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Perineuronal nets in cerebellar nuclei neurons orchestrate social behaviour via regulation of neuronal activity in circuits innervated by the cerebellum.

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Paper

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The authors' code

MATLAB · 151 lines · 3.7 KB · no license

  1. %%
  2. clear all
  3. close all
  5. %% load data
  6. load('data.mat');
  8. %% drop empty channels
  9. for s=1:7
  10. data(s).outputmatrix=data(s).outputmatrix(sum(data(s).outputmatrix,2)~=0,:);
  11. end
  13. %% display the number of discovered signals per cell
  14. % s=1;
  15. % tmp=zeros(size(data(s).cells));
  16. % for i=1:size(data(s).outputmatrix,2)-1
  17. % [row,col] = find(data(s).cells==i);
  18. % tmp(row,col)=data(s).outputmatrix(3,i+1);
  19. % end
  20. % figure;
  21. % imagesc(tmp);
  24. %%
  25. s=5;
  26. excells=ismember(data(s).cells,find(data(s).outputmatrix(2,2:end)>3));
  27. figure;imshow(excells)
  30. %% precalculate binarized expression images
  31. genes={"SLC17A6","TAC1","KCNAB1","ADCYAP1","GAD1","RTN4R","PKIB","XDH"};
  34. % updated thresholds
  35. thresholds=5.*ones(length(genes),1);
  36. thresholds(1)=10;
  37. thresholds(2)=3;
  38. thresholds(5)=7;
  39. thresholds(7)=7;
  40. thresholds(8)=3; %only for chicken2, otherwise 5.
  41. thresholds(3)=10;
  44. for s=1:7
  45. for i=1:numel(genes)
  46. data(s).expression{i}=ismember(data(s).cells,find(data(s).outputmatrix(i,2:end)>thresholds(i)));
  47. end
  48. end
  51. % %% get nissl images
  52. % L=[];
  53. % sections=dir('nisslsegmentation\raw\*.tif');
  54. % for s=1:5
  55. % L(s).nissl=imread(['nisslsegmentation\raw\',sections(s).name]);
  56. % end
  59. %% make custom colormap
  61. % custommap=[cmap([1 0 0],6,10,10);cmap([0 1 0],6,10,10);cmap([1 1 0],3,10,10);repmat([0.4 0.4 0.4],20,1);[0 0 1]];
  62. % map2=[[1 0 0];[0 1 0];[1 0 1];[0 0 1]; [1,0,1];[0 1 1];repmat([0.4 0.4 0.4],50,1);[0.8, 0.8, 0.8];[1,0.55,0]];
  63. map2=[[1 0 0];[0 1 0];[0 0 1];[1 0 1]; [1,1,0];[0 1 1];repmat([0.4 0.4 0.4],50,1);[0.8, 0.8, 0.8];[1,0.55,0]];
  65. %% find subnuclei.
  67. for s=1:7
  69. expression=data(s).expression;
  71. Med=expression{1}.*expression{3}.*(1-expression{4});
  72. MedL=expression{1}.*expression{2};%.*(1-expression{7});
  73. IntX=expression{1}.*expression{4};
  74. %IntP=expression{7}.*(expression{6} | expression{4}).*(1-expression{2});
  75. IntP=expression{1}.*(expression{7}).*(1-expression{3}).*(1-expression{4});
  77. m=makemask(cat(3,Med,MedL,IntX,IntP),0);
  78. L(s).subnuclei=m;
  79. end
  82. clear Med MedL IntX IntP
  83. %% quick subnuclei from saved
  84. %subnucleipal=[hex2rgb('#EA9292');hex2rgb('#FF7F00');hex2rgb('#33a02');hex2rgb('#1F78B4');repmat([0.95 0.95 0.95],60,1)];
  85. %subnucleipal=[hex2rgb('#EA9292');hex2rgb('#ED1C24');hex2rgb('#33a02');hex2rgb('#1F78B4');repmat([0.95 0.95 0.95],60,1)];
  87. subnucleipal=[hex2rgb('#EA9292');hex2rgb('#F7F008');hex2rgb('#33a02');hex2rgb('#1F78B4');repmat([0.95 0.95 0.95],60,1)];
  88. for s=1:7
  89. L(s).ex=data(s).expression{1};
  91. boundaries = bwboundaries(L(s).ex);
  93. n=L(s).subnuclei;
  94. n(L(s).ex>0 & n==0)=57;
  95. figure;
  96. h=imshow(label2rgb(n,subnucleipal,'white'));
  97. title(['section ',int2str(s)]);
  98. ax = gca;
  99. if(s>4)
  100. ax.YDir = 'normal'
  101. end
  102. hold on;
  103. for k=1:numel(boundaries)
  104. b=boundaries{k};
  105. plot(b(:,2),b(:,1),'black');
  106. end
  107. % pause();
  108. print(gcf,['figure_exports/subnuclei',int2str(s)],'-dpdf','-fillpage','-r1000')
  109. close gcf
  110. end
  113. %% look at Class-A vs Class-B.
  115. for s=1:7
  117. expression=data(s).expression;
  119. ClassA=expression{1}.*(1-expression{8});
  120. ClassB=expression{1}.*expression{8};
  122. m=makemask(cat(3,ClassA,ClassB),0);
  123. L(s).classes=m;
  124. end
  126. %% quick Classes from saved
  127. Classpal=[hex2rgb('#ff6347');hex2rgb('#00688b');repmat([0.95 0.95 0.95],60,1)];
  129. for s=1:7
  130. L(s).ex=data(s).expression{1};
  132. boundaries = bwboundaries(L(s).ex);
  134. n=L(s).classes;
  135. n(L(s).ex>0 & n==0)=57;
  136. figure;
  137. h=imshow(label2rgb(n,Classpal,'white'));
  138. title(['section ',int2str(s)]);
  139. ax = gca;
  140. if s>4
  141. ax.YDir = 'normal'
  142. end
  143. hold on;
  144. for k=1:numel(boundaries)
  145. b=boundaries{k};
  146. plot(b(:,2),b(:,1),'black');
  147. end
  148. print(gcf,['figure_exports/classes_ch2',int2str(s)],'-dpdf','-fillpage','-r1000')
  149. close gcf
  150. end

analyse_exdata_updated.m at commit 996c4b2, no license · at the source

Overview

Authors: Kyota Fujita1, Hong Zhu1, Chiharu Tsuji1, Atsuki Kawamura2, Masaaki Nishiyama2,3, Haruhiro Higashida1, Shigeru Yokoyama1
ORCID iDs: Kyota Fujita, Masaaki Nishiyama, Shigeru Yokoyama
  1. Department of Basic Research on Social Recognition and Memory, Research Centre for Child Mental Development, Kanazawa University,Kanazawa, Japan
  2. Department of Histology and Cell Biology, Graduate School of Medical Sciences, Kanazawa University,Kanazawa, Ishikawa Japan
  3. Social Brain Development Research Unit, Next Generation Medical Development Research Core, Institute for Frontier Science Initiative, Kanazawa University,Kanazawa, Ishikawa Japan
Institutions: Kanazawa University (Japan)
Journal: Translational psychiatry, volume 16, issue 1, article 242
Dates: received 22 July 2025; accepted 6 March 2026; published online 13 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-03952-4 · PMID 42129135 · PMCID PMC13172362 · OpenAlex W7161051546
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), autism (population), cellular / molecular (subfield)
Methods: Statistics, Single-unit activity, calcium imaging
Keywords: Molecular neuroscience, Physiology, Autism spectrum disorders
MeSH: Cerebellar Nuclei*, Cerebellum*, Neurons*, Perineuronal Nets*, Social Behavior*, Animals, Cyclic AMP Response Element-Binding Protein, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neuroscience of respiration and sleep (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: Financial support was also provided in the form of a Hokuriku Bank Research Grant for Young Scientists, a grant from Kanazawa University for the “HOZUMINE” Project for the Promotion of Research, a grant from Kanazawa University “JIKO-CHOKOKU” project for promotion of research and a grant from the Daiichi Sankyo Foundation of Life Science
Citations: not cited yet (Europe PMC); 99 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.

justuskebschull/cncode_final

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 996c4b28afdac96cd7015d89047f4f462e05f4e4, 6 August 2021
Languages: MATLAB (80), R (24), Python (3), Shell (2)
Size: 158 files, 109 scripts
Software Heritage: not archived
Found in: the text, “Data collection from scRNA-seq”
Holds: README, 23 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (23 files), tidyverse (21 files), patchwork (18 files), cowplot (17 files), Statistics and Machine Learning Toolbox (15 files), circlize (14 files), ComplexHeatmap (14 files), ggplot2 (14 files), Image Processing Toolbox (9 files), SciPy (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
110 files
At the source: github.com/justuskebschull/CNcode_final?tab=readme-ov-file

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;
  • 109 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 statement

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

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41398-026-03952-4.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 3 keywords, 10 MeSH terms, 1 funder, 98 references.

Cite

This paper

Fujita, K., Zhu, H., Tsuji, C., Kawamura, A., Nishiyama, M., Higashida, H., & Yokoyama, S. (2026). Perineuronal nets in cerebellar nuclei neurons orchestrate social behaviour via regulation of neuronal activity in circuits innervated by the cerebellum. Translational psychiatry, 16(1), 242. https://doi.org/10.1038/s41398-026-03952-4

BibTeX

@article{fujita2026perineuronal,
author = {Fujita, Kyota and Zhu, Hong and Tsuji, Chiharu and Kawamura, Atsuki and Nishiyama, Masaaki and Higashida, Haruhiro and Yokoyama, Shigeru},
title = {{Perineuronal nets in cerebellar nuclei neurons orchestrate social behaviour via regulation of neuronal activity in circuits innervated by the cerebellum}},
journal = {Translational psychiatry},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {242},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-03952-4},
url = {https://doi.org/10.1038/s41398-026-03952-4},
pmid = {42129135},
pmcid = {PMC13172362}
}

RIS

TY - JOUR
AU - Fujita, Kyota
AU - Zhu, Hong
AU - Tsuji, Chiharu
AU - Kawamura, Atsuki
AU - Nishiyama, Masaaki
AU - Higashida, Haruhiro
AU - Yokoyama, Shigeru
TI - Perineuronal nets in cerebellar nuclei neurons orchestrate social behaviour via regulation of neuronal activity in circuits innervated by the cerebellum
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/05/13
VL - 16
IS - 1
SP - 242
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-03952-4
UR - https://doi.org/10.1038/s41398-026-03952-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-03952-4",
"type": "article-journal",
"title": "Perineuronal nets in cerebellar nuclei neurons orchestrate social behaviour via regulation of neuronal activity in circuits innervated by the cerebellum",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Fujita",
"given": "Kyota"
},
{
"family": "Zhu",
"given": "Hong"
},
{
"family": "Tsuji",
"given": "Chiharu"
},
{
"family": "Kawamura",
"given": "Atsuki"
},
{
"family": "Nishiyama",
"given": "Masaaki"
},
{
"family": "Higashida",
"given": "Haruhiro"
},
{
"family": "Yokoyama",
"given": "Shigeru"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "242",
"DOI": "10.1038/s41398-026-03952-4",
"PMID": "42129135",
"PMCID": "PMC13172362",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-03952-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

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