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Neuronal differentiation of neuroblastoma cell lines for neurological disease modeling.

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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › mRNA-sequencing and expression analysis ↔ analysis_deseq2.R, lines 41–105 · score 0.70 · binomial GLM, DESeq2, dispersion, Wald, Salmon, genes
  2. [2] § STAR★Methods › Method details › mRNA-sequencing and expression analysis ↔ trim.sh, the whole file · a weak match · score 0.62 · fastQC, ea, trimmed, mcf, quality, sequencing
  3. [3] § STAR★Methods › Method details › Mutation and fusion gene analysis with RNA-seq data ↔ filter_common_variants.sh, the whole file · a weak match · score 0.59 · RNA edit sites, common variants, waterfall, filter, GATK
  4. [4] § STAR★Methods › Method details › Mutation and fusion gene analysis with RNA-seq data ↔ spec_sens2.R, lines 1–57 · score 0.57 · RNA edit sites, filter steps, HaplotypeCaller, depth, variants, GATK

Paper

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

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The authors' code

R · 106 lines · 3.2 KB · MIT · 1 match

  1. #####################################
  2. ## R script for reading in salmon quant.sf
  3. ## and analysis via DESeq2
  4. ## annotation via ensembl meta-data
  5. #####################################
  9. #####################################
  10. ## Load Libraries
  11. #####################################
  12. library(DESeq2)
  13. library(gplots)
  14. library(tximportData)
  15. library(GenomicFeatures)
  16. library(tximport)
  21. #####################################
  22. ## READ META-DATA
  23. #####################################
  24. pdata = read.table('file2sample.csv',header=T,sep=',')
  25. pdata = pdata
  26. rownames(pdata) = pdata[,1]
  27. dirElem = strsplit(getwd(),'/')[[1]];
  28. expname = paste("_salmon_shiny_",dirElem[length(dirElem)],sep='')
  30. files = dir(path='counts/', pattern=".*\\.sf$", recursive=TRUE)
  31. o = pmatch(paste(pdata$sampleName,".salmon/",sep=""), files)
  32. files = files[o];
  33. files = paste('counts',files,sep='/')
  34. pdata$fileName = files
  35. pdata$group = factor(pdata$group)
  36. pdata$project = factor(pdata$project)
  41. #####################################
  42. ## summarise transcripts to genes
  43. #####################################
  44. TxDb <- makeTxDbFromGFF(file = "/path/to/gtf_file/gencode/37/gencode.v37.annotation.gtf") #
  45. k <- keys(TxDb, keytype = "TXNAME")
  46. tx2gene <- select(TxDb, k, "GENEID", "TXNAME")
  47. head(tx2gene)
  49. txi <- tximport(files, type = "salmon", tx2gene = tx2gene)
  50. colnames(txi$counts) = pdata$sampleName
  51. colnames(txi$abundance) = pdata$sampleName
  56. #####################################
  57. ## NORMALIZE DATA, DESeq2
  58. ## negative binomial GLM test
  59. ## alternative test: nbionomLRT (chi-square)
  60. #####################################
  61. dds <- DESeqDataSetFromTximport(
  62. txi,
  63. colData = pdata,
  64. design= (~ project + group) # experimental design, multiple factors possible
  65. )
  66. dds <- DESeq(dds) # includes estimation of size factors, dispersion + nbinomWaldTest; betaPrior=F for >2 factors
  68. conds = labels(terms(design(dds)))[1]
  69. ens.str <- substr(rownames(dds), 1, 15)
  70. rownames(dds) = ens.str
  71. resultsNames(dds)
  72. nexprs = counts(dds,normalized=T)
  77. #####################################
  78. ## annotation via download from website ensembl biomart
  79. #####################################
  80. anno = read.csv(gzfile("/path/to/ensembl_meta_data/biomart_ens103_210308.txt.gz"), header=T, as.is=T, sep="\t")
  81. colnames(anno) = c("ensembl_gene_id", "description", "chromosome_name", "gene_start_position", "gene_end_position", "strand", "external_gene_name", "entrez_gene_id")
  82. anno = anno[order(anno$external_gene_name),]
  83. anno = anno[grep("^CHR", anno$chromosome_name,invert=T),]
  84. anno = anno[!duplicated(anno$ensembl_gene_id),] # one annotation for one gene
  85. id_type= "ensembl_gene_id"
  86. anno = anno[anno$ensembl_gene_id%in%rownames(nexprs), ]
  88. anexprs = data.frame(nexprs)
  89. colnames(anexprs) = colnames(nexprs)
  90. anexprs = merge(anno,anexprs,by.x='ensembl_gene_id',by.y=0,all.y=T)
  91. tpm <- txi$abundance
  92. ens <- substr(rownames(tpm), 1, 15)
  93. rownames(tpm) = ens
  94. atpm = merge(anno,data.frame(tpm),by.x='ensembl_gene_id',by.y=0,all.y=T)
  96. write.table(anexprs,paste('tables/NormData',expname, '.csv',sep=''), row.names=F,quote=F, sep='\t',na="")
  97. write.table(atpm, file=,paste('tables/TPM',expname, '.csv',sep=''), row.names=F,quote=F, sep='\t',na="")
  100. # save R objects for later/further analysis
  101. save(pdata,txi,dds,nexprs,anexprs,atpm,expname,tx2gene,file="R_salmon.rda")
  105. sessionInfo()

analysis_deseq2.R, under MIT · at the source

Overview

Authors: Claudia Pommerenke1, Vivien Hauer2, Sonja Eberth2, Lisa Werr3,4, Hannah Kallnischkies2, Ulfert Rand2, Stefan Nagel2, Christoph Bartenhagen3, Wilhelm Gerhard Dirks2, Matthias Fischer3, Laura Steenpass2, Haicui Wang2
ORCID iDs: Haicui Wang
  1. Department of Bioinformatics, IT, and Databases, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, 38124 Braunschweig, Germany
  2. Department of Human and Animal Cell Lines, Leibniz Institute DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, 38124 Braunschweig, Germany
  3. Department of Experimental Pediatric Oncology, University Children’s Hospital of Cologne, 50931 Cologne, Germany
  4. Department of Translational Genomics, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
Institutions: Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures (Germany); University of Cologne (Germany); University Hospital Cologne (Germany)
Journal: iScience, volume 29, issue 7, article 116469
Dates: received 28 December 2025; accepted 2 June 2026; published online 18 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116469 · PMID 42375529 · PMCID PMC13312023 · OpenAlex W7165108420
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Neuroscience, Molecular neuroscience, Transcriptomics
Topic: Neuroblastoma Research and Treatments (Neurology, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (SFB1588, ID 413326622, SFB1399, FI 1926/2-1, ID 493872418)
Citations: not cited yet (Europe PMC); 41 references in the paper
Research resources: Anti-N-MYC, rabbit polyclonal RRID:AB_10692664, Anti-Vimentin (VIM), mouse polyclonal RRID:AB_10917747, Anti-GAPDH, mouse monoclonal RRID:AB_2107448, Anti-Vimentin (VIM), rabbit polyclonal RRID:AB_2273020, RRID:AB_2534069, RRID:AB_2534079, Anti-TH, rabbit polyclonal RRID:AB_2716568, RRID:AB_2918622, Anti-Ki67, mouse monoclonal RRID:AB_393778, RRID:AB_477590, RRID:AB_772193, RRID:AB_772206, Anti-Neurofilament-L, rabbit polyclonal RRID:AB_823575, SH-SY5Y RRID:CVCL_0019, IMR-32 RRID:CVCL_0346, LAN-5 RRID:CVCL_0389, SK-N-BE(2) RRID:CVCL_0528, CHP-126 RRID:CVCL_1123, CHP-134 RRID:CVCL_1124, GI-ME-N RRID:CVCL_1232, LAN-6 RRID:CVCL_1363, MHH-NB-11 RRID:CVCL_1412, SIMA RRID:CVCL_1695, LAN-1 RRID:CVCL_1827, LAN-2 RRID:CVCL_1829, KELLY RRID:CVCL_2092, LS RRID:CVCL_2105, NBL-S RRID:CVCL_2136, NGP RRID:CVCL_2141, NMB RRID:CVCL_2143

Abstract

Neurological disorders are often associated with neuronal dysfunction. Neuroblastoma (NB) cell line SH-SY5Y is widely used as in vitro model as it can differentiate into neuron-like cells. Many other NB cell lines can also respond to differentiation treatment but their potential in neuronal modeling remained unexplored. We evaluated 18 NB cell lines using RNA-seq and differentiation treatment with retinoic acid and brain-derived neurotrophic factor. Only few mutations in neurotransmitter pathway genes were detected. Transcription factor activities and differentially expressed genes classified NB cell lines into adrenergic (ADRN) and mesenchymal (MES) types. Each ADRN-type cell line exhibited a unique expression profile of neurotransmitter pathway genes. Differentiation treatment of ADRN-type cell lines CHP-134, LAN-5, and SIMA resulted in neuron-like cells expressing synaptic marker and several neurotransmitter pathway genes. Our results revealed ADRN-type cell lines can be selected carefully based on molecular background, differentiation potential and neuronal gene expression for neurological disease modeling.

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 4 matches between paragraphs and lines of code.

Zenodo 6401600

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “mRNA-sequencing and expression analysis”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DESeq2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
6 files
At the source: zenodo.org/records/6401600

Zenodo 13759327

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Mutation and fusion gene analysis with RNA-seq d”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

claupomm/rna-seq_snv_tumour_only

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 030323f5c73396179a1494664c0ccf0243d81516, 10 January 2025
Languages: R (9), Shell (6), Perl (1)
Size: 296 files, 16 scripts
Software Heritage: not archived
Found in: the text, “Mutation and fusion gene analysis with RNA-seq d”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (8 files), tidyverse (8 files), ggplot2 (7 files), cowplot (4 files), psych (4 files), BCFtools (1 file), ggpubr (1 file), SAMtools (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
17 files
At the source: github.com/claupomm/RNA-seq_snv_tumour_only/tree/v1.0.0

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

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 20 scripts, each with its path and the digest of its content;
  • 4 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 and code availability

All data supporting the findings of this study are available within the article and its supplementary files. Additional raw data are available from the lead contact upon request.

Raw and processed RNA-seq data have been deposited at ArrayExpress: E-MTAB-14737. Additionally, RNA-seq data are accessible at DSMZCellDive (https://celldive.dsmz.de/) for interactive visualization.

WGS data for cell lines have been deposited at European Nucleotide Archive: PRJEB45367.30

This study did not generate or use any custom code.

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 2, 28 September 2026

  • Authors: added Haicui Wang (0000-0003-3720-3859); removed Haicui Wang

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 1 funder, 41 references, 30 RRIDs.

Cite

This paper

Pommerenke, C., Hauer, V., Eberth, S., Werr, L., Kallnischkies, H., Rand, U., Nagel, S., Bartenhagen, C., Dirks, W. G., Fischer, M., Steenpass, L., & Wang, H. (2026). Neuronal differentiation of neuroblastoma cell lines for neurological disease modeling. iScience, 29(7), 116469. https://doi.org/10.1016/j.isci.2026.116469

BibTeX

@article{pommerenke2026neuronal,
author = {Pommerenke, Claudia and Hauer, Vivien and Eberth, Sonja and Werr, Lisa and Kallnischkies, Hannah and Rand, Ulfert and Nagel, Stefan and Bartenhagen, Christoph and Dirks, Wilhelm Gerhard and Fischer, Matthias and Steenpass, Laura and Wang, Haicui},
title = {{Neuronal differentiation of neuroblastoma cell lines for neurological disease modeling}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {7},
pages = {116469},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116469},
url = {https://doi.org/10.1016/j.isci.2026.116469},
pmid = {42375529},
pmcid = {PMC13312023}
}

RIS

TY - JOUR
AU - Pommerenke, Claudia
AU - Hauer, Vivien
AU - Eberth, Sonja
AU - Werr, Lisa
AU - Kallnischkies, Hannah
AU - Rand, Ulfert
AU - Nagel, Stefan
AU - Bartenhagen, Christoph
AU - Dirks, Wilhelm Gerhard
AU - Fischer, Matthias
AU - Steenpass, Laura
AU - Wang, Haicui
TI - Neuronal differentiation of neuroblastoma cell lines for neurological disease modeling
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/18
VL - 29
IS - 7
SP - 116469
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116469
UR - https://doi.org/10.1016/j.isci.2026.116469
LA - en
ER -

CSL-JSON

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