OSCR

Jagged-mediated lateral induction patterns Notch3 signaling within adult neural stem cell populations.

Code ↔ Paper

1 match between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 1 match · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Notch3 signaling levels are spatially patterned among adult NSPCs in situ ↔ BAC/pairplot.py, the whole file · a weak match · score 0.53 · maximal radius, N3ICD sum, apical area, axis, classify, surrounding

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 88 lines · 3.4 KB · BSD-3-Clause · 1 match

  1. import pandas as pd
  2. from matplotlib import pyplot as plt
  3. import seaborn as sns
  4. from main_functions import *
  6. sns.set_theme(style="white")
  9. if __name__ == "__main__":
  10. # Data reading
  11. df0 = pd.read_excel("aires_bac.xlsx", sheet_name=0)
  13. df = [df0]
  15. # Maximal radius to plot and proportion for creating groups
  16. r = 10.0
  17. q = 0.2
  19. # Reference mark for x-axis and marks to plot wrt reference
  20. mark_id = "Apical Area"
  21. mark_list = ["N3ICD mean", "N3ICD Sum"]
  23. aux_low = 0.0
  24. aux_mid = 0.0
  25. aux_high = 0.0
  26. voisinages = []
  27. n = 0
  28. r_list = [10.0]
  30. fig, ax = plt.subplots(2, 1, figsize=(7, 8), layout="tight")
  32. for group_id, df_1 in enumerate(df):
  33. marks = df_1[mark_id].to_numpy()
  34. X, Y = df_1["X"].to_numpy(), df_1["Y"].to_numpy()
  36. # Determine window of study using average radius of nuclei
  37. areas = df_1['Apical Area']
  38. radius = np.sqrt(areas / np.pi)
  39. mean_radius = radius.mean()
  41. min_x, max_x = np.min(X), np.max(X)
  42. min_y, max_y = np.min(Y), np.max(Y)
  44. len_x, len_y = max_x - min_x + 2 * mean_radius, max_y - min_y + 2 * mean_radius
  45. for i, y_mark_id in enumerate(mark_list):
  46. # Marks for classification in groups.
  47. y_marks = df_1[y_mark_id].to_numpy()
  49. # Compute lower and upper bounds for marks
  50. lower_bound = np.quantile(y_marks, q)
  51. upper_bound = np.quantile(y_marks, 1 - q)
  53. # Compute signal inside circle of radius r for each individual cell
  54. signal, _ = individual_marked_K_func(X, Y, r_list, len_x, len_y, lower_bound, upper_bound, y_marks, y_marks, m_r2_func)
  56. # Colours according to class (y_marks)
  57. colors = [1.0 if mark > upper_bound else -1.0 if mark < lower_bound else 0.0 for mark in y_marks]
  59. # Indices to separate outliers (points with no surrounding signal)
  60. positive_id = signal > 0.0
  61. null_id = signal == 0.0
  63. # Plotting of points coloured by surrounding signal
  64. ax[i].scatter(marks[null_id], y_marks[null_id], c="k", edgecolors="w", linewidths=0.5, zorder=10)
  65. sc = ax[i].scatter(marks[positive_id], y_marks[positive_id], c=np.abs(signal[positive_id]), cmap="Reds_r", linewidths=0.5,
  66. vmax=np.sort(signal)[-1],
  67. edgecolors="k", zorder=11)
  68. cb = plt.colorbar(sc, ax=ax[i], label="Total surrounding signal")
  70. # Plot of groups according to lower and upper groups
  71. plot_with_confidence([np.min(marks), np.max(marks)], [np.min(y_marks), np.min(y_marks)],
  72. [lower_bound, lower_bound],
  73. ax=ax[i], c="#440154", alpha=0.4, coef_alpha=0.0)
  74. plot_with_confidence([np.min(marks), np.max(marks)], [upper_bound, upper_bound],
  75. [np.max(y_marks), np.max(y_marks)],
  76. ax=ax[i], c="#fde725", alpha=0.4, coef_alpha=0.0)
  78. plot_with_confidence([np.min(marks), np.max(marks)], [lower_bound, lower_bound],
  79. [upper_bound, upper_bound],
  80. ax=ax[i], c="#21918c", alpha=0.4, coef_alpha=0.0)
  82. ax[1].set_xlabel(mark_id)
  84. fig.suptitle("BAC")
  86. fig.savefig("images/bac_pairplot.pdf", format="pdf", bbox_inches="tight")
  88. plt.show()

pairplot.py at commit ca2a78a, under BSD-3-Clause · at the source

Overview

Authors: Sara Ortica1, Miguel Martinez Herrera1, Louis Degroux1, Bastian Rochette1, Nicolas Dray1, Laure Bally-Cuif1
ORCID iDs: Sara Ortica, Miguel Martinez Herrera, Nicolas Dray, Laure Bally-Cuif
  1. Institut Pasteur, Université Paris Cité, CNRS UMR3738, Zebrafish Neurogenetics Unit, F-75015 Paris, France
Institutions: Centre National de la Recherche Scientifique (France); Institut Pasteur (France); Université Paris Cité (France); Biologie du Développement et Cellules Souches (France)
Journal: Nature communications, volume 17, issue 1, article 3986
Dates: received 11 August 2025; accepted 23 February 2026; published online 14 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-70478-0 · PMID 41832179 · PMCID PMC13136484 · OpenAlex W7135371441
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: zebrafish (organism)
Methods: Preprocessing, Spectral & time-frequency, Statistics, Evoked potentials, Connectivity
Keywords: Neural stem cells, Adult stem cells, Stem-cell niche
MeSH: Adult Stem Cells*, Jagged-1 Protein*, Neural Stem Cells*, Receptor, Notch3*, Signal Transduction*, Zebrafish Proteins*, Animals, Intracellular Signaling Peptides and Proteins, Membrane Proteins, SOXB1 Transcription Factors, Telencephalon, Zebrafish (* major topic)
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Agence Nationale de la Recherche (French National Research Agency) (LabEx Revive ANR-10-LABX-0073); Fondation pour la Recherche Médicale (Foundation for Medical Research in France) (EQU202203014636); EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) (ERC SyG PEPS 101071786)
Citations: cited by 3 papers (Europe PMC); 100 references in the paper

Abstract

In the adult brain, Notch3 signaling promotes neural stem cell (NSC) quiescence and stemness. It remains unknown how Notch3 signaling levels are controlled and relate to these NSC decisions. Here we directly measure the nuclear translocation of the Notch3 intracellular fragment (N3ICD) and quantify Notch3 signaling in NSCs of the zebrafish adult telencephalon in situ. We report that Notch3 signaling levels match NSC quiescence and stemness levels. In physical space, Notch3 signaling is patterned and high signaling levels surround N3ICDlow cells, which also express the deltaA (dla) ligand. Another ligand, jagged1b (jag1b), expressed in all NSCs, activates Notch3 signaling and sustains expression of the stemness factor Sox2. Finally, lowering jag1b preserves the structured distribution of Notch3 signaling levels in space but attenuates their variance. We propose that Notch3 signaling integrates Dla-mediated lateral inhibition and Jag1b-mediated lateral induction to control quiescence and stemness and their spatiotemporal dynamics in adult NSCs.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Zenodo 18244815

License: BSD-3-Clause
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: seaborn (14 files), pandas (13 files), Matplotlib (11 files), NumPy (10 files), SciPy (4 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
18 files
At the source: zenodo.org/records/18244815

migmtz/spatialn3icd

License: BSD-3-Clause
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ca2a78ad7ac084f44ba62b139f13711b8be87ae2, 12 December 2025
Languages: Python (15), Jupyter (1)
Size: 41 files, 16 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (requirements.txt), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: seaborn (14 files), pandas (13 files), Matplotlib (11 files), NumPy (10 files), SciPy (4 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
18 files
At the source: github.com/migmtz/spatialN3ICD/tree/v1.0.0

Code availability

The spatial statistics analysis was coded in Python. Computing the Km and Lm functions along with the hypothesis testing procedures were entirely implemented by leveraging classic functions from pandas, NumPy and Scipy libraries and parallelization was performed using the multiprocessing package. All data (raw and processed), codes to compute the quantities and to generate all images in Figs. 3, 6 and Supplementary Fig. S4, and a Readme can be found in https://zenodo.org/records/18244815 (ref. 100).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 32 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Lead contact: availability: all materials and transgenic lines generated in this study are available upon request to data are provided in this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 12 MeSH terms, 3 funders, 95 references.

Cite

This paper

Ortica, S., Martinez Herrera, M., Degroux, L., Rochette, B., Dray, N., & Bally-Cuif, L. (2026). Jagged-mediated lateral induction patterns Notch3 signaling within adult neural stem cell populations. Nature communications, 17(1), 3986. https://doi.org/10.1038/s41467-026-70478-0

BibTeX

@article{ortica2026jagged,
author = {Ortica, Sara and Martinez Herrera, Miguel and Degroux, Louis and Rochette, Bastian and Dray, Nicolas and Bally-Cuif, Laure},
title = {{Jagged-mediated lateral induction patterns Notch3 signaling within adult neural stem cell populations}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3986},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70478-0},
url = {https://doi.org/10.1038/s41467-026-70478-0},
pmid = {41832179},
pmcid = {PMC13136484}
}

RIS

TY - JOUR
AU - Ortica, Sara
AU - Martinez Herrera, Miguel
AU - Degroux, Louis
AU - Rochette, Bastian
AU - Dray, Nicolas
AU - Bally-Cuif, Laure
TI - Jagged-mediated lateral induction patterns Notch3 signaling within adult neural stem cell populations
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/14
VL - 17
IS - 1
SP - 3986
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70478-0
UR - https://doi.org/10.1038/s41467-026-70478-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-70478-0",
"type": "article-journal",
"title": "Jagged-mediated lateral induction patterns Notch3 signaling within adult neural stem cell populations",
"container-title": "Nature communications",
"author": [
{
"family": "Ortica",
"given": "Sara"
},
{
"family": "Martinez Herrera",
"given": "Miguel"
},
{
"family": "Degroux",
"given": "Louis"
},
{
"family": "Rochette",
"given": "Bastian"
},
{
"family": "Dray",
"given": "Nicolas"
},
{
"family": "Bally-Cuif",
"given": "Laure"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3986",
"DOI": "10.1038/s41467-026-70478-0",
"PMID": "41832179",
"PMCID": "PMC13136484",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70478-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
14
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1126/sciadv.aed3625
Mice produce interneurons in the septum as a response to aversive experiences and antidepressant treatment.
Journal: Science advances
In common: 10 references
[2] doi:10.1016/j.cub.2026.07.041 [code]
Notch-mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or -receiving roles.
Journal: Current biology : CB
In common: seaborn, pandas, SciPy, 2 other tools, 5 references
[3] doi:10.1073/pnas.2522727123 [code]
3D epithelial cell topology tunes signaling range to promote precise patterning.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: pandas, SciPy, Matplotlib, 1 other tool, 3 references
[4] doi:10.1096/fj.202504890rr
Purinergic Receptor P2Y2 Regulates Neurogenesis and Functional Recovery Following Spinal Cord Injury in Zebrafish.
Journal: FASEB journal : official publication of the Federation of American Societies for Experimental Biology
In common: zebrafish, 4 references
[5] doi:10.1038/s44319-026-00731-6 [code]
Multifaceted conserved functions of Notch during post-embryonic neurogenesis in the annelid Platynereis.
Journal: EMBO reports
In common: 4 references
[6] doi:10.1016/j.stemcr.2026.103015 [code]
Brain injury reactivates a developmental program driving genesis and integration of transient LGE-class interneurons.
Journal: Stem cell reports
In common: seaborn, pandas, SciPy, 2 other tools, 2 references
[7] doi:10.1016/j.isci.2026.116206 [code]
Gut distension evokes rapid neural dynamics in vagal and hindbrain populations of larval zebrafish.
Journal: iScience
In common: seaborn, pandas, SciPy, 2 other tools, zebrafish, 1 reference
[8] doi:10.1016/j.stemcr.2026.102977 [code]
Integrative analysis of drug-gene signatures in human pluripotent stem cells reveals prazosin as a novel SQSTM1 regulator for ALS therapeutics.
Journal: Stem cell reports
In common: pandas, SciPy, Matplotlib, 1 other tool, zebrafish, 1 reference
[9] doi:10.64898/2026.03.30.715222 [code]
Synthetic lumen rounding directs neural progenitor division mode
Journal: bioRxiv (preprint)
In common: seaborn, pandas, SciPy, 2 other tools, 1 reference
[10] doi:10.7554/elife.100880 [code]
An applicable and efficient retrograde monosynaptic circuit mapping tool for larval zebrafish.
Journal: eLife
In common: seaborn, pandas, SciPy, 2 other tools, zebrafish

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.