5.1 Get gene signatures

Here, we’ll get the leading edge genes from the GSEA analysis, for one human interneuron signature and one astrocyte signature, both enriched in G34R/V tumors.

dge_le <- read_tsv(here("bulk_transcriptome_epigenome/output/02/fgsea_results_padj<0.01_IDH_top_le.tsv"))

## Parsed with column specification:
## cols(
##   Comparison = col_character(),
##   Signature = col_character(),
##   Sample = col_character(),
##   Age = col_character(),
##   Cell_type = col_character(),
##   pval = col_double(),
##   padj = col_double(),
##   ES = col_double(),
##   NES = col_double(),
##   nMoreExtreme = col_double(),
##   size = col_double(),
##   leadingEdge = col_character(),
##   Dataset = col_character(),
##   leadingEdge_top = col_character()
## )

load(here("reference_datasets/2017_Nowakowski/processed_data/nowakowski_signatures.Rda"))

# Newborn interneuron signature
interneuron_genes    <- nowakowski_signatures$hg_sym$nIN1 
interneuron_genes_mm <- nowakowski_signatures$mm_sym$nIN1

# Astrocyte signature
astro_genes          <- nowakowski_signatures$hg_sym$Astrocyte
astro_genes_mm       <- nowakowski_signatures$mm_sym$Astrocyte

5.2 Human fetal telencephalon (Nowakowski et al, 2017)

data_nowakowski <- data.frame(
  interneuron_signature = cytobox::meanGeneExpression(nowa, interneuron_genes)$Mean_marker_expression,
  astrocyte_signature   = cytobox::meanGeneExpression(nowa, astro_genes)$Mean_marker_expression,
  cluster               = nowa@meta.data$WGCNAcluster,
  celltype              = nowa@meta.data$Cell_type
)

# Filter to cell types of interest for this analysis: astrocytes, inhibitory neurons
# and ganglionic eminence radial glia
data_nowakowski <- data_nowakowski %>% 
  filter(cluster %in% c("Astrocyte",
                        "nIN1", "nIN2", "nIN3", "nIN4",
                        "IN-CTX-CGE1", "IN-CTX-MGE1",
                        "MGE-IPC1", "MGE-IPC2", "MGE-IPC3",
                        "MGE-RG1", "MGE-RG2", "MGE-div"))

# Clean up
rm(nowa)

Generate the scatterplot:

data_nowakowski %>% 
  rr_ggplot(aes(x = interneuron_signature, y = astrocyte_signature, colour = cluster), plot_num = 1) +
  geom_point(shape = 16, size = 1, alpha = 0.2) +
  scale_color_manual(values = palette_nowakowski, name = "Cell type") +
  geom_density_2d() +
  ggtitle("Human fetal telencephalon")

_{[figure/source data @ G34-gliomas/singlecell_normal/figures/03//nowakowski_scatterplot…]}

5.3 Developing mouse forebrain (Jessa et al, 2019)

In the mouse atlas:

data_atlas <- data.frame(
  interneuron_signature = cytobox::meanGeneExpression(joint_cortex_small, interneuron_genes_mm)$Mean_marker_expression,
  astrocyte_signature   = cytobox::meanGeneExpression(joint_cortex_small, astro_genes_mm)$Mean_marker_expression,
  cluster               = gsub("_", " ", joint_cortex_small@meta.data$ID_20190730_with_blacklist_and_refined)
)

## NOTE: [Best3, 5830418K08Rik] undetected in Forebrain E12.5

## NOTE: [NA, Sirpb1c, Gm9733, Sirpb1a, Gm5150, B3galtl, Pmp2] undetected in Forebrain E12.5

# Filter to relevant cell tpyes
data_atlas <- data_atlas %>% 
  filter(grepl("ASTR|VRGC|MGIN|CINH|INIP", cluster) & !grepl("BLACKLIST", cluster))

# Clean up
rm(joint_cortex_small)

Plot:

data_atlas %>% 
  rr_ggplot(aes(x = interneuron_signature, y = astrocyte_signature, colour = cluster), plot_num = 1) +
  geom_point(shape = 16, size = 1, alpha = 0.2) +
  scale_color_manual(values = palette_atlas, name = "Cell type") +
  geom_density_2d() +
  ggtitle("Normal mouse brain")

_{[figure/source data @ G34-gliomas/singlecell_normal/figures/03//mouse_forebrain_scatter…]}

5.4 H3G34R/V tumors

Load Seurat object with all samples combined, and re-constitute a V2 object from the V3 object:

load("/lustre03/project/6004736/vlisi/fromHydra/SCRATCH/vlisi/LEGACY/G34/inHouseDataCombined/data/integrated_g34.seurat_v3.save_v2.Rda")

# get cell type projections used in Figure 5
load("/lustre03/project/6004736/vlisi/fromHydra/SCRATCH/vlisi/LEGACY/G34/allSamplesCombined/data/ScSnMetaDataWithNormals.RDat")
cell_meta <- ScSnDataWithNormals

# convert the object to V3
seurat_joint <- CreateSeuratObject(raw.data = integrated_g34@assays$RNA@counts,
                                   meta.data = integrated_g34@meta.data)
seurat_joint <- NormalizeData(seurat_joint)
seurat_joint <- SetDimReduction(seurat_joint, reduction.type = "umap", slot = "cell.embeddings",
                                integrated_g34@reductions[["umap"]]@cell.embeddings)
seurat_joint <- SetDimReduction(seurat_joint, reduction.type = "umap", slot = "key", "UMAP_")

# add in the cell type projections used in Figure 5
seurat_joint <- AddMetaData(seurat_joint, ScSnDataWithNormals[, c("ConsensusLineageRefinedConsensus"), drop = FALSE])
seurat_joint@meta.data$Cell_type_projection <- seurat_joint@meta.data$ConsensusHybrid2L3NP.smoothk10
seurat_joint@meta.data$Cell_type_broad_Fig5 <- seurat_joint@meta.data$ConsensusLineageRefinedConsensus
seurat_joint@meta.data$Status_normal_malignant <- seurat_joint@meta.data$CNVnormals

seurat_joint@meta.data <- seurat_joint@meta.data[, c(colnames(seurat_joint@meta.data)[1:22],
                                                     "Status_normal_malignant",
                                                     "Cell_type_projection",
                                                     "Cell_type_broad_Fig5")]

# clean up
rm(integrated_g34)
rm(ScSnDataWithNormals)

save(seurat_joint, file = glue("{out}/integrated_seurat.Rda"))

load(glue("{out}/integrated_seurat.Rda"))

Subset the same signatures used in the normal brain to the genes detected in the tumors:

interneuron_genes_keep <- base::intersect(interneuron_genes, rownames(seurat_joint@data))
astro_genes_keep <- base::intersect(astro_genes, rownames(seurat_joint@data))

Calculate mean expression:

data_tumors <- data.frame(
  interneuron_signature = cytobox::meanGeneExpression(seurat_joint, interneuron_genes_keep)$Mean_marker_expression,
  astrocyte_signature   = cytobox::meanGeneExpression(seurat_joint, astro_genes_keep)$Mean_marker_expression,
  cluster_broad         = seurat_joint@meta.data$Cell_type_broad_Fig5
)

Plot:

data_tumors %>% 
  filter(cluster_broad %in% c("neurons", "astro")) %>% 
  rr_ggplot(aes(x = interneuron_signature, y = astrocyte_signature, colour = cluster_broad), plot_num = 1) +
  geom_point(shape = 16, size = 1, alpha = 0.2) +
  scale_color_manual(values = c("astro" = "#FF3F38", "neurons" = "#084081"), name = "Cell type") +
  geom_density_2d() +
  ggtitle("G34R/V HGGs")

_{[figure/source data @ G34-gliomas/singlecell_normal/figures/03//tumor_scatter…]}