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. 2017 Mar 28;4:170033. doi: 10.1038/sdata.2017.33

Transcriptomic profiling of 39 commonly-used neuroblastoma cell lines

Jo Lynne Harenza 1, Maura A Diamond 1, Rebecca N Adams 2, Michael M Song 3, Heather L Davidson 3, Lori S Hart 1, Maiah H Dent 1, Paolo Fortina 2, C Patrick Reynolds 3, John M Maris 1,4,a
PMCID: PMC5369315  PMID: 28350380

Abstract

Neuroblastoma cell lines are an important and cost-effective model used to study oncogenic drivers of the disease. While many of these cell lines have been previously characterized with SNP, methylation, and/or mRNA expression microarrays, there has not been an effort to comprehensively sequence these cell lines. Here, we present raw whole transcriptome data generated by RNA sequencing of 39 commonly-used neuroblastoma cell lines. These data can be used to perform differential expression analysis based on a genetic aberration or phenotype in neuroblastoma (e.g., MYCN amplification status, ALK mutation status, chromosome arm 1p, 11q and/or 17q status, sensitivity to pharmacologic perturbation). Additionally, we designed this experiment to enable structural variant and/or long-noncoding RNA analysis across these cell lines. Finally, as more DNase/ATAC and histone/transcription factor ChIP sequencing is performed in these cell lines, our RNA-Seq data will be an important complement to inform transcriptional targets as well as regulatory (enhancer or repressor) elements in neuroblastoma.

Subject terms: Paediatric cancer, Cancer genomics, RNA sequencing, Gene expression analysis

Background & Summary

An estimated 15,780 children were diagnosed with cancer in 2014 In the United States, and per year globally, this number is nearly 250,000 (ref. 1). Although the 5-year survival rate of pediatric cancers is ~80%, many of the most commonly diagnosed childhood cancers: brain tumors, Wilms tumor, rhabdomyosarcoma, and high-risk neuroblastoma, have devastatingly low rates of survival1, demonstrating the continued need for research progress in these areas. Here, we focus on neuroblastoma, the most common extracranial solid tumor in children. This disease has an estimated incidence of 1 in 8,000 to 10,000 births2 and a 5-year survival rate of >95% for children in the low and intermediate risk groups. However, children with high-risk disease have only a 40% likelihood of survival2. Culturing of neuroblastoma cell lines dates back to the 1940s (ref. 3), during which the sole purpose of culturing was for diagnosis. However, producing cell lines from neuroblastoma tumors quickly became routine (see review4) and today, they are commonly-used, highly-characterized models used in laboratories across the world. Neuroblastoma cell lines nicely model a tumor’s histopathology, gene expression, aneuploidy, and drug sensitivity, thus they are routinely used to investigate oncogenes or signaling pathways pharmacologically (drug screens, drug sensitivity/resistance) and/or genetically (siRNA, shRNA, CRISPR).

The genomics of neuroblastoma cell lines have been previously characterized using SNP5, methylation5,6, and/or mRNA expression microarrays7–9, however, there has not been an effort to profile a large panel of these cell lines with high-throughput sequencing techniques. The motivation behind this study was to comprehensively profile the mRNA and non-coding RNA transcriptome of commonly-used neuroblastoma cell line models with a major goal of using this information as a complement to the epigenomic data currently available and the many data in the process of being generated. Integration of RNA expression patterns with histone and/or transcription factor chromatin immunoprecipitation (ChIP) sequencing is necessary for inferring transcriptional regulatory events. Neuroblastomas can be classified into various groups based on genetic lesions, for example: MYCN copy number amplification, harboring an activating ALK mutation, harboring a chromosomal loss (e.g.,: 1p, 3p, 11q) or gain (17q), TERT rearrangements (for review of neuroblastoma genomics, see ref. 10). Utilizing a panel of cell lines which harbor a mixture of these characteristics enables differential expression analyses on the basis of a genetic lesion, mutation of interest, or expression of a gene of interest.

These data have reuse value to inform selection of cell lines for experimental investigation of putative neuroblastoma oncogenes and/or tumor suppressors. For example, choice of knock-down or over-expression studies require a priori knowledge of basal expression of the gene of interest for rational experimental design. These data allow the experimenter to quickly determine which cell lines are high, mid, or low expressers of a gene of interest without requiring tedious quantitative, real-time PCR analysis or western blotting of multiple cell lines prior to initiating a gene over-expression or knockdown experiment.

Here, we describe transcriptome-wide profiling of 39 neuroblastoma cell lines, the hTERT-immortalized retinal pigmented epithelial cell line, RPE-1, and pooled human fetal brain tissue. Careful and stringent technical design at each experimental stage has allowed generation of a high-quality RNA-Seq dataset which has tremendous reuse value for the neuroblastoma community. An overview of the study design is depicted in Fig. 1. Briefly, cell lines were thawed, grown, and collected at 60–80% confluency over a two-month period. Once all cell lines were pelleted, RNA extractions were performed, quality of RNA inspected, and RNA sequencing was performed. Raw FASTQ files were generated and are publicly-available for reuse (see Data Citation 1). Additionally, we provide a processed file of gene-level mRNA abundances for each sample. We anticipate this data being a valuable tool for the neuroblastoma research community as we continue investigation into oncogenomic mechanisms of this disease.

Figure 1. Experimental and data analysis workflow.

Figure 1

Cell lines were thawed and cultured to ~60–80% confluence before passaging and finally, pelleting. RNA was extracted, sequencing performed, and data analysis performed as described.

Methods

Cell lines and culturing

Cell line stocks were obtained from the Children’s Oncology Group (COG) Cell Culture and Xenograft Repository at Texas Tech University Health Sciences Center (www.COGcell.org), the American Type Culture Collection (Manassas, VA), or the Children’s Hospital of Philadelphia (CHOP) cell line bank. Several of the COG-derived cell lines were established direct-to-culture in parallel with a patient-derived xenograft model11 that are being characterized separately (see Table 1 (available online only)). All cell culturing for this experiment was performed at CHOP. Each cell line was thawed for 2–3 min in a 37 °C water bath, added to a 15 ml tube containing its respective growth medium, and pelleted by centrifugation at 300×g for 3 min. The supernatant was discarded to remove the DMSO-containing freezing medium. Cells were re-suspended in 1 ml of growth medium and transferred into a T75 flask containing an additional 10 ml of growth medium. Once cells were ~70–80% confluent, they were transferred to a 150 mm dish. At ~70–80% confluency, cells were split into two 150 mm dishes and at ~70–80% confluency, each dish of cells was pelleted, washed 3x with 1X PBS, and frozen at −80C until nucleic acids were extracted. See Table 1 (available online only) for a complete listing of cell lines, whether a matched patient-derived xenograft (PDX) exists, and their growth medium. Cell lines appended with ‘nb’ were grown in serum-free neurobasal medium. The following were purchased from Thermo Fisher Scientific (Waltham, MA): Iscove’s IMDM (Cat# 12440053), RPMI 1640 with 25 mM HEPES (Cat# 22400089), Neurobasal-A Medium (Cat# 10888022), L-glutamine (Cat# 25030081), antibiotic/antimycotic (Cat# 15240062), 50X B-27 serum-free supplement (Cat# 18504044), 100X N-2 supplement (Cat# 17502048). The following growth factors were purchased from VWR (Radnor, PA): rhFGF (fibroblast growth factor, Cat# PAG5071) and rhEGF (epidermal growth factor, Cat# PAG5021). Insulin/Transferrin/Selenium (ITS) premix culture supplement was purchased from Corning Life Sciences (Tewksbury, MA, Cat# 354351). Hyclone Fetal bovine serum was purchased from Fisher Scientific (Cat# SH30071.03) and the lot remained consistent across the different medium formulations throughout the duration of the experiment. Of note, SK-N-BE(2)-C is a subclone derived from the parental SK-N-BE(2) cell line12 and SH-SY5Y was derived from the SH-SY subclone of the parental SK-N-SH cell line13.

Table 1. Associated metadata for the cell lines used in this study.

Source Name Characteristics [organism] Characteristics [organism part] Characteristics [Matched PDX] Method Sample Name Factor Value [Growth Media] Factor Value [Detachment Method]
Listed are the cell lines used in this study, matched PDX if applicable, and culturing media.              
CHP-134 cell line Homo sapiens Neuroblastoma NA Total RNA extraction CHP-134 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
CHP-212 cell line Homo sapiens Neuroblastoma NA Total RNA extraction CHP-212 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
COG-N-415 cell line Homo sapiens Neuroblastoma COG-N-415x Total RNA extraction COG-N-415 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement n/a
COG-N-440 cell line Homo sapiens Neuroblastoma COG-N-440x Total RNA extraction COG-N-440 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
COG-N-453 cell line Homo sapiens Neuroblastoma COG-N-453x Total RNA extraction COG-N-453 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement n/a
COG-N-471nb cell line Homo sapiens Neuroblastoma COG-N-471x Total RNA extraction COG-N-471nb Neurobasal-A plus 50 ng/ml rhEGF, 50 ng/ml rhFGF, 2 mM L-glutamine, 1X B27, and 1X N2 0.02% Versene
COG-N-496 cell line Homo sapiens Neuroblastoma COG-N-496x Total RNA extraction COG-N-496 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
COG-N-519 cell line Homo sapiens Neuroblastoma COG-N-519x Total RNA extraction COG-N-519 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
COG-N-534 cell line Homo sapiens Neuroblastoma COG-N-534m Total RNA extraction COG-N-534 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
COG-N-549 cell line Homo sapiens Neuroblastoma COG-N-549x Total RNA extraction COG-N-549 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
COG-N-557nb cell line Homo sapiens Neuroblastoma COG-N-557x Total RNA extraction COG-N-557nb Neurobasal-A plus 50 ng/ml rhEGF, 50 ng/ml rhFGF, 2 mM L-glutamine, 1X B27, and 1X N2 0.02% Versene
COG-N-561 cell line Homo sapiens Neuroblastoma COG-N-561x Total RNA extraction COG-N-561 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
COG-N-573 cell line Homo sapiens Neuroblastoma COG-N-573x Total RNA extraction COG-N-573 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
Felix cell line Homo sapiens Neuroblastoma FELIXx Total RNA extraction Felix (COG-N-426) IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine, 1:1,000 ITS Premix Supplement 0.02% Versene
IMR-05 cell line Homo sapiens Neuroblastoma NA Total RNA extraction IMR-05 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
IMR-32 cell line Homo sapiens Neuroblastoma NA Total RNA extraction IMR-32 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
KELLY cell line Homo sapiens Neuroblastoma NA Total RNA extraction KELLY RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
LA-N-5 cell line Homo sapiens Neuroblastoma NA Total RNA extraction LA-N-5 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
LA-N-6 cell line Homo sapiens Neuroblastoma NA Total RNA extraction LA-N-6 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NB-1 cell line Homo sapiens Neuroblastoma NA Total RNA extraction NB-1 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine n/a
NB-16 cell line Homo sapiens Neuroblastoma NA Total RNA extraction NB-16 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.05% Trypsin/EDTA
NB-1,643 cell line Homo sapiens Neuroblastoma NB-1,643 Total RNA extraction NB-1,643 IMDM, 20% FBS, 1% Penicillin/Streptomycin, 2 mM L-glutamine n/a
NB-1,691 cell line Homo sapiens Neuroblastoma NB-1,691 Total RNA extraction NB-1,691 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.05% Trypsin/EDTA
NB-69 cell line Homo sapiens Neuroblastoma NA Total RNA extraction NB-69 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NB-EBc1 cell line Homo sapiens Neuroblastoma NB-EBc1 Total RNA extraction NB-EBc1 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NB-SD cell line Homo sapiens Neuroblastoma NB-SD Total RNA extraction NB-SD RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NBL-S cell line Homo sapiens Neuroblastoma NA Total RNA extraction NBL-S RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NGP cell line Homo sapiens Neuroblastoma NA Total RNA extraction NGP RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NLFcell line Homo sapiens Neuroblastoma NA Total RNA extraction NLF RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
NMB cell line Homo sapiens Neuroblastoma NA Total RNA extraction NMB RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
RPE-1 cell line Homo sapiens Epithelial NA Total RNA extraction RPE-1 RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.05% Trypsin/EDTA
SH-SY5Y cell line Homo sapiens Neuroblastoma NA Total RNA extraction SH-SY5Y RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
SK-N-AS cell line Homo sapiens Neuroblastoma SK-N-AS xenograft from cell line; not patient-derived Total RNA extraction SK-N-AS RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.05% Trypsin/EDTA
SK-N-BE(2) cell line Homo sapiens Neuroblastoma NA Total RNA extraction SK-N-BE(2) RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
SK-N-BE(2)-C cell line Homo sapiens Neuroblastoma NA Total RNA extraction SK-N-BE(2)-C RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
SK-N-DZ cell line Homo sapiens Neuroblastoma NA Total RNA extraction SK-N-DZ RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
SK-N-FI cell line Homo sapiens Neuroblastoma NA Total RNA extraction SK-N-FI RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
SK-N-SH cell line Homo sapiens Neuroblastoma NA Total RNA extraction SK-N-SH RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.05% Trypsin/EDTA
SMS-KAN cell line Homo sapiens Neuroblastoma NA Total RNA extraction SMS-KAN RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
SMS-SAN cell line Homo sapiens Neuroblastoma NA Total RNA extraction SMS-SAN RPMI 1,640, 10% FBS, 1% Penicillin/Streptomycin, 2 mM L-Glutamine 0.02% Versene
Human Fetal Brain Homo sapiens Brain NA NA Brain NA NA

Throughout the duration of the study, randomization was implemented to ensure unbiased data production. Cell lines were thawed in random order, nucleic acid extractions were performed randomly, and library preps and sequencing were performed randomly. Phenotypic characteristics of each cell line were also assessed as quality control during the cell growth stage. No unusual morphologies or growth rates were noted.

DNA extraction and STR profiling

From separate cell pellets, DNA was extracted using the DNeasy Blood & Tissue Kit (Cat# 69504, Qiagen, Valencia, CA). DNA was quantitated using the Nanodrop 1000 (Thermo Fisher Scientific) and Short Tandem Repeat (STR) profiling employed either the AmpFLSTR Identifiler PCR Amplification kit (Applied Biosystems, Foster City, CA) by the Children’s Hospital of Philadelphia Nucleic Acids and Protein Core or the PowerPlex Fusion kit (Promega, Madison, WI) by Guardian Forensic Sciences (Abington, PA). All cell line STRs matched publicly-available references listed at http://strdb.cogcell.org/.

RNA extraction

Control human fetal brain total RNA (Cat# 636526, Lot#1605061A) was purchased from Clontech Laboratories (Mountain View, CA). This RNA was a pool of normal brain tissue from 21 spontaneously aborted male/female Caucasian fetuses of ages 26–40 weeks and was isolated using a modified guanidinium thiocyanate method14. For all cell lines, RNA was extracted using the miRNeasy Mini kit (Cat# 217004) from Qiagen (Valencia, CA) according to the manufacturer’s protocol. RNA purity was assessed using the Nanodrop 2000 (Thermo Fisher Scientific) and quantitated with the Qubit 2.0 Fluorometer (Thermo Fisher Scientific). Quality and RNA integrity numbers (RINs) were assessed using the TapeStation 2200 (Agilent Technologies, Santa Clara, CA). Each cell line RNA sample had a RIN≥8.7 and the RIN for the fetal brain RNA was 7.6, thus all RNA was of high quality.

Library preparation and RNA sequencing

Libraries were prepared using 1 ug RNA according to the TruSeq Stranded Total RNA Sample Preparation guide (Part# 15031048 Rev. E, October 2013, Illumina, San Diego, CA). Ribosomal RNA removal was performed using the Gold rRNA Removal Mix per Illumina's recommendations. Quality of each library assessed with the Agilent TapeStation 2,200. Six to eight libraries were pooled (N=6–8) and sequenced using v2 chemistry, 2×100 bp, on one high-output flow-cell of an Illumina NextSeq 500 to achieve at least 50 million paired reads per sample. Upon run completion, libraries were demultiplexed, Illumina adapters trimmed, and FASTQ files were generated using the Illumina NextSeq Control Software version 2.02.

Sequencing quality control

First, sample reads were concatenated for each paired read group. Next, FASTQC V0.11.4 (Babraham Institute, available for download at http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was run on all samples and inspected for sequencing quality. Next, Picard tools version 1.140 (Broad Institute, Cambridge, MA, available for download at https://github.com/broadinstitute/picard/releases/tag/1.140) was used to calculate insert sizes for GEO according to the following parameters:

$ java -jar picard.jar CollectInsertSizeMetrics INPUT=Aligned.sortedByCoord.out.bam OUTPUT=filename

Alignment and generation of counts

The Spliced Transcripts Alignment to Reference (STAR) version 2.4.2a aligner (available for download at https://github.com/alexdobin/STAR/releases/tag/STAR_2.4.2a)15 was used to index the full hg19 genome fasta file from UCSC using the following parameters:

$ STAR --runMode genomeGenerate --runThreadN 16 --genomeDir idx_dir --genomeFastaFiles ucsc.hg19.fa --sjdbGTFfile refSeq_hg19_2016-03-03.gtf --sjdbOverhang 100

The GTF file was downloaded using the genePredToGtf command from the kent utility (available for download at http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/):

$ genePredToGtf hg19 knownGene knownGene.gtf

Next, sequences were aligned and counts per gene were generated using the following parameters in two-pass mode:

$ STAR --runMode alignReads --runThreadN 16 --twopassMode Basic --twopass1readsN -1 --chimSegmentMin 15 --chimOutType WithinBAM --genomeDir dir --genomeFastaFiles ucsc.hg19.fa --readFilesIn R1.fastq.gz R2.fastq.gz --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --outFileNamePrefix $cellline. --quantMode TranscriptomeSAM GeneCounts --sjdbGTFfile refSeq_hg19_2016-03-03.gtf --sjdbOverhang 100

Alignment resulted in an average of 66 million uniquely-mapped reads per sample. STAR two-pass mode alignment was chosen as it has been shown to have 99% alignment accuracy and has nearly 20x faster processing speed compared with TopHat2 and similar processing speed as HISAT two-pass mode16.

Generation of FPKM

A custom R script was used to generate gene fragments per kilobase of exons per million reads (FPKM) from the count data produced from STAR. The Genomic Features Package version 1.22.13 (available for download at https://bioconductor.org/packages/release/bioc/html/GenomicFeatures.html) was used with R Version 3.2.2 (Fire Safety) to make the transcriptome database and figures were produced using ggplot2 version 2.1.0 (http://ggplot2.org/).

Differential expression analyses

Differential expression of genes based on MYCN amplification status was performed separately for cell lines and primary neuroblastoma tumor samples using the R package, DESeq2 (version 1.10.1)17. FASTQ files and MYCN status for patient tumors were obtained with consent through the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Consortium (see Data Citation 2, https://ocg.cancer.gov/programs/target/data-matrix). Next, the differentially-expressed genes’ log2-transformed mean expression and the log2 fold-change were correlated between the cell lines and patient samples.

Code availability

R scripts for generation of FPKM and differential expression analyses are available for download at: https://github.com/marislab/NBL-cell-line-RNA-seq.

Data Records

All raw RNA-sequencing data (paired FASTQ files) as well as the processed FPKM matrix from this study have been deposited into the Gene Expression Omnibus (GEO) under Accession Number GSE89413 (see Data Citation 1). For associated specimen metadata, see Table 1 (available online only) and for associated assay metadata, see Table 2 (available online only). Raw single nucleotide polymorphism (SNP) array IDAT files and processed Genome Studio files for 27 of the cell lines have been deposited into GEO under Accession Number GSE89968 (see Data Citation 3). Together, these data make up the GEO Super Series GSE89969.

Table 2. Associated metadata for the RNA-Seq assay.

Cell Line Data ID Assay Name Raw Data File Name 1 Raw Data File Name 2 Data Repository Data Accession Number Derived Data File Name Data Repository Data Accession Number
Listed are the RNA sequencing files available for each cell line, along with GEO accession numbers.                  
CHP-134 CHP134 RNA-Seq CHP134.R1.fq CHP134.R2.fq GEO GSM2371222 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
CHP-212 CHP212 RNA-Seq CHP212.R1.fq CHP212.R2.fq GEO GSM2371223 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-415 COGN415 RNA-Seq COGN415.R1.fq COGN415.R2.fq GEO GSM2371224 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-440 COGN440 RNA-Seq COGN440.R1.fq COGN440.R2.fq GEO GSM2371225 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-453 COGN453 RNA-Seq COGN453.R1.fq COGN453.R2.fq GEO GSM2371226 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-471nb COGN471 RNA-Seq COGN471.R1.fq COGN471.R2.fq GEO GSM2371227 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-496 COGN496 RNA-Seq COGN496.R1.fq COGN496.R2.fq GEO GSM2371228 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-519 COGN519 RNA-Seq COGN519.R1.fq COGN519.R2.fq GEO GSM2371229 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-534 COGN534 RNA-Seq COGN534.R1.fq COGN534.R2.fq GEO GSM2371230 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-549 COGN549 RNA-Seq COGN549.R1.fq COGN549.R2.fq GEO GSM2371231 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-557nb COGN557 RNA-Seq COGN557.R1.fq COGN557.R2.fq GEO GSM2371232 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-561 COGN561 RNA-Seq COGN561.R1.fq COGN561.R2.fq GEO GSM2371233 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
COG-N-573 COGN573 RNA-Seq COGN573.R1.fq COGN573.R2.fq GEO GSM2371234 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
Felix (COG-N-426) FELIX RNA-Seq FELIX.R1.fq FELIX.R2.fq GEO GSM2371235 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
IMR-05 IMR05 RNA-Seq IMR05.R1.fq IMR05.R2.fq GEO GSM2371236 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
IMR-32 IMR32 RNA-Seq IMR32.R1.fq IMR32.R2.fq GEO GSM2371237 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
KELLY KELLY RNA-Seq KELLY.R1.fq KELLY.R2.fq GEO GSM2371238 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
LA-N-5 LAN5 RNA-Seq LAN5.R1.fq LAN5.R2.fq GEO GSM2371239 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
LA-N-6 LAN6 RNA-Seq LAN6.R1.fq LAN6.R2.fq GEO GSM2371240 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-1 NB1 RNA-Seq NB1.R1.fq NB1.R2.fq GEO GSM2371241 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-16 NB16 RNA-Seq NB16.R1.fq NB16.R2.fq GEO GSM2371242 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-1643 NB1643 RNA-Seq NB1643.R1.fq NB1643.R2.fq GEO GSM2371243 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-1691 NB1691 RNA-Seq NB1691.R1.fq NB1691.R2.fq GEO GSM2371244 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-69 NB69 RNA-Seq NB69.R1.fq NB69.R2.fq GEO GSM2371245 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-EBc1 NBEBC1 RNA-Seq NBEBC1.R1.fq NBEBC1.R2.fq GEO GSM2371246 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NB-SD NBSD RNA-Seq NBSD.R1.fq NBSD.R2.fq GEO GSM2371247 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NBL-S NBLS RNA-Seq NBLS.R1.fq NBLS.R2.fq GEO GSM2371248 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NGP NGP RNA-Seq NGP.R1.fq NGP.R2.fq GEO GSM2371249 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NLF NLF RNA-Seq NLF.R1.fq NLF.R2.fq GEO GSM2371250 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
NMB NMB RNA-Seq NMB.R1.fq NMB.R2.fq GEO GSM2371251 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
RPE-1 RPE1 RNA-Seq RPE1.R1.fq RPE1.R2.fq GEO GSM2371252 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SH-SY5Y SHSY5Y RNA-Seq SHSY5Y.R1.fq SHSY5Y.R2.fq GEO GSM2371253 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SK-N-AS SKNAS RNA-Seq SKNAS.R1.fq SKNAS.R2.fq GEO GSM2371254 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SK-N-BE(2) SKNBE2 RNA-Seq SKNBE2.R1.fq SKNBE2.R2.fq GEO GSM2371255 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SK-N-BE(2)-C SKNBE2C RNA-Seq SKNBE2C.R1.fq SKNBE2C.R2.fq GEO GSM2371256 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SK-N-DZ SKNDZ RNA-Seq SKNDZ.R1.fq SKNDZ.R2.fq GEO GSM2371257 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SK-N-FI SKNFI RNA-Seq SKNFI.R1.fq SKNFI.R2.fq GEO GSM2371258 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SK-N-SH SKNSH RNA-Seq SKNSH.R1.fq SKNSH.R2.fq GEO GSM2371259 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SMS-KAN SMSKAN RNA-Seq SMSKAN.R1.fq SMSKAN.R2.fq GEO GSM2371260 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
SMS-SAN SMSSAN RNA-Seq SMSSAN.R1.fq SMSSAN.R2.fq GEO GSM2371261 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413
Human Fetal Brain HU-FETAL-BRAIN RNA-Seq HU-FETAL-BRAIN.R1.fq HU-FETAL-BRAIN.R2.fq GEO GSM2371262 GSE89413_2016-10-30-NBL-cell-line-STAR-fpkm.txt.gz GEO GSE89413

Technical Validation

As a technical validation of our RNA-Seq data, we generated FPKM for all genes (See Methods and Data Citation 1) and compared MYCN FPKM with each cell line’s known copy number amplification status across cell lines (Fig. 2 and Table 3 (available online only)) . Of note, the tumor from which the NLF cell line was derived was MYCN copy number amplified by the fluorescence in situ hybridization, however, it is not amplified at the protein level18 and therefore, as expected, has the lowest MYCN FPKM of all cell lines designated as MYCN amplified. All cell lines were concordant with known MYCN amplification status.

Figure 2. Validation of MYCN genomic amplification status in neuroblastoma cell lines.

Figure 2

Plotted are rank-ordered MYCN FPKM values for the human fetal brain sample and each cell line, colored by known MYCN copy number status. These data validate known MYCN amplification status for each cell line.

Table 3. Genetic lesion profiles for the neuroblastoma cell lines.

Cell Line MYCN status 1p36 del 3p26 del 11q23 del 17q21-qter unbal gain ALK mutation p53 mutation
Listed are collated data for common genetic lesions in neuroblastoma: MYCN amplification status, 1p24, 3p25, and 11q26 deletion status, 17q unbalanced gain27 status, ALK mutation status18, and TP53 mutation status18. Alterations for 1p, 3p, 11q, and 17q are reported from both visual inspection of SNP arrays in Illumina’s Genome Studio as well as processed files from Nexus Copy Number software (Biodiscovery, El Segundo, CA); see Data Citation 3 (del=deletion, cnLOH=copy neutral loss of heterozygosity, AI=allelic imbalance).              
CHP-134 Amplified LOH p32.3-pter; Gain p34.3-p36.22; Loss p36.22-pter Gain/AI p26.3 None Gain q12-qter WT WT
CHP-212 Amplified Loss p13.2-pter Gain/AI p26.3 cnLOH 23.3 Gain q12-qter WT WT
COG-N-415 Amplified Unknown Unknown Unknown Unknown F1174L WT
COG-N-440 Amplified Unknown Unknown Unknown Unknown WT WT
COG-N-453 Amplified Unknown Unknown Unknown Unknown F1174L WT
COG-N-471nb Amplified Unknown Unknown Unknown Unknown WT WT
COG-N-496 Amplified Unknown Unknown Unknown Unknown Unknown Unknown
COG-N-519 Amplified Unknown Unknown Unknown Unknown Unknown Unknown
COG-N-534 Non-amplified Unknown Unknown Unknown Unknown Unknown Unknown
COG-N-549 Non-amplified Unknown Unknown Unknown Unknown Unknown Unknown
COG-N-557nb Amplified Unknown Unknown Unknown Unknown Unknown Unknown
COG-N-561 Amplified Unknown Unknown Unknown Unknown Unknown Unknown
COG-N-573 Amplified Unknown Unknown Unknown Unknown Unknown Unknown
FELIX (COG-N-426) Non-amplified Unknown Unknown Unknown Unknown F1245C WT
IMR-05 Amplified Gain+LOH p32.3-pter Gain/AI p24.3-26.3 Loss q22.1-qter Gain q21.2-qter WT WT
IMR-32 Amplified Loss p32.3-pter Loss p12.3 cnLOH q23.1 Gain q21.2-qter WT WT
KELLY Amplified LOH p21.3-pter; Loss p36.32; Gain p36.33 Loss p26.2 Loss q23.3-qter Gain q21.2-qter F1174L P177T
LA-N-5 Amplified Loss p33-pter None None Gain q21.2-qter R1275Q WT
LA-N-6 Non-amplified p36.12-pter; cnLOH p35.3-p36.11 Loss p14.3-pter Loss q13.4-qter Gain q12-qter WT WT
NB-1 Amplified Loss p32.2-pter Gain p24.1-pter cnLOH q23.1 Gain q22-qter WT; amplified WT
NB-16 Non-amplified None None None Unresolvable WT R248W, R175H
NB-1,643 Amplified Loss p34.2-pter None Loss q23.1-qter Gain q21.31-qter R1275Q WT
NB-1,691 Amplified None Loss p26.1 LOH q11-qter; Loss q22.1-q24.3 None? WT WT
NB-69 Non-amplified Loss p13.3-pter None cnLOH q23.1, q23.3 Gain q12-qter WT WT
NB-EBc1 Non-amplified Loss p35.2-pter Gain p26.1-pter Loss q21-24.2 Gain q21.31-qter WT WT
NB-SD Amplified Loss p21.3-pter Loss p13-pter cnLOH q23.2-23.3 Gain q12-qter F1174L C176F
NBL-S Non-amplified None None Loss q14.1-qter None WT WT
NGP Amplified cnLOH p32.3-pter Gain p25.3-pter Loss q22.1-qter Gain q21.1-qter WT A159D, C141W
NLF Amplified Loss p32.2-pter Loss/AI p26.1; AI p26.2; Gain 26.3 Loss/AI q23.3-qter Gain q21.2-qter WT V203M
NMB Amplified cnLOH p34.2-pter None Loss q21-qter LOH/Gain q11.2-qter WT G245S
RPE-1 Non-amplified Unknown Unknown Unknown Unknown WT WT
SH-SY5Y Non-amplified None None Loss q22.1-q24.2 Gain q21.31-qter F1174L WT
SK-N-AS Non-amplified Loss p36.22-36.32 Loss p14.2-pter q13.4-qter Gain q21.31-qter WT H168R
SK-N-BE(2) Amplified cnLOH p21.3-pter Loss p14.2-pter Gain/AI q13.1-qter Gain q12-qter WT C135F
SK-N-BE(2)-C Amplified cnLOH p21.3-pter Loss p14.2-pter LOH q11-qter; Gain q13.4-qter Gain q12-qter WT C135F
SK-N-DZ Amplified None Gain/AI p26.1-pter Loss q21-qter Gain q21.2-qter WT R110L
SK-N-FI Non-amplified None None None Gain q21.31-qter WT M246R
SK-N-SH Non-amplified None None None Gain q21.31-qter F1174L WT
SMS-KAN Amplified Loss p13.3-pter None Loss q14.2-q23.3 Gain q11.1-qter WT WT
SMS-SAN Amplified Loss p32.3-pter None Loss q25 Gain q21.2-qter F1174L WT

Next, for both cell lines and neuroblastoma patient data, we performed differential expression analyses based on MYCN genomic amplification status using the R package, DESeq2 (ref. 17). We correlated the DESeq2 base mean of the common differentially-expressed genes (N=2,395) between cell lines and primary patient tumors, which were significantly correlated (Fig. 3a, Pearson’s R=0.824, t=71.131, df=2,393, 95% CI=0.811–0.836, P<2.2 e-16). The fold changes of these genes were also significantly correlated between the cell lines and patient samples (Fig. 3b, Pearson’s R=0.73, t=52.231, df=2,393, 95% CI=0.711–0.748, P<2.2 e-16), not only supporting the technical validity of our dataset, but also emphasizing the utility of these cell lines as a surrogate model for neuroblastoma.

Figure 3. Concordance of differentially-expressed genes between neuroblastoma cell lines and primary tumors.

Figure 3

(a) Across the neuroblastoma cell lines, 3,940 genes were differentially-expressed (DE) based on MYCN amplification status and of those, 2,395 were differentially-expressed based on MYCN amplification status in primary tumors and were significantly correlated (Pearson’s R=0.824, P<2.2 e-16). (b) The fold changes of these DE genes were significantly correlated between the cell line dataset and the patient tumor dataset (Pearson’s R=0.73, P<2.2 e-16). (c) A significant correlation between the common 6,523 genes that were not DE in cell lines and tumors was observed (Pearson’s R=0.829, P<2.2 e-16). (d) As expected, correlation of the non-DE genes’ fold changes was close to zero (Pearson’s R=0.052, P<3 e-5).

Finally, we correlated non-differentially expressed genes (DESeq2 p-adjusted > 0.20) between the cell lines and patient tumors (N=6,523). As expected, base mean expression of the genes correlated significantly (Fig. 3c, Pearson’s R=0.829, t=119.74, df=6,521, 95% CI=0.821–0.837, P<2.2 e-16). While correlating fold-change yields a significant P-value because of the large number of genes analyzed, it is clear that the relationship is weak, as the correlation is close to zero (Fig. 3d, Pearson’s R=0.052, t=4.1,766, df=6,521, 95% CI=0.027–0.076, P<3 e-5). This is expected, as all fold-changes of non-DE genes are close to zero.

Usage Notes

All raw FASTQ files and the associated FPKM matrix file can be downloaded from the Gene Expression Omnibus (GEO) under Accession Number GSE89413. STAR-Fusion (https://github.com/STAR-Fusion/STAR-Fusion) enables detection of fusion transcripts. Alternative gene expression analyses can be performed using RSEM19 and/or transcript level analyses can be performed using kallisto20. Use of kallisto will also allow quantification of non-coding RNA abundances. Differential expression analyses may be performed using the common R packages, limma21 or DESeq2 (ref. 17). Differentially expressed gene lists can be explored for enrichment in signaling pathways using Ingenuity Pathway Analysis (Qiagen, http://www.ingenuity.com/products/ipa) and/or gene ontologies using ToppGene22 or the Gene Ontology Consortium tool23. Finally, these expression data can be integrated with epigenomics datasets (e.g.: ChIP-Seq, DNase-Seq/ATAC-Seq, Histone ChIP-Seq) to infer transcriptional regulation or repression.

Additional Information

How to cite this article: Harenza, J. L. et al. Transcriptomic profiling of 39 commonly-used neuroblastoma cell lines. Sci. Data 4:170033 doi: 10.1038/sdata.2017.33 (2017).

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

sdata201733-isa1.zip (4.1KB, zip)

Acknowledgments

We thank the National Cancer Institute Pediatric Preclinical Testing Program (PPTP) and the Children’s Oncology Group (COG) Cell Culture and Xenograft Repository for the neuroblastoma cell lines. We thank Stephen Mahoney and Kristen Hunter from The Children’s Hospital of Philadelphia Nucleic Acids and Protein Core as well as Arthur Young and Katherine Cross from Guardian Forensic Sciences for performing and interpreting STR profiles for the cell lines. We also acknowledge and thank the TARGET consortium for providing access to patient RNA-Seq and clinical data. This work was supported by NIH grants CA124709 (JMM) and CA180692 (JMM), the Giulio D’Angio Endowed Chair (JMM), and Alex’s Lemonade Stand Foundation.

Footnotes

The authors declare no competing financial interests.

Data Citations

  1. Harenza J., Diamond M. A., Hart L. S., Maris J. M. 2016. Gene Expression Omnibus . GSE89413
  2. 2009. NBCI Bioproject . PRJNA89523
  3. Harenza J., Diamond M. A., Maris J. M. 2016. Gene Expression Omnibus . GSE89968

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Harenza J., Diamond M. A., Hart L. S., Maris J. M. 2016. Gene Expression Omnibus . GSE89413
  2. 2009. NBCI Bioproject . PRJNA89523
  3. Harenza J., Diamond M. A., Maris J. M. 2016. Gene Expression Omnibus . GSE89968

Supplementary Materials

sdata201733-isa1.zip (4.1KB, zip)

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