Combining in vitro and in silico approaches to model neural tube patterning and isthmic organizer formation.
Overview
- Computational Science for Health and Environment, Department of Earth and Environmental Sciences, Lund University,Lund, Sweden
- Department of Experimental Medical Science, Lund University,Lund, Sweden
- Department of Neuroscience, University of Copenhagen,Copenhagen, Denmark
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University,Lund, Sweden
- Wallenberg Neuroscience Center and Lund Stem Cell Center, Lund University,Lund, Sweden
Abstract
During early embryonic development, the human neural tube is formed and patterned through spatial regionalization of cell identity, driven by gene regulatory responses to morphogen gradients. However, many of the underlying mechanisms remain unclear. Here, we integrate single-cell RNA sequencing data from in vitro emulation of neural tube patterning to develop computational models of rostral-caudal and dorsal-ventral patterning. By embedding these models in a 3D geometry, we reveal how transient morphogen signals induce irreversible patterns consistent with developmental biology and experimental data. Notably, our framework accurately captures the formation and maintenance of the isthmic organizer at the mid-hindbrain boundary, providing a realistic and mechanistic picture of neural tube patterning. This integrated approach bridges in vitro experimentation and computational modeling to uncover fundamental principles of neural development.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
No file of the authors' code could be read here: it is described below, and read at its source.
codebyad/model_neural_tube_patterning_and_isthmic_organizer_formation
Availability: 1 check, the latest on 27 September 2026: the link is dead
- 27 September 2026: the link is dead
Code availability
The custom computer code, scripts, and simulation workflows developed and used in this study to model neural tube patterning and isthmic organizer formation are publicly available on GitHub at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Tracing map
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Data
No dataset and no data link were found in the paper.
Data availability
Processed MiSTR single-cell and single-nucleus transcriptomic datasets from Rathore et al. are publicly available through the UCSC Cell Browser at https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 9 MeSH terms, 6 funders, 51 references, 1 RRID.
Cite
This paper
Bertilsson, F., Degener, A., Ibek, P., Rathore, G. S., Andersson, E., Rifes, P., Kirkeby, A., & Olariu, V. (2026). Combining in vitro and in silico approaches to model neural tube patterning and isthmic organizer formation. NPJ systems biology and applications, 12(1), 126. https://
BibTeX
@article{bertilsson2026c
author = {Bertilsson, Fredrik and Degener, Alexander and Ibek, Paulina and Rathore, Gaurav Singh and Andersson, Emil and Rifes, Pedro and Kirkeby, Agnete and Olariu, Victor},
title = {{Combining in vitro and in silico approaches to model neural tube patterning and isthmic organizer formation}},
journal = {NPJ systems biology and applications},
year = {2026},
month = aug,
volume = {12},
number = {1},
pages = {126},
publisher = {Nature Publishing Group},
issn = {2056-7189},
doi = {10.1038/
url = {https://
pmid = {42629366},
pmcid = {PMC13498569}
}
RIS
TY - JOUR
AU - Bertilsson, Fredrik
AU - Degener, Alexander
AU - Ibek, Paulina
AU - Rathore, Gaurav Singh
AU - Andersson, Emil
AU - Rifes, Pedro
AU - Kirkeby, Agnete
AU - Olariu, Victor
TI - Combining in vitro and in silico approaches to model neural tube patterning and isthmic organizer formation
T2 - NPJ systems biology and applications
J2 - NPJ Syst Biol Appl
PY - 2026
DA - 2026/
VL - 12
IS - 1
SP - 126
SN - 2056-7189
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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