Update PFCdevApp.qmd

PFCdevApp.qmd

@@ -12,11 +12,11 @@ format:
 ---
 # Home
 
-<p style="font-size: 50px; font-weight: bold; text-align: center;">Molecular and spatial signatures of the developing mouse prefrontal cortex</p>
+<p style="font-size: 50px; font-weight: bold; text-align: center;">Single-cell spatiotemporal transcriptome dynamics of mouse prefrontal cortex intratelencephalic neurons during postnatal development</p>
 
 <br>
 
-<img src="https://ZhengTiger.github.io/picx-images-hosting/PFCdev/main.9kgd3316wd.webp" style="width: 100%;">
+<img src="https://ZhengTiger.github.io/picx-images-hosting/PFCdev/Figure_1A.2h8jm97w9c.webp" style="width: 100%;">
 
 <br>
 <br>
@@ -26,7 +26,7 @@ Summary
 </p>
 
 <p style="font-size: 20px; text-align: justify;">
-Prefrontal cortex (PFC) is the high-level center of brain cognitive function, which regulates emotion, memory, decision-making, behavior, neuroendocrine and other brain functions. The sculpting of neural circuits during PFC development involves cellular and molecular reconfiguration. Here, we integrated analysis of single-cell transcriptome, spatial transcriptome and connectome data to decipher the spatial construction and molecular basis of neural circuits in the developing mouse prefrontal cortex.
+The prefrontal cortex (PFC) is a core regulatory center for higher cognitive functions in the mammalian brain. In early postnatal mice, the PFC remains incompletely mature, particularly the intratelencephalic (IT) neurons located in superficial layers. However, the molecular developmental trajectories and regulatory mechanisms of IT neurons during the critical postnatal period (1-2 weeks) for circuit establishment, remain poorly characterized at single-cell resolution. Here, we performed single-cell RNA sequencing analysis on mouse PFC isolated at different developmental time points, and integrated with spatial transcriptomic data. Our study systematically deciphered the spatiotemporal transcriptome dynamics of mouse PFC during postnatal development, with particular emphasis on the heterogeneity of IT neurons. We identified dynamically expressed genes during postnatal development, including cadherin and axon guidance molecule genes, as well as specific transcriptional regulators that control the maturation of IT neurons in different layers. Furthermore, we also revealed that glial cells play crucial regulatory roles in IT neuron development through specific signaling pathways. Our study provides a comprehensive single-cell atlas of postnatal mouse PFC development, offering important insights into the mechanisms of neural development and circuit assembly in PFC.
 </p>
 
 
@@ -45,9 +45,8 @@ Our scRNAseq data sequenced the PFC of mice at four different stages (P1, P4, P1
 </p>
 
 - Select dataset: Select datasets containing different cell types
-- Select time: Select datasets containing different developmental stages
 - Select celltype: Select different resolutions to view cell clusters on UMAP
-- Select gene: Select different genes to view their expression on UMAP
+- Select gene: Select different genes to view their expression
 
 
 
@@ -56,14 +55,13 @@ Spatial data
 </p>
 
 <p style="font-size: 20px; text-align: justify;">
-We collected the whole brain stereo-seq datasets of P0 and Adult mice from [(Han et al., Neuron, 2025)](https://doi.org/10.1016/j.neuron.2025.02.015), extracted and analyzed the PFC brain region. Users can browse the following content through the spatial page:
+We collected the whole brain stereo-seq datasets of P1 and Adult mice from [(Han et al., Neuron, 2025)](https://doi.org/10.1016/j.neuron.2025.02.015), extracted and analyzed the PFC brain region. Users can browse the following content through the spatial page:
 </p>
 
-- Spatial Clustering: Select different resolutions to view the spatial distribution of cell clusters
+- Spatial Clustering: Select different cell subtypes to view their spatial distribution
 - Spatial Gene Expression: Select different genes to view their spatial expression
 
 
-
 <p style="font-size: 20px; font-weight: bold; text-align: left;">
 Download
 </p>
@@ -88,11 +86,33 @@ library(dplyr)
 
 source("R/Palettes.R")
 
-seu.downsample <- readRDS('data/seu.harmony.downsample.rds')
-seu.downsample <- seu.downsample[,
-  !(seu.downsample$orig.ident=="Adult" &
-  seu.downsample$SubType_v4 %in% c("Im L2/3 IT","Im L4/5 IT","Im L5 IT","Im L6 IT","NPC"))]
-#seu.harmony@meta.data <- seu.harmony.metadata
+# scrnaseq
+seu.downsample <- readRDS('data/seu.all.HVGs.rds')
+seu.downsample$orig.ident <- factor(seu.downsample$orig.ident,
+                                    levels = c("P0","P4","P10","Adult"))
+seu.downsample$SubType <- seu.downsample$SubType_v4
+
+# spatial
+column <- c("x_rotated","y_rotated","Im.L2.3.IT","Im.L4.5.IT","Im.L5.IT","Im.L6.IT","L2.3.IT","L4.5.IT","L5.IT","L6.IT","L5.PT","L5.NP","L6.CT","Lamp5","Pvalb","Sst","Vip","NPC","Astro","Endo","Microglia","Oligo","OPC")
+P1_cell2loc <- read.csv("data/P1_cell2location.csv", row.names = 1)
+P1_cell2loc <- P1_cell2loc[,column]
+P1_cell2loc$Endo <- 0
+P1_cell2loc$Oligo <- 0
+colnames(P1_cell2loc) <- c("x_rotated","y_rotated",names(col_cluster[["SubType"]]))
+P1_cell2loc$SubType <- as.character(apply(P1_cell2loc[,names(col_cluster[["SubType"]])[-18]], 1, function(x){
+  names(which.max(x))
+}))
+
+P77_cell2loc <- read.csv("data/P77_cell2location.csv", row.names = 1)
+P77_cell2loc <- P77_cell2loc[,column]
+P77_cell2loc$Endo <- 0
+colnames(P77_cell2loc) <- c("x_rotated","y_rotated",names(col_cluster[["SubType"]]))
+P77_cell2loc$SubType <- as.character(apply(P77_cell2loc[,names(col_cluster[["SubType"]])[-18]], 1, function(x){
+  names(which.max(x))
+}))
+
+sp_p1 <- readRDS("data/P1_bin50_PFC.rds")
+sp_p77 <- readRDS("data/P77_bin50_PFC.rds")
 ```
 
 
@@ -104,15 +124,11 @@ seu.downsample <- seu.downsample[,
 ## {.sidebar}
 
 ```{r}
-selectInput('dataset', 'Select dataset', c("All cells","Neurons"))
-```
-
-```{r}
-selectInput('time', 'Select time', c("All","P0","P4","P10","Adult"), selected = "All")
+selectInput('dataset', 'Select dataset', c("All cells","Neurons"), selected = "Neurons")
 ```
 
 ```{r}
-selectInput('celltype', 'Select celltype', c("MainType","SubType_v4"), selected = "SubType_v4")
+selectInput('celltype', 'Select celltype', c("MainType","SubType"), selected = "SubType")
 ```
 
 ```{r}
@@ -131,12 +147,16 @@ selectInput('gene', 'Select gene', rownames(seu.downsample), selected = "Cux2")
 plotOutput('cluster_plot')
 ```
 
+
+### Row
+
 #### Column
 
 ```{r}
 plotOutput('gene_plot')
 ```
 
+
 ### Row
 
 #### Column
@@ -146,68 +166,67 @@ plotOutput('vln_plot')
 ```
 
 
-
 ```{r}
 #| context: server
 
 output$cluster_plot <- renderPlot({
-  if (input$time == "All"){
-    seu <- seu.downsample
-  }else{
-    seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$orig.ident==input$time])
-  }
   if (input$dataset=="Neurons"){
     seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType==c("Excitatory", "Inhibitory")])
+  }else{
+    seu <- seu.downsample
   }
   seu@meta.data[,input$celltype] <- factor(
     seu@meta.data[,input$celltype],
     levels = names(col_cluster[[input$celltype]])
   )
-  DimPlot(
-    seu,
-    reduction = 'umap',
-    group.by = input$celltype,
-    cols = col_cluster[[input$celltype]],
-    pt.size = 2,
-    label = F
-  ) +
-    theme_bw(base_size = 15) +
-    theme(panel.grid = element_blank()) +
-    coord_fixed() +
-    labs(title = "")
+    DimPlot(
+      seu,
+      reduction = 'umap',
+      group.by = input$celltype,
+      split.by = "orig.ident",
+      ncol = 4,
+      cols = col_cluster[[input$celltype]],
+      pt.size = 0.5,
+      label = F) +
+      theme_bw(base_size = 15) +
+      theme(panel.grid = element_blank(), legend.position = "right",
+            strip.text = element_text(size = 20),
+            strip.background = element_rect(color="white", fill="white",)) +
+      coord_fixed() +
+      labs(title = "") + 
+      guides(color = guide_legend(ncol = 1, override.aes = list(size = 3)))
 })
 
 
 output$gene_plot <- renderPlot({
-  if (input$time == "All"){
-    seu <- seu.downsample
-  }else{
-    seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$orig.ident==input$time])
-  }
   if (input$dataset=="Neurons"){
     seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType==c("Excitatory", "Inhibitory")])
+  }else{
+    seu <- seu.downsample
   }
   FeaturePlot(
     seu,
     features = input$gene,
     reduction = 'umap',
+    split.by = "orig.ident",
+    ncol=4,
     order = T,
-    pt.size = 2
-  ) +
-    theme_bw(base_size = 15) +
-    theme(panel.grid = element_blank(), plot.title = element_text(hjust = 0.5)) +
-    coord_fixed()
+    pt.size = 0.5
+  ) &
+    theme_bw(base_size = 15) &
+    theme(panel.grid = element_blank(), plot.title = element_text(hjust = 0.5),
+          strip.text = element_text(size = 20, face = "bold"),
+          legend.position = c(0.1,0.25)) &
+    coord_fixed() &
+    scale_color_gradientn(colours = c("lightblue3", "lightblue", "white", "red", "red4"), limits=c(0,2), breaks=c(0,2), na.value = "red4")
 })
 
 
 output$vln_plot <- renderPlot({
-  if (input$time == "All"){
-    seu <- seu.downsample
-  }else{
-    seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$orig.ident==input$time])
-  }
   if (input$dataset=="Neurons"){
     seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType==c("Excitatory", "Inhibitory")])
+  }else{
+    seu <- seu.downsample
   }
   seu@meta.data[,input$celltype] <- factor(
     seu@meta.data[,input$celltype],
@@ -215,7 +234,223 @@ output$vln_plot <- renderPlot({
   )
   VlnPlot(seu, features = input$gene, group.by = input$celltype,
           cols = col_cluster[[input$celltype]]) +
-    NoLegend()
+    NoLegend() +
+    labs(x="")
 })
 ```
 
+
+
+
+
+
+# Spatial {scrolling="true"}
+
+## {.sidebar}
+
+```{r}
+selectInput('sp_celltype', 'Select celltype', names(col_cluster[["SubType"]])[-18], selected = "Im L2/3 IT")
+```
+
+```{r}
+selectInput('sp_gene', 'Select gene', rownames(seu.downsample), selected = "Cux2")
+```
+
+
+
+## Column
+
+### Row
+
+#### Column
+
+```{r}
+plotOutput('p1_cluster_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('p77_cluster_plot')
+```
+
+
+### Row
+
+#### Column
+
+```{r}
+plotOutput('p1_subtype_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('p1_gene_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('p77_subtype_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('p77_gene_plot')
+```
+
+
+
+
+```{r}
+#| context: server
+
+output$p1_cluster_plot <- renderPlot({
+  data <- P1_cell2loc
+  ggplot(data, mapping = aes(x_rotated, y_rotated, color=SubType)) +
+    geom_point(size = 2) +
+    scale_color_manual(values = col_cluster[["SubType"]]) +
+    coord_fixed() +
+    #ggdark::dark_theme_void() +
+    theme_void() +
+    theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
+    guides(color = guide_legend(title="", ncol = 1, override.aes = list(size = 3))) +
+    labs(title="P1 SubType")
+})
+
+output$p77_cluster_plot <- renderPlot({
+  data <- P77_cell2loc
+  ggplot(data, mapping = aes(x_rotated, y_rotated, color=SubType)) +
+    geom_point(size = 0.8) +
+    scale_color_manual(values = col_cluster[["SubType"]]) +
+    coord_fixed() +
+    #ggdark::dark_theme_void() +
+    theme_void() +
+    theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) + 
+    guides(color = guide_legend(title="", ncol = 1, override.aes = list(size = 3))) +
+    labs(title="P77 SubType")
+})
+
+output$p1_subtype_plot <- renderPlot({
+  data <- P1_cell2loc
+  subtype_i <- input$sp_celltype
+  df_i <- data.frame(
+    x = data$x_rotated,
+    y = data$y_rotated,
+    col = data[,subtype_i]
+  )
+  if (subtype_i %in% names(col_cluster[["SubType"]])[c(16,17,18,19,21)]){
+    df_i$col[!data$SubType==subtype_i] <- 0
+  }
+  df_i$col[df_i$col>1] <- 1
+  df_i$col[df_i$col<0.05] <- 0
+  low = "#f7f7f7"
+  if (length(which(df_i$col>0))==0){
+    high = "#f7f7f7"
+  }else{
+    high = col_cluster[["SubType"]][subtype_i]
+  }
+  ggplot(df_i, aes(x=x, y=y, color=col)) +
+    geom_point(size=2) +
+    scale_color_gradient(low = low, high=high,
+                         na.value = "#f7f7f7", limits = c(0,max(df_i$col)),
+                         breaks = c(0,max(df_i$col))) +
+    coord_fixed() +
+    theme_void() +
+    theme(legend.position = "none",
+          plot.title = element_text(hjust = 0.5)) +
+    labs(title=paste("P1", subtype_i))
+})
+
+
+output$p1_gene_plot <- renderPlot({
+  data <- data.frame(
+    x = sp_p1$x_rotated, y = sp_p1$y_rotated,
+    gene = scale(sp_p1@assays$RNA$counts[input$sp_gene,])
+    )
+  data$gene[data$gene<0] <- 0
+  data$gene[data$gene>3] <- 3
+  color <- col_cluster[["gene"]]
+  ggplot(data, aes(x=x, y=y, color=gene)) +
+    geom_point(size=2) +
+    scale_color_gradientn(colours = color, na.value = "lightgray",
+                          limits = c(0,3), 
+                          breaks = c(0,3)) +
+    coord_fixed() +
+    #ggdark::dark_theme_void() +
+    theme_void()+
+    theme(legend.position = c(0.9,0.2), legend.title = element_blank(),
+          plot.title = element_text(hjust = 0.5)) +
+    labs(title=paste("P1", input$sp_gene))
+})
+
+
+
+output$p77_subtype_plot <- renderPlot({
+  data <- P77_cell2loc
+  data$`L5 IT`[data$`L5 IT`<0.25] <- 0
+  subtype_i <- input$sp_celltype
+  df_i <- data.frame(
+    x = data$x_rotated,
+    y = data$y_rotated,
+    col = data[,subtype_i]
+  )
+  if (subtype_i %in% names(col_cluster[["SubType"]])[c(16,17,18,20,21)]){
+    df_i$col[!data$SubType==subtype_i] <- 0
+  }
+  df_i$col[df_i$col>1] <- 1
+  df_i$col[df_i$col<0.05] <- 0
+  ggplot(df_i, aes(x=x, y=y, color=col)) +
+    geom_point(size=0.8) +
+    scale_color_gradient(low = "#f7f7f7", high=col_cluster[["SubType"]][subtype_i],
+                         na.value = "#f7f7f7", limits = c(0,max(df_i$col)),
+                         breaks = c(0,max(df_i$col))) +
+    coord_fixed() +
+    theme_void() +
+    theme(legend.position = "none",
+          plot.title = element_text(hjust = 0.5)) +
+    labs(title=paste("P77",subtype_i))
+})
+
+
+output$p77_gene_plot <- renderPlot({
+  data <- data.frame(
+    x = sp_p77$x_rotated, y = sp_p77$y_rotated,
+    gene = scale(sp_p77@assays$RNA$counts[input$sp_gene,])
+    )
+  data$gene[data$gene<0] <- 0
+  data$gene[data$gene>3] <- 3
+  color <- col_cluster[["gene"]]
+  ggplot(data, aes(x=x, y=y, color=gene)) +
+    geom_point(size=0.8) +
+    scale_color_gradientn(colours = color, na.value = "lightgray",
+                          limits = c(0,3), 
+                          breaks = c(0,3)) +
+    coord_fixed() +
+    #ggdark::dark_theme_void() +
+    theme_void() +
+    theme(legend.position = c(0.9,0.2), legend.title = element_blank(),
+          plot.title = element_text(hjust = 0.5)) +
+    labs(title=paste("P77", input$sp_gene))
+})
+```
+
+
+
+
+
+# Download
+
+<p style="font-size: 20px; text-align: justify;">
+The raw single cell RNA-seq data are available from GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE298260">GSE298260</a>). The raw stereo-seq data are available from GEO
+</p>
+
+<p style="font-size: 20px; text-align: justify;">
+The processed data can be downloaded here:</p>
+
+- All cells data: <a href="https://huggingface.co/TigerZheng/PFCdev-data/resolve/main/seu.harmony.rds?download=true">sc_seu.rds</a>
+- Spatial data: <a href="https://huggingface.co/TigerZheng/PFCdev-data/resolve/main/P1_bin50_PFC.rds?download=true">p1_seu.rds</a>, <a href="https://huggingface.co/TigerZheng/PFCdev-data/resolve/main/P77_bin50_PFC.rds?download=true">p77_seu.rds</a>
+
+