OSCR

Ribosomal modifications are associated with mesenchymal fate selection in the neural crest lineage.

A correction to this paper has been published: the notice, 42660968, from Europe PMC.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Survival prognosis analysis in SEQC neuroblastoma dataset ↔ survival_analysis/survival_curves.ipynb, lines 10–43 · score 0.69 · package GSVA, survival curves, signature score, gene signature, ssgsea
  2. [2] § Results › Ribosomal control and rRNA modifications program in CNCC fates ↔ survival_analysis/survival_curves.ipynb, lines 151–200 · score 0.61 · ribosomal assembly, Ruvbl2, Wdr75, Ddx31, Ncl, Ruvbl1
  3. [3] § Results › Ribosomal control and rRNA modifications program in CNCC fates ↔ single-cell_analysis/NC_lineage_visualization.ipynb, lines 44–51 · score 0.53 · ribosomal assembly, Ruvbl2, Wdr75, Ddx31, Ncl, Ruvbl1
  4. [4] § Results › Ribosomal control and rRNA modifications signature in neuroblastoma ↔ survival_analysis/survival_curves.ipynb, lines 206–230 · score 0.52 · signature score, gene signature, high risk, 3–4, Survival, curves

Paper

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

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The authors' code

Jupyter notebook · 286 lines · 9.7 KB · no license · 3 matches

  1. # %%
  2. library(survminer)
  3. library(ggfortify)
  4. library(tidyverse)
  5. library(ggplot2)
  6. library(survival)
  7. library(cowplot)
  8. library(patchwork)
  10. # %%
  11. calculate_gene_signature_scores <- function(exp, ano, signatures, method = "colSums") {
  12. if (method == "ssgsea" && !requireNamespace("GSVA", quietly = TRUE)) {
  13. stop("Package 'GSVA' is required for ssgsea method. Install it using install.packages('GSVA').")
  14. }
  16. for (fate in names(signatures)) {
  17. genes_in_set <- signatures[[fate]]
  18. valid_genes <- rownames(exp) %in% genes_in_set
  20. if (sum(valid_genes) == 1) {
  21. warning(paste("Only one gene found for signature:", fate))
  22. single_gene <- rownames(exp)[valid_genes]
  23. ano[[paste0("gene_set:", fate)]] <- exp[single_gene, ]
  24. } else if (sum(valid_genes) > 1) {
  25. if (method == "colSums") {
  26. ano[[paste0("gene_set:", fate)]] <- colSums(exp[valid_genes, , drop = FALSE])
  27. } else if (method == "ssgsea") {
  28. gsva_output <- GSVA::gsva(exp, signatures, method = "ssgsea", verbose = FALSE)
  29. signature_scores <- t(gsva_output)
  30. for (mv in colnames(signature_scores)) {
  31. ano[[paste0("gene_set:", mv)]] <- signature_scores[, mv]
  32. }
  33. } else {
  34. stop("Invalid method. Choose either 'colSums' or 'ssgsea'.")
  35. }
  36. } else {
  37. warning(paste("No matching genes found for signature:", fate))
  38. ano[[paste0("gene_set:", fate)]] <- rep(NA, ncol(exp))
  39. }
  40. }
  42. return(ano)
  43. }
  46. # %%
  47. surv_ad <- function(gs, exp, ano, stages = NULL, mycn = NULL, high_risk = NULL, out = NULL) {
  48. # Default title
  49. title <- "All samples"
  51. if (!is.null(mycn) && !is.null(high_risk)) {
  52. title <- paste("High risk =", high_risk, "and MYCN =", ifelse(mycn == 1, "amplified", "non-amplified"))
  53. } else if (!is.null(mycn)) {
  54. title <- paste("MYCN =", ifelse(mycn == 1, "amplified", "non-amplified"))
  55. } else if (!is.null(high_risk)) {
  56. title <- paste("High risk =", high_risk)
  57. }
  59. if (!is.null(stages)) {
  60. title <- paste(title, "| Stage(s):", paste(stages, collapse=", "))
  61. }
  63. if (startsWith(gs, "gene_set:")) {
  64. res <- ano
  65. res$exp <- ano[[gs]]
  66. } else {
  67. if (!(gs %in% rownames(exp)))
  68. return()
  70. res <- data.frame("exp" = as.numeric(exp[gs, ]), ano)
  71. }
  73. res <- res[!is.na(res$exp), ]
  75. # Filter by stages
  76. if (!is.null(stages)) {
  77. res <- res[res$inss %in% stages, ]
  78. }
  80. # Filter by MYCN status
  81. if (!is.null(mycn)) {
  82. res <- res[!is.na(res$mycn.status) & res$mycn.status == mycn, ]
  83. }
  85. # Filter by high-risk
  86. if (!is.null(high_risk)) {
  87. res <- res[!is.na(res$high.risk) & res$high.risk == high_risk, ]
  88. }
  90. cut1 <- quantile(res$exp, 0.75) #0.75
  91. cut2 <- quantile(res$exp, 0.25) #.25
  93. res1 <- res[res$exp >= cut1, ]
  94. res2 <- res[res$exp <= cut2, ]
  96. res1$group <- "High freq (>75%)"
  97. res2$group <- "Low freq (<25%)"
  99. res <- rbind(res1, res2)
  101. # Convert time to years
  102. res$time <- res$time / 365
  104. fit <- survfit(Surv(time, vital_status) ~ group, data = res)
  105. pv <- surv_pvalue(fit, data = res)
  107. p <- ggsurvplot(
  108. fit, data = res, pval = TRUE,
  109. risk.table = TRUE,
  110. risk.table.col = "strata",
  111. palette = c("#9d1403", "#3172b8"), # Custom colors
  112. title = title,
  113. legend.title = "",
  114. legend.labs = c("High freq (>75%)", "Low freq (<25%)"),
  115. xlab = "Time from diagnosis (years)",
  116. ylab = "Probability of OS",
  117. risk.table.y.text = FALSE,
  118. risk.table.title = "Numbers at risk",
  119. pval.method = FALSE,
  120. pval.coord = c(0, 0.2) # Adjust position of p-value
  121. )
  123. if (is.null(out)) {
  124. return(p$plot)
  125. } else {
  126. return(p)
  127. }
  129. }
  132. # %%
  135. # %%
  136. # pre-processing of SEQC dataset:
  138. dat=readRDS('/home/irina/survival_cancers_data/neuroblastoma_bulk_survival/GSE49711.dat.rds')
  140. exp=dat$dat
  141. ano=dat$ano
  143. ano$time=as.numeric(ano$OS)
  144. ano$vital_status=ano$OS.event
  145. ano$inss=ano$inss.stage
  148. # %%
  149. table(ano$inss, useNA = "ifany")
  151. # %%
  152. signatures = list()
  153. signatures$mesenchymal_early=c(
  154. "MYC", "RUVBL1", "DIXDC1", "RAI14", "ID1", "TWIST1", "LRIG3", "PSMG1", "MYO1B", "IFRD2",
  155. "RHOJ", "DDX31", "KCTD1", "DUSP6", "COLEC12", "SPRY4", "ANXA7", "NCL", "ITGA8", "SH3BP5",
  156. "HSPA9", "COL9A1", "RBP1", "PHLDA1", "DESI1", "WDR74", "IMPDH1", "ACTN1", "CDH11", "CCND3",
  157. "FHL3", "MOGAT2", "DLC1", "ZNHIT6", "MEST", "EEF1D", "IL11RA", "SLC7A5", "PGM2", "ATIC",
  158. "EIF4EBP1", "WDR75", "EMG1", "GUSB", "MEIS2", "RIN2", "SRM", "S1PR2", "MRPL12", "RUVBL2",
  159. "TRAF4", "NME1", "PEG3", "CDC42EP5", "PRDX6", "SLC1A5", "FZD2", "NHP2", "SNAI1", "HOPX",
  160. "RRP8", "CSN3", "PTGIS", "SULF2", "CCN1", "CCL15", "CCL23"
  161. )
  163. signatures$ribosomal_assembly = c('WDR74', 'WDR75', 'NCL', 'NHP2', 'EEF1D', 'RUVBL1', 'RUVBL2', 'DDX31', 'EMG1',
  164. 'RRP8', 'MRPL12', "ZNHIT6")
  166. signatures$ribosomal_structural_proteins = c('RPL22', 'RPL11', 'RPS8', 'RPL5', 'RPS27', 'RPS24', 'RPS13',
  167. 'RPS25', 'RPLP2', 'RPL27A', 'RPS3', 'RPL6', 'RPLP0', 'RPS26', 'RPL41', 'RPL21', 'RPL10L', 'RPS29',
  168. 'RPL36AL', 'RPS27L', 'RPL4', 'RPS17', 'RPLP1', 'RPL3L', 'RPS2', 'RPS15A', 'RPL13', 'RPL26', 'RPL23', 'RPL23A',
  169. 'RPL19', 'RPL27', 'RPL38', 'RPL17', 'RPS16', 'RPL18', 'RPS15', 'RPL36', 'RPS28', 'RPL18A', 'RPS19', 'RPL13A', 'RPS11',
  170. 'RPS9', 'RPL28', 'RPS5', 'RPS7', 'RPS27A', 'RPL31', 'RPL37A', 'RPS21', 'RPL3', 'RPL32', 'RPL29', 'RPL24',
  171. 'RPL22L1', 'RPL39L', 'RPL15', 'RPSA', 'RPL14', 'RPL35A', 'RPL9', 'RPL34', 'RPS3A', 'RPL37', 'RPS23', 'RPS14',
  172. 'RPL26L1', 'RPS10', 'RPS18', 'RPL10A', 'RPL7L1', 'RPS12', 'RPS20', 'RPL7', 'RPL30', 'RPL8', 'RPS6', 'RPL35',
  173. 'RPL12', 'RPL7A', 'RPS4X', 'RPL39', 'RPL36A', 'RPL10', 'RPS4Y1', 'RPS4Y2')
  175. signatures$mesenchymal_late= c(
  176. "FSTL1", "LRRC58", "REEP5", "VCAM1", "STAMBPL1", "WNT5A", "NKD2", "FARP1", "SSBP3", "CDK6",
  177. "FLI1", "SERPINF1", "CYTH3", "S1PR3", "FLRT2", "SPIN2A", "COL3A1", "RHOD", "SLC25A4", "TMEM200B",
  178. "TCF7L1", "PREX2", "RAB3IL1", "NET1", "FRMD6", "VEGFC", "FBLN5", "SEMA5A", "LIN28B", "SIX1",
  179. "PPFIBP2", "SMPDL3B", "IGFBP4", "PRICKLE1", "NOLC1", "LUM", "VASN", "PCDH18", "TAF4B", "SLC38A4",
  180. "C15orf39", "PDGFRA", "MTHFD1", "TRIL", "HDAC7", "HAPLN1", "ITM2C", "AMOT", "LRP1", "CPED1",
  181. "LIMA1", "ALX1", "FAM43A", "MFAP4", "NID1", "ITGB5", "RARG", "MMP2", "CSRP2", "XPO5",
  182. "CITED1", "ADORA2B", "PDGFRB", "CDC14C", "CDC14B", "EML3", "BMPR1B", "COL4A1", "CD248", "SNAI2",
  183. "RARB", "FOXC1", "SDC1", "METTL13", "DHRS3", "COL23A1", "PRRX1", "SERTAD4", "EVA1B", "PUS7",
  184. "SHC1", "PRRX2", "PDLIM4", "PWP2", "MRPL34", "PLVAP", "MRPS34", "DDR2", "PACSIN2", "DUSP12",
  185. "MRC2", "CD63", "TRAF3IP2", "ITGA9", "ARG1", "CNN2", "RAB32", "CRYM", "UBL4B", "TPM4",
  186. "A2M", "BGN", "EEF2K", "MTHFD2", "POLR2E", "PITPNC1", "ALX3", "FLNC", "SIX2", "ATAD3A",
  187. "HMCES", "OBSL1", "DUSP7", "AK2", "SLC25A22", "CLMP", "S100A6", "PLCB1", "NOL9", "LAMP5",
  188. "TNS1", "MFAP2", "COL26A1", "PDE4A", "EMILIN1", "PPAN", "TNFAIP6", "PCOLCE", "AIMP2", "TMEM119",
  189. "DACT3", "ALX4", "TBX2", "DDX56", "ST6GALNAC4", "FPGS", "SH2D3C", "DCHS1", "EBF3", "TBX3", "SPRED2"
  190. )
  192. signatures$s_phase=c('MCM5','PCNA','TYMS','FEN1','MCM7','MCM4','RRM1','UNG','GINS2','MCM6','CDCA7','DTL','PRIM1',
  193. 'UHRF1','CENPU','HELLS','RFC2','POLR1B','NASP','RAD51AP1','GMNN','WDR76','SLBP','CCNE2','UBR7',
  194. 'POLD3','MSH2','ATAD2','RAD51','RRM2','CDC45','CDC6','EXO1','TIPIN','DSCC1','BLM','CASP8AP2',
  195. 'USP1','CLSPN','POLA1','CHAF1B','MRPL36','E2F8')
  196. signatures$g2m_phase=c('HMGB2','CDK1','NUSAP1','UBE2C','BIRC5','TPX2','TOP2A','NDC80','CKS2','NUF2','CKS1B',
  197. 'MKI67','TMPO','CENPF','TACC3','PIMREG','SMC4','CCNB2','CKAP2L','CKAP2','AURKB','BUB1',
  198. 'KIF11','ANP32E','TUBB4B','GTSE1','KIF20B','HJURP','CDCA3','JPT1','CDC20','TTK','CDC25C',
  199. 'KIF2C','RANGAP1','NCAPD2','DLGAP5','CDCA2','CDCA8','ECT2','KIF23','HMMR','AURKA','PSRC1',
  200. 'ANLN','LBR','CKAP5','CENPE','CTCF','NEK2','G2E3','GAS2L3','CBX5','CENPA')
  203. # %%
  204. ### gene signature score as colSums (total of expression)
  206. # %%
  207. ano = calculate_gene_signature_scores(exp, ano, signatures, method = "colSums")
  209. fit=NULL
  210. p0 = NULL
  212. plots <- list()
  213. dataset_title <- "Survival Analysis - colSums - noMYCN, stages = 3,4 Patients"
  215. for (fate in names(signatures)) {
  216. print(fate)
  217. p <- surv_ad(paste0('gene_set:', fate), exp, ano, stages = c(3,4), mycn = 0, high_risk = NULL)
  219. if (!is.null(p)) {
  220. plots[[fate]] <- p + ggtitle(fate) # Add individual title
  221. }
  222. }
  224. if (length(plots) > 0) {
  225. combined_plot <- wrap_plots(plots, ncol = 4) + # Set to 4 columns per row
  226. plot_annotation(title = dataset_title)
  228. ggsave("figures/fig_survival/colSums.survival.noMYCN.stages3-4_combined.pdf",
  229. combined_plot, width = 14.4, height = ceiling(length(plots) / 4) * 4)
  230. }
  233. # %%
  234. fate = "ribosomal_assembly"
  235. p <- surv_ad(paste0('gene_set:', fate), exp, ano, stages = c(3,4), mycn = 0, high_risk = NULL, out = "full")
  236. print(p)
  238. # %%
  241. # %%
  242. ## SSGSEA_SCORES
  244. # %%
  245. ano = calculate_gene_signature_scores(exp, ano, signatures, method = "ssgsea")
  248. # %%
  249. fate = "ribosomal_assembly"
  250. p <- surv_ad(paste0('gene_set:', fate), exp, ano, stages = c(3,4), mycn = 0, high_risk = NULL, out = "full")
  251. print(p)
  253. # %%
  254. plots <- list()
  255. dataset_title <- "Survival Analysis - MYCN, stages 3, 4 Patients" # Adjust this title accordingly
  257. for (fate in names(signatures)) {
  258. print(fate)
  259. p <- surv_ad(paste0('gene_set:', fate), exp, ano, stages = NULL, mycn = 0, high_risk = NULL)
  261. if (!is.null(p)) {
  262. plots[[fate]] <- p + ggtitle(fate) # Add individual title
  263. }
  264. }
  266. if (length(plots) > 0) {
  267. combined_plot <- wrap_plots(plots, ncol = 4) + # Set to 4 columns per row
  268. plot_annotation(title = dataset_title)
  270. ggsave("figures/fig_survival/ssgsea_score.ssGSEA.survival.MYCN.stages_3-4_combined.pdf",
  271. combined_plot, width = 14.4, height = ceiling(length(plots) / 4) * 4)
  273. }
  276. # %%
  279. # %%
  280. sessionInfo()
  282. # %%
  285. # %%

survival_curves.ipynb at commit d9f90b2, no license · at the source

Overview

Authors: Irina Poverennaya1, Aliia Murtazina2, Lei Li3, Lorena Maili4, Lukas Sourada5,6, Luis Fernando Montano-Gutierrez1, Rozalina Galimullina1, Tobias Steinschaden1, Marketa Kaiser7, Tomas Zikmund7, Adna Goralija5,6, Teng Gao8,9, Aurore Attina10, Ornella Clara11, Christoph Bartenhagen12,13, Alek G Erickson2,14, Yaakov Gershtein1, Shiyuan Chen4, Kristyna Polaskova5,15, Jaroslav Sterba15
and 15 other authorsBettina Semsch16, Emma R Andersson16, Varsha Prakash17, Theresa Vincent17,18, Maria Arceo19, Per Kogner20, Susanne Schlisio19, Peter V Kharchenko21, Alexandre David10,22, Jozef Kaiser7, Matthias Fischer12,13, Jan Skoda5,6, Paul A Trainor4,23, Andrei S Chagin3,24, Igor Adameyko1,2
ORCID iDs: Lukas Sourada, Rozalina Galimullina, Tobias Steinschaden, Marketa Kaiser, Adna Goralija, Teng Gao, Ornella Clara, Alek G Erickson, Shiyuan Chen, Emma R Andersson, Per Kogner, Susanne Schlisio, Peter V Kharchenko, Alexandre David, Jozef Kaiser, Matthias Fischer, Jan Skoda, Paul A Trainor, Andrei S Chagin, Igor Adameyko
24 affiliations
  1. Department of Neuroimmunology, Center for Brain Research, Medical University of Vienna, Vienna, Austria
  2. Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
  3. Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Centre for Bone and Arthritis Research at the Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
  4. Stowers Institute for Medical Research, Kansas City, MO USA
  5. Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czech Republic
  6. International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic
  7. Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic
  8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA USA
  9. Present Address: Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA USA
  10. IRCM, Université de Montpellier, ICM, INSERM, Montpellier, France
  11. Aix-Marseille University, CNRS, UMR 7288, IBDM, Marseille, France
  12. Department of Experimental Pediatric Oncology, University Children’s Hospital of Cologne, Cologne, Germany
  13. Center for Molecular Medicine Cologne, Medical Faculty, University of Cologne, Cologne, Germany
  14. Department of Molecular Biosciences, the Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
  15. Department of Pediatric Oncology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic
  16. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
  17. Department of Microbiology, NYU Grossman School of Medicine, New York, NY USA
  18. Division of Pathology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
  19. Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
  20. Childhood Cancer Research Unit, Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
  21. Altos Labs, San Diego Institute of Science, San Diego, CA USA
  22. IRMB-PPC, INM, CHU Montpellier, INSERM, Université de Montpellier, CNRS, Montpellier, France
  23. Department of Cell Biology and Physiology, University of Kansas Medical Center, Kansas City, KS USA
  24. Science for Life Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
Institutions: Medical University of Vienna (Austria); Karolinska Institutet (Sweden); University of Gothenburg (Sweden); Stowers Institute for Medical Research (United States); Masaryk University (Czechia); International Clinical Research Center, St. Anne's University Hospital Brno (Czechia); Central European Institute of Technology (Czechia); Brno University of Technology (Czechia); Boston Children's Hospital (United States); Harvard University (United States); Inserm (France); Université de Montpellier (France); Institut de Recherche en Cancérologie de Montpellier (France); Centre National de la Recherche Scientifique (France); Aix-Marseille Université (France); Institut de Biologie du Développement Marseille (France); University of Cologne (Germany); University Hospital Cologne (Germany); Stockholm University (Sweden); University Hospital Brno (Czechia); New York University (United States); Altos Labs; Institute for Regenerative Medicine & Biotherapy (France); Institute for Neurosciences of Montpellier (France); University of Kansas Medical Center (United States)
Journal: Nature communications, volume 17, issue 1, article 2326
Dates: received 4 January 2024; accepted 25 February 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70375-6 · PMID 41803115 · PMCID PMC12976135 · OpenAlex W7134279380
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population)
Methods: Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Differentiation, Gene regulatory networks, Cell lineage, CNS cancer
MeSH: Mesoderm*, Neural Crest*, Ribosomes*, RNA, Ribosomal, 18S*, Animals, Cell Differentiation, Cell Line, Tumor, Cell Lineage, Humans, Mice, Neuroblastoma, Ribosomal Proteins, RNA Processing, Post-Transcriptional (* major topic)
Topic: RNA modifications and cancer (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Research Council (856529); European Union’s Horizon 2020 research and innovation program; NIDCR NIH HHS (F32 DE033617); NCI NIH HHS (R01 CA270241); ALSF; Austrian Science Fund Project
Citations: cited by 2 papers (Europe PMC); 157 references in the paper
Notices: A correction to this paper has been published (42660968, from Europe PMC)

Abstract

Neural crest cells contribute to craniofacial formation by differentiating into skeletogenic mesenchyme and neuro-glial lineages. Using Smart-seq2 single-cell transcriptomics, we show that mesenchymal fate commitment correlates specifically with the expression of rRNA-modifying and ribosome assembly factors, rather than structural ribosomal proteins. Notably, EMG1 and NHP2 introduce key post-transcriptional modifications into 18S rRNA, including m¹acp³ψ at U1248, which requires TSR3 for final maturation. Disrupting NHP2 or TSR3 in vitro and in vivo perturbs cranial neural crest differentiation; post-migratory temporal knockout of Polr1a or Polr1c also causes craniofacial malformations. These findings align with cell type-specific m¹acp³ψ levels during neural crest differentiation. Given the neural crest contribution to neuroblastoma, we analyze patient data to find that elevated ribosomal control and rRNA-modifying proteins predict poorer outcomes. Complementary experiments in neuroblastoma cell lines reveal functional roles for TSR3 and WDR74 in mesenchymal-like tumor states. Together, our results link rRNA modifications and ribosome assembly to fate decisions, suggesting ribosomal heterogeneity shapes both normal development and tumor progression.

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

Repository

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ipoverennaya/ribo_modification_paper

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State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d9f90b25e772040dcd013814d8c66774a74c6888, 29 January 2026
Languages: Jupyter (2)
Size: 5 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: anndata (1 file), cowplot (1 file), ggplot2 (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), patchwork (1 file), Scanpy (1 file), scVelo (1 file), survival (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/ipoverennaya/ribo_modification_paper

Code availability

The code used for single-cell analysis can be found at the GitHub link: https://github.com/ipoverennaya/ribo_modification_paper.

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

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Data

Datasets cited

Data availability

All new mouse sequencing data associated with this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) under the accession numbers GSE290341 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE290341) (temporal Polr1a knockout experiment) and GSE308372 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE308372) (in vitro overexpression of Nhp2, Emg1, and Tsr3). Sequencing data from neuroblastoma patients are available via the controlled-access European Genome-phenome Archive (EGA) under Study ID EGAS50000001103 (https://ega-archive.org/studies/EGAS50000001103) and Dataset ID: EGAD50000001596 (https://ega-archive.org/datasets/EGAD50000001596). Access requests should be submitted to the Data Access Committee which will respond to requests within 4-6 weeks. Access will be granted upon completion of a Data Access Agreement. The previously published mouse cranial neural crest datasets (E8.5, E9.5, and E10.5) and trunk postotic neural crest datasets (E9.5 and E10.5) analyzed in this study are available in GEO under accession numbers GSE201257 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE201257) and GSE129114 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129114), respectively. The processed data including mouse cranial neural crest dataset can be browsed from: https://adameykolab.hifo.meduniwien.ac.at/cellxgene_public/. Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at https://www.stowers.org/research/publications/LIBPB-2601. Source data are provided with this paper in the Supplementary Information/Source Data Files. Any additional materials will be available from the corresponding author upon request. Source data are provided with this paper.

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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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 35 authors, 4 keywords, 13 MeSH terms, 6 funders, 154 references, 1 integrity notice.

Cite

This paper

Poverennaya, I., Murtazina, A., Li, L., Maili, L., Sourada, L., Montano-Gutierrez, L. F., Galimullina, R., Steinschaden, T., Kaiser, M., Zikmund, T., Goralija, A., Gao, T., Attina, A., Clara, O., Bartenhagen, C., Erickson, A. G., Gershtein, Y., Chen, S., Polaskova, K., . . . Adameyko, I. (2026). Ribosomal modifications are associated with mesenchymal fate selection in the neural crest lineage. Nature communications, 17(1), 2326. https://doi.org/10.1038/s41467-026-70375-6

BibTeX

@article{poverennaya2026ribosomal,
author = {Poverennaya, Irina and Murtazina, Aliia and Li, Lei and Maili, Lorena and Sourada, Lukas and Montano-Gutierrez, Luis Fernando and Galimullina, Rozalina and Steinschaden, Tobias and Kaiser, Marketa and Zikmund, Tomas and Goralija, Adna and Gao, Teng and Attina, Aurore and Clara, Ornella and Bartenhagen, Christoph and Erickson, Alek G and Gershtein, Yaakov and Chen, Shiyuan and Polaskova, Kristyna and Sterba, Jaroslav and Semsch, Bettina and Andersson, Emma R and Prakash, Varsha and Vincent, Theresa and Arceo, Maria and Kogner, Per and Schlisio, Susanne and Kharchenko, Peter V and David, Alexandre and Kaiser, Jozef and Fischer, Matthias and Skoda, Jan and Trainor, Paul A and Chagin, Andrei S and Adameyko, Igor},
title = {{Ribosomal modifications are associated with mesenchymal fate selection in the neural crest lineage}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {2326},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70375-6},
url = {https://doi.org/10.1038/s41467-026-70375-6},
pmid = {41803115},
pmcid = {PMC12976135}
}

RIS

TY - JOUR
AU - Poverennaya, Irina
AU - Murtazina, Aliia
AU - Li, Lei
AU - Maili, Lorena
AU - Sourada, Lukas
AU - Montano-Gutierrez, Luis Fernando
AU - Galimullina, Rozalina
AU - Steinschaden, Tobias
AU - Kaiser, Marketa
AU - Zikmund, Tomas
AU - Goralija, Adna
AU - Gao, Teng
AU - Attina, Aurore
AU - Clara, Ornella
AU - Bartenhagen, Christoph
AU - Erickson, Alek G
AU - Gershtein, Yaakov
AU - Chen, Shiyuan
AU - Polaskova, Kristyna
AU - Sterba, Jaroslav
AU - Semsch, Bettina
AU - Andersson, Emma R
AU - Prakash, Varsha
AU - Vincent, Theresa
AU - Arceo, Maria
AU - Kogner, Per
AU - Schlisio, Susanne
AU - Kharchenko, Peter V
AU - David, Alexandre
AU - Kaiser, Jozef
AU - Fischer, Matthias
AU - Skoda, Jan
AU - Trainor, Paul A
AU - Chagin, Andrei S
AU - Adameyko, Igor
TI - Ribosomal modifications are associated with mesenchymal fate selection in the neural crest lineage
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/09
VL - 17
IS - 1
SP - 2326
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70375-6
UR - https://doi.org/10.1038/s41467-026-70375-6
LA - en
ER -

CSL-JSON

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"language": "en",
"issued": {
"date-parts": [
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}
}

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