Single-cell MultiOmics and spatial transcriptomics demonstrate neuroblastoma developmental plasticity
Bibliographic record
Abstract
Neuroblastoma, the most prevalent extracranial pediatric solid tumor, arises from neural crest progeny cells. It exhibits substantial developmental plasticity and intratumoral heterogeneity, leading to survival rates below 50% in high-risk cases. The regulatory mechanisms underlying this plasticity remain largely elusive. In this integrative study, we used single-cell MultiOmics from a mouse spontaneous tumor model and spatial transcriptomics from human patient samples to dissect the transcriptional and epigenetic landscapes that govern developmental states in neuroblastoma. We identified developmental intermediate states in high-risk neuroblastomas critical for malignant transitions and uncovered extensive epigenetic priming with latent capacity for diverse state transitions. Furthermore, we mapped enhancer gene regulatory networks (eGRNs) and tumor microenvironments sustaining these aggressive states. State transitions and malignancy could be interfered with by targeting transcription factors controlling the eGRNs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".