seurat subset multiple conditions

Flow cytometry using the multimer probe approach (Extended Data Fig. BCR diversity was slightly reduced in S+ CD21CD27FcRL5+ compared with S+ CD21+ resting Bm cells (Extended Data Fig. Frequencies in g were compared using two-proportions z-test with Bonferronis multiple testing correction. Is this workflow indeed the best? 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. Memory lymphocytes are usually long-lived and provide faster and more vigorous immune responses upon secondary contact with their specific antigen2. Assa Yeroslaviz 1.8k. | RestoreLegend | Restores a legend after removal | d, Representative histograms (left) and violin plots of indicated markers on S+ Bm cell subsets (right) postVac were derived from the flow cytometry dataset (n=37). a, Heatmap compares V heavy (VH; left) and VL (right) gene usage in indicated S+ Bm cell subsets and S Bm cells (non-binders) from scRNA-seq data of SARS-CoV-2-infected patients at months 6 and 12 post-infection. I used ?%in% but it didn't work. Nat. Moreover, our multimer staining approach might miss low-affinity antigen binders50. It works, however, for some types of cells, not very well. ), Filling the Gap Program of UZH (to M.E.R. J. Immunol. I have been subsetting a cluster from a Seurat object to find subclusters. isn't the whole point of integration to remove batch effects? Samples in cf were compared using KruskalWallis test with Dunns multiple comparison, showing adjusted P values. The images or other third party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. These dynamics were comparable in patients with mild and severe COVID-19 (Extended Data Fig. 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 M.B.S. Unless a gene is not expressed (n-reads) at 1/p* try to forget about it just like a bad day (p* being the relative mean gene expression taking into account cDNA library construction efficiency, which in the case of 10x is 15%, or 1/p* = 1/0.15 7 reads/cell/gene). ; and #310030-200669 and #310030-212240 to O.B. Red dashed lines indicate minimal and maximal cumulative enrichment values. How to have multiple colors with a single material on a single object? 6c). 6, eabg6916 (2021). 8e,f). Thank you @satijalab for this amazing tool and the amazing tutorials !!!! Takes either a list of cells to use as a subset, or a Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. | WhichCells(object = object, ident = "ident.keep") | WhichCells(object = object, idents = "ident.keep") | Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. If I want to select a subset of data in R, I can use the subset function. ## [4] igraph_1.4.1 lazyeval_0.2.2 sp_1.6-0 5 Flow cytometry analysis of tonsillar and circulating SARS-CoV-2-specific B. 4e). 8g). Seurat provides many prebuilt themes that can be added to ggplot2 plots for quick customization, | Theme | Function | Can be used to downsample the data to a certain At the transcriptional level, S+ Bm cells at month 6 post-infection upregulated genes associated with B cell activation and recent GC emigration35, such as NKFBIA, JUND, MAP3K8, CXCR4 and CD83, compared with S+ Bm cells at month 12 (Extended Data Fig. Samples were stained as described for spectral flow cytometry using biotinylated SWT, RBD, Sbeta and Sdelta (MiltenyiBiotec) and hemagglutinin (SinoBiological) that were multimerized at 4:1 molar ratios with fluorescently labeled and/or barcoded SAV (TotalSeqC, BioLegend). Independent datasets were then integrated using Seurats anchoring-based integration method. The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection. Cell Rep. 34, 108684 (2021). Samples were acquired on a Cytek Aurora cytometer using the SpectroFlo software. Briefly, lists of differentially expressed genes were preranked in decreasing order by the negative logarithm of their P value, multiplied for the sign of their average log-fold change (in R, -log(P_val)*sign(avg_log2FC)). and JavaScript. 1 Overview of SARS-CoV-2 cohorts analyzed in this study. Haga, C. L., Ehrhardt, G. R. A., Boohaker, R. J., Davis, R. S. & Cooper, M. D. Fc receptor-like 5 inhibits B cell activation via SHP-1 tyrosine phosphatase recruitment. One limitation of our study is that we performed the clonal analysis after vaccination recall, because the numbers of S+ Bm cells during acute SARS-CoV-2 infection were too low for our sequencing approach. How a top-ranked engineering school reimagined CS curriculum (Ep. GSEA was performed on this preranked list using the R package fgsea (v.1.2). Thank you @satijalab for this amazing analysis package. 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. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. rev2023.4.21.43403. Seurat (version 3.1.4) Subsetting the before integrating data to interested cells and then do the whole integration, followed by PCA, umap, findneighbors and findclusters seemed reasonale to me. BCR-seq showed similar SHM counts in SWT+ Bm cells in blood and tonsils (Fig. Y.Z. 208, 25992606 (2011). No VH or VL chain segments were significantly differentially used between S+ Bm cell subsets. ## [1] systemfonts_1.0.4 sn_2.1.0 plyr_1.8.8 Science 371, eabf4063 (2021). 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. b, N+ (left) and S+ (right) Bm cell frequencies were determined in paired blood and tonsils of SARS-CoV-2-vaccinated (n=8) and SARS-CoV-2-recovered individuals (n=8). The S+ Bm cell subset distribution of newly detected clones (n=1,357 clones) at month 12 post-infection (post-vaccination) was comparable to the persistent clones (Fig. 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). 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. Antibody affinity shapes the choice between memory and germinal center B cell fates. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. a, Sorting strategy for SARS-CoV-2 S+ Bm cells and S B cells, gated on CD19+ non-PB, for scRNA-seq is provided. 2e, as are preVac and nonVac SHM counts. ## [61] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1 Also, cells previously occurring as cluster outliers from cl7 found their way to the corresponding clusters. Find centralized, trusted content and collaborate around the technologies you use most. In the SARS-CoV-2 Infection Cohort, cells with fewer than 200 or more than 2,500 detected genes and cells with more than 10% detected mitochondrial genes were excluded from the analysis. Box plots show medians, box limits and interquartile ranges (IQRs), with whiskers representing 1.5 IQR and outliers (also applies to subsequent figures). | AddMetaData(object = object, metadata = vector, col.name = "name") | object$name <- vector | 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. f,g, GSEA of CD21CD27FcRL5+ S+ Bm cells versus CD21+ resting S+ Bm cells are shown for indicated gene sets. Longitudinal tracking of S+ Bm cell clones between month 6 and month 12 post-infection identified 30 persistent clones in individuals vaccinated during that period (Fig. VH/VL were clustered hierarchically, with colors indicating frequencies. 64). Note that plotting functions now return ggplot2 objects, so you can add themes, titles, and, "2,700 PBMCs clustered using Seurat and viewed\non a two-dimensional tSNE", # Plotting helper functions work with ggplot2-based scatter plots, such as DimPlot, FeaturePlot, CellScatter, and. A multiple hypothesis correction procedure was applied to obtain adjusted P values. Since Seurat v3.0, weve made improvements to the Seurat object, and added new methods for user interaction. max.cells.per.ident = Inf, d, Percentages of Ki-67+ S+ Bm cells are provided in paired blood and tonsil samples of SARS-CoV-2-vaccinated and recovered individuals (n=16). Default is INF. We used a two-tailed Wilcoxon matched-pairs signed-rank test in b, d and g, and two-sided Wilcoxon test in e. The HolmBonferroni method was used for P value adjustment of multiple comparisons. Policy. Look at what 1||2||3 evaluates to: and you'd get the same using | instead. For example, In FeaturePlot, one can specify multiple genes and also split.by to further split to multiple the conditions in the meta.data. So I guess FindVariableFeatures of the subset cells should be tried. Robbiani, D. F. et al. Here, we address a few key goals: For convenience, we distribute this dataset through our SeuratData package. We found that S+ CD21CD27 Bm cells showed signs of increased antigen processing and presentation; how much this might translate into truly increased capacity of antigen presentation is unclear43. 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. ## Peer reviewer reports are available. 128, 45884603 (2018). ## [67] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0 Clonal diversity between Bm cell subsets was investigated using the alphaDiversity function of Immcantations package Alakazam (v1.2.0) (ref. Seurat provides many prebuilt themes that can be added to ggplot2 plots for quick customization. Nat. 7, eabf5314 (2022). In a, P values were calculated by fitting a linear model to count data using edgeR. Use MathJax to format equations. The probes were mixed in 1:1 Brilliant Buffer (BD Bioscience) and FACS buffer (PBS with 2% FBS and 2mM EDTA) with 5M of free d-biotin. and O.B. @MediciPrime That looks correct to me, though your resolution=0.2 parameter is quite low. Jenks, S. A. et al. 22,54). The method is named sctransform, and avoids some of the pitfalls of standard normalization workflows, including the addition of a pseudocount, and log-transformation. Poon, M. M. L. et al. Y.Z. subsetting seurat object with multiple samples, Traffic: 1812 users visited in the last hour, User Agreement and Privacy 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). Andrews, S. F. et al. c, Heat map shows selected, significantly differentially expressed genes in indicated S+ Bm cell subsets. Subsets and markers of antigen-specific B cells and antigen-specific B cell subsets were evaluated only if more than nine or three specific cells per sample were detected, respectively. 6, 748 (2019). Density plots indicate count distributions across binding score ranges are shown on top and on the side. By default, this is set to the VariableFeatures. Of these, 35 received SARS-CoV-2 mRNA vaccination between month 6 and month 12, and 3 subjects between acute infection and month 6. Thanks for contributing an answer to Stack Overflow! Sci. Because we are confident in having identified common cell types across condition, we can ask what genes change in different conditions for cells of the same type. Google Scholar. 351 2 15. ## 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. i, SHM counts are provided for nave B cells (n=1,607), blood (n=170) and tonsillar SWT+ Bm cells (n=1,128). 16 patients undergoing tonsillectomies for unrelated conditions were included and paired blood and tonsil samples obtained. These methods first identify cross-dataset pairs of cells that are in a matched biological state (anchors), can be used both to correct for technical differences between datasets (i.e. ## [70] labeling_0.4.2 rlang_1.0.6 reshape2_1.4.4 Below, we demonstrate how to modify the Seurat integration workflow for datasets that have been normalized with the sctransform workflow. But I am not sure which assay should be used for FindVariableFeatures of the subset cells, RNA, SCT, or Integrated? VASPKIT and SeeK-path recommend different paths. | SetIdent(object = object, ident.use = "new.idents") | Idents(object = object) <- "new.idents" | J. Which one to choose? Viral Hepat. # S3 method for Assay Which was the first Sci-Fi story to predict obnoxious "robo calls"? Generic Doubly-Linked-Lists C implementation. ## [5] stxBrain.SeuratData_0.1.1 ssHippo.SeuratData_3.1.4 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. e, Representative CD69 histograms in S+ Bm cells of patient CoV-T2 (left) and percentages of CD69+ S+ Bm cells (right) in blood and tonsils. Article ## [52] metap_1.8 viridisLite_0.4.1 xtable_1.8-4 Immunol. 3g,h and Extended Data Fig. d. Should ScaleData be run on the subset prior to PCA even though the subset comes from an integrated object prepped from SCT? Cells with LIBRA scores >0 for the respective antigens were defined as antigen-specific, and in the SARS-CoV-2 infection, cohort cells were considered S+ if any of the antigens used for baiting (SWT, Sbeta, Sdelta, RBD) were defined as specific. Sakharkar, M. et al. Gene set variation analysis with the package gsva (v1.42.0) was used to estimate gene set enrichments for more than two groups61. # Lastly, we observed poor enrichments for CCR5, CCR7, and CD10 - and therefore remove them from the matrix (optional), "~/Downloads/pbmc3k/filtered_gene_bc_matrices/hg19/", # Get cell and feature names, and total numbers, # Set identity classes to an existing column in meta data, # Subset Seurat object based on identity class, also see ?SubsetData, # Subset on the expression level of a gene/feature, # Subset on a value in the object meta data, # Downsample the number of cells per identity class, # View metadata data frame, stored in object@meta.data, # Retrieve specific values from the metadata, # Retrieve or set data in an expression matrix ('counts', 'data', and 'scale.data'), # Get cell embeddings and feature loadings, # FetchData can pull anything from expression matrices, cell embeddings, or metadata, # Dimensional reduction plot for PCA or tSNE, # Dimensional reduction plot, with cells colored by a quantitative feature, # Scatter plot across single cells, replaces GenePlot, # Scatter plot across individual features, repleaces CellPlot, # Note that plotting functions now return ggplot2 objects, so you can add themes, titles, and options onto them, '2,700 PBMCs clustered using Seurat and viewed\non a two-dimensional tSNE', # Plotting helper functions work with ggplot2-based scatter plots, such as DimPlot, FeaturePlot, CellScatter, and FeatureScatter, # 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, satijalab/seurat: Tools for Single Cell Genomics. Creates a Seurat object containing only a subset of the cells in the original object. (default), then this list will be computed based on the next three 7, eabq3277 (2022). b, Scatter plots as in a display binding scores for SWT, RBD, Sbeta and Sdelta antigen constructs against each other. I was able to achieve this in the following way: Would be interesting to know if Seurat provides such functionality out of the box. Find corresponding symbol for gene used in Seurat, Subsetting a Seurat object based on colnames. We did not assume normal distribution for the flow cytometry data and used nonparametric tests such as KruskalWallis to test for differences between continuous variables in more than two groups, and P values were adjusted for multiple testing using Dunns method. d, Venn diagram displays clonal overlap of SARS-CoV-2-specific clones at months 6 and 12 post-infection. a. ## [79] mathjaxr_1.6-0 ggridges_0.5.4 evaluate_0.20 Resulting scores were used to compute fold changes and significance levels for enrichment score comparisons between cell subsets in limma (v3.50.3) (ref. ), A vector of cell names to use as a subset. control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE, features = Variable Features(control_subset)), Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Could you please let me know if the steps below are the correct way to go about identifying clusters and markers? Biol. I have added them all together and created the VlnPlot to check for the quality of the samples. Lines connect shared clones. 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. I have a seurat object with 10 samples (5 in duplicates). 3fh and Extended Data Fig. Hoehn, K. B., Pybus, O. G. & Kleinstein, S. H. Phylogenetic analysis of migration, differentiation, and class switching in B cells. 4c). Internet Explorer). Hi @attal-kush , 2f). d, Clonality of S+ Bm cells was analyzed preVac and postVac in scRNA-seq dataset. The flow cytometry dataset is available upon request from the corresponding authors. 59). @attal-kush Your questions are so comprehensive and I am also curious if there is a practical way to analyse the subsetted cells. then the answer is to run it on the integrated assay). However, antibody responses to several previously applied vaccines were normal in T-bet-deficient patients30. Circulating TFH cells, serological memory, and tissue compartmentalization shape human influenza-specific B cell immunity. Overexpression of T-bet in HIV infection is associated with accumulation of B cells outside germinal centers and poor affinity maturation. subset.name = NULL, 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). Viant, C. et al. Sci. column name in object@meta.data, etc. Conversely, the frequency of S+ CD21CD27 Bm cells rose quickly and remained stable over 150days post-vaccination, accounting for about 20% of S+ Bm cells (Fig. B, WNNUMAP analysis of Bm cells from COVID-19 patients is provided at months 6 and 12 post-infection, colored by clustering based on single-cell transcriptome and cell surface protein levels (left) and by indicated surface protein markers (right). Content Discovery initiative April 13 update: Related questions using a Review our technical responses for the 2023 Developer Survey, R: subsetting data frame by both certain column names (as a variable) and field values. 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. Any argument that can be retreived e, Violin plots of geometric mean fluorescence intensities (gMFI) or percentages of indicated markers in S+ Bm cells at indicated time points. 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. I.E.A. Immunity 49, 725739.e6 (2018). Notice that many of the top genes that show up here are the same as the ones we plotted earlier as core interferon response genes. Genewise statistics were conducted using empirical Bayes quasi-likelihood F-tests. Can I general this code to draw a regular polyhedron? We associated this with an incident during sample preparation in one of our experiments and decided to exclude most cells of this dataset from the analysis. Lines connect samples of same individual. 9eg) and visualization of Bm cells on the Monocle UMAP space identified two branches, which strongly separated CD21CD27+CD71+ activated and CD21CD27FcRL5+ Bm cells, both branching out from CD21+ resting Bm cells (Fig. After defining such subclusters, i would like to bring back the clusterinfo of the new subclusters to the parent Seurat object, in order to find (sub)-clustermarkers specific for the new subclusters in relation to all cells (and clusters) of the parent object. If they had a confirmed SARS-CoV-2 infection and/or SARS-CoV-2 nucleocapsid-specific antibodies, they were considered SARS-CoV-2-recovered. Choose a subset of cells, and use the integration assay to Run PCA, umap, findneighbors and findclusters to do subclustering. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. After sorting, cell suspensions were pelleted at 400g for 10min at 4C, resuspended and loaded into the Chromium Chip following the manufacturers instructions. I integrated samples across multiple batch conditions and diets after performing SCTransform (according to your most recent vignette for integration with SCTransform - Compiled: 2019-07-16). g, Frequencies (n=29 pairs; left) and pie charts (right) of indicated S+ Bm cell subsets are provided at indicated timepoints. I have tried several approaches before and i think they may be helpful: did the Seurat team ever respond to this..? Sci. eLife 8, e41641 (2019). In humans, resting Bm cells are typically CD21hi, and express the tumor necrosis factor (TNF) receptor superfamily member CD27. | object@meta.data$name | object$name | f, Violin plots of IgG1+ (left) and IgG3+ percentages (right) are shown in each S+ Bm cell subset from the same samples as in e. g, Pie charts represent percentages of S+ Bm cells among all cells in scRNA-seq dataset, separated by Bm cell subsets. Cell 185, 15881601.e14 (2022). Taken together, resting antigen-specific Bm cells were found in the tonsils after SARS-CoV-2 exposure, and they carried signs of tissue adaptation and clonal connection to their circulating counterparts.

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