OSCR

Flexible ensheathment of axons enables myelination of complex CNS networks.

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
  1. [1] § Human oligodendrocytes form paranodal bridges ↔ OJames_OrgData.m, lines 48–177 · score 0.51 · cell ID, chain length, conversion, organoid, paranodal bridge, Quantification

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

MATLAB · 177 lines · 6.1 KB · GPL-3.0 · 1 match

  1. addpath(genpath('D:\GitHubRepos\Call_ParanodalBridge_2022'));
  2. prop_tbl = readtable('D:\GitHubRepos\Call_ParanodalBridge_2022\OwenUpdatedData\Proportion_cells_with_bridges_updated.xlsx');
  3. prop_tbl = prop_tbl(1:24,:);
  5. nonbridged = prop_tbl.Cells_with_0;
  6. bridged = prop_tbl.Cells_with_1 + prop_tbl.Cells_with_2 + prop_tbl.Cells_with_3;
  8. prop = bridged ./ (nonbridged + bridged);
  10. %% sheaths per cell
  11. data = readtable('D:\GitHubRepos\Call_ParanodalBridge_2022\OwenUpdatedData\iPSC_myelinoid_quantification_sheathlengths_aggregatedpercell.csv');
  12. brg_idx = contains(data.Cell_type,'OLs with bridges');
  13. nonbrg_idx = contains(data.Cell_type,'OLs without bridges');
  15. sheathsPerCell_brg = table2array(data(brg_idx,10));
  16. sheathsPerCell_nonbrg = table2array(data(nonbrg_idx,10));
  17. avg = [mean(sheathsPerCell_nonbrg), mean(sheathsPerCell_brg)];
  18. sem = [calcSEM(sheathsPerCell_nonbrg,1), calcSEM(sheathsPerCell_brg,1)];
  20. [ct,cu] = getFigColors;
  22. figure
  23. plotSpread({sheathsPerCell_nonbrg,sheathsPerCell_brg},'distributionMarker','o','distributionColors',{ct,cu});
  24. hold on
  25. errorbar(avg,sem,'ko','MarkerSize',3,'MarkerFaceColor','k','CapSize',0,'LineWidth',1.5);
  26. hold off
  27. xlim([0 3])
  28. xticklabels({})
  29. ylim([0 25])
  30. figQuality(gcf,gca,[2.4 2.2])
  32. %% sheath length per cell
  33. sheathLnth_brg = table2array(data(brg_idx,8));
  34. sheathLnth_nonbrg = table2array(data(nonbrg_idx,8));
  35. avg = [mean(sheathLnth_nonbrg), mean(sheathLnth_brg)];
  36. sem = [calcSEM(sheathLnth_nonbrg,1), calcSEM(sheathLnth_brg,1)];
  38. figure
  39. plotSpread({sheathLnth_nonbrg,sheathLnth_brg},'distributionMarker','o','distributionColors',{ct,cu});
  40. hold on
  41. errorbar(avg,sem,'ko','MarkerSize',3,'MarkerFaceColor','k','CapSize',0,'LineWidth',1.5);
  42. hold off
  43. xlim([0 3])
  44. xticklabels({})
  45. ylim([0 200])
  46. figQuality(gcf,gca,[2.4 2.2])
  48. %% length per cell per sheath type
  49. data = readtable('D:\GitHubRepos\Call_ParanodalBridge_2022\OwenUpdatedData\iPSC_myelinoid_quantification_sheathlengths.csv');
  51. nonbrg_idx = contains(data.Sheath_type,'Regular');
  52. anchr_idx = contains(data.Sheath_type,'Anchored');
  53. brg_idx = contains(data.Sheath_type,'Bridged');
  55. anchr_data = data(anchr_idx,:);
  56. CellIDs = anchr_data.CellID;
  57. uniqCells = unique(anchr_data.CellID,'stable');
  58. mean_anchrLnths = NaN(size(uniqCells));
  59. j = 1;
  60. k = 1;
  61. while j <= length(CellIDs)
  62. templnths = [];
  63. while contains(CellIDs{j},uniqCells{k})
  64. templnths = [templnths; anchr_data.Sheath_length(j)];
  65. % fprintf([CellIDs{j},'\n',uniqCells{k},'\n'])
  66. j = j+1;
  67. if j > length(CellIDs)
  68. % fprintf('got to here\n')
  69. break
  70. end
  71. end
  72. mean_anchrLnths(k) = mean(templnths);
  73. k = k+1;
  74. end
  75. splitData = cellfun(@(x) strsplit(x, '.'), uniqCells, 'UniformOutput', false);
  76. splitAnchrs = vertcat(splitData{:});
  78. nonbrg_data = data(nonbrg_idx,:);
  79. CellIDs = nonbrg_data.CellID;
  80. uniqCells = unique(nonbrg_data.CellID,'stable');
  81. mean_nonbrgLnths = NaN(size(uniqCells));
  82. j = 1;
  83. k = 1;
  84. while j <= length(CellIDs)
  85. templnths = [];
  86. while contains(CellIDs{j},uniqCells{k})
  87. templnths = [templnths; nonbrg_data.Sheath_length(j)];
  88. % fprintf([CellIDs{j},'\n',uniqCells{k},'\n'])
  89. j = j+1;
  90. if j > length(CellIDs)
  91. % fprintf('got to here\n')
  92. break
  93. end
  94. end
  95. mean_nonbrgLnths(k) = mean(templnths);
  96. k = k+1;
  97. end
  98. splitData = cellfun(@(x) strsplit(x, '.'), uniqCells, 'UniformOutput', false);
  99. splitNonbrgs = vertcat(splitData{:});
  101. brg_data = data(brg_idx,:);
  102. CellIDs = brg_data.CellID;
  103. uniqCells = unique(brg_data.CellID,'stable');
  104. mean_brgLnths = NaN(size(uniqCells));
  105. j = 1;
  106. k = 1;
  107. while j <= length(CellIDs)
  108. templnths = [];
  109. while contains(CellIDs{j},uniqCells{k})
  110. templnths = [templnths; brg_data.Sheath_length(j)];
  111. % fprintf([CellIDs{j},'\n',uniqCells{k},'\n'])
  112. j = j+1;
  113. if j > length(CellIDs)
  114. % fprintf('got to here\n')
  115. break
  116. end
  117. end
  118. mean_brgLnths(k) = mean(templnths);
  119. k = k+1;
  120. end
  121. splitData = cellfun(@(x) strsplit(x, '.'), uniqCells, 'UniformOutput', false);
  122. splitBrgs = vertcat(splitData{:});
  124. data2 = readtable('D:\GitHubRepos\Call_ParanodalBridge_2022\OwenUpdatedData\iPSC_myelinoid_quantification_sheathlengths_reorganised_for_Chainlengths.csv');
  126. chain_idx = ~isnan(data2.Chain_length);
  127. data2_chains = data2(chain_idx,:);
  128. uniqCells = unique(data2_chains.CellID,'stable');
  129. CellIDs = data2_chains.CellID;
  130. meanCellChains = NaN(size(uniqCells));
  131. j = 1;
  132. k = 1;
  133. while j <= length(CellIDs)
  134. templnths = [];
  135. while contains(CellIDs{j},uniqCells{k})
  136. templnths = [templnths; data2_chains.Chain_length(j)];
  137. % fprintf([CellIDs{j},'\n',uniqCells{k},'\n'])
  138. j = j+1;
  139. if j > length(CellIDs)
  140. % fprintf('got to here\n')
  141. break
  142. end
  143. end
  144. meanCellChains(k) = mean(templnths);
  145. k = k+1;
  146. end
  147. splitData = cellfun(@(x) strsplit(x, '.'), uniqCells, 'UniformOutput', false);
  148. splitChains = vertcat(splitData{:});
  150. lengths = num2cell([mean_brgLnths;mean_nonbrgLnths;mean_anchrLnths;meanCellChains]);
  151. types = [repmat({'brg'},[length(mean_brgLnths),1]);...
  152. repmat({'nonbrg'},[length(mean_nonbrgLnths),1]);...
  153. repmat({'anch'},[length(mean_anchrLnths),1]);...
  154. repmat({'chain'},[length(meanCellChains),1])];
  155. ids = [splitBrgs;splitNonbrgs;splitAnchrs;splitChains];
  156. concatdata = [lengths types ids];
  158. T = cell2table(concatdata, 'VariableNames', {'Length', 'Type', 'Cells', 'Conversion', 'Organoid', 'Cell'});
  159. mdl = fitlme(T,'Length ~ Type + (1|Cells) + (1|Conversion) + (1|Organoid) + (1|Cell)')
  161. sheathLnth_brg = mean_brgLnths;
  162. sheathLnth_nonbrg = mean_nonbrgLnths;
  163. sheathLnth_anchr = mean_anchrLnths;
  164. sheathLnth_chain = meanCellChains;
  166. avg = [mean(sheathLnth_nonbrg), mean(sheathLnth_anchr), mean(sheathLnth_brg), mean(sheathLnth_chain)];
  167. sem = [calcSEM(sheathLnth_nonbrg,1), calcSEM(sheathLnth_anchr,1), calcSEM(sheathLnth_brg,1), calcSEM(sheathLnth_chain,1)];
  169. figure
  170. plotSpread({sheathLnth_nonbrg,sheathLnth_anchr,sheathLnth_brg,sheathLnth_chain},'distributionMarker','o','distributionColors',{ct,[52 75 160]./255,cu,[0.5 0.5 0.5]});
  171. hold on
  172. errorbar(avg,sem,'ko','MarkerSize',3,'MarkerFaceColor','k','CapSize',0,'LineWidth',1.5);
  173. hold off
  174. xlim([0 5])
  175. xticklabels({})
  176. ylim([0 400])
  177. figQuality(gcf,gca,[2.5 2.2])

OJames_OrgData.m at commit c1c35fc, under GPL-3.0 · at the source

Overview

Authors: Cody L. Call1,2, Sarah A. Neely3, Jason J. Early3, Owen G. James4,5,6,7, Lida Zoupi8,9,10, Anna C. Williams8,9, Yu Kang T. Xu1, Siddharthan Chandran4,5,6,7,11,12, David A. Lyons3,9, Kelly R. Monk2, Dwight E. Bergles1,13
ORCID iDs: Cody L. Call, Jason J. Early, Owen G. James, Lida Zoupi, Anna C. Williams, Yu Kang T. Xu, Siddharthan Chandran, David A. Lyons, Kelly R. Monk, Dwight E. Bergles
13 affiliations
  1. The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University,Baltimore, MD USA
  2. Vollum Institute, Oregon Health and Science University,Portland, OR USA
  3. Centre for Discovery Brain Sciences, University of Edinburgh,Edinburgh, UK
  4. UK Dementia Research Institute at the University of Edinburgh,Edinburgh, UK
  5. Centre for Clinical Brain Sciences, University of Edinburgh,Edinburgh, UK
  6. Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh,Edinburgh, UK
  7. Anne Rowling Regenerative Neurology Clinic, University of Edinburgh,Edinburgh, UK
  8. Centre for Regenerative Medicine, Institute of Regeneration and Repair, University of Edinburgh,Edinburgh, UK
  9. Present Address: Institute for Neuroscience and Cardiovascular Research, The University of Edinburgh,Edinburgh, UK
  10. Present Address: Simons Initiative for the Developing Brain, The University of Edinburgh,Edinburgh, UK
  11. MS Society UK Edinburgh Centre for MS Research, Edinburgh, UK
  12. Centre for Brain Development and Repair, inStem,Bangalore, India
  13. Kavli Neuroscience Discovery Institute, Johns Hopkins University,Baltimore, MD USA
Institutions: Johns Hopkins University (United States); Oregon Health & Science University (United States); University of Edinburgh (United Kingdom); Institute for Stem Cell Biology and Regenerative Medicine (India)
Journal: Nature, volume 654, issue 8119, pages 724-733
Dates: received 25 July 2024; accepted 20 February 2026; published online 1 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41586-026-10312-1 · PMID 41922759 · PMCID PMC13275323 · OpenAlex W7147372314
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), zebrafish (organism), cellular / molecular (subfield)
Methods: Evoked potentials, Statistics
Keywords: Oligodendrocyte, Cell biology
MeSH: Axons*, Central Nervous System*, Myelin Sheath*, Aging, Animals, Cerebral Cortex, Female, Interneurons, Male, Mice, Oligodendroglia, Ranvier's Nodes, Zebrafish (* major topic)
Topic: Nerve injury and regeneration (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (214244/Z/18/Z); NIA NIH HHS (R01 AG072305)
Citations: cited by 4 papers (Europe PMC); 65 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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

clcall/Call_ParanodalBridge_2022

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c1c35fcb7c832a56a62fa37a0b91eb75ed7fabce, 9 March 2026
Languages: MATLAB (55)
Size: 650 files, 55 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: license file, 1 notebook
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (12 files), Image Processing Toolbox (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
56 files
At the source: github.com/clcall/Call_ParanodalBridge_2022

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41586-026-10312-1.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 55 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41586-026-10312-1.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 13 MeSH terms, 2 funders, 65 references.

Cite

This paper

Call, C. L., Neely, S. A., Early, J. J., James, O. G., Zoupi, L., Williams, A. C., Xu, Y. K. T., Chandran, S., Lyons, D. A., Monk, K. R., & Bergles, D. E. (2026). Flexible ensheathment of axons enables myelination of complex CNS networks. Nature, 654(8119), 724-733. https://doi.org/10.1038/s41586-026-10312-1

BibTeX

@article{call2026flexible,
author = {Call, Cody L. and Neely, Sarah A. and Early, Jason J. and James, Owen G. and Zoupi, Lida and Williams, Anna C. and Xu, Yu Kang T. and Chandran, Siddharthan and Lyons, David A. and Monk, Kelly R. and Bergles, Dwight E.},
title = {{Flexible ensheathment of axons enables myelination of complex CNS networks}},
journal = {Nature},
year = {2026},
month = apr,
volume = {654},
number = {8119},
pages = {724--733},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10312-1},
url = {https://doi.org/10.1038/s41586-026-10312-1},
pmid = {41922759},
pmcid = {PMC13275323}
}

RIS

TY - JOUR
AU - Call, Cody L.
AU - Neely, Sarah A.
AU - Early, Jason J.
AU - James, Owen G.
AU - Zoupi, Lida
AU - Williams, Anna C.
AU - Xu, Yu Kang T.
AU - Chandran, Siddharthan
AU - Lyons, David A.
AU - Monk, Kelly R.
AU - Bergles, Dwight E.
TI - Flexible ensheathment of axons enables myelination of complex CNS networks
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/04/01
VL - 654
IS - 8119
SP - 724
EP - 733
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10312-1
UR - https://doi.org/10.1038/s41586-026-10312-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41586-026-10312-1",
"type": "article-journal",
"title": "Flexible ensheathment of axons enables myelination of complex CNS networks",
"container-title": "Nature",
"author": [
{
"family": "Call",
"given": "Cody L."
},
{
"family": "Neely",
"given": "Sarah A."
},
{
"family": "Early",
"given": "Jason J."
},
{
"family": "James",
"given": "Owen G."
},
{
"family": "Zoupi",
"given": "Lida"
},
{
"family": "Williams",
"given": "Anna C."
},
{
"family": "Xu",
"given": "Yu Kang T."
},
{
"family": "Chandran",
"given": "Siddharthan"
},
{
"family": "Lyons",
"given": "David A."
},
{
"family": "Monk",
"given": "Kelly R."
},
{
"family": "Bergles",
"given": "Dwight E."
}
],
"container-title-short": "Nature",
"volume": "654",
"issue": "8119",
"page": "724-733",
"DOI": "10.1038/s41586-026-10312-1",
"PMID": "41922759",
"PMCID": "PMC13275323",
"ISSN": "0028-0836",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41586-026-10312-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
1
]
]
}
}

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:10.1002/glia.70141 [code]
Conservation of Neuron-Astrocyte Correlated Activity in Developing Sensory Pathways.
Journal: Glia
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse, cellular / molecular, author Dwight E Bergles
[2] doi:10.1038/s41467-026-73366-9 [code]
Cortical and white matter myelination proceed in concert during early infancy.
Journal: Nature communications
In common: 4 references
[3] doi:10.1038/s41467-026-72935-2 [code]
Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures.
Journal: Nature communications
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, cellular / molecular, 1 reference
[4] doi:10.1038/s41586-026-10329-6 [code]
Synthetic super-enhancers enable precision viral immunotherapy.
Journal: Nature
In common: mouse, cellular / molecular, author Anna Williams
[5] doi:10.1126/sciadv.aef6777 [code]
Astrocyte redox imbalance underlies prelimbic neuronal hypoactivity and maladaptive affective behaviors in epilepsy.
Journal: Science advances
In common: mouse, cellular / molecular, author Dwight E Bergles
[6] doi:10.1038/s41586-026-10323-y [code]
Genetically encoded assembly recorder temporally resolves cellular history.
Journal: Nature
In common: mouse, cellular / molecular, author Dwight E Bergles
[7] doi:10.1038/s41467-026-75490-y [code]
Topographically organized dorsal raphe activity modulates forebrain sensory-motor representations and contributes to defensive behaviors.
Journal: Nature communications
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, zebrafish
[8] doi:10.1016/j.isci.2026.117187 [code]
Functional and structural characterization of dendritic spine pathology in a mouse model of tauopathy.
Journal: iScience
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse
[9] doi:10.1073/pnas.2603966123 [code]
Oxytocin selectively biases sensory-prefrontal communication through network-level suppression and theta coupling.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse
[10] doi:10.1016/j.celrep.2026.117456 [code]
Anterior lateral motor cortex enables contextual decision-making via dynamic reconfiguration of local circuits.
Journal: Cell reports
In common: shadedErrorBar, Image Processing Toolbox, Statistics and Machine Learning Toolbox, mouse

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.