Inferring and simulating a gene regulatory network for the sympathoadrenal differentiation from single-cell transcriptomics in human

2025 · doi:10.1101/2025.03.21.644507
preprint OA: closed CC-BY-4.0
📄 Open PDF Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-07, 2026-07-04 · read from full text ⓘ

The study investigates molecular mechanisms underlying human sympathoadrenal differentiation during neuroblastoma development, focusing on how neural-crest derived lineages transition into sympathoblasts and chromaffin cells, using a published single-cell RNA-seq dataset. The authors infer an integrated gene regulatory network with CARDAMOM and then simulate network dynamics with HARISSA to model regulatory relationships beyond what RNA velocity alone can provide. They report a 97-gene GRN that captures the transition from Schwann cell precursors to chromaffin cells and sympathoblasts, including self-reinforcing loops and toggle-switch behavior, and show simulations that reproduce experimentally observed gene expression distributions. A caveat noted by the authors is that dynamical prediction statements could not be verified and were deleted. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Background Neuroblastoma is a malignant childhood cancer with significant inter- and intrapatient heterogeneity arising from the abnormal differentiation of neural crest cells into sympathetic neurons. The lack of actionable mutations limits therapeutic options, highlighting the need to better understand the molecular mechanisms that drive this differentiation. Although RNA velocity has provided some insights, modeling regulatory relationships is limited. Methods To address this, we applied our integrated gene regulatory network (GRNs) inference (CARDAMOM) and simulation (HARISSA) tools using a published single-cell RNAseq dataset from human sympathoadrenal differentiation. Results Our analysis identified a 97-gene GRN that drives the transition from Schwann cell precursors to chromaffin cells and sympathoblasts, highlighting dynamic interactions such as self-reinforcing loops and toggle switches. The simulation of that GRN was able to reproduce very satisfactorily the experimentally observed gene expression distributions. Conclusions Altogether, these findings demonstrate the utility of our GRN model framework for inferring GRN structure, even in the absence of a time-resolved dataset.
Full text 1,458 characters · extracted from oa-doi-fallback · 4 sections · click to expand

Abstract

Background Neuroblastoma is a malignant childhood cancer with significant inter- and intrapatient heterogeneity arising from the abnormal differentiation of neural crest cells into sympathetic neurons. The lack of actionable mutations limits therapeutic options, highlighting the need to better understand the molecular mechanisms that drive this differentiation. Although RNA velocity has provided some insights, modeling regulatory relationships is limited.

Methods

To address this, we applied our integrated gene regulatory network (GRNs) inference (CARDAMOM) and simulation (HARISSA) tools using a published single-cell RNAseq dataset from human sympathoadrenal differentiation.

Results

Our analysis identified a 97-gene GRN that drives the transition from Schwann cell precursors to chromaffin cells and sympathoblasts, highlighting dynamic interactions such as self-reinforcing loops and toggle switches. The simulation of that GRN was able to reproduce very satisfactorily the experimentally observed gene expression distributions.

Conclusions

Altogether, these findings demonstrate the utility of our GRN model framework for inferring GRN structure, even in the absence of a time-resolved dataset. Competing Interest Statement The authors have declared no competing interest. Footnotes The section regarding dynamical prediction proved to contain statements that could not be backed up by additional verifications, and was therefore deleted.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-22T02:00:06.705733+00:00
License: CC-BY-4.0