{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Olivier Gandrillon"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-16741"],"description":["Neuroblastoma (NB), a pediatric cancer arising from disrupted sympathetic neuron differentiation, exhibits marked heterogeneity and limited therapeutic options.  To better understand its molecular circuitry dynamics, we applied CARDAMOM, a novel Gene Regulatory Network (GRN) inference framework to single-cell RNA-seq data from patient-derived tumoroids. This approach models gene regulation via piecewise deterministic Markov processes, capturing transcriptional bursting and protein-mediated feedback, overcoming limitations of RNA velocity (e.g., gene independence and lack of biological time). We identified a continuous chromaffin-to-sympathoblast differentiation trajectory, validated by RNA velocity and scFates. Using velocity pseudotime, we selected 85 dynamically relevant genes enriched in cell cycle and DNA replication functions. Notably, 9 genes overlapped with those driving normal sympathoadrenal differentiation, underscoring tumor-normal similarity. The inferred 85-genes network reproduced quite well experimental gene expression patterns in silico, and allowed to predict  protein-level dynamics. Furthermore, it allowed to assess the effect of in silico perturbations (both knock-out and overexpression) of hub genes (e.g., TCF4 and PLK1). We show that those perturbations significantly altered cell fate proportions, with TCF4 knockout increasing chromaffin-like cells and reducing proliferative late sympathoblasts — suggesting a therapeutic strategy to promote differentiation.  Our work therefore demonstrates that NB tumoroids retain a dynamic, differentiation-like architecture amenable to GRN modeling. Predicted druggable targets (e.g., PLK1, TCF4) offer testable therapeutic avenues, including repurposing BET inhibitors (JQ1) or PLK1 inhibitors (BI2536), potentially in combination."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Sample Collection - PDTs were derived as described in : \"Nguyen, T. N. T., et al. (2025). Multiscale modeling of the spatial structure of stem cells in neuroblastoma patient-derived tumoroids reveals a critical role for a short range diffusive process. BioRXiv\\","Sequencing - Sequenced at  tthe IGFL sequencing PSI platform: https://igfl.ens-lyon.fr/offres-et-technologies/platforms/sequencing-platform on an ILLUMINA NEXTSEQ500","Nucleic Acid Extraction - Single-cell RNA sequencing was performed using RevGel-seq (Komatsu, 2023), adapted by Scipio Bioscience. PDTs were trypsinized into a single-cell suspension and chemically labeled with a bifunctional linker (polyA and hydrophobic moiety) in DPBS for 5 minutes. Labeled cells (300 cells/µL, 15,000 total) were mixed with barcoded beads (50 µL each) and homogenized to form cell-bead complexes via collisions. The mixture was diluted in 2.4 mL hydrogel, gelated on ice for 20 minutes, and lysed to release RNA, which hybridized to bead-bound poly-T oligos.","Library Construction - After degelation, bead-bound RNA underwent reverse transcription (Maxima H Minus RT kit) and second-strand synthesis (S3 Supermix). cDNA was PCR-amplified (KAPA HiFi), purified with SPRIselect, and quality-checked via TapeStation and Qubit. Libraries were prepared using the Illumina Nextera XT Kit (5-minute fragmentation, 10 PCR cycles) and sequenced on the NextSeq500 platform (paired-end, 25/0/0/67)."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - median count depth normalization with log1p transformation"],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["NextSeq 500"],"study_type":["RNA-seq of coding RNA from single cells"],"species":["Homo sapiens"],"pubmed_authors":["Olivier Gandrillon"],"additional_accession":[]},"is_claimable":false,"name":"scRNAseq dataset from neuroblastomas tumoroids","description":"Neuroblastoma (NB), a pediatric cancer arising from disrupted sympathetic neuron differentiation, exhibits marked heterogeneity and limited therapeutic options.  To better understand its molecular circuitry dynamics, we applied CARDAMOM, a novel Gene Regulatory Network (GRN) inference framework to single-cell RNA-seq data from patient-derived tumoroids. This approach models gene regulation via piecewise deterministic Markov processes, capturing transcriptional bursting and protein-mediated feedback, overcoming limitations of RNA velocity (e.g., gene independence and lack of biological time). We identified a continuous chromaffin-to-sympathoblast differentiation trajectory, validated by RNA velocity and scFates. Using velocity pseudotime, we selected 85 dynamically relevant genes enriched in cell cycle and DNA replication functions. Notably, 9 genes overlapped with those driving normal sympathoadrenal differentiation, underscoring tumor-normal similarity. The inferred 85-genes network reproduced quite well experimental gene expression patterns in silico, and allowed to predict  protein-level dynamics. Furthermore, it allowed to assess the effect of in silico perturbations (both knock-out and overexpression) of hub genes (e.g., TCF4 and PLK1). We show that those perturbations significantly altered cell fate proportions, with TCF4 knockout increasing chromaffin-like cells and reducing proliferative late sympathoblasts — suggesting a therapeutic strategy to promote differentiation.  Our work therefore demonstrates that NB tumoroids retain a dynamic, differentiation-like architecture amenable to GRN modeling. Predicted druggable targets (e.g., PLK1, TCF4) offer testable therapeutic avenues, including repurposing BET inhibitors (JQ1) or PLK1 inhibitors (BI2536), potentially in combination.","dates":{"release":"2026-07-07T00:00:00Z","modification":"2026-07-07T05:25:37.518Z","creation":"2026-03-11T22:24:34.72Z"},"accession":"E-MTAB-16741","cross_references":{"ENA":["ERP190690"],"EFO":["EFO_0002944","EFO_0004170","EFO_0005684","EFO_0005518","EFO_0003816","EFO_0004184"]}}