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An electrophysiological and proteomics roadmap for human induced glutamatergic neurons: fine-tuning of culture conditions for pathophysiological studies.

Code ↔ Paper

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  1. [1] § Materials and methods › Taxonomic-based peptide annotation ↔ R/main.r, lines 2–52 · score 0.85 · mono culture sample, Rattus norvegicus, Spectronaut peptide, peptides quantified, annotate peptides, Proteoclade
  2. [2] § Materials and methods › Differential protein abundance analysis ↔ R/main.r, lines 2–52 · score 0.83 · median normalization, peptide quantification, proDa, annotated peptides, transformed, raw
  3. [3] § Materials and methods › Taxonomic-based peptide annotation ↔ python/main.py, the whole file · a weak match · score 0.66 · create pcdb, merge fastas, species, Spectronaut, max, peptides
  4. [4] § Results › PEI and PLO adhesion factors did not affect the neuronal networks’ electrophysiology ↔ R/main.r, lines 54–111 · score 0.60 · NB HD, NB LD, BP HD, BP LD, fitting, BrainPhys
  5. [5] § Results › Functional synaptic characterization of the best culture conditions to maintain long term and highly functional network ↔ R/main.r, lines 313–368 · score 0.56 · synaptic transmission, term synaptic, presynaptic, signals, synapses, Neurobasal
  6. [6] § Materials and methods › Gene set enrichment analysis ↔ R/main.r, lines 371–422 · score 0.54 · enrichment score, Gene Ontology, neuro, axon, filtered

Paper

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The authors' code

R · 422 lines · 21 KB · no license · 5 matches

  2. #################################
  3. # Differential abundance analysis
  4. #################################
  6. ## Reading Proteoclade- and Spectronaut-annotate peptide raw intensity matrix
  8. suppressPackageStartupMessages(library(dplyr))
  10. # 1 - Filtering out peptides quantified in rat mono-culture and peptides not quantified in human cultures with rat-only annotation
  11. # 2 - Re-arrange Spectronaut peptide gene annotation, more accurate than corresponding Proteoclade annotation, to accomodate further median averagin of peptide intensity
  12. # 3 - Exclude Rat mono-culture samples from differential abundance analysis
  14. annotated_peptide_matrix <- read.delim("../inputs/annotated_raw_peptide_intensity_matrix.txt")
  16. annotated_peptide_matrix <- annotated_peptide_matrix %>%
  17. rename(Proteoclade_gene_anno = genes, Proteoclade_species_anno = species) %>%
  18. select(!organisms) %>%
  19. mutate(Rn_mono_intensity_sum = rowSums(annotated_peptide_matrix %>% select(grep(colsample_ids(.), pattern = "Rat_Astro")), na.rm = T),
  20. Hs_mono_intensity_sum = rowSums(annotated_peptide_matrix %>% select(grep(colsample_ids(.), pattern = paste(c("iPSC", "NPC"), collapse = "|"))), na.rm = T),
  21. Spectronaut_bool_hs_only_anno = ifelse(grepl(Spectronaut_species_anno, pattern = "Rattus norvegicus"), FALSE, TRUE),
  22. Proteoclade_bool_hs_only_anno = ifelse(grepl(Proteoclade_species_anno, pattern = "Rattus norvegicus"), FALSE, TRUE)) %>%
  23. filter(Rn_mono_intensity_sum == 0) %>%
  24. filter(Hs_mono_intensity_sum != 0 & Spectronaut_bool_hs_only_anno == TRUE | Hs_mono_intensity_sum != 0 & Proteoclade_bool_hs_only_anno == TRUE) %>%
  25. select(!c(Sequence, Rn_mono_intensity_sum, Hs_mono_intensity_sum, Spectronaut_bool_hs_only_anno, Proteoclade_bool_hs_only_anno, Spectronaut_species_anno, Proteoclade_gene_anno, Proteoclade_species_anno)) %>%
  26. mutate(Spectronaut_gene_anno = unlist(lapply(Spectronaut_gene_anno, function(x) {
  28. gene_anno <- unique(unlist(stringr::str_split(toupper(x), pattern = ";")));
  29. gene_anno <- paste(gene_anno[setdiff(1:length(gene_anno), which(gene_anno %in% ""))], collapse = "|")
  31. }))) %>%
  32. select(!grep(colnames(.), pattern = "Rat_Astro"))
  35. ## Formatting peptide matrix to fit proDa differential analysis pipeline
  37. suppressPackageStartupMessages(library(proDA))
  39. # Median averaging of peptide raw intensity followed by log2 transformation and median normalization
  41. median_collapsed_peptide_matrix <- apply(annotated_peptide_matrix[,-ncol(annotated_peptide_matrix)], 2, function(x) {
  44. tapply(x, annotated_peptide_matrix$Spectronaut_gene_anno, function(y) {
  46. mvec <- median(y, na.rm = T);
  48. })
  49. }) %>%
  50. log2() %>%
  51. median_normalization() %>%
  52. `colnames<-`(sub(colnames(.), pattern = "\\.raw.*", replacement = ".Median.Normalized.Pep.Quantity"))
  54. # Sample metadata organization to fit proDA model
  56. metadata <- read.delim("../inputs/proDA_formatted_sample_metadata.txt")
  58. fit_concat_variables <- proDA(median_collapsed_peptide_matrix, design = ~ 0 + Concat_variables + Cell_line, col_data = metadata)
  60. fit_phenotype <- proDA(median_collapsed_peptide_matrix, design = ~ 0 + Phenotype + Cell_line, col_data = metadata)
  63. contrasts <- lapply(c("Concat_variablesiNeurons_Neurobasal_HD-Concat_variablesiNeurons_Brainphysis_HD",
  64. "Concat_variablesiNeurons_Neurobasal_LD-Concat_variablesiNeurons_Brainphysis_LD",
  65. "Concat_variablesiNeurons_Brainphysis_LD-Concat_variablesiNeurons_Brainphysis_HD",
  66. "Concat_variablesiNeurons_Neurobasal_LD-Concat_variablesiNeurons_Neurobasal_HD",
  67. "Concat_variablesiNeurons_Neurobasal_LD-Concat_variablesiNeurons_Brainphysis_HD"),
  68. function(x) {
  70. test_diff(fit, x)
  72. }) %>%
  73. `names<-` (c("iNeu_NB_HD vs iNeu_BP_HD",
  74. "iNeu_NB_LD vs iNeu_BP_LD",
  75. "iNeu_BP_LD vs iNeu_BP_HD",
  76. "iNeu_NB_LD vs iNeu_NB_HD",
  77. "iNeu_NB_LD vs iNeu_BP_HD"))
  80. # openxlsx::write.xlsx(contrasts, "/home/ennio/repo/rna_seq_tutorial/cytof_tutorial/pclade/contrast_cc/contrasts.xlsx", quote = F, rowNames = F)
  82. # Target gene distribution heatmap accross iNeurons culture conditions
  84. target_gene_list <- openxlsx::read.xlsx("../resources/Common interested genes iN vs iPSC_Mod.xlsx")
  86. Concat_variables <- c("iPSC", "iNeurons_Brainphysis_LD", "iNeurons_Brainphysis_HD", "iNeurons_Neurobasal_LD", "iNeurons_Neurobasal_HD"),
  88. median_collapsed_concat_variables <- t(apply(t(median_collapsed_peptide_matrix), 2, function(x) {
  90. tapply(x, metadata$Concat_variables, function(y) median(y, na.rm = T))
  93. })) %>%
  94. `rownames<-`(NULL)
  97. pdf("iNeurons vs iPSC - Common interesting genes_median.pdf", height = 12, width = 12);
  98. p <- Heatmap(median_collapsed_concat_variables[target_gene_list[,1], Concat_variables], name = "Normalized Median",
  99. column_order = Concat_variables,
  100. row_order = target_gene_list[,1],
  101. na_col = "black",
  102. right_annotation = NULL,
  103. cluster_rows = FALSE,
  104. cluster_columns = FALSE,
  105. col = circlize::colorRamp2(c(min(median_collapsed_concat_variables, na.rm = T), max(median_collapsed_concat_variables, na.rm = T)), c("lightyellow", "darkred")),
  106. column_names_gp = gpar(fontsize = 10, fontface = "bold"),
  107. column_title_gp = gpar(fontface = "bold"),
  108. row_names_gp = gpar(fontface = "bold"),
  109. row_title_gp = gpar(fontface = "bold"));
  110. plot(p);
  111. dev.off()
  115. # Expression heatmap of target protein list
  116. suppressPackageStartupMessages(library(ComplexHeatmap))
  118. sample_info_df_ineu_filt <- sample_info_df %>% filter(phenotype == "iNeurons") %>% mutate(medium_density = paste0(medium, " ", density))
  120. target_gene_list <- openxlsx::read.xlsx("../resources/Lista geni sinapsi per Ennio.xlsx") %>% pull()
  122. target_gene_list <- c("ADGRB1", target_gene_list)
  124. # LFC hmap
  126. contrasts <- c("Neurobasal_LD vs Brainphysis_LD", "Neurobasal_LD vs Brainphysis_HD", "Neurobasal_LD vs Neurobasal_HD")
  128. daa_tabs_syngo_filtered <- do.call("rbind", lapply(1:3, function(x) { tab <- openxlsx::read.xlsx("../results/daa_table_syngo_filtered.xlsx", sheet = x) %>% mutate(contrast = rep(contrasts[x], nrow(.))) })) %>%
  129. filter(name %in% target_gene_list) %>%
  130. dplyr::select(name, LogFC = diff, contrast) %>%
  131. tidyr::spread(contrast, LogFC) %>%
  132. tibble::column_to_rownames("name") %>%
  133. as.matrix()
  135. col_fun <- circlize::colorRamp2(c(min(daa_tabs_syngo_filtered, na.rm = T), 0, max(daa_tabs_syngo_filtered, na.rm = T)), c("blue", "black", "red"));
  137. colnames(daa_tabs_syngo_filtered) <- NULL
  139. pdf("../article_graphs/iNeu_contrast_logFC_hmap.pdf", height = 1, width = 8)
  141. p <- Heatmap(daa_tabs_syngo_filtered, name = "LogFC",
  142. row_split = NULL,
  143. na_col = "darkgrey",
  144. right_annotation = NULL,
  145. cluster_rows = FALSE,
  146. cluster_columns = FALSE,
  147. col = col_fun,
  148. column_split = contrasts,
  149. column_title_rot = 45,
  150. column_names_gp = gpar(fontface = "bold", fontsize = 8),
  151. column_title_gp = gpar(fontface = "bold"),
  152. row_names_gp = gpar(fontface = "bold"),
  153. row_title_gp = gpar(fontface = "bold"))
  155. plot(p)
  157. dev.off()
  159. # PCA score plots displaying first two dimensions respectively labelled by Concat_variable and Cell_line
  161. pca <- prcomp(t(na.omit(median_collapsed_peptide_matrix)), center = TRUE, scale = TRUE)
  163. summ <- summary(pca)$importance
  165. panel <- ggarrange(score_plot1 <- cbind(as.data.frame(pca$x), metadata) %>% ggplot(aes(x = PC1, y = PC2, color = Concat_variables)) +
  166. geom_point(size = 5) +
  167. ggprism::theme_prism() +
  168. xlab(paste0("PC1 (", summ[2,1]*100, "%)")) +
  169. ylab(paste0("PC1 (", summ[2,2]*100, "%)")),
  171. score_plot2 <- cbind(as.data.frame(pca$x), metadata) %>% ggplot(aes(x = PC1, y = PC2, color = Cell_line)) +
  172. geom_point(size = 5) +
  173. ggprism::theme_prism() +
  174. xlab(paste0("PC1 (", summ[2,1]*100, "%)")) +
  175. ylab(paste0("PC1 (", summ[2,2]*100, "%)")), ncol = 2, align = "h", common.legend = F)
  178. pdf("../article_graphs/pca_pc1_pc2_merged_cond_1.pdf", height = 8, width = 16)
  179. annotate_figure(panel, top = text_grob("Pattern Recognition Analysis - Score Plot",
  180. face = "bold", size = 18))
  181. dev.off()
  183. ##################################################
  184. # Geneset enrichment analysis (GSEA) - GO database
  185. ##################################################
  187. suppressPackageStartupMessages(library(clusterProfiler))
  188. suppressPackageStartupMessages(library(org.Hs.eg.db))
  190. dir <- "../results/"
  191. files <- list.files(dir)[grep(list.files(dir), pattern ="iNeurons.*iPSC")]
  192. contrasts <- lapply(files, function(x) {openxlsx::read.xlsx(paste0(dir, x)) }) %>% `names<-`(files)
  194. # Converting HUGO gene symbols to ENTREZID
  195. ids <- bitr(unlist(stringr::str_split(contrasts[[1]][,"name"], pattern = "\\|")), fromType="SYMBOL", toType=c("ENTREZID"), OrgDb="org.Hs.eg.db")
  197. # GSEA of iNeurons vs iPSC contrasts
  199. gseas <- lapply(contrasts, function(x) {
  201. lfc_gene_vector <- x[match(ids$SYMBOL, x$name), "diff"] %>%
  202. arrange(desc(diff)) %>%
  203. select(diff) %>%
  204. pull() %>%
  205. `names<-`(ids[match(x$name, ids$SYMBOL),])
  208. ego <- gseGO(geneList = lfc_gene_vector,
  209. OrgDb = org.Hs.eg.db,
  210. ont = "BP",
  211. pvalueCutoff = 0.05,
  212. eps = 0,
  213. verbose = FALSE)
  215. })
  219. # Heatmap of top 20 down-regulated gene ontologies - iNeurons vs iPSC contrasts
  221. suppressPackageStartupMessages(library(ggplot2))
  223. heatmap_titles <- c("iNeurons_Brainphysis_HD vs iPSC",
  224. "iNeurons_Brainphysis_LD vs iPSC",
  225. "iNeurons_Neurobasal_HD vs iPSC",
  226. "iNeurons_Neurobasal_LD vs iPSC")
  228. pdf("../article_graphs/iNeu_v_iPSC_hmap_top_20_downregulated_ontologies.pdf", height = 16, width = 16)
  229. hmap <- do.call("rbind", lapply(c(1:4), function(x) {
  230. gseas[[x]][c(1:20),] %>%
  231. mutate(contrast = rep(titles[x], nrow(.)))})) %>%
  232. select(Description, contrast, NES) %>%
  233. merge(expand.grid(nes_matrix_top_20_dr_ont$Description, nes_matrix_top_20_dr_ont$contrast) %>%
  234. dplyr::rename(Description = Var1, contrast = Var2),., by = c("Description", "contrast"), all = T) %>%
  235. ggplot(aes(x = contrast, y = Description, fill = NES)) +
  236. geom_tile() +
  237. ggprism::theme_prism() +
  238. labs(fill = "NES") +
  239. theme(axis.title.y = element_blank(),
  240. axis.title.x = element_blank(),
  241. axis.text.x = element_text(hjust = 1, vjust = 1, angle = 45),
  242. legend.text = element_text(face = "bold"),
  243. legend.title = element_text(face = "bold")) +
  244. scale_fill_gradient(low="darkblue", high="skyblue", na.value = "grey") +
  245. ggtitle("GSEA - Gene Ontology Biological Process")
  246. plot(hmap)
  247. dev.off()
  250. # Tables of top 20 down-regulated gene ontologies - iNeurons vs iPSC contrasts
  251. ont_lists <- lapply(gseas, function(x) {
  253. x@result[order(x@result$NES, decreasing = F)[1:20],] %>%
  254. dplyr::select(Description, enrichmentScore, NES, p.adjust, core_enrichment_entrez_id = core_enrichment) %>%
  255. mutate(core_enrichment_hugo_symbol = unlist(lapply(x@result[order(x@result$NES, decreasing = F)[1:20], "core_enrichment"]), function(y) {
  256. ncbi_id <- as.integer(unlist(stringr::str_split(y, pattern = "/")))
  257. paste(ids[which(ids$ENTREZID %in% ncbi_id), "SYMBOL"], collapse = "/")
  258. }))
  260. }) %>% `names<-`(heatmap_titles)
  262. openxlsx::write.xlsx(ont_lists, "../results/iNeu_v_iPSC_table_top_20_downregulated_ontologies.xlsx", quote = F, rowNames = F)
  264. # Heatmap of target gene ontologies - iNeurons vs iPSC contrasts
  266. pathway_of_interest <- openxlsx::read.xlsx("../resources/Files to re-order the heatmap.xlsx")
  267. pathway_of_interest <- reord[-nrow(pathway_of_interest),]
  270. nes_matrix <- do.call("rbind", lapply(as.list(c(1:4)), function(x) {
  272. gseas[[x]] %>%
  273. filter(Description %in% pathway_of_interest$Description) %>%
  274. mutate(contrast = rep(heatmap_titles[x], nrow(.)))
  276. })) %>%
  277. select(contrast, Description, NES) %>%
  278. tidyr::spread(contrast, NES) %>%
  279. tibble::column_to_rownames("Description") %>%
  280. t()
  282. nes_matrix <- nes_matrix[c("iNeurons_Neurobasal_HD vs iPSC",
  283. "iNeurons_Neurobasal_LD vs iPSC",
  284. "iNeurons_Brainphysis_HD vs iPSC",
  285. "iNeurons_Brainphysis_LD vs iPSC"),]
  289. rownames(nes_matrix) <- NULL
  291. col_fun <- circlize::colorRamp2(c(min(nes_matrix, na.rm = T), max(nes_matrix, na.rm = T)), c("yellow", "red"));
  293. pdf("../article_graphs/iNeu_v_iPSC_hmap_target_ontologies.pdf", height = 16, width = 18)
  294. p <- Heatmap(nes_matrix, name = "NES",
  295. row_split = c("iNeurons_Neurobasal_HD vs iPSC",
  296. "iNeurons_Neurobasal_LD vs iPSC",
  297. "iNeurons_Brainphysis_HD vs iPSC",
  298. "iNeurons_Brainphysis_LD vs iPSC"),
  299. column_order = colnames(nes_matrix),
  300. na_col = "darkgrey",
  301. right_annotation = NULL,
  302. cluster_rows = FALSE,
  303. cluster_columns = FALSE,
  304. col = col_fun,
  305. column_names_gp = gpar(fontsize = 14, fontface = "bold"),
  306. column_title_gp = gpar(fontface = "bold"),
  307. row_names_gp = gpar(fontface = "bold"),
  308. row_title_gp = gpar(fontface = "bold"),
  309. column_names_max_height = unit(18, "cm"))
  310. plot(p)
  311. dev.off()
  313. # Tables of target gene ontologies - iNeurons vs iPSC contrasts
  315. ont_lists <- lapply(gseas, function(x) {
  317. x@result %>%
  318. filter(Description %in% pathway_of_interest$Description) %>%
  319. dplyr::select(Description, enrichmentScore, NES, p.adjust, core_enrichment_entrez_id = core_enrichment) %>%
  320. mutate(core_enrichment_hugo_symbol = unlist(lapply(x@result[order(@result$NES, decreasing = F)[1:20], "core_enrichment"]), function(y) {
  321. ncbi_id <- as.integer(unlist(stringr::str_split(y, pattern = "/")))
  322. paste(ids[which(ids$ENTREZID %in% ncbi_id), "SYMBOL"], collapse = "/")
  323. })) %>%
  324. arrange(desc(NES))
  326. }) %>%
  327. `names<-`(heatmap_titles)
  329. openxlsx::write.xlsx(ont_lists, "../results/iNeu_v_iPSC_table_target_ontologies.xlsx", quote = F, rowNames = F)
  332. # GSEA of contrasts between iNeurons culture conditions
  334. files <- list.files(dir)[grep(list.files(dir), pattern ="iNeurons.*iNeurons")][-1]
  336. contrasts <- lapply(as.list(files), function(x) {openxlsx::read.xlsx(paste0(dir, x)) %>% mutate(name = toupper(name))})
  338. gseas <- lapply(contrasts, function(x) {
  340. lfc_gene_vector <- x[match(ids$SYMBOL, x$name), "diff"] %>%
  341. arrange(desc(diff)) %>%
  342. select(diff) %>%
  343. pull() %>%
  344. `names<-`(ids[match(x$name, ids$SYMBOL),])
  347. <- gs(geneList = lfc_gene_vector,
  348. OrgDb = org.Hs.eg.db,
  349. ont = "BP",
  350. pvalueCutoff = 0.05,
  351. eps = 0,
  352. verbose = FALSE)
  354. })
  357. heatmap_titles <- c("iNeurons_Brainphysis_LD vs iNeurons_Brainphysis_HD ", "iNeurons_Neurobasal_HD vs iNeurons_Brainphysis_HD", "iNeurons_Neurobasal_LD vs iNeurons_Brainphysis_LD", "iNeurons_Neurobasal_LD vs iNeurons_Neurobasal_HD")
  360. ont_ind_to_exclude <- list(
  362. c("synaptic signaling", "regulation of trans-synaptic signaling", "chemical synaptic transmission"),
  364. c("anterograde trans-synaptic signaling", "trans-synaptic signaling", "chemical synaptic transmission","synapse organization","long-term synaptic potentiation","regulation of long-term synaptic potentiation","synapse assembly"),
  366. c("trans-synaptic signaling", "synapse organization", "synapse assembly", "regulation of synapse assembly","presynapse organization","synapse organization")
  368. )
  371. # Tables of target gene ontologies - contrasts between iNeurons culture conditions
  373. ont_lists <- lapply(as.list(c(1:3)), function(x) {
  375. gseas[[x]] %>%
  376. filter(grepl(Description, pattern = paste(c("axon", "synap", "neuro"), collapse = "|"))) %>%
  377. filter(!Description %in% ont_ind_to_exclude[[x]]) %>%
  378. dplyr::select(Description, enrichmentScore, NES, p.adjust, core_enrichment_entrez_id = core_enrichment) %>%
  379. mutate(core_enrichment_hugo_symbol = unlist(lapply(x@result[order(@result$NES, decreasing = F)[1:20], "core_enrichment"]), function(y) {
  380. ncbi_id <- as.integer(unlist(stringr::str_split(y, pattern = "/")))
  381. paste(ids[which(ids$ENTREZID %in% ncbi_id), "SYMBOL"], collapse = "/")
  382. })) %>%
  383. arrange(desc(NES))
  384. }) %>%
  385. `names<-`(heatmap_titles)
  387. openxlsx::write.xlsx(ont_lists, "../results/iNeu_v_iNeu_hmap_target_ontologies_list.xlsx", quote = F, rowNames = F)
  389. # Heatmap of target gene ontologies - contrasts between iNeurons culture conditions
  391. nes_matrix <- expand.grid(tab4$Description, heatmap_titles) %>%
  392. dplyr::rename(Description = Var1, contrast = Var2) %>%
  393. merge(.,do.call("rbind", lapply(as.list(c(1:3)), function(x) {
  395. gseas[[x]][-ro[[x]],] %>% mutate(contrast = rep(titles[x], nrow(.)))
  397. })), by = c("Description", "contrast"), all = T) %>%
  398. select(contrast, Description, NES) %>% distinct(NES, .keep_all = TRUE) %>%
  399. tidyr::spread(., contrast, NES) %>%
  400. tibble::column_to_rownames("Description") %>%
  401. t() %>%
  402. `rownames<-`(NULL)
  405. col_fun <- circlize::colorRamp2(c(min(nes_matrix, na.rm = T), max(nes_matrix, na.rm = T)), c("yellow", "red"));
  407. pdf("./article_graphs/iNeu_v_iNeu_hmap_target_ontologies.pdf", height = 10, width = 14)
  408. p <- Heatmap(nes_matrix, name = "NES",
  409. row_split = contrast,
  410. column_order = colnames(nes_matrix),
  411. na_col = "darkgrey",
  412. right_annotation = NULL,
  413. cluster_rows = FALSE,
  414. cluster_columns = FALSE,
  415. col = col_fun,
  416. column_names_gp = gpar(fontsize = 12, fontface = "bold"),
  417. column_title_gp = gpar(fontface = "bold"),
  418. row_names_gp = gpar(fontface = "bold"),
  419. row_title_gp = gpar(fontface = "bold"),
  420. column_names_max_height = unit(11, "cm"))
  421. plot(p)
  422. dev.off()

main.r at commit b2eed08, no license · at the source

Overview

Authors: Martina Servetti1,2, Giulia Parodi3, Martino Caramia2, Ennio Nano3, Martina Bartolucci4, Antonella Marte1,3, Giacomo Mazzoni1, Simone Giubbolini1, Farah Diab1, Andrea Petretto4, Pierluigi Valente1,3, Sergio Martinoia3,5, Simona Baldassari6,7, Anna Fassio1,3, Fabio Benfenati2,3, Anna Corradi1,3, Bruno Sterlini1,3
ORCID iDs: Martina Bartolucci, Giacomo Mazzoni, Simone Giubbolini, Sergio Martinoia, Simona Baldassari, Fabio Benfenati, Bruno Sterlini
  1. Dipartimento di Medicina Sperimentale, Università di Genova,Genoa, Italy
  2. Center for Synaptic Neuroscience and Technology, Istituto Italiano di Tecnologia,Genoa, Italy
  3. IRCCS Azienda Ospedaliera Metropolitana,Genoa, Italy
  4. Core Facility for Omics Science, IRCCS Istituto Giannina Gaslini,Genoa, Italy
  5. Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova,Genoa, Italy
  6. Unit of Medical Genetics, IRCCS Istituto Giannina Gaslini,Genoa, Italy
  7. Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genova,Genoa, Italy
Institutions: University of Genoa (Italy); Italian Institute of Technology (Italy); IRCCS Azienda Ospedaliera Metropolitana (Italy); Istituto Giannina Gaslini (Italy)
Journal: Cell death discovery, volume 12, issue 1, article 333
Dates: received 28 August 2025; accepted 26 May 2026; published online 5 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41420-026-03185-w · PMID 42248859 · PMCID PMC13458295 · OpenAlex W7163688118
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Development of the nervous system, Stem-cell differentiation, Neurophysiology
Topic: Pluripotent Stem Cells Research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Ministero dell’Istruzione, dell’Università e della Ricerca (#NEXTGENERATIONEU, PRIN2022MPCKWW); #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022); Fondazione Telethon (Telethon Foundation) (GMR24T1085, GJC22066); Ricerca Corrente and “5X1000”, IRCCS San MArtino, Genova, Italy
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

Induced glutamatergic neurons (iGluNeurons) generated by Neurogenin-2 (NGN2) overexpression in human pluripotent stem cells are a powerful model for studying human neuronal maturation and function; however, NGN2-based protocols still lack standardized culture conditions that critically affect neuronal development and function. Three key factors have been identified by previous literature, namely the composition of extracellular matrix coating, the initial plating density, and the choice of culture medium, but the differential effects of their combination have not been thoroughly analyzed. Here, we investigated the combinatorial effects of these three variables, testing eight distinct culture conditions resulting from the combinations of two coatings (poly-L-ornithine and polyethyleneimine), two media (BrainPhys and Neurobasal), and two cell densities (4800 and 1200 cells/mm²). We assessed electrophysiological properties at the single-cell and network levels, characterized morphofunctional and proteomic features across multiple developmental stages. Electrophysiological data indicate that medium composition and plating density, rather than substrate coating, determine neuronal maturation dynamics, with BrainPhys and high density promoting rapid but transient maturation while Neurobasal and low density supporting gradual and sustained network development. Morphofunctional analyzes of synapses and the axon initial segment, together with neuronal maturation markers, support an early BrainPhys-driven acceleration of development that is later exceeded by Neurobasal. To enable accurate proteome profiling of the iGluNeuron system—comprising human neurons and rat astrocytes—we developed a robust taxonomic filtering algorithm that selectively identifies human-specific proteins. This approach confirmed the presence of a conserved core of NGN2-driven differentiation pathways across all settings, in addition to condition-specific signatures. Finally, in the optimal conditions identified through our experimental analyzes, robust spontaneous and evoked synaptic activity was observed. These results provide a framework for optimizing iGluNeuron cultures, balancing rapid maturation and long-term functional stability, and establishing a benchmark for human neuronal models in disease research and drug screening.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

mathworks.com/matlabcentral/fileexchange

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State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Quantitative image analysis with ImageJ”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 2 checks, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source: mathworks.com/matlabcentral/fileexchange/28181-ais-quantification

ennionano/functional_roadmap_for_induced_glut-neurons

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b2eed084c19115a196048dfda737faecfdc5ff9f, 11 March 2026
Languages: R (1), Python (1)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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3 files
At the source: github.com/ennionano/functional_roadmap_for_induced_glut-neurons

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The code is available on the following GitHub page “https://github.com/ennionano/functional_roadmap_for_induced_glut-neurons.” and will be fully accessible upon request.

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

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Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 3 keywords, 4 funders, 88 references.

Cite

This paper

Servetti, M., Parodi, G., Caramia, M., Nano, E., Bartolucci, M., Marte, A., Mazzoni, G., Giubbolini, S., Diab, F., Petretto, A., Valente, P., Martinoia, S., Baldassari, S., Fassio, A., Benfenati, F., Corradi, A., & Sterlini, B. (2026). An electrophysiological and proteomics roadmap for human induced glutamatergic neurons: fine-tuning of culture conditions for pathophysiological studies. Cell death discovery, 12(1), 333. https://doi.org/10.1038/s41420-026-03185-w

BibTeX

@article{servetti2026electrophysiological,
author = {Servetti, Martina and Parodi, Giulia and Caramia, Martino and Nano, Ennio and Bartolucci, Martina and Marte, Antonella and Mazzoni, Giacomo and Giubbolini, Simone and Diab, Farah and Petretto, Andrea and Valente, Pierluigi and Martinoia, Sergio and Baldassari, Simona and Fassio, Anna and Benfenati, Fabio and Corradi, Anna and Sterlini, Bruno},
title = {{An electrophysiological and proteomics roadmap for human induced glutamatergic neurons: fine-tuning of culture conditions for pathophysiological studies}},
journal = {Cell death discovery},
year = {2026},
month = jun,
volume = {12},
number = {1},
pages = {333},
publisher = {Nature Publishing Group},
issn = {2058-7716},
doi = {10.1038/s41420-026-03185-w},
url = {https://doi.org/10.1038/s41420-026-03185-w},
pmid = {42248859},
pmcid = {PMC13458295}
}

RIS

TY - JOUR
AU - Servetti, Martina
AU - Parodi, Giulia
AU - Caramia, Martino
AU - Nano, Ennio
AU - Bartolucci, Martina
AU - Marte, Antonella
AU - Mazzoni, Giacomo
AU - Giubbolini, Simone
AU - Diab, Farah
AU - Petretto, Andrea
AU - Valente, Pierluigi
AU - Martinoia, Sergio
AU - Baldassari, Simona
AU - Fassio, Anna
AU - Benfenati, Fabio
AU - Corradi, Anna
AU - Sterlini, Bruno
TI - An electrophysiological and proteomics roadmap for human induced glutamatergic neurons: fine-tuning of culture conditions for pathophysiological studies
T2 - Cell death discovery
J2 - Cell Death Discov
PY - 2026
DA - 2026/06/05
VL - 12
IS - 1
SP - 333
SN - 2058-7716
PB - Nature Publishing Group
DO - 10.1038/s41420-026-03185-w
UR - https://doi.org/10.1038/s41420-026-03185-w
LA - en
ER -

CSL-JSON

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