CCL19-expressing FRCs in murine lung tumors
Chrysa Papadopoulou
Last updated: 2024-08-21
Checks: 7 0
Knit directory: CCL19_FRCs_lung_cancer/
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Load packages
suppressPackageStartupMessages({
library(here)
library(purrr)
library(dplyr)
library(stringr)
library(patchwork)
library(Seurat)
library(Matrix)
library(dittoSeq)
library(gridExtra)
library(gsubfn)
library(ggsci)
library(slingshot)
})Set directory
basedir <- here()Read CCL19-EYFP⁺ cell data from naïve lungs and excised LLC-gp33 tumors on day 23
CCL19_EYFP <- readRDS(paste0(basedir,"/data/Mouse/CCL19_EYFP_nonmCOV.rds"))CCL19-EYFP⁺ cells (Figure 5E)
Umap colored per celltype
#Define color palette
palet <-c("#2ED9FF","#90AD1C","#FEAF16","#B00068","#1CFFCE","#3283FE","#16FF32","#AA0DFE")
names(palet) <-c(paste0("Smoc1", expression("\u207A "), "AdvFB"),paste0("Cd34", expression("\u207A "), "AdvFB"),paste0("Npnt", expression("\u207A "), "AlvFB"),paste0("Hhip", expression("\u207A "), "AdvFB"),paste0("Sulf1", expression("\u207A "), "TRC"),paste0("Rgs5", expression("\u207A "), "PRC"),"SMC/PC","AlvFB/SMC/PC")
DimPlot(CCL19_EYFP, reduction = "umap", group.by = "annot", cols=palet)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle(paste0("Ccl19-EYFP", "\U207A ", "cells"))
Umap colored per tumor presence
CCL19_EYFP_origin <- subset(CCL19_EYFP, origin %in% c("naive","tumor"))
CCL19_EYFP_origin$origin[which(CCL19_EYFP_origin$origin == "naive")] <- "Lung"
CCL19_EYFP_origin$origin[which(CCL19_EYFP_origin$origin == "tumor")] <- "Tumor/Day 23"
DimPlot(CCL19_EYFP_origin, reduction = "umap", group.by = "origin", cols = c("lightgrey", "red"))+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
Dotplot (Figure 5F)
data_conv <-CCL19_EYFP
data_conv <-Remove_ensebl_id(data_conv)
Idents(data_conv) <- data_conv$annot
levels(data_conv)<-levels(data_conv)[order(match(levels(data_conv),c("Hhip⁺ AdvFB","Cd34⁺ AdvFB","Smoc1⁺ AdvFB",paste0("Npnt", expression("\u207A "), "AlvFB"),
"Sulf1⁺ TRC","SMC/PC","AlvFB/SMC/PC","Rgs5⁺ PRC")))]
data_conv$annot <- factor(as.character(data_conv@active.ident), levels = rev(c("Hhip⁺ AdvFB","Cd34⁺ AdvFB","Smoc1⁺ AdvFB",paste0("Npnt", expression("\u207A "), "AlvFB"),"Sulf1⁺ TRC","SMC/PC","AlvFB/SMC/PC","Rgs5⁺ PRC")))
gene_list <-c("Cd34","Pdpn","Npnt","Hhip","Sulf1","Ccl11","Il33","Cxcl9","Tnc","Pdgfrb","Notch3","Rgs5","Acta2","Des","Myh11")
gg <-DotPlot(data_conv, features = gene_list, group.by = "annot", scale = TRUE, cols = c("lightgrey", "#C51B7D"),
scale.min = 0, scale.max = 100,col.min = 0 , dot.scale = 4) + xlab(" ") + ylab(" ")
gg + theme(axis.text.x = element_text(angle = 45,hjust = 1))
CCL19-EYFP⁺ cells in different timepoints (Figure 5K)
Umap colored per timepoint of tumor detection
CCL19_EYFP$TimePoint[which(CCL19_EYFP$TimePoint == "d0")] <- "Naive"
CCL19_EYFP$TimePoint[which(CCL19_EYFP$TimePoint == "d15")] <- "Day 15"
CCL19_EYFP$TimePoint[which(CCL19_EYFP$TimePoint == "d23")] <- "Day 23"
DimPlot(CCL19_EYFP, reduction = "umap", group.by = "TimePoint", cols = c("red", "gray" , "gray"))+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle(paste0("Ccl19-EYFP", "\U207A ", "cells"))
Umap of unaffected Lung (Naive)
CCL19_EYFP_naive <- subset(CCL19_EYFP, TimePoint %in% c("Naive"))
DimPlot(CCL19_EYFP_naive, reduction = "umap", group.by = "TimePoint", cols = c("gray"))+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("Naive")
Umap of Tumor at Day 15
CCL19_EYFP_d15 <- subset(CCL19_EYFP, TimePoint %in% c("Day 15"))
DimPlot(CCL19_EYFP_d15, reduction = "umap", group.by = "TimePoint", cols = c("red"))+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("Day 15")
Umap of Tumor at Day 23
CCL19_EYFP_d23 <- subset(CCL19_EYFP, TimePoint %in% c("Day 23"))
DimPlot(CCL19_EYFP_d23, reduction = "umap", group.by = "TimePoint", cols = c("grey"))+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("Day 23")
Des in Day 15 vs Day 23 (Supplementary Figure 6C)
CCL19_EYFP_15_vs_23 <- subset(CCL19_EYFP, TimePoint %in% c("Day 15","Day 23"))
FeaturePlot(CCL19_EYFP_15_vs_23, reduction = "umap", split.by = "TimePoint",
features = get_full_gene_name('Des',CCL19_EYFP),raster=FALSE,
cols=c("lightgrey", "darkred"))
Differentiation trajectories on CCL19-EYFP⁺ cells based on Slingshot algorithm (Figure 5L)
Annotation from RNA_snn_res.0.25 to perform trajectory analysis
#Define color palette
palet_new <-c("#2ED9FF","#90AD1C","#FEAF16","#B00068","#1CFFCE","#3283FE","#16FF32","#AA0DFE")
names(palet_new) <-c("5","0","2","3","4","6","1","7" )
# This column was used to annotate the dataset
DimPlot(CCL19_EYFP, reduction = "umap", group.by = "RNA_snn_res.0.25",cols = palet_new)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle("RNA_snn_res.0.25")
Differentiation trajectories for Sulf1⁺ TRC and Rgs5⁺ PRC
#Set color palette
palet_new <-c("#90AD1C","#16FF32","#FEAF16","#B00068","#1CFFCE","#2ED9FF","#3283FE","#AA0DFE")
names(palet_new) <-c(1,2,3,4,5,6,7,8)
#Calculation of CCL19-EYFP`r knitr::asis_output("\U207A")` cell differentiation trajectories
clustering <- as.integer(CCL19_EYFP@meta.data$RNA_snn_res.0.25)
dimred <- CCL19_EYFP@reductions$umap@cell.embeddings
# Slingshot for TRC
pto_TRC <- slingshot(dimred, clustering, start.clus = '6', end.clus = '5' ,reducedDim = 'umap',extend="n",stretch=0.07,thresh=0.05)
pto_TRC <- as.SlingshotDataSet(pto_TRC)
# Slingshot for PRC
pto_PRC <- slingshot(dimred, clustering, start.clus = '2', end.clus = '7' ,reducedDim = 'umap',extend="n",stretch=0.07,thresh=0.05)
pto_PRC <- as.SlingshotDataSet(pto_PRC)
plot(dimred, col = palet_new[clustering], asp = 1, pch = 16)
lines(pto_PRC@curves$Lineage4, lwd = 3, col = 'magenta')
lines(pto_TRC@curves$Lineage2, lwd = 3, col = 'black')
legend("bottomright", legend = c("T1","T2"), col = c('black','magenta'), lty = 1, lwd = 2.5, cex = 1.1)
legend("topright", legend = names(palet), col = palet, pch = 2, pt.cex = 1, cex = 0.72)
CCL19-EYFP⁺ cells (no fate) and CCL19-iEYFP ⁺ cells sorted from doxycycline-treated LLC-gp33 tumors (Fatemapping experiment) (Figure 5N)
Umap
CCL19_EYFP$FateMapping[which(CCL19_EYFP$FateMapping == "no fate")] <- "Ccl19-EYFP"
CCL19_EYFP$FateMapping[which(CCL19_EYFP$FateMapping == "fate mapped")] <- "Ccl19-iEYFP (Day 15 progeny)"
DimPlot(CCL19_EYFP, reduction = "umap", group.by = "FateMapping", cols = c("#00BFC4","#F8766D"))+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") 
Umap indicating differentiation trajectories
CCL19_EYFP$FateMapping_anot <- CCL19_EYFP$FateMapping
CCL19_EYFP$FateMapping_anot[which(CCL19_EYFP$FateMapping == "no fate")] <- 1
CCL19_EYFP$FateMapping_anot[which(CCL19_EYFP$FateMapping == "fate mapped")] <- 2
#Set color palette
cols_vec <- c("#00BFC4","#F8766D")
names(cols_vec) <-c("Ccl19-EYFP","Ccl19-iEYFP (Day 15 progeny)")
clustering <- CCL19_EYFP@meta.data$FateMapping_anot
plot(dimred, col = cols_vec[clustering], asp = 1, pch = 16)
lines(pto_PRC@curves$Lineage4, lwd = 3, col = 'magenta')
lines(pto_TRC@curves$Lineage2, lwd = 3, col = 'black')
legend("bottomright", legend = c("T1","T2"), col = c('black','magenta'), lty = 1, lwd = 2.5, cex = 1)
legend("topright", legend = names(cols_vec), col = cols_vec, pch = 2, pt.cex = 1, cex = 0.6)
Session info
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] slingshot_2.8.0 TrajectoryUtils_1.8.0
[3] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[5] Biobase_2.60.0 GenomicRanges_1.52.0
[7] GenomeInfoDb_1.36.1 IRanges_2.34.1
[9] S4Vectors_0.38.1 BiocGenerics_0.46.0
[11] MatrixGenerics_1.12.3 matrixStats_1.0.0
[13] princurve_2.1.6 ggsci_3.0.0
[15] gsubfn_0.7 proto_1.0.0
[17] gridExtra_2.3 dittoSeq_1.12.1
[19] ggplot2_3.4.2 Matrix_1.6-0
[21] SeuratObject_4.1.3 Seurat_4.3.0.1
[23] patchwork_1.1.2 stringr_1.5.0
[25] dplyr_1.1.2 purrr_1.0.1
[27] here_1.0.1 magrittr_2.0.3
[29] circlize_0.4.15 tidyr_1.3.0
[31] tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] polyclip_1.10-4 lifecycle_1.0.3
[7] tcltk_4.3.1 rprojroot_2.0.3
[9] globals_0.16.2 processx_3.8.2
[11] lattice_0.21-8 MASS_7.3-60
[13] plotly_4.10.2 sass_0.4.7
[15] rmarkdown_2.23 jquerylib_0.1.4
[17] yaml_2.3.7 httpuv_1.6.11
[19] sctransform_0.3.5 sp_2.0-0
[21] spatstat.sparse_3.0-2 reticulate_1.36.1
[23] cowplot_1.1.1 pbapply_1.7-2
[25] RColorBrewer_1.1-3 abind_1.4-5
[27] zlibbioc_1.46.0 Rtsne_0.16
[29] RCurl_1.98-1.12 git2r_0.33.0
[31] GenomeInfoDbData_1.2.10 ggrepel_0.9.3
[33] irlba_2.3.5.1 listenv_0.9.0
[35] spatstat.utils_3.1-0 pheatmap_1.0.12
[37] goftest_1.2-3 spatstat.random_3.1-5
[39] fitdistrplus_1.1-11 parallelly_1.36.0
[41] DelayedMatrixStats_1.22.0 leiden_0.4.3
[43] codetools_0.2-19 DelayedArray_0.28.0
[45] tidyselect_1.2.0 shape_1.4.6
[47] farver_2.1.1 spatstat.explore_3.2-1
[49] jsonlite_1.8.7 ellipsis_0.3.2
[51] progressr_0.13.0 ggridges_0.5.4
[53] survival_3.5-5 systemfonts_1.0.4
[55] tools_4.3.1 ragg_1.2.5
[57] ica_1.0-3 Rcpp_1.0.11
[59] glue_1.6.2 SparseArray_1.2.4
[61] xfun_0.39 withr_2.5.0
[63] fastmap_1.1.1 fansi_1.0.4
[65] callr_3.7.3 digest_0.6.33
[67] R6_2.5.1 mime_0.12
[69] textshaping_0.3.6 colorspace_2.1-0
[71] scattermore_1.2 tensor_1.5
[73] spatstat.data_3.0-1 utf8_1.2.3
[75] generics_0.1.3 data.table_1.14.8
[77] httr_1.4.6 htmlwidgets_1.6.2
[79] S4Arrays_1.2.1 whisker_0.4.1
[81] uwot_0.1.16 pkgconfig_2.0.3
[83] gtable_0.3.3 lmtest_0.9-40
[85] XVector_0.40.0 htmltools_0.5.5
[87] scales_1.2.1 png_0.1-8
[89] knitr_1.43 rstudioapi_0.15.0
[91] reshape2_1.4.4 nlme_3.1-162
[93] cachem_1.0.8 zoo_1.8-12
[95] GlobalOptions_0.1.2 KernSmooth_2.23-22
[97] parallel_4.3.1 miniUI_0.1.1.1
[99] pillar_1.9.0 grid_4.3.1
[101] vctrs_0.6.3 RANN_2.6.1
[103] promises_1.2.0.1 xtable_1.8-4
[105] cluster_2.1.4 evaluate_0.21
[107] cli_3.6.1 compiler_4.3.1
[109] rlang_1.1.1 crayon_1.5.2
[111] future.apply_1.11.0 labeling_0.4.2
[113] ps_1.7.5 getPass_0.2-4
[115] plyr_1.8.8 fs_1.6.3
[117] stringi_1.7.12 viridisLite_0.4.2
[119] deldir_1.0-9 munsell_0.5.0
[121] lazyeval_0.2.2 spatstat.geom_3.2-4
[123] sparseMatrixStats_1.12.2 future_1.33.0
[125] shiny_1.7.4.1 highr_0.10
[127] ROCR_1.0-11 igraph_1.5.0.1
[129] bslib_0.5.0 date()[1] "Wed Aug 21 10:10:11 2024"
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] slingshot_2.8.0 TrajectoryUtils_1.8.0
[3] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[5] Biobase_2.60.0 GenomicRanges_1.52.0
[7] GenomeInfoDb_1.36.1 IRanges_2.34.1
[9] S4Vectors_0.38.1 BiocGenerics_0.46.0
[11] MatrixGenerics_1.12.3 matrixStats_1.0.0
[13] princurve_2.1.6 ggsci_3.0.0
[15] gsubfn_0.7 proto_1.0.0
[17] gridExtra_2.3 dittoSeq_1.12.1
[19] ggplot2_3.4.2 Matrix_1.6-0
[21] SeuratObject_4.1.3 Seurat_4.3.0.1
[23] patchwork_1.1.2 stringr_1.5.0
[25] dplyr_1.1.2 purrr_1.0.1
[27] here_1.0.1 magrittr_2.0.3
[29] circlize_0.4.15 tidyr_1.3.0
[31] tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.1
[3] later_1.3.1 bitops_1.0-7
[5] polyclip_1.10-4 lifecycle_1.0.3
[7] tcltk_4.3.1 rprojroot_2.0.3
[9] globals_0.16.2 processx_3.8.2
[11] lattice_0.21-8 MASS_7.3-60
[13] plotly_4.10.2 sass_0.4.7
[15] rmarkdown_2.23 jquerylib_0.1.4
[17] yaml_2.3.7 httpuv_1.6.11
[19] sctransform_0.3.5 sp_2.0-0
[21] spatstat.sparse_3.0-2 reticulate_1.36.1
[23] cowplot_1.1.1 pbapply_1.7-2
[25] RColorBrewer_1.1-3 abind_1.4-5
[27] zlibbioc_1.46.0 Rtsne_0.16
[29] RCurl_1.98-1.12 git2r_0.33.0
[31] GenomeInfoDbData_1.2.10 ggrepel_0.9.3
[33] irlba_2.3.5.1 listenv_0.9.0
[35] spatstat.utils_3.1-0 pheatmap_1.0.12
[37] goftest_1.2-3 spatstat.random_3.1-5
[39] fitdistrplus_1.1-11 parallelly_1.36.0
[41] DelayedMatrixStats_1.22.0 leiden_0.4.3
[43] codetools_0.2-19 DelayedArray_0.28.0
[45] tidyselect_1.2.0 shape_1.4.6
[47] farver_2.1.1 spatstat.explore_3.2-1
[49] jsonlite_1.8.7 ellipsis_0.3.2
[51] progressr_0.13.0 ggridges_0.5.4
[53] survival_3.5-5 systemfonts_1.0.4
[55] tools_4.3.1 ragg_1.2.5
[57] ica_1.0-3 Rcpp_1.0.11
[59] glue_1.6.2 SparseArray_1.2.4
[61] xfun_0.39 withr_2.5.0
[63] fastmap_1.1.1 fansi_1.0.4
[65] callr_3.7.3 digest_0.6.33
[67] R6_2.5.1 mime_0.12
[69] textshaping_0.3.6 colorspace_2.1-0
[71] scattermore_1.2 tensor_1.5
[73] spatstat.data_3.0-1 utf8_1.2.3
[75] generics_0.1.3 data.table_1.14.8
[77] httr_1.4.6 htmlwidgets_1.6.2
[79] S4Arrays_1.2.1 whisker_0.4.1
[81] uwot_0.1.16 pkgconfig_2.0.3
[83] gtable_0.3.3 lmtest_0.9-40
[85] XVector_0.40.0 htmltools_0.5.5
[87] scales_1.2.1 png_0.1-8
[89] knitr_1.43 rstudioapi_0.15.0
[91] reshape2_1.4.4 nlme_3.1-162
[93] cachem_1.0.8 zoo_1.8-12
[95] GlobalOptions_0.1.2 KernSmooth_2.23-22
[97] parallel_4.3.1 miniUI_0.1.1.1
[99] pillar_1.9.0 grid_4.3.1
[101] vctrs_0.6.3 RANN_2.6.1
[103] promises_1.2.0.1 xtable_1.8-4
[105] cluster_2.1.4 evaluate_0.21
[107] cli_3.6.1 compiler_4.3.1
[109] rlang_1.1.1 crayon_1.5.2
[111] future.apply_1.11.0 labeling_0.4.2
[113] ps_1.7.5 getPass_0.2-4
[115] plyr_1.8.8 fs_1.6.3
[117] stringi_1.7.12 viridisLite_0.4.2
[119] deldir_1.0-9 munsell_0.5.0
[121] lazyeval_0.2.2 spatstat.geom_3.2-4
[123] sparseMatrixStats_1.12.2 future_1.33.0
[125] shiny_1.7.4.1 highr_0.10
[127] ROCR_1.0-11 igraph_1.5.0.1
[129] bslib_0.5.0