Single-cell RNA-seq: Clustering Analysis - In-depth-NGS-Data-Analysis ## [91] RANN_2.6.1 pbapply_1.7-0 future_1.31.0 Lines connect samples of same individual. Immunol. However there are a few times that i found some genes that are primary markers for one certain subtype of the cells i want to sub clustering do not exist in the integration assay, which may lead to some problems. No VH or VL chain segments were significantly differentially used between S+ Bm cell subsets. In Hafemeister and Satija, 2019, we introduced an improved method for the normalization of scRNA-seq, based on regularized negative binomial regression. 6dg). VASPKIT and SeeK-path recommend different paths. Immunol. Of these, 35 received SARS-CoV-2 mRNA vaccination between month 6 and month 12, and 3 subjects between acute infection and month 6. The ideal workflow is not clear to me and perusing the vignettes and past issues did not clarify it fully. For scRNA-seq data, distribution was assumed to be normal, but this was not formally tested. 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). DefaultAssay(control_subset) <- "integrated" 11, eaax0904 (2019). d, Violin plots comparing frequencies of CD21CD27+, CD21CD27, CD21+CD27+ and CD21+CD27 S+ Bm cell subsets are separated by timepoints post-infection and mild (acute infection, n=15; month 6, n=33; month 12, n=10) and severe COVID-19 (acute infection, n=8; month 6, n=19; month 12, n=6). 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). We longitudinally studied antigen-specific Bm cells in a cohort of 65 patients with COVID-19, 33 females and 32 males, including 42 with mild and 23 with severe disease course, during their acute SARS-CoV-2 infection and at months 6 and 12 post-infection. JCI Insight 2, e92943 (2017). | MergeSeurat(object1 = object1, object2 = object2) | merge(x = object1, y = object2) |. 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. SARS-CoV-2-nave healthy controls (n=11) were sampled before their SARS-CoV-2 mRNA vaccination, at week 2 post-second dose, month 6 post-second dose and at week 2 post-third dose. Immunol. r rna-seq single-cell seurat Share Pseudobulking was done only for patients with more than 20 cells in each cell subset. and J.N. Immunol. 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. 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. Red dashed lines indicate minimal and maximal cumulative enrichment values. 3a,b). low.threshold = -Inf, Following 20min staining with fixable viability dye eFluor 780 (eBioscience) and TruStain FcX and subsequently 1h antigen-specific staining mix, cells were incubated at 4C for 30min with a surface staining mix containing fluorescently labeled and barcoded antibodies, and each sample was marked with a hashtag antibody that allowed multiplexing (Supplementary Table 6). 7ac). after integration, I subsetted my cells of interest using the integrated assay, and I still see apparent batch effects. A multiple hypothesis correction procedure was applied to obtain adjusted P values. ## [64] pkgconfig_2.0.3 sass_0.4.5 uwot_0.1.14 HolmBonferroni method was used for P value adjustment of multiple comparisons. Warnatz, K. et al. 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. subset.name = NULL, Hi Seurat team, Thank you for developing Seurat. ## [136] rmarkdown_2.20 Rtsne_0.16 spatstat.explore_3.0-6 What are the differences between "=" and "<-" assignment operators? Moreover, our multimer staining approach might miss low-affinity antigen binders50. ## [34] jsonlite_1.8.4 progressr_0.13.0 spatstat.data_3.0-0 Seurat continues to use t-distributed stochastic neighbor embedding (t-SNE) as a powerful tool to visualize and explore these datasets. Durable SARS-CoV-2 B cell immunity after mild or severe disease. '||', where the operator is quoted. J. Immunol. then the answer is to run it on the integrated assay). The scRNA-seq dataset identified a trend towards increased clonality of S+ Bm cells in the six patients vaccinated between month 6 and month 12 post-infection when comparing pre-vaccination with post-vaccination (Fig. I have a seurat object with 10 samples (5 in duplicates). g, Percentages (mean SD) of FcRL4+ Bm cells in paired blood (n=15) and tonsil (n=16) and S+ Bm cells in tonsil samples, separated by SARS-CoV-2-vaccinated (n=8) and recovered patients (n=8). Thanks for contributing an answer to Stack Overflow! Alice. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. a, Gating strategy for SARS-CoV-2 spike (S)+ and receptor-binding domain (RBD)+ Bm cells. Here we plot 2-3 strong marker genes for each of our 14 clusters. control_subset <- RunPCA(control_subset, npcs = 30, verbose = FALSE, features = Variable Features(control_subset)), 1. ISSN 1529-2908 (print). One way to look broadly at these changes is to plot the average expression of both the stimulated and control cells and look for genes that are visual outliers on a scatter plot. 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 number of samples and subjects and the statistical tests used in each experiment are indicated in the corresponding figure legends. limma powers differential expression analyses for RNA-sequencing and microarray studies. (I ask because in the new integration vignette, it explicitly mentions not to run ScaleData after running the IntegrateData function)? Find corresponding symbol for gene used in Seurat, Subsetting a Seurat object based on colnames. d, Heatmap displays V light (VL) gene usage in RBD+ and RBD Bm cells from scRNA-seq dataset of SARS-CoV-2-infected patients at month 6 and 12 post-infection. ), 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. Whereas subdivision of labor in terms of tissue homing and effector functions has been well characterized for memory T cells, functionally different subsets also exist for memory B (Bm) cells. Following subtraction of raw counts of baiting-negative control from those of all other antigen-baiting constructs in every cell, cutoffs for background binding levels were manually determined for every construct by inspection of bimodal distributions of count frequencies across all cells, and all binding counts below thresholds were set to zero and classified as nonbinding. To learn more, see our tips on writing great answers. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Flow cytometry analysis of S+ Bm cells showed an upregulation of Blimp-1 at week 2 post-second dose compared with month 6, and increased expression of T-bet, FcRL5, CD71 and Ki-67 at week 2 post-second dose and post-third dose (Extended Data Fig. Knight and colleagues report altered granulopoiesis and increased frequency of immature neutrophil subsets with immunosuppressive properties in a subset of patients with sepsis with poor outcome. 1 Answer Sorted by: 1 There are a few ways to address this. I followed a similar approach to @attal-kush. # S3 method for Assay However, antibody responses to several previously applied vaccines were normal in T-bet-deficient patients30. control_subset <- ScaleData(control_subset, verbose = FALSE, vars.to.regress = c( "percent.mt")) Raw counts obtained from the cellranger gene expression matrix were used to create cell datasets, which were preprocessed using the Monocle 3 pipeline. | object@idents | Idents(object = object) | Bm cells can be subdivided into phenotypically and functionally distinct subsets10. assay = NULL, Markers were scaled with arcsinh transformation (cofactor 6,000), samples were subsetted to maximally 25 S+ Bm cells per sample. # To see all keys for all objects, use the Key function. 65 patients were included and followed-up until month 12 post-infection. Here, we take the average expression of both the stimulated and control naive T cells and CD14 monocyte populations and generate the scatter plots, highlighting genes that exhibit dramatic responses to interferon stimulation. We included a total of 65 patients of the full cohort51,52 on the basis of a power calculation from pre-experiments and according to sample availability of at least paired samples from two timepoints. 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. I have been following the SCTransform integration tutorial and it doesn't mention how to FindClusters or identify cluster specific markers. Nave B cell clusters were identified on the basis of their surface protein expression of CD27, CD21 and IgD and their transcriptional levels of TCLA1, IL4R, BACH2, IGHD and BTG1. Seurat v4 includes a set of methods to match (or align) shared cell populations across datasets. Immunoglobulin signature predicts risk of post-acute COVID-19 syndrome. Density plots indicate count distributions across binding score ranges are shown on top and on the side. ## [88] fs_1.6.1 fitdistrplus_1.1-8 purrr_1.0.1 Atypical B cells up-regulate costimulatory molecules during malaria and secrete antibodies with T follicular helper cell support. Hnzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. The pro of this approach is that I use this method to solve the problem in the previous approach and now i have the genes that are primary markers for the cell sub types. conceived the project, designed experiments and interpreted 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). But as you can see, %in% is far more useful and less verbose in such circumstances. I want to know: https://doi.org/10.1038/s41590-023-01497-y, DOI: https://doi.org/10.1038/s41590-023-01497-y. Cao, J. et al. ## [7] pbmcsca.SeuratData_3.0.0 pbmcMultiome.SeuratData_0.1.2 4e). ## [94] nlme_3.1-157 mime_0.12 formatR_1.14 # 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. 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. ## 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. subsetting seurat object with multiple samples, Traffic: 1812 users visited in the last hour, User Agreement and Privacy Best wishes e and f, UMAP represents Monocle 3 analysis on all Bm cells in scRNA-seq dataset, colored by clusters identified (e) or pseudotime annotation (f). Counts of SHM in S+ Bm cells remained high at month 12 (post-vaccination) compared with month 6 post-infection (pre-vaccination) (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). Cell Rep. 34, 108684 (2021). Are || and ! 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). For example, In FeaturePlot, one can specify multiple genes and also split.by to further split to multiple the conditions in the meta.data. Hoehn, K. B., Pybus, O. G. & Kleinstein, S. H. Phylogenetic analysis of migration, differentiation, and class switching in B cells. 269, 118129 (2016). AverageExpression: Averaged feature expression by identity class Collectively, these data identify a durable, IgG1-dominated S+ Bm cell response forming upon SARS-CoV-2 infection. @attal-kush Your questions are so comprehensive and I am also curious if there is a practical way to analyse the subsetted cells. 2d and Supplementary Table 2). ), BRCCH-EDCTP COVID-19 initiative (to A.E.M.) In particular, identifying cell populations that are present across multiple datasets can be problematic under standard workflows. In e, two-sided Wilcoxon rank sum test was used and P values corrected by Bonferroni correction. Gray slices indicate individual clones found at one timepoint only, whereas persistent clones found at both timepoints are labeled by the same color. Samples in f were compared using two-proportions z-test. @kostia Quote the operator: something like, Using multiple criteria in subset function and logical operators. 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). ## [121] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 Jenks, S. A. et al. Austin, J. W. et al. J. Exp. @satijalab, could you please help us? Generic Doubly-Linked-Lists C implementation. Natl Acad. Bm cells specific for RBD, wild-type spike (SWT) or spike variants B.1.351 (Sbeta) and B.1.617.2 (Sdelta) were identified by SAV multimers carrying specific oligonucleotide barcodes. At months 6 and 12 post-infection, CD21+ resting Bm cells were the major Bm cell subset in the circulation and were also detected in peripheral lymphoid organs, where they carried tissue residency markers.
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