OSCR

Resolving cell lineages and gene functions in the developing mouse gastrointestinal tract using in utero transduction.

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches
  1. [1] § Results › In Utero Transduction Targets All Major Cell Types in the Developing Gut. ↔ Preprocessing/Expt2_Preprocessing.R, lines 112–193 · score 0.90 · Col1a1, Upk3b, Phox2b, enterocytes, goblet, Clca1
  2. [2] § Results › In Utero Transduction Targets All Major Cell Types in the Developing Gut. ↔ Preprocessing/Expt3_Preprocessing.R, lines 119–198 · score 0.89 · Col1a1, Upk3b, Phox2b, enterocytes, goblet, Clca1
  3. [3] § Results › In Utero Transduction Targets All Major Cell Types in the Developing Gut. ↔ Preprocessing/Expt1_Processing.Rmd, lines 710–721 · score 0.84 · Top2a, Col6a4, Lepr, Mfap5, Mki67, Tagln
  4. [4] § Results › Clonal Sharing between Gut Segments Reveals Lineage-Specific Timing of Regional Identity Acquisition. ↔ Clonal_analysis/Clonal_Regional.Rmd, lines 484–499 · score 0.60 · Pou3f3, local clones, colonic ENS, regionally, stomach, clonally
  5. [5] § Results › Clonal Sharing between Gut Segments Reveals Lineage-Specific Timing of Regional Identity Acquisition. ↔ Clonal_analysis/Clonal_Regional.Rmd, lines 484–499 · score 0.56 · local clones, colonic ENS, Pou3f3 expression, migration, Regional, stomach
  6. [6] § Results › Barcoded In Utero Transduction Reveals Clonal Relationships in the Developing Gut. ↔ Preprocessing/Expt2_Processing.Rmd, lines 150–185 · score 0.52 · Col6a4, smooth muscle, interstitial, Adam12, Adamdec1, Kcnn3
  7. [7] § Results › Barcoded In Utero Transduction Reveals Clonal Relationships in the Developing Gut. ↔ Preprocessing/Expt1_Processing.Rmd, lines 138–164 · score 0.52 · Col6a4, smooth muscle, interstitial, Adam12, Adamdec1, Kcnn3
  8. [8] § Results › Nano-Injection at Early E7.5 Improves ENS Targeting While Revealing Residual Epiblasts. ↔ Clonal_analysis/Clonal_Regional.Rmd, lines 236–248 · score 0.51 · cross germ layer, single germ layer, multicellular, e7, transduction, clones

Paper

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The authors' code

R Markdown · 645 lines · 20 KB · MIT · 3 matches

  1. ---
  2. title: "Figure 5 — Regional and integrated clonal analysis"
  3. output: html_document
  4. ---
  6. ```{r setup, include=FALSE}
  7. knitr::opts_chunk$set(echo = TRUE)
  8. knitr::opts_knit$set(root.dir = normalizePath(".."))
  9. library(dplyr)
  10. library(Seurat)
  11. options(Seurat.object.assay.version = "v5")
  12. library(ggplot2)
  13. library(stringr)
  14. library(patchwork)
  15. library(scales)
  16. library(circlize)
  17. library(UpSetR)
  18. library(scCustomize)
  19. library(ggalluvial)
  20. library(tibble)
  21. library(tidyr)
  22. library(ComplexHeatmap)
  23. options(future.globals.maxSize = 100 * 1024^3)
  24. ```
  26. ```{r}
  27. fig_dir_regions <- "Figures/Fig5/"
  29. save_fig_regions <- function(name, width = 7, height = 5, device = grDevices::pdf, bg = "white", dpi = 300) {
  30. # Ensure directory exists
  31. if (!dir.exists(fig_dir_regions)) dir.create(fig_dir_regions, recursive = TRUE)
  33. ggsave(
  34. filename = file.path(fig_dir_regions, name),
  35. width = width,
  36. height = height,
  37. bg = bg,
  38. dpi = dpi,
  39. device = device
  40. )
  41. }
  42. ```
  44. ```{r}
  45. # Annotated Seurat objects (cell types + clonal metadata; see Preprocessing and Clonal notebooks)
  46. E16_barcodes <- readRDS("Data/E16_barcodes_E1.rds")
  47. E16_barcodes_E2 <- readRDS("Data/E16_barcodes_E2.rds")
  48. E16_E2_ENS <- readRDS("Data/E16_barcodes_ENS_E2.rds")
  49. ```
  51. ```{r}
  52. distinct11 <- c("#377EB8", "#F5B375", "#A3CB38", "#C73681", "#E41A1C", "#84817b", "#A65628", "#FF7F00", "#FFD92F", "#984EA3", "#4DAF4A")
  53. cell_types <- sort(unique(E16_barcodes$major_cell_type_2))
  54. color_map <- setNames(
  55. rep(distinct11, length.out = length(cell_types)),
  56. cell_types
  57. )
  59. clone_cols <- c(
  60. "No CloneID" = "grey70",
  61. "Singleton" = "grey70",
  62. "Cross-germ clones" = "grey70",
  63. "Region not shared at all" = "#56B4E9",
  64. "Region shared by stomach, ileum" = "#ff7f00",
  66. # E2-only categories
  67. "Region shared by stomach, jej_ileum" = "#006D2C",
  68. "Region shared by stomach, colon" = "#006D2C",
  69. "Region shared by jej_ileum, colon" = "#006D2C",
  70. "Region shared by stomach, jej_ile, colon" = "#D81B60"
  71. )
  72. ```
  74. ```{r}
  75. # Find regionally dispersed clones for E7.5 transduction
  76. clone_metadata <- [email hidden] %>%
  77. filter(clones_per_cell_4 == "Multicellular")
  79. regional_clone_summary <- clone_metadata %>%
  80. dplyr::select(clone_nr, sample, clone_type, assigned_cell_type) %>%
  81. group_by(clone_nr) %>%
  82. summarise(
  83. regions = list(unique(sample)),
  84. n_regions = n_distinct(sample),
  85. clone_types = list(unique(clone_type)),
  86. cell_types = list(unique(assigned_cell_type)),
  87. n_cells = n(),
  88. .groups = "drop"
  89. )
  91. # Identify clones found in multiple regions
  92. dispersed_clones <- regional_clone_summary %>%
  93. filter(n_regions > 1) # 21 clones
  95. summary_row <- dispersed_clones %>%
  96. summarise(
  97. clone_nr = paste0("TOTAL(", length(clone_nr), " clones)"),
  98. regions = list(unique(unlist(regions))),
  99. n_regions = 2,
  100. clone_types = list(unique(unlist(clone_types))),
  101. cell_types = list(unique(unlist(cell_types))),
  102. n_cells = sum(n_cells)
  103. )
  105. dispersed_clones <- dispersed_clones %>%
  106. mutate(clone_nr = as.character(clone_nr)) %>%
  107. unique()
  109. dispersed_clones_with_total <- bind_rows(dispersed_clones, summary_row)
  111. dispersed_clones_df <- dispersed_clones_with_total %>%
  112. mutate(across(where(is.list), ~ sapply(., toString)))
  113. write.csv(dispersed_clones_df, "Data/E1_regionaldispersed_clones.csv", row.names = FALSE)
  114. ```
  116. ```{r}
  117. # Prepare for the UpSet plot
  118. clone_counts <- as.data.frame(table(clone_metadata$clone_nr))
  119. colnames(clone_counts) <- c("clone_nr", "cell_count")
  121. shared_clone_metadata <- clone_metadata %>%
  122. filter(clone_nr %in% dispersed_clones$clone_nr)
  124. clone_analysis <- shared_clone_metadata %>%
  125. dplyr::select(clone_nr, major_cell_type_2)
  127. data_wide <- as.data.frame.matrix(table(clone_analysis$clone_nr, clone_analysis$major_cell_type_2))
  128. celltype <- colnames(data_wide)
  129. set_sizes <- colSums(data_wide)
  131. # Compute total number of cells per clone (sum across all cell types)
  132. clone_sizes <- rowSums(data_wide)
  134. # Convert to binary for UpSet intersections
  135. binary_matrix <- data_wide %>%
  136. mutate(across(everything(), ~ ifelse(. > 0, 1, 0)))
  137. binary_matrix$Clone_Size <- clone_sizes
  138. sets_vec <- colnames(binary_matrix)[-ncol(binary_matrix)]
  139. ```
  141. ```{r,fig.height = 6 , fig.width = 6, fig.align = "center"}
  142. grid::grid.newpage()
  143. upset(
  144. binary_matrix[, -ncol(binary_matrix)],
  145. sets = sets_vec,
  146. order.by = "freq",
  147. sets.x.label = "Total multicellular clones",
  148. point.size = 5,
  149. line.size = 2,
  150. mb.ratio = c(0.6, 0.4),
  151. text.scale = c(2.5, 2.0, 2.0, 1.8, 2.2, 2.2),
  152. nintersects = 30,
  153. mainbar.y.max = 16
  154. ) # Fig 5A
  155. dev.off()
  156. ```
  158. ```{r}
  159. clone_region_map <- [email hidden] %>%
  160. group_by(clone_nr) %>%
  161. summarise(
  162. regions = list(sort(unique(sample))),
  163. clone_type = unique(clone_type)[1],
  164. .groups = "drop"
  165. ) %>%
  166. mutate(
  167. shared_clone_category = case_when(
  168. clone_type == "No CloneID" ~ "No CloneID",
  169. clone_type == "Singleton" ~ "Singleton",
  170. TRUE ~ map_chr(regions, function(r) {
  171. if (length(r) == 1) {
  172. "Region not shared at all"
  173. } else if (setequal(r, c("E16_stomach", "E16_ileum"))) {
  174. "Region shared by stomach, ileum"
  175. } else {
  176. paste("Other combination:", paste(r, collapse = ", "))
  177. }
  178. })
  179. )
  180. )
  182. [email hidden]$shared_clone_category <-
  183. clone_region_map$shared_clone_category[
  184. match([email hidden]$clone_nr, clone_region_map$clone_nr)
  185. ]
  187. [email hidden]$shared_clone_category <- factor(
  188. [email hidden]$shared_clone_category,
  189. levels = c(
  190. "No CloneID",
  191. "Singleton",
  192. "Region not shared at all",
  193. "Region shared by stomach, ileum"
  194. )
  195. )
  197. [email hidden]$shared_clone_category <- factor(
  198. [email hidden]$shared_clone_category,
  199. levels = names(clone_cols)
  200. )
  202. DimPlot(E16_barcodes, group.by = "shared_clone_category", reduction = "umap.harmony", pt.size = 0.5, order = T, cols = clone_cols) + ggtitle("Clones shared between regions")
  203. save_fig_regions("umap_E1_regional_sharedclones.pdf", width = 9, height = 6) # Fig 5B
  204. ```
  206. ```{r,fig.height = 6, fig.width = 8, fig.align = "center"}
  207. clone_numbers_dispersed <- dispersed_clones$clone_nr %>% unique()
  208. df <- [email hidden] %>%
  209. filter(clone_nr %in% clone_numbers_dispersed) %>%
  210. select(sample, major_cell_type_2, clone_nr) %>%
  211. mutate(
  212. sample = factor(sample, levels = c("E16_stomach", "E16_ileum")),
  213. major_cell_type = as.factor(major_cell_type_2)
  214. )
  216. ggplot(
  217. df,
  218. aes(
  219. axis1 = sample,
  220. axis2 = major_cell_type,
  221. axis3 = clone_nr,
  222. y = 1,
  223. fill = major_cell_type
  224. )
  225. ) +
  226. geom_alluvium(aes(fill = major_cell_type), width = 0.1, alpha = 0.8) +
  227. geom_stratum(width = 0.1, fill = "grey90", color = "black") +
  228. geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 5) +
  229. scale_x_discrete(limits = c("Sample", "Cell type","Clone ID"), expand = c(0.1, 0.1)) +
  230. scale_fill_manual(values = color_map) +
  231. theme_void() +
  232. theme(legend.position = "none") # Fig 5C
  233. save_fig_regions("alluvialdiagram_E1_regional.pdf", width = 8, height = 6)
  234. ```
  236. ```{r}
  237. # Find regionally dispersed clones for early E7.5 transduction ( restricted to single-germ-layer clones)
  238. cross_germ_clone_ids <- [email hidden] %>%
  239. filter(clones_per_cell_4 == "Multicellular") %>%
  240. group_by(clone_nr) %>%
  241. summarise(
  242. n_types = n_distinct(major_cell_type_3),
  243. .groups = "drop"
  244. ) %>%
  245. filter(n_types > 1) %>%
  246. pull(clone_nr)
  247. cross_germ_clone_ids
  248. ```
  250. ```{r,fig.height = 6, fig.width = 8, fig.align = "center"}
  251. clone_cols <- c(
  252. "No CloneID" = "grey70",
  253. "Singleton" = "grey70",
  254. "Cross-germ clones" = "grey70",
  255. "Region not shared at all" = "#56B4E9",
  256. "Region shared by stomach, jej_ileum" = "#006D2C",
  257. "Region shared by stomach, colon" = "#006D2C",
  258. "Region shared by jej_ileum, colon" = "#006D2C",
  259. "Region shared by stomach, jej_ile, colon" = "#D81B60"
  260. )
  262. clone_region_map_E2 <- [email hidden] %>%
  263. group_by(clone_nr) %>%
  264. summarise(
  265. regions = list(sort(unique(sample))),
  266. clone_type = dplyr::first(as.character(clone_type)),
  267. .groups = "drop"
  268. ) %>%
  269. mutate(
  270. shared_clone_category = case_when(
  271. clone_type == "No CloneID" ~ "No CloneID",
  272. clone_type == "Singleton" ~ "Singleton",
  273. TRUE ~ map_chr(regions, function(r) {
  274. if (length(r) == 1) {
  275. "Region not shared at all"
  276. } else if (all(c("E16_stomach", "E16_jej_ileum") %in% r) && length(r) == 2) {
  277. "Region shared by stomach, jej_ileum"
  278. } else if (all(c("E16_stomach", "E16_colon") %in% r) && length(r) == 2) {
  279. "Region shared by stomach, colon"
  280. } else if (all(c("E16_jej_ileum", "E16_colon") %in% r) && length(r) == 2) {
  281. "Region shared by jej_ileum, colon"
  282. } else if (all(c("E16_stomach", "E16_jej_ileum", "E16_colon") %in% r) && length(r) == 3) {
  283. "Region shared by stomach, jej_ile, colon"
  284. } else {
  285. stop(paste("Unexpected region combination:", paste(r, collapse = ", ")))
  286. }
  287. })
  288. ),
  289. shared_clone_category = if_else(
  290. clone_nr %in% cross_germ_clone_ids,
  291. "Cross-germ clones",
  292. shared_clone_category
  293. )
  294. )
  296. [email hidden]$shared_clone_category <-
  297. clone_region_map_E2$shared_clone_category[
  298. match([email hidden]$clone_nr, clone_region_map_E2$clone_nr)
  299. ]
  301. [email hidden]$shared_clone_category <- factor(
  302. [email hidden]$shared_clone_category,
  303. levels = c(
  304. "No CloneID",
  305. "Singleton",
  306. "Cross-germ clones",
  307. "Region not shared at all",
  308. "Region shared by stomach, jej_ileum",
  309. "Region shared by stomach, colon",
  310. "Region shared by jej_ileum, colon",
  311. "Region shared by stomach, jej_ile, colon"
  312. )
  313. )
  314. DimPlot(E16_barcodes_E2, group.by = "shared_clone_category", reduction = "umap.harmony", pt.size = 0.5, order = T, cols = clone_cols) + ggtitle(NULL) + xlab("UMAP1") + ylab("UMAP2")
  315. save_fig_regions("umap_E2_regional_sharedclones.pdf", width = 9, height = 6) # Fig 5E
  316. ```
  318. ```{r}
  319. # Prepare for the UpSet plot for E2 (non-epiblast clones only)
  320. distinct_clones_E2 <- clone_metadata_E2%>%
  321. group_by(clone_nr) %>%
  322. filter(n_distinct(major_cell_type_3) == 1) %>%
  323. ungroup() %>%
  324. distinct(clone_nr) %>%
  325. pull(clone_nr)# 851 CloneIDs
  328. shared_clone_metadata_E2 <- clone_metadata_E2 %>%
  329. filter(clone_nr %in% distinct_clones_E2)
  331. regional_clone_summary_E2 <- shared_clone_metadata_E2 %>%
  332. dplyr::select(clone_nr, sample, clone_type, assigned_cell_type) %>%
  333. group_by(clone_nr) %>%
  334. summarise(
  335. regions = list(unique(sample)),
  336. n_regions = n_distinct(sample),
  337. clone_types = list(unique(clone_type)),
  338. cell_types = list(unique(assigned_cell_type)),
  339. n_cells = n(),
  340. .groups = "drop"
  341. )
  343. # Identify clones found in multiple regions
  344. dispersed_clones_E2 <- regional_clone_summary_E2 %>%
  345. filter(n_regions > 1) # 119 clones
  347. summary_row_E2 <- dispersed_clones_E2 %>%
  348. summarise(
  349. clone_nr = paste0("TOTAL(", length(clone_nr), " clones)"),
  350. regions = list(unique(unlist(regions))),
  351. n_regions = 3,
  352. clone_types = list(unique(unlist(clone_types))),
  353. cell_types = list(unique(unlist(cell_types))),
  354. n_cells = sum(n_cells)
  355. )
  357. dispersed_clones_E2 <- dispersed_clones_E2 %>%
  358. mutate(clone_nr = as.character(clone_nr)) %>%
  359. unique()
  361. dispersed_clones_with_total_E2 <- bind_rows(dispersed_clones_E2, summary_row_E2)
  363. dispersed_clones_df_E2 <- dispersed_clones_with_total_E2 %>%
  364. mutate(across(where(is.list), ~ sapply(., toString)))
  365. write.csv(dispersed_clones_df_E2, "Data/E2_regionaldispersed_clones_nonepiblast.csv", row.names = FALSE)
  366. ```
  368. ```{r}
  369. # Prepare for the UpSet plot for E2 (non-epiblast)
  370. clone_analysis_E2 <- shared_clone_metadata_E2 %>%
  371. dplyr::select(clone_nr, major_cell_type_2)
  373. data_wide_E2 <- as.data.frame.matrix(table(clone_analysis_E2$clone_nr, clone_analysis_E2$major_cell_type_2))
  374. celltype_E2 <- colnames(data_wide_E2)
  375. set_sizes_E2 <- colSums(data_wide_E2)
  377. # Compute total number of cells per clone (sum across all cell types)
  378. clone_sizes_E2 <- rowSums(data_wide_E2)
  380. # Convert to binary for UpSet intersections
  381. binary_matrix_E2 <- data_wide_E2 %>%
  382. mutate(across(everything(), ~ ifelse(. > 0, 1, 0)))
  383. binary_matrix_E2$Clone_Size <- clone_sizes_E2
  384. sets_vec_E2 <- colnames(binary_matrix_E2)[-ncol(binary_matrix_E2)]
  385. ```
  387. ```{r,fig.height = 5, fig.width = 6, fig.align = "center"}
  388. pdf(file.path(fig_dir_regions, "Uplot_E2_regional_nonepiblast.pdf"), width = 6, height = 5, onefile = FALSE)
  389. grid::grid.newpage()
  390. upset(
  391. binary_matrix_E2[, -ncol(binary_matrix_E2)],
  392. sets = sets_vec_E2,
  393. order.by = "freq",
  394. sets.x.label = "Total multicellular clones",
  395. point.size = 5,
  396. line.size = 2,
  397. mb.ratio = c(0.6, 0.4),
  398. text.scale = c(2.5, 2.0, 2.0, 1.8, 2.2, 2.2),
  399. nintersects = 30,
  400. mainbar.y.max = 120
  401. ) # Fig 5D
  402. dev.off()
  403. ```
  405. ```{r}
  406. heatmap_matrix <- shared_clone_metadata_E2 %>%
  407. distinct(clone_nr, sample) %>%
  408. mutate(val = 1) %>%
  409. pivot_wider(names_from = sample, values_from = val, values_fill = 0) %>%
  410. column_to_rownames("clone_nr") %>%
  411. as.matrix()
  413. # Prepare the Row Annotation (Cell Type)
  414. row_data <- shared_clone_metadata_E2 %>%
  415. distinct(clone_nr, major_cell_type_2) %>%
  416. group_by(clone_nr) %>%
  417. summarise(type = paste(unique(major_cell_type_2), collapse = "/")) %>%
  418. column_to_rownames("clone_nr")
  420. row_data <- row_data[rownames(heatmap_matrix), , drop = FALSE]
  422. celltype_cols <- c(
  423. "Endothelial" = "#A65628",
  424. "ENS" = "#984EA3",
  425. "Immune" = "#4DAF4A",
  426. "ICC/Fibroblast" = "yellow",
  427. "Endothelial/Fibroblast" = "#377EB8",
  428. "Immune/Fibroblast" = "red"
  429. )
  431. ra = rowAnnotation(
  432. CellType = row_data$type,
  433. col = list(CellType = celltype_cols)
  434. )
  436. # Draw the Heatmap
  437. pdf(file.path(fig_dir_regions, "E2_nonepiblast_clones_3regions.pdf"), width = 6, height = 5, onefile = FALSE)
  438. Heatmap(heatmap_matrix,
  439. name = "Presence",
  440. col = c("white", "navy"),
  441. right_annotation = ra,
  442. show_row_names = FALSE,
  443. cluster_columns = FALSE,
  444. row_title = "Clones",
  445. column_title = "Regional Distribution & Cell Identity",
  446. border = TRUE)
  447. dev.off() # Fig 5F
  448. ```
  450. ```{r,fig.height = 6, fig.width = 6, fig.align = "center"}
  451. clone_sharing_logic <- [email hidden] %>%
  452. filter(clones_per_cell_4 == "Multicellular") %>%
  453. group_by(clone_nr) %>%
  454. filter(n_distinct(major_cell_type_3) == 1) %>%
  455. distinct(clone_nr, sample) %>%
  456. group_by(clone_nr) %>%
  457. summarise(intersection_type = paste(sort(sample), collapse = " & "), .groups = "drop")
  459. sharing_map <- setNames(clone_sharing_logic$intersection_type, clone_sharing_logic$clone_nr)
  460. cells_meta <- [email hidden]
  461. cell_upset_values <- sharing_map[as.character(cells_meta$clone_nr)]
  462. names(cell_upset_values) <- colnames(E16_E2_ENS)
  463. E16_E2_ENS$upset_group <- cell_upset_values
  465. E16_E2_ENS$migration_type <- case_when(
  466. grepl("stomach", E16_E2_ENS$upset_group) & grepl("colon", E16_E2_ENS$upset_group) ~ "Stomach-Colon Migrant",
  467. grepl("&", E16_E2_ENS$upset_group) ~ "Other Shared Clone",
  468. TRUE ~ "Local Clone"
  469. )
  471. DimPlot(E16_E2_ENS,
  472. group.by = "migration_type",
  473. cols = c("Stomach-Colon Migrant" = "#E31A1C",
  474. "Other Shared Clone" = "#1F78B4",
  475. "Local Clone" = "#A6CEE3",
  476. "Unlabeled" = "grey90"),
  477. order = T,
  478. reduction = "umap.harmony") +
  479. labs(title = "ENS: Evidence of Long-Distance Migration",
  480. subtitle = "Red cells share a Clone ID between Stomach and Colon") #Fig 5H
  481. save_fig_regions("ENS_longdistancemigration_nonepiblast.pdf",width = 6, height = 6)
  482. ```
  484. ```{r,fig.height = 6, fig.width = 7, fig.align = "center"}
  485. E16_colon_barcodes_E2 <- subset(E16_E2_ENS, subset = epiblast_label == "non-epiblast" & sample == "E16_colon")
  486. VlnPlot(E2_colon_subset,
  487. features = c("Pou3f3","Pantr1"),
  488. group.by = "migration_type",
  489. pt.size = 0.5, # Shows individual cell jitter
  490. cols = c("Stomach-Colon Migrant" = "#E31A1C",
  491. "Other Shared Clone" = "#1F78B4",
  492. "Local Clone" = "#A6CEE3",
  493. "Unlabeled" = "grey90")) +
  494. geom_boxplot(width = 0.1, fill = "white", outlier.shape = NA) +
  495. labs(title = "Pou3f3 Expression in Colonic ENS by Clonal Origin",
  496. subtitle = "Stomach-derived migrants acquire colonic markers") +
  497. theme(legend.position = "none") # Fig 5I
  498. save_fig_regions("ENS_Vlnplot_nonepiblast.pdf",width = 7, height = 6)
  499. ```
  501. ```{r}
  502. # 1. Prepare metadata with relabeled ENS subtypes
  503. ens_meta_shared <- [email hidden] %>%
  504. filter(!is.na(clone_nr)) %>%
  505. mutate(
  506. refined_type = case_when(
  507. grepl("Progenitor", assigned_cell_type, ignore.case = TRUE) ~ "ENS Progenitors",
  508. grepl("Branch B", assigned_cell_type, ignore.case = TRUE) ~ "ENS Branch B",
  509. assigned_cell_type %in% c("ENS Branch A", "ENC12") ~ "ENS Branch A",
  510. TRUE ~ assigned_cell_type
  511. )
  512. ) %>%
  513. group_by(clone_nr) %>%
  514. #filter(n_distinct(refined_type) > 1, n_distinct(sample) > 1) %>% # filtering the more than 1 sample more than 1 cell type
  515. ungroup()
  517. # 2. Clone annotation data
  518. ens_shared_metadata <- ens_meta_shared %>%
  519. group_by(clone_nr) %>%
  520. summarise(
  521. samples = paste(
  522. unique(sample[order(match(sample, c("E16_stomach", "E16_jej_ileum", "E16_colon")))]),
  523. collapse = " / "
  524. ),
  525. .groups = "drop"
  526. )
  528. # 3. Build clone x subtype proportion matrix
  529. heatmap_matrix_ens <- ens_meta_shared %>%
  530. group_by(clone_nr, refined_type) %>%
  531. tally(name = "cell_count") %>%
  532. group_by(clone_nr) %>%
  533. mutate(prop = cell_count / sum(cell_count)) %>%
  534. ungroup() %>%
  535. select(clone_nr, refined_type, prop) %>%
  536. pivot_wider(
  537. id_cols = clone_nr,
  538. names_from = refined_type,
  539. values_from = prop,
  540. values_fill = 0
  541. ) %>%
  542. column_to_rownames("clone_nr") %>%
  543. as.matrix()
  545. # 4. Align row annotation
  546. row_data_ens <- ens_shared_metadata %>%
  547. filter(clone_nr %in% rownames(heatmap_matrix_ens)) %>%
  548. column_to_rownames("clone_nr")
  550. row_data_ens <- row_data_ens[rownames(heatmap_matrix_ens), , drop = FALSE]
  552. # 5. Region annotation colors
  553. all_unique_combos <- unique(row_data_ens$samples)
  554. ens_colors <- setNames(scales::hue_pal()(length(all_unique_combos)), all_unique_combos)
  556. ra_ens <- rowAnnotation(
  557. region = row_data_ens$samples,
  558. col = list(region = ens_colors),
  559. show_annotation_name = TRUE,
  560. annotation_legend_param = list(
  561. region = list(
  562. title = "Regions",
  563. title_gp = gpar(fontsize = 10, fontface = "bold")
  564. )
  565. )
  566. )
  568. # 6. Order subtype columns
  569. subtype_order <- c(
  570. "ENS Progenitors",
  571. "ENS Neuroblast",
  572. "ENS Branch A",
  573. "ENS Branch B",
  574. "SCP"
  575. )
  577. existing_cols <- intersect(subtype_order, colnames(heatmap_matrix_ens))
  578. heatmap_matrix_ens <- heatmap_matrix_ens[, existing_cols, drop = FALSE]
  580. # 7. Order region groups
  581. region_order <- c(
  582. "E16_stomach / E16_jej_ileum / E16_colon",
  583. "E16_stomach / E16_colon",
  584. "E16_stomach / E16_jej_ileum",
  585. "E16_jej_ileum / E16_colon",
  586. "E16_stomach",
  587. "E16_jej_ileum",
  588. "E16_colon"
  589. )
  591. region_colors <- c(
  592. "E16_stomach / E16_jej_ileum / E16_colon" = "#E41A1C",
  593. "E16_stomach / E16_colon" = "#FF69B4",
  594. "E16_stomach / E16_jej_ileum" = "#FF7F00",
  595. "E16_jej_ileum / E16_colon" = "#41AB5D",
  596. "E16_stomach" = "#8C6D31",
  597. "E16_jej_ileum" ="#FDBF6F",
  598. "E16_colon" = "#CAB2D6"
  599. )
  601. row_data_ens$samples <- factor(row_data_ens$samples, levels = region_order)
  603. # reorder rows to match split order
  604. ord <- order(row_data_ens$samples)
  605. heatmap_matrix_ens <- heatmap_matrix_ens[ord, , drop = FALSE]
  606. row_data_ens <- row_data_ens[ord, , drop = FALSE]
  608. # rebuild annotation AFTER ordering rows
  609. ra_ens <- rowAnnotation(
  610. region = row_data_ens$samples,
  611. col = list(region = region_colors),
  612. show_annotation_name = TRUE,
  613. annotation_legend_param = list(
  614. region = list(
  615. title = "Regions",
  616. title_gp = gpar(fontsize = 10, fontface = "bold")
  617. )
  618. )
  619. )
  621. pdf(file.path(fig_dir_regions, "ENS_E2_subtype_sharing_heatmap_subtype_allclones.pdf"), width = 6, height = 9)
  622. Heatmap(
  623. heatmap_matrix_ens,
  624. name = "Proportion",
  625. right_annotation = ra_ens,
  626. cluster_columns = FALSE,
  627. cluster_rows = TRUE,
  628. cluster_row_slices = FALSE,
  629. row_split = row_data_ens$samples,
  630. row_gap = unit(1.5, "mm"),
  631. show_row_names = FALSE,
  632. border = TRUE,
  633. column_names_side = "top",
  634. column_title = "Long-Distance ENS Migrants: Regional & Subtype Composition",
  635. row_title = "Shared CloneIDs"
  636. ) #Fig 5G
  637. dev.off()
  638. ```
  640. ```{r}
  641. saveRDS(object = E16_barcodes, "Data/E16_barcodes_E1.rds")
  642. saveRDS(object = E16_barcodes_E2, "Data/E16_barcodes_E2.rds")
  643. ```

Clonal_Regional.Rmd at commit 616e26b, under MIT · at the source

Overview

Authors: Ziwei Liu1, Krishnanand Padmanabhan1, Jingyan He2, Katrin Hector2, Bettina Semsch2,3, Jia Sun3, Viktoria Knoflach1, Sarantis Giatrellis2, Johan Lorentz2, Khachatur Dallakyan1, Christian Göritz2, Emma Rachel Andersson2, Ulrika Marklund1
ORCID iDs: Ziwei Liu, Krishnanand Padmanabhan, Jingyan He, Katrin Hector, Jia Sun, Christian Göritz, Emma Rachel Andersson, Ulrika Marklund
  1. Department of Medical Biochemistry and Biophysics, Unit of Molecular Neurobiology, Karolinska Institutet SE-171 77, Stockholm, Sweden
  2. Department of Cell and Molecular Biology, Karolinska Institutet SE-171 77, Stockholm, Sweden
  3. Comparative Medicine, Karolinska Institutet SE-171 77, Stockholm, Sweden
Institutions: Karolinska Institutet (Sweden)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 37, article e2614077123
Dates: received 29 April 2026; accepted 11 August 2026; published online 9 September 2026; in print 15 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2614077123 · PMID 42715084 · PMCID PMC13578780 · OpenAlex W7212009148
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Keywords: lineage tracing, in utero transduction, enteric nervous system, gut patterning, neural crest development
MeSH: Cell Lineage*, Gastrointestinal Tract*, Animals, Cell Differentiation, Enteric Nervous System, Female, Gene Expression Regulation, Developmental, Mice, Neural Crest (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Knut och Alice Wallenbergs Stiftelse (kawforskning) (2020.0109); Vetenskapsrådet (VR) (2020-01129, 2022-01570, 2024-03113, 2021-03537, 2017-01054); Hjärnfonden (Brain Foundation) (FO2025-0169, FO2023-0130, FO2025-0271); European Research Council (101045026, 101171156)
Citations: not cited yet (Europe PMC); 40 references in the paper

Abstract

How diverse cell lineages emerge and are genetically regulated during organogenesis are central questions in understanding the developmental origins of disease. However, the mouse gut, including its intrinsic enteric nervous system (ENS) derived from migratory neural crest, has remained difficult to experimentally target. Here, we introduce an in utero lentiviral nano-injection strategy that enables early and efficient access to progenitor cells of all major cell types within the developing gut as well as gut-innervating ganglia. Leveraging this approach in combination with DNA barcoding and single-cell transcriptomics, we resolve clonal relationships in all gut lineages, including epithelial, neural, immune, and mesenchymal cell types. Clonal coupling between distinct subsets of fibroblasts and either pericytes, mesothelial cells, or interstitial cells of Cajal, suggested a developmental logic whereby the mesenchymal compartment arises from a set of fate-biased progenitors. Yet, mesenchymal regionalization along the anterior–posterior axis establishes early, whereas the ENS displays broad clonal dispersion across gut regions and acquires subsequent regional identities. We further adapted the platform for temporally controlled cell-type specific gene manipulation and, as a proof-of-principle, show that induced expression of the proneural factor Ascl1 biases ENS progenitor cells toward neuronal differentiation. Together, this work provides insights into refined spatiotemporal lineage relationships within a multigerm-layer organ and establishes a broadly applicable in vivo framework for probing gene function during gastrointestinal and neural crest development.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

UMarklundLab/Inutero_clonaltracing_E16Gut

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 616e26bff0494c376446fa645603249409f96ec2, 29 August 2026
Languages: R (17), Python (2)
Size: 84 files, 19 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, environment (renv.lock), 8 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: ggplot2 (15 files), Seurat (14 files), patchwork (13 files), tidyverse (13 files), circlize (7 files), ComplexHeatmap (7 files), cowplot (5 files), reticulate (4 files), NumPy (2 files), pandas (2 files), clusterProfiler (1 file), ggpubr (1 file), Scanpy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
21 files
At the source: github.com/UMarklundLab/Inutero_clonaltracing_E16Gut

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 19 scripts, each with its path and the digest of its content;
  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data, Materials, and Software Availability

Raw and processed single-cell RNA-sequencing data are available at Gene Expression Omnibus (GEO) database under the identifier GSE325733 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE325733) (37). scRNA-seq data used in Fig. 1 can be found at ArrayExpress with the accession number E-MTAB-14817 (https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-14817) (38). Code used to analyze data is summarized at https://github.com/UMarklundLab/Inutero_clonaltracing_E16Gut (39). Summary statistics of generated scRNA-seq dataset can be found in Dataset S2 (http://www.pnas.org/lookup/doi/10.1073/pnas.2614077123#supplementary-materials). Differentially expressed genes for clusters presented in the study are available in Datasets S3 (http://www.pnas.org/lookup/doi/10.1073/pnas.2614077123#supplementary-materials)–S8 (http://www.pnas.org/lookup/doi/10.1073/pnas.2614077123#supplementary-materials). We adapted in situ hybridization images from the database Gene Paint (40) for SI Appendix, Fig. S4 I–G (http://www.pnas.org/lookup/doi/10.1073/pnas.2614077123#supplementary-materials) and the spatial transcriptomic reproductions in SI Appendix, Fig. S1E (http://www.pnas.org/lookup/doi/10.1073/pnas.2614077123#supplementary-materials) are adapted from the Mouse Organogenesis Spatiotemporal Transcriptomic Atlas (MOSTA) interactive viewer at https://db.cngb.org/stomics/mosta/ (13).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 9 MeSH terms, 4 funders, 37 references.

Cite

This paper

Liu, Z., Padmanabhan, K., He, J., Hector, K., Semsch, B., Sun, J., Knoflach, V., Giatrellis, S., Lorentz, J., Dallakyan, K., Göritz, C., Andersson, E. R., & Marklund, U. (2026). Resolving cell lineages and gene functions in the developing mouse gastrointestinal tract using in utero transduction. Proceedings of the National Academy of Sciences of the United States of America, 123(37), e2614077123. https://doi.org/10.1073/pnas.2614077123

BibTeX

@article{liu2026resolving,
author = {Liu, Ziwei and Padmanabhan, Krishnanand and He, Jingyan and Hector, Katrin and Semsch, Bettina and Sun, Jia and Knoflach, Viktoria and Giatrellis, Sarantis and Lorentz, Johan and Dallakyan, Khachatur and Göritz, Christian and Andersson, Emma Rachel and Marklund, Ulrika},
title = {{Resolving cell lineages and gene functions in the developing mouse gastrointestinal tract using in utero transduction}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = sep,
volume = {123},
number = {37},
pages = {e2614077123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2614077123},
url = {https://doi.org/10.1073/pnas.2614077123},
pmid = {42715084},
pmcid = {PMC13578780}
}

RIS

TY - JOUR
AU - Liu, Ziwei
AU - Padmanabhan, Krishnanand
AU - He, Jingyan
AU - Hector, Katrin
AU - Semsch, Bettina
AU - Sun, Jia
AU - Knoflach, Viktoria
AU - Giatrellis, Sarantis
AU - Lorentz, Johan
AU - Dallakyan, Khachatur
AU - Göritz, Christian
AU - Andersson, Emma Rachel
AU - Marklund, Ulrika
TI - Resolving cell lineages and gene functions in the developing mouse gastrointestinal tract using in utero transduction
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/09/09
VL - 123
IS - 37
SP - e2614077123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2614077123
UR - https://doi.org/10.1073/pnas.2614077123
LA - en
ER -

CSL-JSON

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"id": "10.1073/pnas.2614077123",
"type": "article-journal",
"title": "Resolving cell lineages and gene functions in the developing mouse gastrointestinal tract using in utero transduction",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
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"family": "Liu",
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{
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"given": "Jia"
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{
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{
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