W. et al. Rev. Provided by the Springer Nature SharedIt content-sharing initiative, Nature Immunology (Nat Immunol) While I did not test the above, I tested running FindVariableFeatures() (or not), and I recommend re-running FindVariableFeatures(). Antigen-specific CD21CD27+ and CD21CD27 Bm cells have been transiently detected after vaccines12,19,20,21,22 and during infection with certain pathogens21,23,24, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (refs. Finally, CD14 and CXCL10 are genes that show a cell type specific interferon response. ), Digitalization Initiative of the Zurich Higher Education Institutions Rapid-Action Call #2021.1_RAC_ID_34 (to C.C. Profiling B cell immunodominance after SARS-CoV-2 infection reveals antibody evolution to non-neutralizing viral targets. (I assume if I just need to delete the 3 lines of code I just mentioned above and change Compare: For your example, I believe the following should work: See the examples in ?subset for more. Zumaquero, E. et al. In b, significant differences between groups were determined by constructing a bootstrap delta distribution for each pair of unique values between groups. # 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. We used the scRNA-seq of S+ and S Bm cells sorted from recovered individuals with and without subsequent vaccination to interrogate the pathways guiding development of different Bm cell subsets (Extended Data Fig. Nat. Black lines indicate trajectory. Already on GitHub? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. 1a). Thank you @satijalab !!!! b, Paired comparison of S+ Bm cell frequencies within B cells (n=34) was performed at preVac and postVac. Is it necessary to run FindVariableFeatures on the RNA assay of the subset and get new variables to use in PCA in order to properly cluster the subset? max.cells.per.ident = Inf, These data showed that SARS-CoV-2 infection induced a stable CD21+ Bm cell population in the circulation, which continuously matured for more than 6months. 7ac). It is unclear whether the CD21CD27 Bm cells observed post-vaccination can again become resting Bm cells or whether this phenotype is terminally fated. Thank you @satijalab for this amazing tool and the amazing tutorials !!!! | NoAxes | Remove axes and axis text | Adding EV Charger (100A) in secondary panel (100A) fed off main (200A). Colors indicate Bm cell subsets. This issue may help you address your question. Fourteen cycles (in one case 17) of initial cDNA amplification were used for all sample batches, and single-cell sequencing libraries for whole-transcriptome analysis (GEX), BCR profiling (VDJ) and TotalSeq (BioLegend) barcode detection (ADT) were generated. Conversely, CD21+CD27+ and CD21+CD27 Bm cells were prominent at months 6 and 12, amounting to 60.5% and 29.1% of S+ Bm cells at month 12, respectively (Fig. I was wondering, if it make more sense to find subsetting parameters which will comply with all the samples, or one can do it one sample (or one condition) at a time by itself. The flow cytometry and scRNA-seq subcohort characteristics are presented in Supplementary Tables 1 and 2, respectively. column name in object@meta.data, etc. Abela, I. 3d). Whereas S+ Bm cells were predominantly resting CD21+ Bm cells at month 6, vaccination strongly induced the appearance of S+ CD21CD27+ and CD21CD27 Bm cells in blood (Fig. We found that SARS-CoV-2 infection and vaccination induced long-lived and stable antigen-specific Bm cells in the circulation that continued to mature up to 1year post-infection, as evidenced by their elevated proliferation rate at month 6, high SHM counts and improved breadth of SARS-CoV-2 antigen recognition. Increased memory B cell potency and breadth after a SARS-CoV-2 mRNA boost, BNT162b2 vaccine induces divergent B cell responses to SARS-CoV-2 S1 and S2, Systematic comparison of respiratory syncytial virus-induced memory B cell responses in two anatomical compartments, Single-cell epigenomic landscape of peripheral immune cells reveals establishment of trained immunity in individuals convalescing from COVID-19, The germinal centre B cell response to SARS-CoV-2, Anti-SARS-CoV-2 receptor-binding domain antibody evolution after mRNA vaccination, Human CD8+ T cell cross-reactivity across influenza A, B and C viruses, SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8+ T cells, Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection, https://github.com/Moors-Code/MBC_Plasticity_Moor_Boyman_Collaboration. Hi @vertesy , Science 371, eabf4063 (2021). Samples in b were compared using a KruskalWallis test with Dunns multiple comparison correction, in ce with a two-tailed Wilcoxon matched-pairs signed-rank test and in i with a two-sided Wilcoxon test with Holm multiple comparison correction. and O.B. A minor scale definition: am I missing something? 5c). Briefly, FASTQ files were aligned to the human GRCh38 genome using Cell Rangers cellranger multi pipeline (10x Genomics, v6.1.2) with default settings, which allowed one to process together the paired GEX, ADT and VDJ libraries for each sample batch. 1b. Koutsakos, M. et al. Generic Doubly-Linked-Lists C implementation. 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. Returns a Seurat object containing only the relevant subset of cells, Run the code above in your browser using DataCamp Workspace, SubsetData: Return a subset of the Seurat object, pbmc1 <- SubsetData(object = pbmc_small, cells = colnames(x = pbmc_small)[. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Sci. high.threshold = Inf, These dynamics were comparable in patients with mild and severe COVID-19 (Extended Data Fig. You signed in with another tab or window. Hello, But I am not sure which assay should be used for FindVariableFeatures of the subset cells, RNA, SCT, or Integrated? Our data showing expression of ZEB2 in CD21CD27 Bm cells suggest unidirectional plasticity, as ZEB2 acts together with T-bet to commit CD8+ effector T cells to a terminal differentiation state and has been proposed to act similarly in B cells16,40. | object@data | GetAssayData(object = object) | 212, 20412056 (2015). c, Venn diagram shows clonal overlap of SARS-CoV-2-specific clones in different Bm cell subsets. How can I find help page about "%in%"? All plotting functions will return a ggplot2 plot by default, allowing easy customization with ggplot2. Circulating TFH cells, serological memory, and tissue compartmentalization shape human influenza-specific B cell immunity. 3i). subset.name = NULL, We probed the Bm cell response to antigen reexposure in 35 of the 65 patients with COVID-19 who had received mRNA vaccination between month 6 and month 12 post-infection (Extended Data Fig. Dominguez, C. X. et al. Find corresponding symbol for gene used in Seurat, Subsetting a Seurat object based on colnames. | WhichCells(object = object, max.cells.per.ident = 500) | WhichCells(object = object, downsample = 500) | 37, 521546 (2019). We also introduce simple functions for common tasks, like subsetting and merging, that mirror standard R functions. Also, instead of changing the default assay to "RNA", finding the variable features, and changing the default assay back to "integrated", would it be make more sense to just delete those lines of code and just change: 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. The S+ CD21CD27 Bm cells identified here were transcriptionally very similar to their atypical counterparts in SLE. For full details, please read our tutorial. These circulating resting Bm cells might be able to rapidly respond to antigen rechallenge with the acquisition of different Bm cell fates or they might home to secondary lymphoid and peripheral organs to form a CD69+ tissue-resident Bm cells. 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. Serum and blood was obtained, and peripheral blood mononuclear cells were isolated by density centrifugation, washed and frozen in fetal bovine serum (FBS) with 10% dimethyl sulfoxide and stored in liquid nitrogen until use. How about saving the world? ## [67] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 5d,e). 7, eabf5314 (2022). c, Average expression of indicated genes was derived at preVac and postVac in persistent S+ Bm cell clones that contained at least one CD21CD27FcRL5+ S+ Bm cell (n=14 clones). I am trying to subset the object based on cells being classified as a 'Singlet' under seurat_object@meta.data[["DF.classifications_0.25_0.03_252"]] and can achieve this by doing the following: I would like to automate this process but the _0.25_0.03_252 of DF.classifications_0.25_0.03_252 is based on values that are calculated and will not be known in advance. Use MathJax to format equations. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. AutoPointSize: Automagically calculate a point size for ggplot2-based. c, Stacked bar graphs show single patient contribution to the WNN clusters. My assumption was that it would start with 1 and if it does evaluate to "false" it would go on to 2 and than to 3, and if none matches the statement after == is "false" and if one of them matches, it is "true". BCR-seq detected shared clones mostly between S+ CD21+CD27+ and CD21CD27+CD71+ activated Bm cells, as well as the CD21CD27FcRL5+ Bm cell subset (Extended Data Fig. Samples were compared using paired t-test (c) or two-sided Wilcoxon test (f). By clicking Sign up for GitHub, you agree to our terms of service and The method is named sctransform, and avoids some of the pitfalls of standard normalization workflows, including the addition of a pseudocount, and log-transformation. Is it possible and valid instead to use values from the "data" slot of the SCT assay (log-normalized corrected values) for the MAST test? Connect and share knowledge within a single location that is structured and easy to search. ## 24, 389396 (2017). @attal-kush Your questions are so comprehensive and I am also curious if there is a practical way to analyse the subsetted cells. Moreover, our multimer staining approach might miss low-affinity antigen binders50. So, my here is my workflow: 6dg). I simply used the FindNeighbors and FindClusters command in order to create the 'seurat_clusters' list in the meta.data. Using this subsetted data, I tried 4 different approaches: Approach 1: Default reintegration > Re-cluster (following, Approach 2: SCT reintegration > Re-cluster (following, Approach 3: No re-integration > Re-scale > Re-cluster (following, Approach 4: No re-integration > SC transform > Re-cluster (following. Replies here and in some other GitHub issues have slightly different approaches but they all make general sense. Red line represents fitted second-order polynomial function (R2=0.1298). Is there a way to do that? Y.Z. I was able to achieve this in the following way: Would be interesting to know if Seurat provides such functionality out of the box. Memory lymphocytes are usually long-lived and provide faster and more vigorous immune responses upon secondary contact with their specific antigen2. a, Donut plots of BCR sequences of S+ Bm cells in three representative patients preVac and postVac. 2d). Clustering was performed using the Louvain algorithm and a resolution of 0.4. Correspondence to The cohort size was based on sample availability. 197, 10171022 (2016). | SetIdent(object = object, cells.use = 1:10, ident.use = "new.idents") | Idents(object = object, cells = 1:10) <- "new.idents" | Bioinformatics 32, 28472849 (2016). ## [9] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2 The following tutorial is designed to give you an overview of the kinds of comparative analyses on complex cell types that are possible using the Seurat integration procedure. Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination. J. Exp. The transient occurrence of vaccine-specific CD21CD27 Bm cells has been described during responses to the influenza vaccine12,20, with one study reporting this Bm cell subset in de novo rather than recall responses20. f,g, GSEA of CD21CD27FcRL5+ S+ Bm cells versus CD21+ resting S+ Bm cells are shown for indicated gene sets. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | object@hvg.info | HVFInfo(object = object) | 8 SARS-CoV-2-specific B. ), Forschungskredit Candoc grant from UZH (FK-20-022; to S.A.), Young Talents in Clinical Research program of the SAMW and G. & J. Bangerter-Rhyner Foundation (YTCR 08/20; to M.E.R. rowSums () determines how many non-zero counts you have. BCR-seq showed similar SHM counts in SWT+ Bm cells in blood and tonsils (Fig. BMC Bioinformatics 14, 7 (2013). @satijalab, could you please help us? ), A vector of cell names to use as a subset. ## [76] cachem_1.0.7 cli_3.6.0 generics_0.1.3 This work was funded by the Swiss National Science Foundation (#4078P0-198431 to O.B. Peer reviewer reports are available. Can I general this code to draw a regular polyhedron? In h, a two-sided Wilcoxon rank sum test was used, and P values corrected by Bonferroni correction. 5a and Extended Data Fig. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. B cells that differentiate in the GC undergo affinity maturation through somatic hypermutation (SHM) of the B cell receptor (BCR) following which B cells can become long-lived plasma cells or Bm cells4,5,6. The commands are largely similar, with a few key differences: Now that the datasets have been integrated, you can follow the previous steps in this vignette identify cell types and cell type-specific responses.Session Info Cells are colored by timepoint (left) and by clusters identified by PhenoGraph algorithm (right). contributed to patient recruitment. Reincke, M. E. et al. A, scRNA-seq subcohort of SARS-CoV-2 Infection Cohort. The authors declare no competing interests. Hi @attal-kush , Extended Data Fig. 124, 10171030 (1966). ## [40] polyclip_1.10-4 gtable_0.3.1 leiden_0.4.3 1d). as.Seurat: Convert objects to 'Seurat' objects; as.SingleCellExperiment: Convert objects to SingleCellExperiment objects; as.sparse: Cast to Sparse; AugmentPlot: Augments ggplot2-based plot with a PNG image. 9d). All tests were performed two-sided. From reading the other issues posted regarding the topic it appears that any kind of re-analysis prior to integration is not recommended, and that once subsetted a integrated data set should just be re-scaled and the pipeline followed on from this point on. FindMarkers between conditions Issue #2733 satijalab/seurat By using uniform manifold approximation and projection (UMAP) we visualized S+ Bm cells from the flow cytometry dataset obtained in nonvaccinated post-infection samples and performed a PhenoGraph clustering (Extended Data Fig. b, Representative flow cytometry plots show gating strategy for RBD+ Bm cells in patient CoV-P1, as in Fig. CD21 Bm cells were the predominant subsets during acute infection and early after severe acute respiratory syndrome coronavirus 2-specific immunization. For example, to only cluster cells using a single sample group, control, we could run the following: . 11, 2664 (2020). Gene set enrichment analysis (GSEA) was done as described51. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Ogega, C. O. et al. 1c and Extended Data Fig. Another cohort (Extended Data Fig. Frequencies were compared in c using two-tailed Mann Whitney test, in d and e with a two-tailed Wilcoxon matched-pairs signed rank test and in g with a Kruskal-Wallis test with a Dunns multiple comparison correction, showing adjusted P values. | object@dr$pca | object[["pca"]] | A. et al. The best answers are voted up and rise to the top, Not the answer you're looking for? This scRNA-seq approach detected frequencies of about 30% of RBD+ Bm cells within S+ Bm cells that were comparable to flow cytometry (Extended Data Figs. Sci. Unexpected uint64 behaviour 0xFFFF'FFFF'FFFF'FFFF - 1 = 0? The commands are largely similar, with a few key differences: Normalize datasets individually by SCTransform (), instead of NormalizeData () prior to integration d. Should ScaleData be run on the subset prior to PCA even though the subset comes from an integrated object prepped from SCT? low.threshold = -Inf, Transl. between condition A cluster 1 vs. condition B cluster 1 cells). In the scRNA-seq dataset, CD21+CD27+ resting Bm cells were the main S+ Bm cell subset at months 6 and 12 post-infection in nonvaccinated individuals, whereas CD21CD27+CD71+ activated and CD21CD27FcRL5+ Bm cells became predominant post-vaccination at month 12 post-infection (Fig. Sci. I hope it is useful. ## control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE, features = Variable Features(control_subset)) how to make a subset of cells expressing certain gene in seurat R SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease. I did integration with SCTransform. to your account. You can read more about sctransform in the manuscript or our SCTransform vignette. a, Gating strategy is provided for identification of SARS-CoV-2 S+ and nucleocapsid (N+) germinal center (GC) and Bm cells in tonsil from a SARS-CoV-2-recovered and vaccinated individual (CoV-T2).