OSCR

Notch-mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or -receiving roles.

Code ↔ Paper

9 matches 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 9 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Star Methods › Method details › Area fold-change analysis ↔ FoldChangeNCAreas.ipynb, the whole file · a weak match · score 0.64 · Fold change, transcribing NCs, apical area, 2.5 min, windows
  2. [2] § Star Methods › Method details › Modelling ↔ ImposedPerimeter/RunImposedPerimeterModel.m, lines 15–59 · score 0.63 · inhibiting activator expression, Hill coefficients, Notch, spl
  3. [3] § Results › E(spl)-m8 transcription correlates with morphological changes in NCs ↔ FoldChangeNCAreas.ipynb, the whole file · a weak match · score 0.62 · apical area fold, fold changes, transcribing NCs, W1, W2, 2.5 min
  4. [4] § Results › A bias in apical size prefigures transcriptional onset within clusters ↔ MultivariateModels.ipynb, lines 30–39 · score 0.62 · logistic regression, random forests, Multivariate, splits, classification, model
  5. [5] § Star Methods › Method details › Modelling ↔ ImposedPerimeter/RunImposedPerimeterModel.m, lines 15–59 · score 0.59 · Hill coefficients, Hill function, Activated, Delta, thresholds, inhibiting
  6. [6] § Star Methods › Method details › Modelling ↔ SOP_DefaultParams_rho_adapted_weighted.m, lines 5–94 · score 0.57 · Hill coefficients, Hill function, Activated, Delta, thresholds, inhibiting
  7. [7] § Results › Lateral inhibition model incorporating cell perimeter and tension-differences can replicate signalling properties ↔ SOP_DefaultParams_rho_adapted_weighted.m, lines 5–94 · score 0.56 · lateral inhibition, cis inhibition, dimensional, connectivity, weighted, trans
  8. [8] § Results › Lateral inhibition model incorporating cell perimeter and tension-differences can replicate signalling properties ↔ DynamicPerimeter/IterateOverDifferentThresholdsDynamicPerimeterModelForPaper.m, lines 176–180 · score 0.53 · success probability, perimeter model, initial perimeter, simulation, dynamic
  9. [9] § Star Methods › Method details › Cross-correlation analysis ↔ CrossCorrelationsOnCombinedData.m, lines 1–7 · score 0.53 · NC apical area, NB apical area, correlation, Cross, transcribing

Paper

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

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The authors' code

Jupyter notebook · 150 lines · 3.8 KB · AGPL-3.0 · 2 matches

  1. # %%
  2. # Script that analyses area fold changes in transcribing and non-transcribing NCs shown in Figure 5
  4. # Imports
  5. import pandas as pd
  6. import seaborn as sns
  7. import matplotlib.pyplot as plt
  8. from scipy.stats import ttest_rel
  10. # Load dataset
  11. df = pd.read_csv('AreasInWindowsNaNMean.csv')
  13. # Identify window columns
  14. windows = [col for col in df.columns if col != 'Signalling']
  16. # Calculate fold change
  17. df_fc = df.copy()
  18. df_fc[windows] = df_fc[windows].div(df_fc[windows[0]], axis=0)
  20. # Split signalling vs non-signalling
  21. df_signal = df_fc[df_fc['Signalling'] == 1].drop(columns=['Signalling'])
  22. df_nosignal = df_fc[df_fc['Signalling'] == 0].drop(columns=['Signalling'])
  24. # Heatmap plotting function
  25. def plot_heatmap(df, title, outfile):
  26. plt.figure(figsize=(6, 6))
  27. ax = sns.heatmap(
  28. df,
  29. cmap="GnBu",
  30. cbar=True,
  31. vmin=0.5, vmax=2.0,
  32. linewidths=0,
  33. linecolor="none",
  34. square=False
  35. )
  37. # Rasterize heatmap only (avoids grid artifacts in PDF)
  38. ax.collections[0].set_rasterized(True)
  40. plt.title(title)
  41. plt.xlabel("Windows")
  42. plt.ylabel("Cells")
  43. plt.tight_layout()
  44. plt.savefig(outfile, dpi=300, bbox_inches="tight", pad_inches=0)
  45. plt.show()
  47. # Plot heatmaps
  48. plot_heatmap(
  49. df_signal,
  50. "Fold-change areas across windows (Signalling cells)",
  51. "areas_fc_heatmap_signalling_NaNMean.pdf"
  52. )
  54. plot_heatmap(
  55. df_nosignal,
  56. "Fold-change areas across windows (Non-signalling cells)",
  57. "areas_fc_heatmap_nonsignalling_NaNMean.pdf"
  58. )
  60. # Stats + paired t-tests
  61. def window_stats_and_tests(df_fc, label):
  62. print(f"\n=== {label} cells ===")
  64. windows = df_fc.columns.tolist()
  66. # Mean ± SD per window
  67. stats = df_fc.agg(['mean', 'std']).T
  68. print("\nMean ± SD fold change per window:")
  69. print(stats)
  71. # Paired t-tests between successive windows
  72. print("\nPaired t-tests (successive windows):")
  73. for w1, w2 in zip(windows[:-1], windows[1:]):
  74. tstat, pval = ttest_rel(df_fc[w1], df_fc[w2], nan_policy='omit')
  75. print(f"{w1} → {w2}: t = {tstat:.3f}, p = {pval:.4e}")
  77. return stats
  79. # Convert to long format
  80. def to_long(df_fc):
  81. df_long = df_fc.copy()
  82. df_long['CellID'] = df_long.index
  83. df_long = df_long.melt(
  84. id_vars='CellID',
  85. var_name='Window',
  86. value_name='FoldChange'
  87. )
  88. return df_long
  90. # Boxplot + datapoints + paired lines
  91. def plot_boxplot_with_lines(df_long, title, outfile):
  92. plt.figure(figsize=(7, 5))
  94. sns.boxplot(
  95. data=df_long,
  96. x='Window',
  97. y='FoldChange',
  98. color='lightgray',
  99. showfliers=False
  100. )
  102. sns.stripplot(
  103. data=df_long,
  104. x='Window',
  105. y='FoldChange',
  106. color='black',
  107. size=4,
  108. jitter=0.15,
  109. alpha=0.7
  110. )
  112. # Paired lines
  113. for cell_id, d in df_long.groupby('CellID'):
  114. plt.plot(
  115. d['Window'],
  116. d['FoldChange'],
  117. color='black',
  118. alpha=0.3,
  119. linewidth=0.7
  120. )
  121. plt.ylim(0.2,2.5)
  122. plt.axhline(1, color='red', linestyle='--', linewidth=1)
  123. plt.ylabel("Apical area fold change")
  124. plt.xlabel("Time window")
  125. plt.title(title)
  126. plt.tight_layout()
  127. plt.savefig(outfile, dpi=300, bbox_inches="tight")
  128. plt.show()
  130. # Run stats + boxplots (signalling)
  131. stats_signal = window_stats_and_tests(df_signal, "Signalling")
  132. df_signal_long = to_long(df_signal)
  134. plot_boxplot_with_lines(
  135. df_signal_long,
  136. "Apical area fold change (Signalling cells)",
  137. "areas_fc_boxplot_signalling_NaNMean.pdf"
  138. )
  140. # Run stats + boxplots (non-signalling)
  141. stats_nosignal = window_stats_and_tests(df_nosignal, "Non-signalling")
  142. df_nosignal_long = to_long(df_nosignal)
  144. plot_boxplot_with_lines(
  145. df_nosignal_long,
  146. "Apical area fold change (Non-signalling cells)",
  147. "areas_fc_boxplot_nonsignalling_NaNMean.pdf"
  148. )
  149. df_fc.to_csv("areas_foldchange_dataset_used.csv", index=False)

FoldChangeNCAreas.ipynb at commit 10955e7, under AGPL-3.0 · at the source

Overview

Authors: Prachi Richa1, Charalambos Roussos1, Chengxi Zhu2, Martin O Lenz2, Shahar Kasirer3, David Sprinzak4, Sarah Bray1
ORCID iDs: Prachi Richa, Sarah Bray
  1. Department of Physiology Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
  2. Cambridge Advanced Imaging Centre, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
  3. School of Physics and Astronomy, Tel Aviv University, Tel Aviv 69978, Israel
  4. School of Neurobiology, Biochemistry and Biophysics, Tel Aviv University, Tel Aviv 69978, Israel
Institutions: University of Cambridge (United Kingdom); Cambridge Advanced Imaging Centre (United Kingdom); Tel Aviv University (Israel)
Journal: Current biology : CB, volume 36, issue 17, pages 4297-4309.e6
Dates: published online 12 August 2026; in print 7 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.cub.2026.07.041 · PMID 42586067 · PMCID PMC7619482 · OpenAlex W7202253646
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: drosophila (organism), cellular / molecular (subfield)
Methods: Preprocessing, Statistics, Machine learning, Evoked potentials, fMRI & imaging
Keywords: Notch, Lateral inhibition, live-transcription, cell-mechanics, mathematical modelling
MeSH: Drosophila melanogaster*, Drosophila Proteins*, Neural Stem Cells*, Neurogenesis*, Receptors, Notch*, Signal Transduction*, Animals, Gene Expression Regulation, Developmental (* major topic)
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Wellcome Trust (212936/Z/18/Z, 212207/Z/18, 212207); Isaac Newton Trust; UK Research and Innovation Medical Research Council; NIH HHS (P40 OD018537)
Citations: cited by 1 paper (Europe PMC); 59 references in the paper

Abstract

During neurogenesis, neuroblasts are selected from proneural-competent cells through lateral inhibition, a process controlled by the evolutionarily conserved Notch signalling pathway. By tracking transcription from Notch-target genes and cell morphologies in real time, we discovered that the presumptive neuroblast never initiates target-gene transcription. This implies a pre-existing bias directs Notch signalling. The bias correlates with a heterogeneity in apical cell areas which is further reinforced during neuroblast selection. Additionally, the length and duration of neuroblast-neighbour cell contacts predict the likelihood of transcription. Using mathematical modelling we show that lateral inhibition seeded with subtle morphological differences can bias cells toward signal-sending or receiving roles before transcriptional feedback occurs. Notch activation further alters apical cell area, reinforcing the initial bias. We propose that signalling and cell mechanics work together to ensure the robust selection of a single neural precursor.

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 9 matches between paragraphs and lines of code.

crou607/neurogenesis_analysis

License: AGPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 10955e7fc55709e180c1b6e4711ac2800f277a3e, 9 June 2026
Languages: MATLAB (18), Jupyter (8)
Size: 28 files, 26 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, 8 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (8 files), pandas (8 files), NumPy (5 files), seaborn (5 files), Statistics and Machine Learning Toolbox (4 files), scikit-learn (4 files), SciPy (4 files), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/crou607/neurogenesis_analysis.git

Zenodo 20612238

License: apgl-v3
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (8 files), pandas (8 files), NumPy (5 files), seaborn (5 files), Statistics and Machine Learning Toolbox (4 files), scikit-learn (4 files), SciPy (4 files), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
28 files
At the source: doi.org/10.5281/zenodo.20612238

The paper's code and data availability statement is in the Data section.

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;
  • 52 scripts, each with its path and the digest of its content;
  • 9 matches 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 and code availability

All Data have been deposited at FigShare and are publicly available as of the date of publication at https://figshare.com/s/a3665194df21a645f6ff

All original code has been deposited at Zenodo and is publicly available at DOI 10.5281/zenodo.20612238 (https://doi.org/10.5281/zenodo.20612238) as of the date of publication.

Any additional information required to reanalyze the data reported in this work paper is available from the Lead Contact upon request.

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 3, 28 September 2026

  • Publisher: n/a → Elsevier BV
  • Authors: added Sarah Bray (0000-0002-1642-599X); removed Sarah Bray

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 8 MeSH terms, 4 funders, 58 references.

Cite

This paper

Richa, P., Roussos, C., Zhu, C., Lenz, M. O., Kasirer, S., Sprinzak, D., & Bray, S. (2026). Notch-mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or -receiving roles. Current biology : CB, 36(17), 4297-4309.e6. https://doi.org/10.1016/j.cub.2026.07.041

BibTeX

@article{richa2026notch,
author = {Richa, Prachi and Roussos, Charalambos and Zhu, Chengxi and Lenz, Martin O and Kasirer, Shahar and Sprinzak, David and Bray, Sarah},
title = {{Notch-mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or -receiving roles}},
journal = {Current biology : CB},
year = {2026},
month = aug,
volume = {36},
number = {17},
pages = {4297--4309.e6},
publisher = {Elsevier BV},
issn = {0960-9822},
doi = {10.1016/j.cub.2026.07.041},
url = {https://doi.org/10.1016/j.cub.2026.07.041},
pmid = {42586067},
pmcid = {PMC7619482}
}

RIS

TY - JOUR
AU - Richa, Prachi
AU - Roussos, Charalambos
AU - Zhu, Chengxi
AU - Lenz, Martin O
AU - Kasirer, Shahar
AU - Sprinzak, David
AU - Bray, Sarah
TI - Notch-mediated lateral inhibition is shaped by morphological differences to reinforce bias toward signal-sending or -receiving roles
T2 - Current biology : CB
J2 - Curr Biol
PY - 2026
DA - 2026/08/12
VL - 36
IS - 17
SP - 4297
EP - 4309.e6
SN - 0960-9822
PB - Elsevier BV
DO - 10.1016/j.cub.2026.07.041
UR - https://doi.org/10.1016/j.cub.2026.07.041
LA - en
ER -

CSL-JSON

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"container-title": "Current biology : CB",
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