Cell 185, 15881601.e14 (2022). High-affinity memory B cells induced by SARS-CoV-2 infection produce more plasmablasts and atypical memory B cells than those primed by mRNA vaccines. I know that we shouldn't rescale subsetted data from an integrated object but is it possible to RunUMAP on the subsetted data so I can at least get a plot? Can I general this code to draw a regular polyhedron? As cell identity is only available after intergration and clustering? O.B. However, when I try to do any of the following: I am at loss for how to perform conditional matching with the meta_data variable. 25,26,27,28,29). By default, this is set to the VariableFeatures. 1e). . I have a seurat object with 10 samples (5 in duplicates). 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: We performed scRNA-seq combined with feature barcoding, which allowed us to assess surface phenotype and to perform BCR-seq in sorted S+ Bm cells and S B cells from paired blood and tonsil samples of four patients (two SARS-CoV-2-recovered and two SARS-CoV-2-vaccinated). In particular, identifying cell populations that are present across multiple datasets can be problematic under standard workflows. The S+ CD21CD27 Bm cells identified here were transcriptionally very similar to their atypical counterparts in SLE. | RotatedAxis | Rotates x-axis labels |. 205, 20162025 (2020). 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. Immunity 55, 945964 (2022). rev2023.4.21.43403. How to convert a sequence of integers into a monomial, How to create a virtual ISO file from /dev/sr0. 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. The latter possibility fits well with our clonal data. 124, 10171030 (1966). Immunity 52, 842855.e6 (2020). | WhichCells(object = object, subset.name = "name", low.threshold = low, high.threshold = high) | WhichCells(object = object, expression = name > low & name < high) | Samples were compared using paired t-test (c) or two-sided Wilcoxon test (f). GSEA was performed on this preranked list using the R package fgsea (v.1.2). 2d and 6a. 5c). UMAP and clustering grouped Bm cells by IgG (clusters 15), IgM (clusters 6 and 7) and IgA (clusters 8 and 9) expression and revealed a phenotypical shift from acute infection to months 6 and 12 post-infection characterized by increased expression of CD21 on S+ Bm cells, whereas expression of Blimp-1, Ki-67, CD11c, CD71 and FcRL5 diminished (Extended Data Fig. Immunol. Immunol. The frequency of blood S+ Bm cells was approximately fivefold increased post-vaccination at month 12 compared with pre-vaccination at month 6 post-infection (Fig. @attal-kush Your questions are so comprehensive and I am also curious if there is a practical way to analyse the subsetted cells. Thank you @satijalab for this amazing analysis package. 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. Multifactorial seroprofiling dissects the contribution of pre-existing human coronaviruses responses to SARS-CoV-2 immunity. I have 6 scRNAseq runs of mixed immune cells, I subsetted all T cells (ie. Now, I have a Seurat object with 3 assays: RNA, SCT, and Integrated. Seurat continues to use t-distributed stochastic neighbor embedding (t-SNE) as a powerful tool to visualize and explore these datasets. # 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. Analysis of differentially expressed genes indicated that CD21CD27FcRL5+ B cells were the most distinctive subset and had high expression of TBX21 (encoding T-bet), T-bet-driven genes ZEB2 and ITGAX (encoding CD11c), and TOX (Fig. 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. Atypical memory B cells are greatly expanded in individuals living in a malaria-endemic area. Nature 604, 141145 (2022). a, Cohort overview of SARS-CoV-2 Infection Cohort. In addition, since I am not integrating the subset, is it recommended to use the "scale.data" slot in the SCT assay for DE analysis or continue using the "data" slot in the SCT assay for this subset? Seurat (version 3.1.4) original object. 128, 45884603 (2018). b, Violin plots of frequencies of CD21CD27+, CD21CD27, CD21+CD27+ and CD21+CD27 cells within S+ Bm cells are shown at acute infection (n=23) and months 6 (n=52) and 12 post-infection (n=16). Generally, you'll want use different parameters for each sample. Lines connect samples of same individual. During acute infection S+ CD21CD27+ Bm cells and CD21CD27 Bm cells represented on average 48.1% and 16.4% of total S+ Bm cells, respectively, and they strongly declined at month 6 (6.3% and 5.3%) and month 12 (3.7% and 6.6%) post-infection (Fig. Keller, B. et al. Weighted-nearest neighbor (WNN) clustering identified nave B cells (IgMhiIgDhiFCER2hi), nave/activated B cells (IgMhiIgDhiFCER2hiFCRL5hi), GC B cells (CD27hiCD38hiAICDAhi) and Bm cells (IgMloIgDloCD27int) (Extended Data Fig. Note, that tested this on one data set only so far. Sci. What you say "got the right result" probably misses several cases where bf11 is indeed 1, 2 or 3. b, Representative flow cytometry plots show percentages of decoy-negative SARS-CoV-2 S+ Bm cells (gated as in Extended Data Fig. Anti-SARS-CoV-2 antibodies were measured by a commercially available enzyme-linked immunosorbent assay specific for S1 of SARS-CoV-2 (Euroimmun SARS-CoV-2 IgG and IgA)57 or by a bead-based multiplexed immunoassay58. Frauke Muecksch, Zijun Wang, Michel C. Nussenzweig, R. Camille Brewer, Nitya S. Ramadoss, Tobias V. Lanz, Laila Shehata, Wendy F. Wieland-Alter, Laura M. Walker, Alice Cho, Frauke Muecksch, Michel C. Nussenzweig, Marios Koutsakos, Patricia T. Illing, Katherine Kedzierska, Anastasia A. Minervina, Mikhail V. Pogorelyy, Paul G. Thomas, Nature Immunology b, Hill numbers diversity curves show clonal diversities over a range of diversity orders for indicated S+ Bm cell subsets and nave B cells. 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. For example, to only cluster cells using a single sample group, control, we could run the following: . 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. The integrated assay consists of 3000 features comings from the original integration analysis (so choosed from the whole dataset, and not only from cells of the subset). 4ac). We also introduce simple functions for common tasks, like subsetting and merging, that mirror standard R functions. Raw counts obtained from the cellranger gene expression matrix were used to create cell datasets, which were preprocessed using the Monocle 3 pipeline. 2d and Supplementary Table 2). ## [4] igraph_1.4.1 lazyeval_0.2.2 sp_1.6-0 I was able to achieve this in the following way: Would be interesting to know if Seurat provides such functionality out of the box. Extended Data Fig. Sci. Graphical representations were generated with BioRender.com. B cell clonality analysis was performed mainly with the changeo-10x pipeline from the Immcantation suite65 using the singularity image provided by Immcantation developers. ## [28] ggrepel_0.9.3 rbibutils_2.2.13 textshaping_0.3.6 However, the differentiation path of CD21CD27+ Bm cells and CD21CD27 Bm cells remains ill-defined. Collectively, these data identify a durable, IgG1-dominated S+ Bm cell response forming upon SARS-CoV-2 infection. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. J. Not the answer you're looking for? 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). Dimensionality reduction and clustering analysis of flow cytometry data were performed in R using the CATALYST workflow (CATALYST package, version 1.18.1) (ref. Why does Acts not mention the deaths of Peter and Paul? Compared with their circulating counterparts, tonsillar S+ and N+ Bm cells expressed, on average, more CD69, less Ki-67, reduced T-bet and several chemokine receptors differently (Fig. e, Shown are gating strategy (left) and stacked bar plots (mean+standard deviation; right) of IgG+, IgM+ and IgA+ S+ Bm cells at indicated timepoints (acute, n=23; month 6, n=52; month 12, n=16). # One of these Assay objects is called the "default assay", meaning it's used for all analyses and visualization. Thank you for visiting nature.com. control_subset <- FindVariableFeatures(control_subset, selection.method = "vst", nfeatures = 3000) The scRNA-seq dataset identified a significantly increased SHM count in S+ Bm cells at month 12 compared with month 6 post-infection (Fig. Immunol. The clonality distance threshold was set to 0.20 for the longitudinal analysis of the SARS-CoV-2 Infection Cohort dataset and to 0.05 for the SARS-CoV-2 Tonsil Cohort dataset. 17, 12261234 (2016). and A.E.M. Cell 185, 18751887.e8 (2022). Goel, R. R. et al. At this point the tutorial displayed the UMAP plots with DimPlots and went forward to combine additional human PBMC datasets from eight different technologies. Hnzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. 1c and Supplementary Table 4) with no history of SARS-CoV-2 infection and seronegative for SARS-CoV-2 S S1-specific antibodies. J. Clin. Note that @timoast from the Seurat team recommended otherwise, although I never seen an explanation why would this not best way to go. SHM counts were low in unswitched S+ CD21+ Bm cells, slightly higher in CD21+CD27 resting Bm cells, and high by comparison in CD21+CD27+ resting, CD21CD27+CD71+ activated and CD21CD27 Bm cells (Fig. Statistical significance was established at P<0.05. Is short-circuiting logical operators mandated? (I ask because in the new integration vignette, it explicitly mentions not to run ScaleData after running the IntegrateData function)? "~/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". 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". Immunol. In d, frequencies were compared using a two-tailed, two-proportions z-test with a Bonferroni-based multiple testing correction. ## [130] mnormt_2.1.1 sctransform_0.3.5 multcomp_1.4-22 As one can see in the pic below, the quality is quite different in each of the duplicated conditions. 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. ## [1] systemfonts_1.0.4 sn_2.1.0 plyr_1.8.8 ## [15] SeuratObject_4.1.3 Seurat_4.3.0 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). So I guess FindVariableFeatures of the subset cells should be tried. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Black lines indicate trajectory. Andrews, S. F. et al. Not the answer you're looking for? Thank you for the wonderful package. 2a and 3c). A.E.M. SubsetData( I followed a similar approach to @attal-kush. Use the Previous and Next buttons to navigate the slides or the slide controller buttons at the end to navigate through each slide. | RestoreLegend | Restores a legend after removal | Samples in bd were compared using KruskalWallis test with Dunns multiple comparison correction, showing adjusted P values if significant. In e, two-sided Wilcoxon rank sum test was used and P values corrected by Bonferroni correction. 2a). Thanks for contributing an answer to Bioinformatics Stack Exchange! I am also wondering if there is an official recommendation for this task. Independent datasets were then integrated using Seurats anchoring-based integration method. 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. Commun. Nat Immunol (2023). Each dot represents an individual (n=6). Red line represents fitted second-order polynomial function (R2=0.1298). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Accessing data in Seurat is simple, using clearly defined accessors and setters to quickly find the data needed. ## [136] rmarkdown_2.20 Rtsne_0.16 spatstat.explore_3.0-6 ## [13] bmcite.SeuratData_0.3.0 SeuratData_0.2.2 Med. Can be used to downsample the data to a certain data.table vs dplyr: can one do something well the other can't or does poorly? Finally, we use a t-SNE to visualize our clusters in a two-dimensional space. Python script that identifies the country code of a given IP address. ## loaded via a namespace (and not attached): ## [1] systemfonts_1.0.4 sn_2.1.0 plyr_1.8.8, ## [4] igraph_1.4.1 lazyeval_0.2.2 sp_1.6-0, ## [7] splines_4.2.0 listenv_0.9.0 scattermore_0.8, ## [10] qqconf_1.3.1 TH.data_1.1-1 digest_0.6.31, ## [13] htmltools_0.5.4 fansi_1.0.4 magrittr_2.0.3, ## [16] memoise_2.0.1 tensor_1.5 cluster_2.1.3, ## [19] ROCR_1.0-11 limma_3.54.1 globals_0.16.2, ## [22] matrixStats_0.63.0 sandwich_3.0-2 pkgdown_2.0.7, ## [25] spatstat.sparse_3.0-0 colorspace_2.1-0 rappdirs_0.3.3, ## [28] ggrepel_0.9.3 rbibutils_2.2.13 textshaping_0.3.6, ## [31] xfun_0.37 dplyr_1.1.0 crayon_1.5.2, ## [34] jsonlite_1.8.4 progressr_0.13.0 spatstat.data_3.0-0, ## [37] survival_3.3-1 zoo_1.8-11 glue_1.6.2, ## [40] polyclip_1.10-4 gtable_0.3.1 leiden_0.4.3, ## [43] future.apply_1.10.0 BiocGenerics_0.44.0 abind_1.4-5, ## [46] scales_1.2.1 mvtnorm_1.1-3 spatstat.random_3.1-3, ## [49] miniUI_0.1.1.1 Rcpp_1.0.10 plotrix_3.8-2, ## [52] metap_1.8 viridisLite_0.4.1 xtable_1.8-4, ## [55] reticulate_1.28 stats4_4.2.0 htmlwidgets_1.6.1, ## [58] httr_1.4.5 RColorBrewer_1.1-3 TFisher_0.2.0, ## [61] ellipsis_0.3.2 ica_1.0-3 farver_2.1.1, ## [64] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14, ## [67] deldir_1.0-6 utf8_1.2.3 tidyselect_1.2.0, ## [70] labeling_0.4.2 rlang_1.0.6 reshape2_1.4.4, ## [73] later_1.3.0 munsell_0.5.0 tools_4.2.0, ## [76] cachem_1.0.7 cli_3.6.0 generics_0.1.3, ## [79] mathjaxr_1.6-0 ggridges_0.5.4 evaluate_0.20, ## [82] stringr_1.5.0 fastmap_1.1.1 yaml_2.3.7, ## [85] ragg_1.2.5 goftest_1.2-3 knitr_1.42, ## [88] fs_1.6.1 fitdistrplus_1.1-8 purrr_1.0.1, ## [91] RANN_2.6.1 pbapply_1.7-0 future_1.31.0, ## [94] nlme_3.1-157 mime_0.12 formatR_1.14, ## [97] compiler_4.2.0 plotly_4.10.1 png_0.1-8, ## [100] spatstat.utils_3.0-1 tibble_3.1.8 bslib_0.4.2, ## [103] stringi_1.7.12 highr_0.10 desc_1.4.2, ## [106] lattice_0.20-45 Matrix_1.5-3 multtest_2.54.0, ## [109] vctrs_0.5.2 mutoss_0.1-12 pillar_1.8.1, ## [112] lifecycle_1.0.3 Rdpack_2.4 spatstat.geom_3.0-6, ## [115] lmtest_0.9-40 jquerylib_0.1.4 RcppAnnoy_0.0.20, ## [118] data.table_1.14.8 irlba_2.3.5.1 httpuv_1.6.9, ## [121] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20, ## [124] gridExtra_2.3 parallelly_1.34.0 codetools_0.2-18, ## [127] MASS_7.3-56 rprojroot_2.0.3 withr_2.5.0, ## [130] mnormt_2.1.1 sctransform_0.3.5 multcomp_1.4-22, ## [133] parallel_4.2.0 grid_4.2.0 tidyr_1.3.0, ## [136] rmarkdown_2.20 Rtsne_0.16 spatstat.explore_3.0-6, ## [139] Biobase_2.58.0 numDeriv_2016.8-1.1 shiny_1.7.4, Fast integration using reciprocal PCA (RPCA), Integrating scRNA-seq and scATAC-seq data, Demultiplexing with hashtag oligos (HTOs), Interoperability between single-cell object formats, Create an integrated data assay for downstream analysis, Identify cell types that are present in both datasets, Obtain cell type markers that are conserved in both control and stimulated cells, Compare the datasets to find cell-type specific responses to stimulation, When running sctransform-based workflows, including integration, do not run the. 6, eabk0894 (2021). ; NRP 78 Implementation Programme to C.C. I would also like to know the recommended way of doing this. Unswitched CD21+ Bm cells were IgM+, whereas the other Bm cell subsets expressed mainly IgG, with IgG1 being the dominant subclass (Extended Data Fig. ), Innovation grant of University Hospital Zurich (to O.B. ident.use = NULL, The best answers are voted up and rise to the top, Not the answer you're looking for? Thank you @satijalab !!!! Sci. Colors indicate frequency within RBD+ and RBD Bm cells. Hi @attal-kush , Otherwise, will return an object consissting only of these cells, Parameter to subset on. You can read more on the concept here in Martin's paper. Next, we performed droplet-based scRNA-seq combined with feature barcoding and BCR sequencing (BCR-seq) on sorted S+ and S Bm cells isolated from the blood of nine patients with COVID-19 at months 6 and 12 post-infection; three patients were nonvaccinated, and six received SARS-CoV-2 mRNA vaccination between month 6 and month 12 (Extended Data Fig. Functional groups of genes were ordered by hierarchical clustering. PLoS Comput. @MediciPrime That looks correct to me, though your resolution=0.2 parameter is quite low. Levine, J. H. et al. Immunol. 60). Extended Data Fig. Google Scholar. Below, we demonstrate methods for scRNA-seq integration as described in Stuart*, Butler* et al, 2019 to perform a comparative analysis of human immune cells (PBMC) in either a resting or interferon-stimulated state.
Shaq Projector Commercial,
Sims 4 Royal Cc Maxis Match,
Altitude Of Polaris At New Orleans,
Articles S