OSCR

An intrinsic cytoskeletal oscillator establishes neuronal polarity.

Code ↔ Paper

17 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 17 matches
  1. [1] § Methods › Neurite tracking, growth profiling and local protein intensity profiling ↔ ImageJ_macros/_shared/Proc.py, lines 1–87 · score 0.72 · upper threshold, tip position, tip point, bright, neurite growth, neurite tip
  2. [2] § Methods › Neurite tracking, growth profiling and local protein intensity profiling ↔ ImageJ_macros/_shared/Proc.py, lines 1–87 · score 0.68 · Fiji plugins, neurite tip positions, macro, Jython, kymographs, batch
  3. [3] § ARP2/3 is required for neuronal polarization ↔ figures/EDFig01_coordination/edfig01c_fig01f_slice_neurite_freq_and_xcorr.Rmd, lines 6–58 · score 0.65 · acute cortical slices, mNeonGreen, expressing LYN, neurons expressing, TM, neurite tips
  4. [4] § Methods › Quantification of actin branches and orientation in EM tomograms ↔ figures/EDFig10_em/color_model.py, lines 1–44 · score 0.65 · filament orientation, barbed end, IMOD, contours, vectors, EM
  5. [5] § Methods › Colocalization analysis ↔ NeuriteKymoGeneration.py, lines 65–167 · score 0.63 · rolling radius, Subtract Background, Fiji
  6. [6] § Methods › Optogenetic control of ARP2/3 activation with PA-RAC1 ↔ templates/fig03n_parac1_precursor.Rmd, lines 6–127 · score 0.62 · C450M, T17N, PA RAC1, variant, LSM980, activation
  7. [7] § ARP2/3 distribution correlates with neurite growth ↔ templates/fig03n_parac1_precursor.Rmd, lines 6–127 · score 0.62 · C450M, T17N, PA RAC1, WT neurons, variants, GC
  8. [8] § ARP2/3 counteracts the actomyosin network ↔ figures/Fig05_myosin/fig05l_blebb_ko_neurite_xcorr.Rmd, lines 6–59 · score 0.60 · KO neurons treated, blebbistatin treated, retraction duration, retraction velocities, myosin, DIV
  9. [9] § Methods › Cross-correlation of neurite growth dynamics and local protein intensity fluctuations ↔ templates/fig04o_lyn_live_neurite_xcorr.Rmd, lines 764–800 · score 0.58 · get_summary_stats, cross correlation function, rstatix, ccf, derivative, lag
  10. [10] § Methods › Quantification of actin branches and orientation in EM tomograms ↔ figures/EDFig10_em/object_mod.py, lines 1–37 · score 0.57 · filament orientation, barbed end, IMOD, EM, actin
  11. [11] § Actin fluctuations oscillate with neurite growth ↔ figures/Fig02_actin_wave/fig02d_soma_actin_intensity_pre_wave.Rmd, lines 173–200 · score 0.57 · wave events, soma actin, actin intensity, actin wave, violin, Figure 2
  12. [12] § Methods › Neurite tracking, growth profiling and local protein intensity profiling ↔ figures/EDFig01_coordination/edfig01c_fig01f_slice_neurite_freq_and_xcorr.Rmd, lines 6–58 · score 0.56 · cortical slice cultures, mNeonGreen, neurite growth profiles, LYN, intensity, neurons
  13. [13] § Methods › Cross-correlation of neurite growth dynamics and local protein intensity fluctuations ↔ templates/pooled_replicate_ccf.Rmd, lines 730–765 · score 0.56 · get_summary_stats, cross correlation function, ccf, rstatix, derivative, lag
  14. [14] § Actin fluctuations oscillate with neurite growth ↔ figures/Fig01_coordination/fig01ef_neurite_xcorr_culture_slice.Rmd, lines 6–59 · score 0.55 · acute cortical slices, unpolarized neurons, cultured neurons, soma, retraction, actin
  15. [15] § Methods › Cross-correlation of neurite growth dynamics and local protein intensity fluctuations ↔ templates/pooled_replicate_ccf.Rmd, lines 730–765 · score 0.54 · get_summary_stats, individual neurite, cross correlation, ccf, rstatix, tip
  16. [16] § ARP2/3 is required for neuronal polarization ↔ figures/EDFig10_em/color_model.py, lines 1–44 · score 0.54 · actin filament orientation, barbed ends pointing, model, tips
  17. [17] § Methods › Cross-correlation of neurite growth dynamics and local protein intensity fluctuations ↔ templates/fig04o_lyn_live_neurite_xcorr.Rmd, lines 764–800 · score 0.53 · get_summary_stats, cross correlation function, rstatix, derivatives, Pearson, summed

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 · 147 lines · 5 KB · MIT · 2 matches

  1. """
  2. Proc.py — kymograph-to-time-series Jython post-processor.
  4. Part of the Bradke-lab neurite-growth / kymograph workflow. Called by the
  5. 8 kymograph-family .ijm macros in this directory tree:
  7. ImageJ_macros/edfig02d/edfig02d_lifeact_kymo_batch_interactive.ijm
  8. ImageJ_macros/edfig02d/edfig02d_lifeact_kymo_gen.ijm
  9. ImageJ_macros/edfig03/edfig03q_nested_actin_kymo_gen__240227_kymo_5b8b17be.ijm
  10. ImageJ_macros/edfig03/edfig03q_nested_actin_kymo_gen__241025_analyzed_5f793d08.ijm
  11. ImageJ_macros/fig03/fig03or_perfusion_kymo_gen.ijm
  12. ImageJ_macros/fig03/fig03or_perfusion_wave_kymo_gen.ijm
  13. ImageJ_macros/fig03/fig03or_perfusion_area_kymo_gen.ijm
  14. ImageJ_macros/fig04/fig04o_lyn_live_roi_measure.ijm
  16. Each of those macros calls:
  18. runMacro(macro_dir + "Proc.py", arglst)
  20. where `arglst` is a comma-separated string:
  22. "<Table_path>,<upper_threshold>,<x_median>,<window_length>"
  24. The currently-active image must be a kymograph (time on Y axis, distance
  25. on X axis) where bright pixels above `upper_threshold` mark the
  26. fluorescent structure of interest (e.g. neurite tip in a Lifeact channel).
  28. What this script does:
  30. 1. For each row (= timepoint), scan left-to-right and record the
  31. RIGHTMOST column whose pixel value > upper_threshold. That column
  32. is interpreted as the "tip position" for that timepoint.
  33. 2. Compute a `start_point = tip_position - window_length` per row
  34. (clamped to 0). This defines an integration window of width
  35. `window_length` ending at the tip.
  36. 3. Sum pixel intensities along that window per row → "integrated
  37. intensity" of the tip's trailing neurite segment for that timepoint.
  38. 4. Push three columns to Fiji's Results table:
  39. - Start point
  40. - Tip point
  41. - Integerated intensity ← note: original typo preserved
  42. One row per Y position of the kymograph (= one row per timepoint).
  44. The Results table is left open for the caller to `saveAs("Results", ...)`
  45. into a per-cell CSV.
  47. Authorship
  48. ----------
  49. Authored by:
  51. - Christoph Möhl (Bradke lab, DZNE)
  52. - Mansoureh Aghabeig (Image and Data Analysis Facility, DZNE)
  54. Tien-Chen Lin (Bradke lab, DZNE) integrated this script into the 8
  55. kymograph-family `.ijm` macros listed above and also created the
  56. upstream `IJ_NeuriteGrowth` Fiji plugin (not in this repo) that wraps
  57. the same `Multi Kymograph` + `Proc.py` workflow into a user-facing
  58. neurite-tracing tool.
  60. This file in the repo is the `Project_Kymo` variant of Proc.py (uses
  61. `pixl[0] > upper_value` for thresholded tip detection). An older sibling
  62. variant at `~/Nextcloud/03_Notebooks_and_Code/Neurite activities/
  63. NeuriteGrowthScript/Proc.py` uses `pixl[0] > 0` instead (ignores the
  64. threshold argument) — that one is NOT what the kymograph macros in this
  65. repo expect.
  67. The original lives at:
  68. ~/Nextcloud/03_Notebooks_and_Code/Neurite activities/
  69. NeuriteGrowthScript/Project_Kymo/Proc.py
  71. The macros hard-code:
  72. macro_dir = getDirectory("home") + "Nextcloud\\Neurite activities\\"
  74. so when reproducing outside the original author's environment, either:
  75. - Copy this Proc.py to `~/Nextcloud/Neurite activities/Proc.py`, or
  76. - Edit `macro_dir` in each kymograph macro to point to
  77. `ImageJ_macros/_shared/` (this directory).
  79. See `ImageJ_macros/README.md` for the joint authorship paragraph and
  80. the full kymograph-family file list.
  82. License: MIT (see top-level LICENSE).
  83. """
  84. from ij import IJ
  85. from ij.measure import ResultsTable
  86. import os
  87. import glob
  90. # Getting main parameters
  91. args = getArgument()
  92. arglst= args.split( ",")
  94. Table_path = arglst[0]
  95. upper_value = int(arglst[1])
  96. # x = int(arglst[2]) # x median filter
  97. window_length = int(arglst[3]) # window length
  99. imp = IJ.getImage()
  101. # IJ.run(imp, "Auto Threshold", "method=%s" %method_threshold );
  104. y = imp.getHeight()
  105. x = imp.getWidth()
  108. # Finding the tip point for each row
  109. #(the tip is defined as the nonzero value with hieghest x value)
  110. xlast = 0
  111. tip_point_list = [];
  112. for row in range(y):
  113. xlast = 0
  114. for col in range(x):
  115. pixl = imp.getPixel(col, row)
  116. if pixl[0] > upper_value: # upper_value or 0?
  117. xlast = col
  118. tip_point_list.append(xlast)
  120. # Calculating the start point
  121. start_point_list = [];
  122. for i in range(len(tip_point_list)):
  123. start_point = tip_point_list[i] - window_length
  124. if start_point < 0 :
  125. start_point = 0
  126. start_point_list.append(start_point)
  128. # Calculating the integerated intensity
  129. integrated_intensity_list = []
  130. for row in range(y):
  131. integerated_intensity = 0
  132. for col in range(start_point_list[row],tip_point_list[row]+1):
  133. integerated_intensity = integerated_intensity + imp.getPixel(col, row)[0]
  134. integrated_intensity_list.append(integerated_intensity)
  136. # list tip position for each row (time frame) in results table
  137. table = ResultsTable.getResultsTable()
  138. table.reset()
  139. for i in range(len(tip_point_list)):
  140. table.incrementCounter()
  141. table.addValue('Start point', start_point_list[i])
  142. table.addValue('Tip point', tip_point_list[i])
  143. table.addValue('Integerated intensity', integrated_intensity_list[i])
  144. table.show('Results')
  146. # IJ.saveAs("Results", Table_path);
  147. imp.close()

Proc.py at commit acc1adb, under MIT · at the source

Overview

Authors: Tien-chen Lin1, Charlotte H. Coles1,2, Eissa Alfadil1, Florian Fäßler3,4, Andreas Husch1, Sebastian Dupraz1, Thorben Pietralla1,5, Akihiro Narita6, Max Schelski1,5, Kevin C. Flynn1,7, Sina Stern1, Christoph Möhl8, Brett J. Hilton9, Franz Vauti10, Hans-Henning Arnold10, Florian K. M. Schur3, Frank Bradke1
ORCID iDs: Eissa Alfadil, Florian Fäßler, Thorben Pietralla, Max Schelski, Brett J. Hilton, Franz Vauti, Florian K. M. Schur, Frank Bradke
  1. Laboratory for Axon Growth and Regeneration, German Center for Neurodegenerative Diseases (DZNE),Bonn, Germany
  2. Present Address: Biopharm Discovery, GlaxoSmithKline,Stevenage, UK
  3. Institute of Science and Technology Austria (ISTA),Klosterneuburg, Austria
  4. Present Address: Department of Integrated Structural Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC),Illkirch, France
  5. International Max Planck Research School for Brain and Behavior,Bonn, Germany
  6. Division of Biological Science, Graduate School of Science, Nagoya University,Nagoya, Japan
  7. Present Address: CaseBioscience, Woodbury, MN USA
  8. Image and Data Analysis Facility, German Center for Neurodegenerative Diseases (DZNE),Bonn, Germany
  9. International Collaboration on Repair Discoveries (ICORD), Djavad Mowafaghian Centre for Brain Health, and Department of Cellular and Physiological Sciences, Faculty of Medicine, University of British Columbia,Vancouver, British Columbia Canada
  10. Department of Cellular and Molecular Neurobiology, Technische Universität Braunschweig,Braunschweig, Germany
Institutions: German Center for Neurodegenerative Diseases (Germany); GlaxoSmithKline (United Kingdom) (United Kingdom); Institute of Science and Technology Austria (Austria); Institut de génétique et de biologie moléculaire et cellulaire (France); Max Planck Society (Germany); Nagoya University (Japan); University of British Columbia (Canada); Technische Universität Braunschweig (Germany)
Journal: Nature, volume 657, issue 8130, pages 213-225
Dates: received 1 December 2023; accepted 3 June 2026; published online 8 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41586-026-10755-6 · PMID 42420447 · PMCID PMC13538037 · OpenAlex W7167667301
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Spectral & time-frequency, Statistics, Machine learning, Evoked potentials, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Cell polarity, Neuronal development
MeSH: Cell Polarity*, Cytoskeleton*, Neurons*, Actin-Related Protein 2-3 Complex, Actins, Actomyosin, Animals, Axons, Cells, Cultured, Dendrites, Growth Cones, Mice, Microtubules, Neurites, Optogenetics (* major topic)
Topic: Axon Guidance and Neuronal Signaling (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 4 papers (Europe PMC); 99 references in the paper

Abstract

Neurons acquire polarity by specifying one neurite as the axon, whereas the others become dendrites. But how this fundamental asymmetry is established remains unclear1. Neuronal polarization has been thought to rely primarily on growth cones that sense external cues2. Here we show that growth cones alone do not direct this process and that the soma acts as a central organizer of neuronal polarization. Using live imaging and genetic loss-of-function approaches in vivo, combined with optogenetic control and local cytoskeletal perturbations in cultured neurons, we uncover a soma-initiated oscillatory program that primes axon selection. Periodic actin branching that depends on the actin-related protein 2/3 (ARP2/3) complex at the soma remodels a global actomyosin network, thereby generating an actin wave that retracts neurites before propagating into a single neurite tip. Exposure to this wave relaxes local actomyosin contractility, which drives a transient microtubule-based protrusion and biases this neurite towards axon fate. As the cell exits this oscillatory stage, this neurite can overcome global inhibition and extend independently of ARP2/3, whereas actomyosin activity suppresses axon formation in the remaining neurites so that they subsequently become dendrites. This soma-driven mechanism ensures the emergence of a single axon independent of environmental cues and underpins the unidirectional information flow in neuronal circuits.

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 17 matches between paragraphs and lines of code.

Zenodo 20081075

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 7 files
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

darkbreaker0/IJ_NeuriteGrowthScript

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 36675f71f775dbde346ce667e2b756ff8139cadd, 9 February 2026
Languages: Python (2)
Size: 7 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ImageJ / Fiji (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/darkbreaker0/IJ_NeuriteGrowthScript

darkbreaker0/Arp3_neuronal_polarization_2026

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: acc1adbc0dcb7b603d925cbf7e72a2976cdce718, 9 July 2026
Languages: R (88), Python (9)
Size: 193 files, 97 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, 81 notebooks
Not found: environment file, tests, continuous integration, documentation
Tools: tidyverse (67 files), ggplot2 (66 files), broom (65 files), data.table (63 files), pheatmap (62 files), ggpubr (61 files), rstatix (53 files), Plotly (10 files), ImageJ / Fiji (6 files), UMAP (4 files), cowplot (1 file), emmeans (1 file), lme4 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
99 files
At the source: github.com/darkbreaker0/Arp3_neuronal_polarization_2026

Code availability

The custom ImageJ macro used for generating kymographs, extracting neurite tip positions and protein intensities is available at GitHub (https://github.com/darkbreaker0/IJ_NeuriteGrowthScript) and Zenodo (https://doi.org/10.5281/zenodo.20118606)99. The custom R and Python scripts used in the study are available at GitHub (https://github.com/darkbreaker0/Arp3_neuronal_polarization_2026) and Zenodo (https://doi.org/10.5281/zenodo.20118606)99. The code used for polarity determination of the actin filament in tomograms is available at Zenodo (https://doi.org/10.5281/zenodo.20081075)93.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 99 scripts, each with its path and the digest of its content;
  • 17 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

Datasets cited

Data availability

The raw data of the representative images have been deposited into Zenodo (https://doi.org/10.5281/zenodo.20118606)99. Owing to the large file size of the raw image data and the processed data used in the analyses that generated the graphs, we archived the image files in the read-only file archive at the DZNE institute. We provide raw data files upon request. The request can be directed to and will be fulfilled by the lead contact F.B. Source data are provided with 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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 2 keywords, 15 MeSH terms, 99 references.

Cite

This paper

Lin, T.-c., Coles, C. H., Alfadil, E., Fäßler, F., Husch, A., Dupraz, S., Pietralla, T., Narita, A., Schelski, M., Flynn, K. C., Stern, S., Möhl, C., Hilton, B. J., Vauti, F., Arnold, H.-H., Schur, F. K. M., & Bradke, F. (2026). An intrinsic cytoskeletal oscillator establishes neuronal polarity. Nature, 657(8130), 213-225. https://doi.org/10.1038/s41586-026-10755-6

BibTeX

@article{lin2026intrinsic,
author = {Lin, Tien-chen and Coles, Charlotte H. and Alfadil, Eissa and Fäßler, Florian and Husch, Andreas and Dupraz, Sebastian and Pietralla, Thorben and Narita, Akihiro and Schelski, Max and Flynn, Kevin C. and Stern, Sina and Möhl, Christoph and Hilton, Brett J. and Vauti, Franz and Arnold, Hans-Henning and Schur, Florian K. M. and Bradke, Frank},
title = {{An intrinsic cytoskeletal oscillator establishes neuronal polarity}},
journal = {Nature},
year = {2026},
month = jul,
volume = {657},
number = {8130},
pages = {213--225},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10755-6},
url = {https://doi.org/10.1038/s41586-026-10755-6},
pmid = {42420447},
pmcid = {PMC13538037}
}

RIS

TY - JOUR
AU - Lin, Tien-chen
AU - Coles, Charlotte H.
AU - Alfadil, Eissa
AU - Fäßler, Florian
AU - Husch, Andreas
AU - Dupraz, Sebastian
AU - Pietralla, Thorben
AU - Narita, Akihiro
AU - Schelski, Max
AU - Flynn, Kevin C.
AU - Stern, Sina
AU - Möhl, Christoph
AU - Hilton, Brett J.
AU - Vauti, Franz
AU - Arnold, Hans-Henning
AU - Schur, Florian K. M.
AU - Bradke, Frank
TI - An intrinsic cytoskeletal oscillator establishes neuronal polarity
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/07/08
VL - 657
IS - 8130
SP - 213
EP - 225
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10755-6
UR - https://doi.org/10.1038/s41586-026-10755-6
LA - en
ER -

CSL-JSON

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