Exploration of the dataset

Load libraries

library(Seurat)
library(RColorBrewer)
library(wesanderson)

#Set ggplot theme
theme_set(theme_classic())

Load QC filtered cell dataset

QCFiltered.data dataset was generated following this QC steps

QCFiltered.data <- readRDS("./QC.filtered.cells.RDS")

Assigning cell cycle score

Cell cycle score was assigned following the Seurat v2.4 Cell-Cycle Scoring vignette

# Assign cell-cycle scores based on Tirosh et al, 2015 gene list
s.genes <- c("Mcm5", "Pcna", "Tym5", "Fen1", "Mcm2", "Mcm4", "Rrm1", "Ung", "Gins2", "Mcm6", "Cdca7", "Dtl", "Prim1", "Uhrf1", "Mlf1ip", "Hells", "Rfc2", "Rap2", "Nasp", "Rad51ap1", "Gmnn", "Wdr76", "Slbp", "Ccne2", "Ubr7", "Pold3", "Msh2", "Atad2", "Rad51", "Rrm2", "Cdc45", "Cdc6", "Exo1", "Tipin", "Dscc1", "Blm", " Casp8ap2", "Usp1", "Clspn", "Pola1", "Chaf1b", "Brip1", "E2f8")
g2m.genes <- c("Hmgb2", "Ddk1","Nusap1", "Ube2c", "Birc5", "Tpx2", "Top2a", "Ndc80", "Cks2", "Nuf2", "Cks1b", "Mki67", "Tmpo", " Cenpk", "Tacc3", "Fam64a", "Smc4", "Ccnb2", "Ckap2l", "Ckap2", "Aurkb", "Bub1", "Kif11", "Anp32e", "Tubb4b", "Gtse1", "kif20b", "Hjurp", "Cdca3", "Hn1", "Cdc20", "Ttk", "Cdc25c", "kif2c", "Rangap1", "Ncapd2", "Dlgap5", "Cdca2", "Cdca8", "Ect2", "Kif23", "Hmmr", "Aurka", "Psrc1", "Anln", "Lbr", "Ckap5", "Cenpe", "Ctcf", "Nek2", "G2e3", "Gas2l3", "Cbx5", "Cenpa")

QCFiltered.data <- CellCycleScoring(object = QCFiltered.data,
                                    s.genes = s.genes,
                                    g2m.genes = g2m.genes,
                                    set.ident = F)

DimPlot(QCFiltered.data,
        reduction.use = "spring",
        group.by = "Phase",
        cols.use = wes_palette("GrandBudapest1", 3, type = "discrete")[3:1],
        dim.1 = 1, 
        dim.2 = 2,
        do.label=T,
        label.size = 4,
        no.legend = F )

Manuscript Fig. 1B

Known pallial and sub-pallial marker genes

FeaturePlot(object = QCFiltered.data,
            features.plot = c("Nes", "Sox2", "Tbr1", "Slc17a6", "Gad2", "Dlx5"),
            cols.use = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F)

Manuscript Fig. 1C

Assign cell state scores

We assigned broad transcriptional cell state score based on known and manually curated marker genes

Apical progenitors

APgenes <- c("Rgcc", "Sparc", "Hes5","Hes1", "Slc1a3",
             "Ddah1", "Ldha", "Hmga2","Sfrp1", "Id4",
             "Creb5", "Ptn", "Lpar1", "Rcn1","Zfp36l1",
             "Sox9", "Sox2", "Nr2e1", "Ttyh1", "Trip6")
genes.list <- list(APgenes)
enrich.name <- "AP_signature"
QCFiltered.data <- AddModuleScore(QCFiltered.data,
                                  genes.list = genes.list,
                                  genes.pool = NULL,
                                  n.bin = 5,
                                  seed.use = 1,
                                  ctrl.size = length(genes.list),
                                  use.k = FALSE,
                                  enrich.name = enrich.name,
                                  random.seed = 1)

FeaturePlot(object = QCFiltered.data,
            features.plot = APgenes,
            cols.use = wes_palette("Zissou1", 8, type = "continuous"),
            pt.size = 0.8,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Apical progenitors gene expression

Basal progenitors

BPgenes <- c("Eomes", "Igsf8", "Insm1", "Elavl2", "Elavl4",
             "Hes6","Gadd45g", "Neurog2", "Btg2", "Neurog1")
genes.list <- list(BPgenes)
enrich.name <- "BP_signature"
QCFiltered.data <- AddModuleScore(QCFiltered.data,
                                  genes.list = genes.list,
                                  genes.pool = NULL,
                                  n.bin = 5,
                                  seed.use = 1,
                                  ctrl.size = length(genes.list),
                                  use.k = FALSE,
                                  enrich.name = enrich.name,
                                  random.seed = 1)

FeaturePlot(object = QCFiltered.data,
            features.plot = BPgenes,
            cols.use = wes_palette("Zissou1", 8, type = "continuous"),
            pt.size = 0.8,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Basal progenitors gene expression

Early pallial neurons

ENgenes <- c("Mfap4", "Nhlh2", "Nhlh1", "Ppp1r14a", "Nav1",
             "Neurod1", "Sorl1", "Svip", "Cxcl12", "Tenm4",
             "Dll3", "Rgmb", "Cntn2", "Vat1")
genes.list <- list(ENgenes)
enrich.name <- "EN_signature"
QCFiltered.data <- AddModuleScore(QCFiltered.data,
                                  genes.list = genes.list,
                                  genes.pool = NULL,
                                  n.bin = 5,
                                  seed.use = 1,
                                  ctrl.size = length(genes.list),
                                  use.k = FALSE,
                                  enrich.name = enrich.name,
                                  random.seed = 1)

FeaturePlot(object = QCFiltered.data,
            features.plot = ENgenes,
            cols.use = wes_palette("Zissou1", 8, type = "continuous"),
            pt.size = 0.8,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Early pallial neurons gene expression

Late pallial neurons

LNgenes <- c("Snhg11", "Pcsk1n", "Mapt", "Ina", "Stmn4",
             "Gap43", "Tubb2a", "Ly6h","Ptprd", "Mef2c")
genes.list <- list(LNgenes)
enrich.name <- "LN_signature"
QCFiltered.data <- AddModuleScore(QCFiltered.data,
                                  genes.list = genes.list,
                                  genes.pool = NULL,
                                  n.bin = 5,
                                  seed.use = 1,
                                  ctrl.size = length(genes.list),
                                  use.k = FALSE,
                                  enrich.name = enrich.name,
                                  random.seed = 1)

FeaturePlot(object = QCFiltered.data,
            features.plot = LNgenes,
            cols.use = wes_palette("Zissou1", 8, type = "continuous"),
            pt.size = 0.8,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Late pallial neurons gene expression

Subpallial neurons

SPgenes <- c("Gad2", "Dlx6", "Slc32a1", "Nrxn3","Dlx5")
genes.list <- list(SPgenes)
enrich.name <- "SP_signature"
QCFiltered.data <- AddModuleScore(QCFiltered.data,
                                  genes.list = genes.list,
                                  genes.pool = NULL,
                                  n.bin = 5,
                                  seed.use = 1,
                                  ctrl.size = length(genes.list),
                                  use.k = FALSE,
                                  enrich.name = enrich.name,
                                  random.seed = 1)

FeaturePlot(object = QCFiltered.data,
            features.plot = SPgenes,
            cols.use = wes_palette("Zissou1", 8, type = "continuous"),
            pt.size = 0.8,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Sub-pallial neurons gene expression

Pallial neurons

Palgenes <- c("Tmem163", "Ptprd", "Tbr1", "Wnt7b", "Nrn1", "Slc17a6")
genes.list <- list(Palgenes)
enrich.name <- "Pal_signature"
QCFiltered.data <- AddModuleScore(QCFiltered.data,
                                  genes.list = genes.list,
                                  genes.pool = NULL,
                                  n.bin = 5,
                                  seed.use = 1,
                                  ctrl.size = length(genes.list),
                                  use.k = FALSE,
                                  enrich.name = enrich.name,
                                  random.seed = 1)

FeaturePlot(object = QCFiltered.data,
            features.plot = Palgenes,
            cols.use = wes_palette("Zissou1", 8, type = "continuous"),
            pt.size = 0.8,
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Pallial neurons gene expression

Plot cell state scores onto the Spring embedding

FeaturePlot(object = QCFiltered.data,
            features.plot = c("AP_signature1", "BP_signature1", "EN_signature1",
                              "LN_signature1", "Pal_signature1", "SP_signature1"),
            cols.use = rev(brewer.pal(10,"Spectral")),
            reduction.use = "spring",
            no.legend = T,
            overlay = F,
            dark.theme = F
            )

Manuscript Fig. 1D

Save Seurat object

saveRDS(QCFiltered.data, "./QC.filtered.cells.RDS")

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")

## [1] "30 novembre, 2020, 10,28"

#Packages used
sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
## LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] wesanderson_0.3.6  RColorBrewer_1.1-2 Seurat_2.3.4       Matrix_1.2-17     
## [5] cowplot_1.0.0      ggplot2_3.2.1     
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15          colorspace_1.4-1    class_7.3-17       
##   [4] modeltools_0.2-22   ggridges_0.5.1      mclust_5.4.5       
##   [7] htmlTable_1.13.2    base64enc_0.1-3     rstudioapi_0.11    
##  [10] proxy_0.4-23        farver_2.0.1        npsurv_0.4-0       
##  [13] flexmix_2.3-15      bit64_4.0.2         codetools_0.2-16   
##  [16] splines_3.6.3       R.methodsS3_1.7.1   lsei_1.2-0         
##  [19] robustbase_0.93-5   knitr_1.26          zeallot_0.1.0      
##  [22] jsonlite_1.7.0      Formula_1.2-3       ica_1.0-2          
##  [25] cluster_2.1.0       kernlab_0.9-29      png_0.1-7          
##  [28] R.oo_1.23.0         compiler_3.6.3      httr_1.4.1         
##  [31] backports_1.1.5     assertthat_0.2.1    lazyeval_0.2.2     
##  [34] lars_1.2            acepack_1.4.1       htmltools_0.5.0    
##  [37] tools_3.6.3         igraph_1.2.5        gtable_0.3.0       
##  [40] glue_1.4.1          RANN_2.6.1          reshape2_1.4.3     
##  [43] dplyr_0.8.3         Rcpp_1.0.5          vctrs_0.2.0        
##  [46] gdata_2.18.0        ape_5.3             nlme_3.1-141       
##  [49] iterators_1.0.12    fpc_2.2-3           gbRd_0.4-11        
##  [52] lmtest_0.9-37       xfun_0.18           stringr_1.4.0      
##  [55] lifecycle_0.1.0     irlba_2.3.3         gtools_3.8.1       
##  [58] DEoptimR_1.0-8      MASS_7.3-53         zoo_1.8-6          
##  [61] scales_1.1.0        doSNOW_1.0.18       parallel_3.6.3     
##  [64] yaml_2.2.1          reticulate_1.13     pbapply_1.4-2      
##  [67] gridExtra_2.3       rpart_4.1-15        segmented_1.0-0    
##  [70] latticeExtra_0.6-28 stringi_1.4.6       highr_0.8          
##  [73] foreach_1.4.7       checkmate_1.9.4     caTools_1.17.1.2   
##  [76] bibtex_0.4.2        Rdpack_0.11-0       SDMTools_1.1-221.1 
##  [79] rlang_0.4.7         pkgconfig_2.0.3     dtw_1.21-3         
##  [82] prabclus_2.3-1      bitops_1.0-6        evaluate_0.14      
##  [85] lattice_0.20-41     ROCR_1.0-7          purrr_0.3.3        
##  [88] labeling_0.3        htmlwidgets_1.5.1   bit_4.0.4          
##  [91] tidyselect_0.2.5    plyr_1.8.4          magrittr_1.5       
##  [94] R6_2.4.1            snow_0.4-3          gplots_3.0.1.1     
##  [97] Hmisc_4.3-0         pillar_1.4.2        foreign_0.8-72     
## [100] withr_2.1.2         fitdistrplus_1.0-14 mixtools_1.1.0     
## [103] survival_2.44-1.1   nnet_7.3-14         tsne_0.1-3         
## [106] tibble_2.1.3        crayon_1.3.4        hdf5r_1.3.2.9000   
## [109] KernSmooth_2.23-15  rmarkdown_2.5       grid_3.6.3         
## [112] data.table_1.12.6   metap_1.1           digest_0.6.25      
## [115] diptest_0.75-7      tidyr_1.0.0         R.utils_2.9.0      
## [118] stats4_3.6.3        munsell_0.5.0

---

1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr↩