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Defective ventral neurogenesis due to midfetal Chd8 mutation drives autistic-like behavior in mice.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Elevated plus-maze test ↔ ElevatedPlusMaze.py, lines 115–201 · score 0.78 · elevated plus maze, arm entries, open arms, closed arms, thickness, distance
  2. [2] § Results › Lineage tracing and scRNA-seq analysis reveal cell type–specific gene expression changes due to midfetal induction of Chd8 heterozygous mutation ↔ scRNA-seq_2_gene_exp.ipynb, lines 181–242 · score 0.67 · pri OPC, endothelial cell, inhibitory neuron, NPC, microglia, genes
  3. [3] § Methods › scRNA-seq analysis ↔ scRNA-seq_2_gene_exp.ipynb, lines 181–242 · score 0.62 · rank genes, RNA seq, duplicate, mapped, clustering, cells
  4. [4] § Results › Lineage tracing and scRNA-seq analysis reveal cell type–specific gene expression changes due to midfetal induction of Chd8 heterozygous mutation ↔ scRNA-seq_2_gene_exp.ipynb, lines 118–158 · score 0.56 · endothelial cells, inhibitory neurons, microglia, OPCs, oligodendrocyte, seq

Paper

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The authors' code

Jupyter notebook · 251 lines · 7.3 KB · no license · 3 matches

  1. # %%
  2. import warnings
  3. warnings.filterwarnings('ignore')
  5. import os
  6. import numpy as np
  7. import pandas as pd
  8. import scanpy as sc
  9. import anndata
  10. import matplotlib.pyplot as plt
  11. import seaborn as sns
  13. sc.logging.print_header()
  14. sc.settings.set_figure_params(dpi=100, facecolor='white')
  16. # %%
  17. adata = sc.read_h5ad('./ad_scvimodel.h5ad')
  19. # %%
  20. adata
  22. # %%
  23. sc.pl.umap(adata,
  24. color=['batch', 'Genotype', 'Sex', 'leiden_scVI'],
  25. ncols=2,
  26. frameon=False)
  28. # %%
  29. genotype_colors = {'WT': '#808080', 'MT': '#8B0000'}
  31. sc.pl.umap(adata, color='Genotype', palette=genotype_colors, size=3, frameon=False, title='Genotype')
  33. # %%
  34. sc.pl.umap(adata, color='leiden_scVI', add_outline=True, legend_loc='on data',
  35. legend_fontsize=12, legend_fontoutline=2,frameon=False,
  36. title='clustering of cells')
  38. # %%
  39. sc.pl.umap(adata, color=['Naaa', 'Mki67', 'Ascl1', 'Neurod1', 'Dlx1', 'Aldh1l1', 'Pdgfra','Mbp', 'Cx3cr1', 'Bgn', 'Cldn5'])
  41. # %%
  42. cell_type_names = {
  43. '0': 'Astrocyte',
  44. '1': 'Inhibitory_neuron',
  45. '2': 'OPC',
  46. '3': 'In_progenitor',
  47. '4': 'Ex_neuron',
  48. '5': 'Astrocyte_progenitor',
  49. '6': 'Astrocyte',
  50. '7': 'NPC',
  51. '8': 'OPC',
  52. '9': 'Inhibitory_neuron',
  53. '10': 'Pericyte',
  54. '11': 'Oligodendrocyte',
  55. '12': 'pri-OPC',
  56. '13': 'Microglia',
  57. '14': 'Ex_neuron',
  58. '15': 'Endothelial_cell',
  59. '16': 'Astrocyte',
  60. '17': 'Pericyte'
  61. }
  63. adata.obs['cell_type_name'] = adata.obs['leiden_scVI'].map(cell_type_names)
  65. # %%
  66. sc.pl.umap(adata,
  67. color=['Genotype', 'cell_type_name'],
  68. ncols=2,
  69. frameon=False)
  71. # %%
  72. cluster_to_color = {
  73. 'Astrocyte': '#2ca02c',
  74. 'Astrocyte_progenitor': '#f3fbd4',
  75. 'Endothelial_cell': '#7f7f7f',
  76. 'Ex_neuron': '#ff7f0e',
  77. 'In_progenitor': '#ff9896',
  78. 'Inhibitory_neuron': '#d62728',
  79. 'Microglia': '#8c564b',
  80. 'NPC': '#313695',
  81. 'pri-OPC': '#aec7e8',
  82. 'OPC': '#1f77b4',
  83. 'Oligodendrocyte': '#9467bd',
  84. 'Pericyte': '#ffbb78'
  85. }
  87. adata.obs['cell_type_name_colors'] = adata.obs['cell_type_name'].map(cluster_to_color)
  89. palette = [cluster_to_color[cluster] for cluster in adata.obs['cell_type_name'].cat.categories]
  91. sc.pl.umap(adata,
  92. color=['Genotype', 'cell_type_name'],
  93. ncols=2,
  94. frameon=False)
  96. # %%
  97. genes_of_interest = ['Aldh1l1', 'Plp1', 'Pdgfra', 'Olig2', 'Neurod1', 'Dlx1', 'Naaa', 'Cx3cr1', 'Cldn5', 'Bgn', 'Mki67']
  99. sc.pl.dotplot(
  100. adata,
  101. genes_of_interest,
  102. groupby='cell_type_name',
  103. dendrogram=True,
  104. color_map="Blues",
  105. swap_axes=True,
  106. use_raw=False,
  107. standard_scale='var')
  109. # %%
  110. adata_temp = adata.obs[['Genotype', 'cell_type_name']]
  112. # %%
  113. adata_temp
  115. # %%
  116. adata_temp.groupby('cell_type_name')['Genotype'].value_counts(normalize=False)
  118. # %%
  119. data = {
  120. 'cell_type_name': ['Astrocyte', 'Astrocyte', 'Astrocyte_progenitor', 'Astrocyte_progenitor',
  121. 'Endothelial_cell', 'Endothelial_cell', 'Ex_neuron', 'Ex_neuron',
  122. 'In_progenitor', 'In_progenitor', 'Inhibitory_neuron', 'Inhibitory_neuron',
  123. 'Microglia', 'Microglia', 'NPC', 'NPC', 'OPC', 'OPC',
  124. 'Oligodendrocyte', 'Oligodendrocyte', 'pri-OPC', 'pri-OPC'],
  125. 'Genotype': ['WT', 'MT', 'WT', 'MT', 'MT', 'WT', 'WT', 'MT', 'WT', 'MT',
  126. 'WT', 'MT', 'WT', 'MT', 'WT', 'MT', 'MT', 'WT', 'MT', 'WT',
  127. 'MT', 'WT'],
  128. 'Counts': [2496, 2394, 557, 549, 523, 321, 852, 682, 753, 655, 1461, 1265,
  129. 234, 117, 437, 428, 1681, 1133, 348, 202, 298, 235]
  130. }
  132. df = pd.DataFrame(data)
  134. total_wt = df[df['Genotype'] == 'WT']['Counts'].sum()
  135. total_mt = df[df['Genotype'] == 'MT']['Counts'].sum()
  137. df['Percentage'] = df.apply(lambda x: x['Counts'] / total_wt * 100 if x['Genotype'] == 'WT' else x['Counts'] / total_mt * 100, axis=1)
  139. wt_data = df[df['Genotype'] == 'WT'][['cell_type_name', 'Percentage']].rename(columns={'Percentage': 'WT_Percentage'})
  140. mt_data = df[df['Genotype'] == 'MT'][['cell_type_name', 'Percentage']].rename(columns={'Percentage': 'MT_Percentage'})
  141. combined_data = pd.merge(wt_data, mt_data, on='cell_type_name', how='outer').fillna(0)
  143. fig, ax = plt.subplots(figsize=(10, 8))
  144. indices = range(len(combined_data))
  145. bar_width = 0.35
  147. ax.bar(indices, combined_data['WT_Percentage'], width=bar_width, label='WT', color='#1f77b4')
  148. ax.bar([i + bar_width for i in indices], combined_data['MT_Percentage'], width=bar_width, label='MT', color='#ff7f0e')
  150. ax.set_xlabel('Cell Type')
  151. ax.set_ylabel('Percentage')
  152. ax.set_title('Percentage of Each Cell Type by Genotype')
  153. ax.set_xticks([i + bar_width / 2 for i in indices])
  154. ax.set_xticklabels(combined_data['cell_type_name'], rotation=90)
  155. ax.legend()
  157. plt.tight_layout()
  158. plt.show()
  160. # %%
  161. adata.uns['log1p']["base"] = None
  162. sc.tl.rank_genes_groups(adata, 'leiden_scVI', use_raw=False, layer='counts', method='wilcoxon')
  163. sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
  165. # %%
  166. adata.uns['log1p']["base"] = None
  167. sc.tl.rank_genes_groups(adata, 'cell_type_name', use_raw=False, layer='counts', method='wilcoxon')
  168. sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
  170. # %%
  171. unique_cell_types = adata.obs['cell_type_name'].unique()
  173. for cell_type in unique_cell_types:
  174. temp = adata[adata.obs['cell_type_name'] == cell_type, :]
  176. sc.tl.rank_genes_groups(temp, groupby='Genotype', use_raw=True, method='wilcoxon')
  178. df = sc.get.rank_genes_groups_df(temp, group='MT')
  179. df.to_csv(f'./rank_genes/{cell_type}_rank_genes.csv')
  181. # %%
  182. combined_df = pd.DataFrame()
  184. for cell_type_names, cell_type in cell_type_names.items():
  185. temp_df = pd.read_csv(f'./rank_genes/{cell_type}_rank_genes.csv')
  186. temp_df = temp_df[temp_df['pvals_adj'] < 0.05]
  187. temp_df['cluster'] = cell_type
  188. combined_df = pd.concat([combined_df, temp_df])
  190. combined_df = combined_df.drop_duplicates()
  192. ordered_cell_types = [
  193. 'Ex_neuron',
  194. 'In_progenitor',
  195. 'Inhibitory_neuron',
  196. 'NPC',
  197. 'Astrocyte_progenitor',
  198. 'Astrocyte',
  199. 'pri-OPC',
  200. 'OPC',
  201. 'Oligodendrocyte',
  202. 'Microglia',
  203. 'Endothelial_cell',
  204. 'Pericyte'
  205. ]
  207. combined_df['cluster'] = pd.Categorical(combined_df['cluster'], categories=ordered_cell_types, ordered=True)
  208. combined_df.sort_values('cluster', inplace=True)
  210. color_mapping = {
  211. 'Astrocyte': '#2ca02c',
  212. 'Astrocyte_progenitor': '#f3fbd4',
  213. 'Endothelial_cell': '#7f7f7f',
  214. 'Ex_neuron': '#ff7f0e',
  215. 'In_progenitor': '#ff9896',
  216. 'Inhibitory_neuron': '#d62728',
  217. 'Microglia': '#8c564b',
  218. 'NPC': '#313695',
  219. 'pri-OPC': '#aec7e8',
  220. 'OPC': '#1f77b4',
  221. 'Oligodendrocyte': '#9467bd',
  222. 'Pericyte': '#ffbb78'
  223. }
  225. plt.figure(figsize=(12, 8))
  226. strip_plot = sns.stripplot(x="cluster", y="logfoldchanges", data=combined_df, jitter=0.3, palette=color_mapping)
  228. plt.ylim(-5, 5)
  230. for i, cell_type in enumerate(ordered_cell_types):
  231. num_pos_genes = combined_df[(combined_df['cluster'] == cell_type) & (combined_df['logfoldchanges'] > 0)].shape[0]
  232. num_neg_genes = combined_df[(combined_df['cluster'] == cell_type) & (combined_df['logfoldchanges'] < 0)].shape[0]
  233. plt.text(i, 4.5, f'P={num_pos_genes}', ha='center', va='bottom', fontsize=9)
  234. plt.text(i, -4.5, f'N={num_neg_genes}', ha='center', va='top', fontsize=9)
  236. plt.title('Log2 fold changes for each cell type with significance')
  237. plt.xlabel('Cell Type')
  238. plt.ylabel('Average log2 fold change')
  240. plt.legend([], [], frameon=False)
  242. plt.show()
  244. # %%
  247. # %%
  250. # %%

scRNA-seq_2_gene_exp.ipynb at commit ced6239, no license · at the source

Overview

Authors: Kenta Nitahara1,2,3, Atsuki Kawamura2, Ayumu Tashiro2, Tomoya Iwasaki2, Shin-Ichi Horike4, Jumpei Terakawa4, Takiko Daikoku4, Koichi Higashi5, Ken Kurokawa5, Kiyoko Kato3, Masaaki Nishiyama1,2
ORCID iDs: Kenta Nitahara, Atsuki Kawamura, Ayumu Tashiro, Shin-Ichi Horike, Takiko Daikoku, Koichi Higashi, Ken Kurokawa, Masaaki Nishiyama
  1. Social Brain Development Research Unit, Next Generation Medical Development Research Core, Institute for Frontier Science Initiative, Kanazawa University,Kanazawa, Japan
  2. Department of Histology and Cellular Biology, Graduate School of Medical Sciences, Kanazawa University,Kanazawa, Japan
  3. Department of Gynecology and Obstetrics, Graduate School of Medical Sciences, Kyushu University,Fukuoka, Japan
  4. Research Center for Experimental Modeling of Human Disease, Kanazawa University,Kanazawa, Japan
  5. Genome Evolution Laboratory, National Institute of Genetics,Mishima, Japan
Institutions: Kanazawa University (Japan); Kyushu University (Japan); National Institute of Genetics (Japan)
Journal: Nature communications, volume 17, issue 1, article 4457
Dates: received 16 January 2025; accepted 7 May 2026; published online 27 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73416-2 · PMID 42203765 · PMCID PMC13216556 · OpenAlex W7162508036
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), autism (population), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Single-unit activity, calcium imaging
Keywords: Disease model, Autism spectrum disorders, Epigenetics and behaviour
MeSH: Autism Spectrum Disorder*, Autistic Disorder*, DNA-Binding Proteins*, Mutation*, Neurogenesis*, Transcription Factors*, Animals, Behavior, Animal, Cell Differentiation, Disease Models, Animal, Female, Male, Mice, Mice, Inbred C57BL, Stem Cells (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (JP21H02847, JP24H00627); Japan Agency for Medical Research and Development (JP22gm6310008, JP24wm0625316); Secom Science and Technology Foundation; Astellas Foundation for Research on Metabolic Disorders
Citations: not cited yet (Europe PMC); 90 references in the paper
Research resources: pAAV.Syn.Flex.GCaMP6f.WPRE.SV4089 RRID:Addgene_100833, pENN.AAV.CamKII.GCaMP6f.WPRE.SV40 RRID:Addgene_100834, pAAV-Syn-FLEX-rc[ChrimsonR-tdTomato]48 RRID:Addgene_62723

Abstract

Autism spectrum disorder (ASD) is a common neurodevelopmental condition characterized by behavioral abnormalities. Although mouse models have been widely adopted to recapitulate the pathology of ASD, the identification of specific neural abnormalities responsible for autistic-like behavior has remained challenging. Here we provide insight into this causal relation by identifying the critical period and cell type responsible for the development of such behavior in ASD model mice with a Chd8 mutation. We find that Chd8 mutation induced at embryonic day 14.5 gives rise to ASD-like behavioral phenotypes, including abnormal social interaction and increased anxiety-like behavior, as well as to accelerated cell-cycle exit and differentiation in ventral progenitor cells. Restoration of Chd8 expression in ventral progenitor cells ameliorates both the behavioral phenotypes and aberrant ventral differentiation in Chd8 mutant mice. Our findings indicate that Chd8 mutation during the midfetal period—in particular, in ventral progenitor cells—contributes to the development of autistic-like behavior.

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 4 matches between paragraphs and lines of code.

heal-research/arPLS

License: GPL-2.0
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Evidence: files inventoried
Commit: 8d73e9529e67febbba76e65061229d35c26a1d93, 25 March 2022
Languages: C++ (20), C/C++ (14)
Size: 44 files, 34 scripts
Software Heritage: not archived
Found in: the text, “Fiber photometry”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
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At the source: github.com/heal-research/arPLS

akwamura/Chd8_scRNA-seq

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Tools: anndata (5 files), Matplotlib (5 files), NumPy (5 files), pandas (5 files), Scanpy (5 files), seaborn (5 files), SciPy (2 files), scVelo (1 file), Squidpy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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At the source: github.com/akwamura/Chd8_scRNA-seq

daisukeino/BehavioralAnalysis

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Tools: NumPy (5 files), OpenCV (5 files), Pillow (5 files), Matplotlib (3 files)
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At the source: github.com/daisukeino/BehavioralAnalysis

Zenodo 19105439

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At the source:

Zenodo 19105511

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kentanitahara/chd8_scrna-seq

License: none: the authors keep all their rights
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Evidence: files inventoried
Commit: ced62390bc448df1704584d0ff4ae7f196c55218, 30 October 2024
Languages: Jupyter (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, 5 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
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At the source: github.com/KentaNitahara/Chd8_scRNA-seq/tree/v1.0.0

kentanitahara/behavioralanalysis

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: a88d0964d66a6e368455521975b84128a4d346e2, 5 March 2025
Languages: Python (5)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), OpenCV (5 files), Pillow (5 files), Matplotlib (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
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6 files
At the source: github.com/KentaNitahara/BehavioralAnalysis/tree/v1.0.0

Code availability

The codes for the scRNA-seq and spatial transcriptome analyses used in this study can be found on GitHub: https://github.com/akwamura/Chd8_scRNA-seq, and archived on Zenodo as Kawamura A (2024) Chd8_scRNA-seq: 10.5281/zenodo.19105439. The codes for the behavioral tests used in this study can be found on GitHub: https://github.com/daisukeino/BehavioralAnalysis, and archived on Zenodo as Ino D (2025) BehavioralAnalysis: 10.5281/zenodo.19105511.

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

Tracing map

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What the map holds:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 64 scripts, each with its path and the digest of its content;
  • 4 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 scRNA-seq data have been deposited in GEO under the accession number GSE278323 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE278323) and in DDBJ Sequence Read Archive under the accession number DRA016611 [https://ddbj.nig.ac.jp/search/entry/bioproject/PRJDB14499]. Spatial transcriptome analysis data have been deposited in GEO under the accession number GSE278133 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi]. All other data are available within the Source Data file. Source data are provided in this paper.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 15 MeSH terms, 4 funders, 89 references, 3 RRIDs.

Cite

This paper

Nitahara, K., Kawamura, A., Tashiro, A., Iwasaki, T., Horike, S.-I., Terakawa, J., Daikoku, T., Higashi, K., Kurokawa, K., Kato, K., & Nishiyama, M. (2026). Defective ventral neurogenesis due to midfetal Chd8 mutation drives autistic-like behavior in mice. Nature communications, 17(1), 4457. https://doi.org/10.1038/s41467-026-73416-2

BibTeX

@article{nitahara2026defective,
author = {Nitahara, Kenta and Kawamura, Atsuki and Tashiro, Ayumu and Iwasaki, Tomoya and Horike, Shin-Ichi and Terakawa, Jumpei and Daikoku, Takiko and Higashi, Koichi and Kurokawa, Ken and Kato, Kiyoko and Nishiyama, Masaaki},
title = {{Defective ventral neurogenesis due to midfetal Chd8 mutation drives autistic-like behavior in mice}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {4457},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73416-2},
url = {https://doi.org/10.1038/s41467-026-73416-2},
pmid = {42203765},
pmcid = {PMC13216556}
}

RIS

TY - JOUR
AU - Nitahara, Kenta
AU - Kawamura, Atsuki
AU - Tashiro, Ayumu
AU - Iwasaki, Tomoya
AU - Horike, Shin-Ichi
AU - Terakawa, Jumpei
AU - Daikoku, Takiko
AU - Higashi, Koichi
AU - Kurokawa, Ken
AU - Kato, Kiyoko
AU - Nishiyama, Masaaki
TI - Defective ventral neurogenesis due to midfetal Chd8 mutation drives autistic-like behavior in mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/27
VL - 17
IS - 1
SP - 4457
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73416-2
UR - https://doi.org/10.1038/s41467-026-73416-2
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-73416-2",
"type": "article-journal",
"title": "Defective ventral neurogenesis due to midfetal Chd8 mutation drives autistic-like behavior in mice",
"container-title": "Nature communications",
"author": [
{
"family": "Nitahara",
"given": "Kenta"
},
{
"family": "Kawamura",
"given": "Atsuki"
},
{
"family": "Tashiro",
"given": "Ayumu"
},
{
"family": "Iwasaki",
"given": "Tomoya"
},
{
"family": "Horike",
"given": "Shin-Ichi"
},
{
"family": "Terakawa",
"given": "Jumpei"
},
{
"family": "Daikoku",
"given": "Takiko"
},
{
"family": "Higashi",
"given": "Koichi"
},
{
"family": "Kurokawa",
"given": "Ken"
},
{
"family": "Kato",
"given": "Kiyoko"
},
{
"family": "Nishiyama",
"given": "Masaaki"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4457",
"DOI": "10.1038/s41467-026-73416-2",
"PMID": "42203765",
"PMCID": "PMC13216556",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73416-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
27
]
]
}
}

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

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