OSCR

A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence.

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  1. [1] § Methods › scRNA‐Seq Bioinformatic Analysis ↔ 2. Data Visualization with Seurat Script.qmd, lines 193–256 · score 0.96 · CHI3L1, IL1R1, IL6R, TNFRSF1B, COL1A1, HMOX1
  2. [2] § Methods › scRNA‐Seq Bioinformatic Analysis ↔ 3. Psudotime Analysis Script.qmd, lines 161–220 · score 0.60 · NC lineage, KRT19, MGP, KRT8, TBXT, ACAN

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Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

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  1. ---
  2. title: "General Seurat Figures and Analysis in RStudio"
  3. format: html
  4. editor: visual
  5. ---
  7. ```{r}
  9. ```
  11. All packages required for script to run.
  13. ```{r}
  14. #| echo: false
  15. #if (!require("BiocManager", quietly = TRUE))
  16. #install.packages("BiocManager")
  17. #BiocManager::install("Seurat", force = TRUE)
  18. #install.packages("remotes")
  19. #install.packages("R.utils")
  20. #remotes::install_github("satijalab/seurat-wrappers")
  21. #BiocManager::install(c('BiocGenerics', 'DelayedArray', 'DelayedMatrixStats',
  22. # 'limma', 'lme4', 'S4Vectors', 'SingleCellExperiment',
  23. # 'SummarizedExperiment', 'batchelor', 'HDF5Array',
  24. # 'terra', 'ggrastr'))
  25. #install.packages("devtools")
  26. #devtools::install_github('cole-trapnell-lab/monocle3')
  27. #BiocManager::install("EnhancedVolcano", force = TRUE)
  28. #BiocManager::install("ComplexHeatmap")
  29. #devtools::install_github("jinworks/CellChat")
  30. #install.packages("NMF")
  32. library(Seurat)
  33. library(SeuratWrappers)
  34. library(patchwork)
  35. library(dplyr)
  36. library(ggplot2)
  37. library(monocle3)
  38. library(Matrix)
  39. #library(EnhancedVolcano)
  40. library (ggrepel)
  41. library(RColorBrewer)
  42. library(pheatmap)
  43. library(webr)
  46. #if GK Laptop
  47. #setwd("C:/Users/gkane/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat")
  49. #if BIRI super computer
  50. setwd("C:/Users/kanedag/OneDrive - Cedars-Sinai Health System/Sheyn, Dima's files - Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD model/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat")
  51. ```
  53. ```{r}
  54. #color by cluster
  55. AllData.int$seurat_clusters -> [email hidden]
  56. AllData.umap <-DimPlot(AllData.int, reduction = "umap", pt.size = 0.01, label = TRUE,
  57. label.size = 4, raster = FALSE) + coord_fixed(ratio=1)
  58. AllData.pca
  59. ggsave("AllData umap.png", AllData.umap)
  61. #color by disc
  62. P1 <- DimPlot(AllData.int, reduction = "pca", pt.size = 0.1, label = TRUE, repel = TRUE,
  63. raster = FALSE,group.by = "orig.ident")
  64. P1
  65. ggsave("All discs orig.ident pca.png", P1)
  67. #color by injury stats
  68. P2 <- DimPlot(AllData.int, reduction = "pca", pt.size = 0.1, label = TRUE, repel = TRUE,
  69. raster = FALSE,group.by = "disc.ident")
  70. P2
  71. ggsave("Inj vs Healthy pca.png", P2)
  73. P3 <- DimPlot(AllData.int, reduction = "pca", pt.size = 0.1, label = TRUE, repel = TRUE,
  74. raster = FALSE,group.by = "cell.ident")
  75. P3
  76. ggsave("Cell Ident pca.png", P3)
  78. P4 <- DimPlot(AllData.int, reduction = "pca", pt.size = 0.1, label = TRUE, repel = TRUE,
  79. raster = FALSE,group.by = "cell.state")
  80. P4
  81. ggsave("Cell.state pca.png", P4)
  83. DimPlot(AllData.int, reduction = "pca", pt.size = 0.01, label = TRUE,
  84. label.size = 4, raster = FALSE)
  85. DimPlot(AllData.int, reduction = "pca", pt.size = 0.1, label = TRUE, repel = TRUE,
  86. raster = FALSE,split.by = "orig.ident")
  87. DimPlot(AllData.int, reduction = "pca", pt.size = 0.1, label = TRUE, repel = TRUE,
  88. raster = FALSE,split.by = "disc.ident")
  90. ```
  92. Plot percent cells from each disc per cluster and percent cells injured/healthy per cluster
  94. ```{r Visualization of batch spread within clusters}
  95. #percent cells from each disc per cluster
  96. # Create a table of cluster-orig.ident counts
  97. cluster_orig.ident_counts <- table(AllData.int$seurat_clusters, AllData.int$orig.ident)
  99. # Calculate the percentage of cells per genotype in each cluster
  100. cluster_orig.ident_percentage <- prop.table(cluster_orig.ident_counts, margin = 1) * 100
  102. # Create a data frame for plotting
  103. plot_data <- as.data.frame(cluster_orig.ident_percentage)
  104. colnames(plot_data) <- c('Cluster', 'orig.ident', 'Percentage')
  106. # Create the stacked bar plot using ggplot2
  107. disc.pct.plot <- ggplot(plot_data, aes(x = Cluster, y = Percentage, fill = orig.ident)) +
  108. geom_bar(stat = "identity", position = "stack", width = 0.7) +
  109. labs(x = 'Cluster', y = 'Percentage of Cells') +
  110. theme_minimal() +
  111. ggtitle('Percentage of Cells per orig.ident in Each Cluster') +
  112. guides(fill = guide_legend(title = 'orig.ident'))
  113. disc.pct.plot
  114. ggsave("percent cells per disc.png", disc.pct.plot, width = 10, height = 8, units = "in")
  116. #percent cells injured/healthy per cluster
  117. #Create a table of cluster-disc.ident counts
  118. inj_ctr_counts <- table(AllData.int$seurat_clusters, AllData.int$disc.ident)
  120. # Calculate the percentage of cells per genotype in each cluster
  121. inj_ctr_percentage <- prop.table(inj_ctr_counts, margin = 1) * 100
  123. # Create a data frame for plotting
  124. plot_data <- as.data.frame(inj_ctr_percentage)
  125. colnames(plot_data) <- c('Cluster', 'disc.ident', 'Percentage')
  127. # Create the stacked bar plot using ggplot2
  128. InjCtr.pct.plot <- ggplot(plot_data, aes(x = Cluster, y = Percentage, fill = disc.ident)) +
  129. geom_bar(stat = "identity", position = "stack", width = 0.7) +
  130. labs(x = 'Cluster', y = 'Percentage of Cells') +
  131. theme_minimal() +
  132. ggtitle('Percentage of Cells per disc.ident in Each Cluster') +
  133. guides(fill = guide_legend(title = 'disc.ident'))+
  134. theme(axis.text.x = element_text(angle = 45, hjust = 1))
  135. InjCtr.pct.plot
  136. ggsave("percent cells per inj-ctr.png", InjCtr.pct.plot, width = 10, height = 8, units = "in")
  139. ```
  141. ```{r Pie-Donut plot}
  142. # Extract the cell.ident and seurat_clusters from the Seurat object
  143. cell_data <- data.frame(cell.ident = [email hidden]$cell.ident,
  144. seurat_clusters = [email hidden]$seurat_clusters)
  146. # Summarize the counts for each combination of cell type and cluster
  147. PD <- cell_data %>%
  148. group_by(cell.ident, seurat_clusters) %>%
  149. summarise(n = n()) %>%
  150. ungroup()
  152. # Modify only the labels containing the word "Transitional" to include line breaks
  153. #PD$cell.ident <- ifelse(grepl("Transitional", PD$cell.ident),
  154. # gsub("Transitional ", "Transitional\n", PD$cell.ident),
  155. # as.character(PD$cell.ident))
  157. # Create the nested pie chart with the specified order and label adjustments
  158. PieDonut(PD, aes(cell.ident, seurat_clusters, count = n),
  159. title = "Cell Types and Clusters Distribution",
  160. showPieName = FALSE,
  161. start=pi/2,
  162. r0 = 0
  163. )
  165. ```
  167. ```{r Healthy vs Injured cluster breakdown bar}
  168. # Extract meta.data from Seurat object
  169. meta_data <- [email hidden]
  171. # Ensure the relevant columns are factors
  172. meta_data$disc.ident <- as.factor(meta_data$disc.ident)
  173. meta_data$seurat_clusters <- as.factor(meta_data$seurat_clusters)
  175. meta_data_summary <- meta_data %>%
  176. dplyr::count(disc.ident, cell.ident) %>%
  177. group_by(disc.ident) %>%
  178. mutate(percentage = n / sum(n) * 100)
  181. # Create the bar plot using ggplot2
  182. ggplot(meta_data, aes(x = disc.ident, fill = cell.ident)) +
  183. geom_bar(position = "fill", stat = "count") +
  184. scale_y_continuous(labels = scales::percent) +
  185. theme_minimal() +
  186. labs(x = "Cell Ident", y = "Percentage", fill = "Cell Identity") +
  187. ggtitle("Distribution of cell identities across Discs")
  191. ```
  193. First run through for cluster cell type identification and visualization of select interesting genes
  195. ```{r Initial cell identification and visiualization of interesting genes}
  196. marker_test_res <- top_markers(cds, group_cells_by="cell.ident", reference_cells=1000, cores=8)
  198. top_specific_markers <- marker_test_res %>%
  199. filter(fraction_expressing >= 0.5) %>%
  200. group_by(cell_group) %>%
  201. top_n(13, pseudo_R2)
  202. top_specific_marker_ids <- unique(top_specific_markers %>% pull(gene_id))
  204. top_specific_marker.int <- plot_genes_by_group(cds,
  205. top_specific_marker_ids,
  206. group_cells_by="cell.ident",
  207. ordering_type="maximal_on_diag",
  208. max.size=3)
  209. top_specific_marker.int
  211. mmp.adam.timp.markers <- DotPlot(AllData.int, features = c("MMP1","MMP2","MMP3", "MMP7",
  212. "MMP11","MMP12","MMP13","MMP14","MMP15","MMP16",
  213. "MMP17","MMP19","MMP20", "MMP23B", "MMP24",
  214. "MMP25","MMP27","MMP28","ADAMTS1","ADAMTS2","ADAMTS3",
  215. "ADAMTS4","ADAMTS5","ADAMTS6","ADAMTS7","ADAMTS9",
  216. "ADAMTS10","ADAMTS12","ADAMTS14",
  217. "ADAMTS17","TIMP1","TIMP2","TIMP3","TNFAIP6")) +
  218. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  219. theme(axis.text.x = element_text(angle = 90, hjust = 1))
  220. mmp.adam.timp.markers
  221. ggsave("MMP-ADAMTS-TIMP dotplot.png", mmp.adam.timp.markers)
  223. assort.mark <- DotPlot(AllData.int, features = c("CDKN1A", "CDKN2A", "E2F7","EIF4", "CDC25A","CDC42", "CDKN2D","CDK12", "TNFRSF1B", "TP53", "IFNG","TNF","IL1B", "IL2","IL6","IL10", "CCL2","CCL7", "CXCL1","CXCL5","CXCL9","CXCL10","MCSF", "RANKL","VEGFA", "IL1R2", "CCL6","CCR6","ADAM8", "TLR1", "TLR2","TLR7","TLR9")) +
  224. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  225. theme(axis.text.x = element_text(angle = 90, hjust = 1))
  226. assort.mark
  227. ggsave("Assorted markers dotplot.png", assort.mark)
  228. hog <- DotPlot(AllData.int, features = c("KRT8", "KRT18", "MGP","DCN","PRRX1","CRLF1","PDE1A","ABLIM1","HHIP", "HHIPL2","CDON","BOC","SMO")) +
  229. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  230. theme(axis.text.x = element_text(angle = 90, hjust = 1))
  231. hog
  232. ggsave("Hedgehog dotplot.png",hog, width = 8, height = 7, units = "in")
  233. #WNT related markers "WNT5B","DVL1","DVL3","LRP5","CTNNB1","GSK3B","CSNK1E","LRP6","SFRP1"
  234. #assorted differentiation markers - ,"SOX5","SOX6","SOX9", "CHRD","GDF5","GREM1"
  236. celltype.overview <- DotPlot(AllData.int, features = list(
  237. "NC" = c("TBXT","KRT8","KRT18","CD24"),
  238. "Trans" = c("ACAN","OGN","ABLIM1","FOSB","PODN","CCNL1","SOX9","COL11A1","FN1","COL2A1","TIMP3"),
  239. "NP" = c( "PAX1","COL9A1","SOX5","SOX6","MGP","DCN","LUM"),
  240. "Pro" = c("TOP2A","CDK1","BIRC5"),
  241. "EC" = c("PECAM1","ENG", "CDH5"))) +
  242. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  243. theme(axis.text.x = element_text(angle = 45, hjust = 1))
  244. celltype.overview
  245. ggsave("AllData initial cell overview dotplot.png", celltype.overview)
  247. dpi.overview <- DotPlot(AllData.int, features = list(
  248. "Degeneration" = c("COL1A1", "COL3A1", "FN1", "MMP16","ADAMTS6","TIMP1","TIMP3","CHI3L1"),
  249. "Inflammation" = c("IL1R1","IL6R","IL16","IL18","TNFRSF1B","IER3","HMOX1"),
  250. "Pain/Innervation" = c("NTN1","NTN4","SEMA3D","SEMA3E","CCN2","TRPV4"))) +
  251. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  252. theme(axis.text.x = element_text(angle = 45, hjust = 1))
  253. dpi.overview
  254. ggsave("degen pain inflam marker overview dotplot.png", celltype.overview)
  256. ```
  258. ```{r}
  259. DefaultAssay(AllData.int) <- "RNA"
  260. celltype.overview <- DotPlot(AllData.int, features = c("ACAN", "COL2A1","SOX9","MIA","KRT8","KRT18","SLC2A1", "CD81","FN1","CRTAC1","CALR","COL1A1","COL1A2", "COL5A1","COL5A2","COL12A1","CD14", "TBXT","PECAM1","ENG", "CDH5", "PTPRC","LYZ" )) +
  261. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  262. theme(axis.text.x = element_text(angle = 90, hjust = 1))
  263. celltype.overview
  265. #Identification of endothelial cells - Cluster 14
  266. Endo.clust <- VlnPlot(AllData.int, features = c("ENG", "PECAM1", "CDH5", "ICAM1", "TEK", "KDR"),pt.size = 0)
  267. Endo.clust
  270. #notochordal/progenitor cells - Cluster 0,1,3,8,9,11,13-16,18,20, maybe 17
  271. noto.clust <- VlnPlot(AllData.int, features = c("TBXT","KRT8","KRT18","PDGFRA","PRRX1","PROCR","ANGPT1","PAX1"), pt.size = 0)
  272. noto.clust
  274. AllData.int$cell.ident <- as.factor(ifelse(AllData.int$seurat_clusters %in% c("0", "1", "3", "9", "20"), "Notochordal",
  275. ifelse(AllData.int$seurat_clusters %in% c("2", "4", "5", "6", "7", "12"), "Nucleus Pulposus",
  276. ifelse(AllData.int$seurat_clusters %in% c("10", "19"), "Fibrocartilage",
  277. ifelse(AllData.int$seurat_clusters %in% c("11", "13"), "Transitional Fibrocartilage",
  278. ifelse(AllData.int$seurat_clusters %in% c("8", "14", "15", "16", "18"), "Transitional Nucleus Pulposus",
  279. ifelse(AllData.int$seurat_clusters == "17", "Immune",
  280. ifelse(AllData.int$seurat_clusters == "21", "Endothelial", ""))))))))
  282. AllData.int$cell.state <- as.factor(ifelse(AllData.int$cell.ident %in% c("Notochordal"), "Progenitor",
  283. ifelse(AllData.int$cell.ident %in% c("Transitional Nucleus Pulposus", "Transitional Fibrocartilage"), "Transitional",
  284. ifelse(AllData.int$cell.ident %in% c("Nucleus Pulposus", "Fibrocartilage", "Immune", "Endothelial"), "Terminal", NA))))
  286. ```
  288. Find and export cluster vs all unique markers
  290. ```{r Export cluster vs all unique genes for IPA analysis}
  291. DefaultAssay(AllData.int) <- "RNA"
  292. top_genes_list <- list()
  293. # Loop through each cluster, find unique genes, and export to CSV
  294. for (i in 0:21) {
  296. cluster_markers <- FindMarkers(AllData.int, ident.1 = i, min.pct = 0.25)
  297. top_genes <- head(cluster_markers[order(cluster_markers$p_val_adj), ],10)
  298. top_genes_list[[paste("Cluster", i)]] <- top_genes
  300. # write.csv(cluster_markers, sprintf("C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/Cluster Unique Genes/Cluster%d.csv", i), row.names = TRUE)
  301. print(paste("File for cluster", i, "written."))
  302. }
  303. print(top_genes_list)
  306. top_gene_names <- unique(unlist(lapply(top_genes_list, rownames)))
  308. # Remove markers that start with "ENSSS"
  309. top_gene_names <- top_gene_names[!grepl("^ENSSS", top_gene_names)]
  311. top5.dot <- DotPlot(AllData.int, features = top_gene_names) +
  312. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  313. theme(axis.text.x = element_text(angle = 90, hjust = 1))
  314. top5.dot
  315. ggsave("Top 10 genes dotplot.png", top5.dot, width = 30, height = 7, units = "in")
  317. ```
  319. Find and export unique markers between injured vs healthy discs
  321. ```{r Export inj vs ctr unique markers for IPA analysis}
  323. Idents(AllData.int) <- AllData.int$disc.ident
  325. # Find markers between groups
  327. InjVCtr <- FindMarkers(AllData.int, ident.1 = "injured", ident.2 = "healthy", min.pct = 0.25)
  329. # Print the top 10 markers
  331. head(InjVCtr, n = 10)
  333. # Write the markers to a CSV file
  335. write.csv(InjVCtr, file = "C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/Cluster Unique Genes/Inj_Vs_Ctr_markers.csv", row.names = TRUE)
  337. ```
  339. ```{r NP-FC-Transitional Clusters comparison}
  340. Idents(AllData.int) <- AllData.int$seurat_clusters
  342. # Define the subset of clusters to process
  343. clusters_to_process <- c(2, 4:8, 10:16, 18:19)
  345. # Loop through each specified cluster
  346. for (i in clusters_to_process) {
  347. # Find markers in the current cluster compared to all other clusters in the subset
  348. cluster_markers <- FindMarkers(AllData.int, ident.1 = i, ident.2 = clusters_to_process[clusters_to_process != i], min.pct = 0.25)
  350. # Construct the file path using sprintf for organized file naming
  351. file_path <- sprintf("C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/NP-FC-Transitional Clusters/Cluster%d.csv", i)
  353. # Write the result to a CSV file
  354. write.csv(cluster_markers, file_path, row.names = TRUE)
  356. # Print message after writing the file
  357. print(paste("File for cluster", i, "written."))
  358. }
  359. ```
  361. ```{r NP-FC cluster comparison}
  362. Idents(AllData.int) <- AllData.int$seurat_clusters
  364. # Define the subset of clusters to process
  365. clusters_to_process <- c(2, 4:7, 10,12)
  367. # Loop through each specified cluster
  368. for (i in clusters_to_process) {
  369. # Find markers in the current cluster compared to all other clusters in the subset
  370. cluster_markers <- FindMarkers(AllData.int, ident.1 = i, ident.2 = clusters_to_process[clusters_to_process != i], min.pct = 0.25)
  372. # Construct the file path using sprintf for organized file naming
  373. file_path <- sprintf("C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/NP-FC Clusters/Cluster%d.csv", i)
  375. # Write the result to a CSV file
  376. write.csv(cluster_markers, file_path, row.names = TRUE)
  378. # Print message after writing the file
  379. print(paste("File for cluster", i, "written."))
  380. }
  382. ```
  384. ```{r Notochordal cluster comparison}
  385. Idents(AllData.int) <- AllData.int$seurat_clusters
  387. # Define the subset of clusters to process
  388. clusters_to_process <- c(0, 1, 3, 8, 9, 11, 14:16, 18, 20)
  390. # Loop through each specified cluster
  391. for (i in clusters_to_process) {
  392. # Find markers in the current cluster compared to all other clusters in the subset
  393. cluster_markers <- FindMarkers(AllData.int, ident.1 = i, ident.2 = clusters_to_process[clusters_to_process != i], min.pct = 0.25)
  395. # Construct the file path using sprintf for organized file naming
  396. file_path <- sprintf("C:/Users/gkane/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/Notochordal Clusters/Cluster%d.csv", i)
  398. # Write the result to a CSV file
  399. write.csv(cluster_markers, file_path, row.names = TRUE)
  401. # Print message after writing the file
  402. print(paste("File for cluster", i, "written."))
  403. }
  405. ```
  407. ```{r Cell type comparison}
  408. Idents(AllData.int) <- AllData.int$cell.ident
  410. # Define the subset of clusters to process
  411. ident_to_process <- c("Notochordal","Fibrocartilage", "Transitional Fibrocartilage","Transitional Nucleus Pulposus","Endothelial","Immune","Nucleus Pulposus")
  413. # Loop through each specified cluster
  414. for (i in ident_to_process) {
  415. # Find markers in the current cluster compared to all other clusters in the subset
  416. ident_markers <- FindMarkers(AllData.int, ident.1 = i, ident.2 = ident_to_process[ident_to_process != i], min.pct = 0.25)
  418. # Construct the file path using sprintf for organized file naming
  419. file_path <- sprintf("C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/Comparison by cell type/%s.csv", i)
  421. # Write the result to a CSV file
  422. write.csv(ident_markers, file_path, row.names = TRUE)
  424. # Print message after writing the file
  425. print(paste("File for ident", i, "written."))
  426. }
  428. marker_test_res <- top_markers(cds, group_cells_by="cell.ident", reference_cells=1000, cores=8)
  430. top_specific_markers <- marker_test_res %>%
  431. filter(fraction_expressing >= 0.5) %>%
  432. group_by(cell_group) %>%
  433. top_n(10, pseudo_R2)
  434. top_specific_marker_ids <- unique(top_specific_markers %>% pull(gene_id))
  436. top_specific_marker.int <- plot_genes_by_group(cds,
  437. top_specific_marker_ids,
  438. group_cells_by="cell.ident",
  439. ordering_type="maximal_on_diag",
  440. max.size=3)
  441. top_specific_marker.int
  443. ```
  445. ```{r Problem vs non-problem children}
  446. #all clusters with > 65% of one disc type will be classificed as that disc, clusters will 50/50 to 65/40 splits will be considered mixed.
  447. AllData.int$disc.prop <- as.factor(ifelse(AllData.int$seurat_clusters %in% c("0", "1", "3", "9", "11", "13", "14" ,"15","16","17","18","20"), "Healthy",
  448. ifelse(AllData.int$seurat_clusters %in% c("2", "4", "5", "6", "8","9", "12","19"), "Injured",
  449. ifelse(AllData.int$seurat_clusters %in% c("7","10","21"), "Mixed",""))))
  451. Idents(AllData.int) <- AllData.int$disc.prop
  453. # Find markers between groups
  455. comp <- FindMarkers(AllData.int, ident.1 = "Injured", ident.2 = "Healthy", min.pct = 0.25)
  457. # Write the markers to a CSV file
  459. write.csv(comp, file = "C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/Inj Vs Ctr cluster comparison.csv", row.names = TRUE)
  461. DotPlot(AllData.int, features = features_to_plot) +
  462. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu")))+
  463. theme(axis.text.x = element_text(angle = 90, hjust = 1))
  467. Idents(AllData.int) <- AllData.int$seurat_clusters
  469. ```
  471. ```{r problem children comparison}
  472. Idents(AllData.int) <- AllData.int$seurat_clusters
  474. # Define the subset of clusters to process
  475. clusters_to_process <- c("2", "4", "5", "6", "7", "10", "12")
  477. # Loop through each specified cluster
  478. for (i in clusters_to_process) {
  479. # Find markers in the current cluster compared to all other clusters in the subset
  480. cluster_markers <- FindMarkers(AllData.int, ident.1 = i, ident.2 = clusters_to_process[clusters_to_process != i], min.pct = 0.25)
  482. # Construct the file path using sprintf for organized file naming
  483. file_path <- sprintf("C:/Users/kanedag/Box/Box - Sheyn Lab/Projects/Ongoing projects/Porcine IVD/Data/scRNA Seq/RStudio and Seurat Analysis/3. Data Visualization via Seurat/Problem Children Comparison/Cluster%s.csv", i)
  485. # Write the result to a CSV file
  486. write.csv(cluster_markers, file_path, row.names = TRUE)
  488. # Print message after writing the file
  489. print(paste("File for cluster", i, "written."))
  490. }
  491. ```
  493. ```{r}
  494. DefaultAssay(AllData.int) <- "RNA"
  496. # Define gene lists (adjust if you already have them stored)
  497. EXO <- c("TSG101","PDCD6IP","VPS4A","VPS4B","CHMP2A","CHMP2B","CHMP4A","CHMP4B",
  498. "CHMP1A","CHMP1B","IST1","HGS","STAM","STAM2","RAB27A","RAB27B","RAB35",
  499. "RAB11A","RAB11B","ARF6","VAMP7","VAMP8","SNAP23","SYTL4","ANXA1","ANXA2",
  500. "CD9","CD63","CD81")
  502. CONST <- c("SAR1A","SAR1B","SEC23A","SEC23B","SEC24A","SEC24B","SEC24C","SEC24D",
  503. "SEC13","SEC31A","SEC31B","ARF1","GBF1","COPA","COPB1","COPB2","COPG1",
  504. "COPG2","COPZ1","COPZ2","SEC61A1","SEC61B","SSR1","HSPA5","DNAJB11",
  505. "STX3","STX4","VAMP3","VAMP8","SNAP23")
  507. CAV <- c("CAV1","CAV2","CAVIN1","CAVIN2","CAVIN3","EHD2")
  509. REG <- c("RAB3A","RAB3B","RAB3C","RAB3D","SYT1","SYT7","CPLX1","CPLX2","STX1A",
  510. "VAMP2","SNAP25")
  512. # Add module scores with unique names
  513. AllData.int <- AddModuleScore(AllData.int, features = list(EXO), name = "EXOscore")
  514. AllData.int <- AddModuleScore(AllData.int, features = list(CONST), name = "CONSTscore")
  515. AllData.int <- AddModuleScore(AllData.int, features = list(CAV), name = "CAVscore")
  516. AllData.int <- AddModuleScore(AllData.int, features = list(REG), name = "REGscore")
  518. # Create composite AltTraffickingScore
  519. AllData.int$AltTraffickingScore <-
  520. AllData.int$EXOscore1 + AllData.int$CONSTscore1 -
  521. AllData.int$CAVscore1 - AllData.int$REGscore1
  523. #Cell communication style by cluster
  524. scores <- FetchData(AllData.int, vars = c("seurat_clusters",
  525. "EXOscore1","CONSTscore1","CAVscore1","REGscore1","AltTraffickingScore"))
  527. avg_scores <- scores %>%
  528. group_by(seurat_clusters) %>%
  529. summarise(across(everything(), mean, na.rm = TRUE))
  531. avg_scores_long <- tidyr::pivot_longer(avg_scores,
  532. cols = -seurat_clusters,
  533. names_to = "ScoreType",
  534. values_to = "MeanScore")
  536. ggplot(avg_scores_long, aes(x = seurat_clusters, y = MeanScore, fill = ScoreType)) +
  537. geom_bar(stat="identity", position="dodge") +
  538. theme_classic() +
  539. theme(axis.text.x = element_text(angle=45, hjust=1)) +
  540. ylab("Average module score") +
  541. xlab("Cluster")
  543. #Cell communication style by cell type
  544. scores <- FetchData(AllData.int, vars = c("cell.ident",
  545. "EXOscore1","CONSTscore1","CAVscore1","REGscore1","AltTraffickingScore"))
  547. avg_scores <- scores %>%
  548. group_by(cell.ident) %>%
  549. summarise(across(everything(), mean, na.rm = TRUE))
  551. avg_scores_long <- tidyr::pivot_longer(avg_scores,
  552. cols = -cell.ident,
  553. names_to = "ScoreType",
  554. values_to = "MeanScore")
  556. ggplot(avg_scores_long, aes(x = cell.ident, y = MeanScore, fill = ScoreType)) +
  557. geom_bar(stat="identity", position="dodge") +
  558. theme_classic() +
  559. theme(axis.text.x = element_text(angle=45, hjust=1)) +
  560. ylab("Average module score") +
  561. xlab("Cluster")
  563. #Cell communication style by select cell type
  564. # Pick the identities you care about
  565. selected_idents <- c("Notochordal","Transitional Nucleus Pulposus","Nucleus Pulposus")
  567. scores <- FetchData(AllData.int, vars = c("cell.ident",
  568. "EXOscore1","CONSTscore1","CAVscore1","REGscore1","AltTraffickingScore")) %>%
  569. filter(cell.ident %in% selected_idents)
  571. avg_scores <- scores %>%
  572. group_by(cell.ident) %>%
  573. summarise(across(everything(), mean, na.rm = TRUE), .groups="drop")
  575. avg_scores_long <- pivot_longer(avg_scores,
  576. cols = -cell.ident,
  577. names_to = "ScoreType",
  578. values_to = "MeanScore")
  580. ggplot(avg_scores_long, aes(x = cell.ident, y = MeanScore, fill = ScoreType)) +
  581. geom_bar(stat="identity", position="dodge") +
  582. theme_classic() +
  583. theme(axis.text.x = element_text(angle=45, hjust=1)) +
  584. ylab("Average module score") +
  585. xlab("Cell identity")
  587. ```
  589. ```{r}
  590. NC <- DotPlot(AllData.int, features = c("TBXT","KRT8", "KRT18", "KRT19", "CD24","LGALS3","CAV1","SHH", "NOTO", "FOXA2","FOXJ1", "CHRD","NOG")) +
  591. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu"))) +
  592. theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  593. labs(title = "Notochordal Marker Expression")
  594. NC
  596. # NP marker plot (bottom)
  597. NP <- DotPlot(AllData.int, features = c("ACAN", "COL2A1","COL3A1","COL6A1","COL9A1", "COL11A1", "DCN","MGP","COMP","TRPV4","SERPINE2","TIMP1","TIMP3", "PAX1", "SOX5", "SOX6")) +
  598. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu"))) +
  599. theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  600. labs(title = "NP Marker Expression")
  601. NP
  603. NC + NP
  605. DotPlot(AllData.int, features = c("PECAM1", "VWF", "CDH5", "KDR", "FLT1", "ENG", "ESAM", "PLVAP", "CD34", "MCAM", "CLDN5", "TEK", "FABP4")) +
  606. scale_color_gradientn(colors = rev(brewer.pal(11, "RdBu"))) +
  607. theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  608. labs(title = "Endothelial Cell Marker Expression")
  612. ```

2. Data Visualization with Seurat Script.qmd at commit d8bb4fc, no license · at the source

Overview

Authors: Giselle Kaneda1,2,3, Jacob T Wechsler1,2, Melissa Chavez1,2, Julia Sheyn1,2, Karandeep Cheema4,5, Chushu Shen4,5, Dante Rigo De Righi4, Lixia Wang4, Yin‐Chen Hsu4, Pablo Avalos2, Yibin Xie4, Wafa Tawackoli1,2,3,4,6,7, Candace Floyd8, Debiao Li4,5, Dmitriy Sheyn1,2,3,6,7
ORCID iDs: Giselle Kaneda, Jacob T Wechsler, Karandeep Cheema, Chushu Shen, Lixia Wang, Yin‐Chen Hsu, Pablo Avalos, Yibin Xie, Wafa Tawackoli, Candace Floyd, Debiao Li, Dmitriy Sheyn
  1. Orthopaedic Stem Cell Research Laboratory, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  2. Board of Governors Regenerative Medicine Institute, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  3. Department of Biomedical Sciences, Cedars‐Sinai Medical Center, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  4. Biomedical Imaging Research Institute, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  5. Department of Bioengineering, University of California, Los Angeles, California, USA
  6. Department of Orthopaedics, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  7. Department of Surgery, Cedars‐Sinai Medical Center, Los Angeles, California, USA
  8. Department of Emergency Medicine, Emory University, Atlanta, Georgia, USA
Institutions: Cedars-Sinai Medical Center (United States); University of California, Los Angeles (United States); Emory University (United States)
Journal: JOR spine, volume 9, issue 3, article e70200
Dates: received 13 November 2025; accepted 8 June 2026; published online 9 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/jsp2.70200 · PMID 42428568 · PMCID PMC13347629 · OpenAlex W4416797438
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.24.690318
Status: code verified
Categories: genetics / omics (modality), human (organism), other (organism), pain (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: biobehavioral tests, discogenic pain, intervertebral disc degeneration, low back pain, notochordal cells, nucleus pulposus, porcine model
Topic: Spine and Intervertebral Disc Pathology (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: NIAMS NIH HHS (R01 AR066517, R01 AR082041); NINDS NIH HHS (RF1 NS135504, R34 NS126032)
Citations: not cited yet (Europe PMC); 128 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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gkaneda/Pig-IVD-Scripts

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d8bb4fc3c123dbf52a58d68bb08b9507839af1f9, 18 September 2025
Languages: Quarto (4)
Size: 4 files, 4 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: 4 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), Monocle 3 (4 files), patchwork (4 files), Seurat (4 files), tidyverse (4 files), ComplexHeatmap (2 files), reticulate (2 files), igraph (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/gkaneda/Pig-IVD-Scripts

The paper's code and data availability statement is in the Data section.

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Data

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Read it in the paper: doi.org/10.1002/jsp2.70200.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 7 keywords, 2 funders, 127 references.

Cite

This paper

Kaneda, G., Wechsler, J. T., Chavez, M., Sheyn, J., Cheema, K., Shen, C., Rigo De Righi, D., Wang, L., Hsu, Y., Avalos, P., Xie, Y., Tawackoli, W., Floyd, C., Li, D., & Sheyn, D. (2026). A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence. JOR spine, 9(3), e70200. https://doi.org/10.1002/jsp2.70200

BibTeX

@article{kaneda2026porcine,
author = {Kaneda, Giselle and Wechsler, Jacob T and Chavez, Melissa and Sheyn, Julia and Cheema, Karandeep and Shen, Chushu and Rigo De Righi, Dante and Wang, Lixia and Hsu, Yin‐Chen and Avalos, Pablo and Xie, Yibin and Tawackoli, Wafa and Floyd, Candace and Li, Debiao and Sheyn, Dmitriy},
title = {{A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence}},
journal = {JOR spine},
year = {2026},
month = jul,
volume = {9},
number = {3},
pages = {e70200},
publisher = {Wiley},
issn = {2572-1143},
doi = {10.1002/jsp2.70200},
url = {https://doi.org/10.1002/jsp2.70200},
pmid = {42428568},
pmcid = {PMC13347629}
}

RIS

TY - JOUR
AU - Kaneda, Giselle
AU - Wechsler, Jacob T
AU - Chavez, Melissa
AU - Sheyn, Julia
AU - Cheema, Karandeep
AU - Shen, Chushu
AU - Rigo De Righi, Dante
AU - Wang, Lixia
AU - Hsu, Yin‐Chen
AU - Avalos, Pablo
AU - Xie, Yibin
AU - Tawackoli, Wafa
AU - Floyd, Candace
AU - Li, Debiao
AU - Sheyn, Dmitriy
TI - A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence
T2 - JOR spine
J2 - JOR Spine
PY - 2026
DA - 2026/07/09
VL - 9
IS - 3
SP - e70200
SN - 2572-1143
PB - Wiley
DO - 10.1002/jsp2.70200
UR - https://doi.org/10.1002/jsp2.70200
LA - en
ER -

CSL-JSON

{
"id": "10.1002/jsp2.70200",
"type": "article-journal",
"title": "A Porcine Model of Intervertebral Disc Injury Recapitulates Human Discogenic Pain Via Notochordal Cell Loss and Pain-Inducing Nucleus Pulposus Cell Emergence",
"container-title": "JOR spine",
"author": [
{
"family": "Kaneda",
"given": "Giselle"
},
{
"family": "Wechsler",
"given": "Jacob T"
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{
"family": "Chavez",
"given": "Melissa"
},
{
"family": "Sheyn",
"given": "Julia"
},
{
"family": "Cheema",
"given": "Karandeep"
},
{
"family": "Shen",
"given": "Chushu"
},
{
"family": "Rigo De Righi",
"given": "Dante"
},
{
"family": "Wang",
"given": "Lixia"
},
{
"family": "Hsu",
"given": "Yin‐Chen"
},
{
"family": "Avalos",
"given": "Pablo"
},
{
"family": "Xie",
"given": "Yibin"
},
{
"family": "Tawackoli",
"given": "Wafa"
},
{
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"given": "Candace"
},
{
"family": "Li",
"given": "Debiao"
},
{
"family": "Sheyn",
"given": "Dmitriy"
}
],
"container-title-short": "JOR Spine",
"volume": "9",
"issue": "3",
"page": "e70200",
"DOI": "10.1002/jsp2.70200",
"PMID": "42428568",
"PMCID": "PMC13347629",
"ISSN": "2572-1143",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/jsp2.70200",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
9
]
]
}
}

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