OSCR

Defective EV-mediated transport of SHH alters neural fate specification in EPM1 epilepsy.

Code ↔ Paper

7 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 7 matches
  1. [1] § MATERIALS AND METHODS › scRNA-seq sample preparation › scRNA-seq data analysis ↔ vCO_02_normalization.ipynb, lines 80–90 · score 0.73 · preliminary clustering, log1p, PCA, tl, pp, neighborhood
  2. [2] § MATERIALS AND METHODS › scRNA-seq sample preparation › scRNA-seq data analysis ↔ vCO_06_annotation.ipynb, lines 339–346 · score 0.68 · preliminary clustering, log1p, PCA, tl, pp, neighborhood
  3. [3] § RESULTS › EPM1 ventral progenitors and neurons change cell identity ↔ vCO_08_nomenclature_post_integration-PT2.ipynb, lines 166–170 · score 0.62 · THSD7A, CACNA2D1, NEUROG2, DLX2, EOMES, NEUROD1
  4. [4] § RESULTS › EPM1 ventral progenitors and neurons change cell identity ↔ vCO_08_nomenclature_post_integration-PT2.ipynb, lines 172–191 · score 0.62 · Neurons subclusters, THSD7A, CACNA2D1, NEUROG2, DLX2, NEUROD1
  5. [5] § MATERIALS AND METHODS › scRNA-seq sample preparation › scRNA-seq data analysis ↔ vCO_01_quality_control.ipynb, lines 220–230 · score 0.61 · scDblFinder, SingleCellExperiment, seed, doublets, cells
  6. [6] § RESULTS › EPM1 ventral progenitors and neurons change cell identity ↔ vCO_08_nomenclature_post_integration-PT2.ipynb, lines 295–297 · score 0.54 · Progenitor subclusters, vCTRL, vEPM1, UMAP, clustered
  7. [7] § MATERIALS AND METHODS › scRNA-seq sample preparation › scRNA-seq data analysis ↔ vCO_01_quality_control.ipynb, lines 110–119 · score 0.52 · mitochondrial gene, low quality, mt

Paper

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The authors' code

Jupyter notebook · 507 lines · 17 KB · MIT · 3 matches

  1. # %%
  2. #I would like to dissect better the Neurons cluster..
  4. # %%
  5. adata = sc.read_h5ad('02_Results/CSTB_annotated_09.h5ad')
  7. # %%
  8. adata
  10. # %%
  11. sc.pl.umap(adata, color=['DSCAM','type'], show=False,vmax=0.5,frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.1)
  13. # %%
  14. adata.obs['annotated'].value_counts()
  16. # %%
  17. #Subcluster
  18. adata_MatNeurons = adata[adata.obs['annotated']=='Neurons'].copy()
  20. # %%
  21. sc.tl.leiden(adata_MatNeurons, resolution=0.2, restrict_to = ['annotated',['Neurons']], key_added='leiden_R1')
  23. # %%
  24. from pylab import rcParams
  26. sc.pl.umap(adata_MatNeurons, color='leiden_R1', legend_loc='right margin' , legend_fontsize = 8, frameon=False)
  28. # %%
  29. # Define your categories of interest
  31. neurons_categories = ['Mature Neurons,0', 'Neurons,1', 'Neurons,2']
  33. # Subset the adata to only include cells where 'annotated' is one of the progenitor categories
  35. adata_Neuro_clusters = adata_MatNeurons[adata_MatNeurons.obs['leiden_R1'].isin(neurons_categories)].copy()
  38. # %%
  39. num_tot_cells = adata_Neuro_clusters.obs.groupby(['sample']).count()
  40. num_tot_cells = dict(zip(num_tot_cells.index, num_tot_cells.scDblFinder_class))
  41. num_tot_cells
  43. # %%
  44. cell_type_counts = adata_Neuro_clusters.obs.groupby(['type','sample', 'leiden_R1']).count()
  45. cell_type_counts = cell_type_counts[cell_type_counts.sum(axis = 1) > 0].reset_index()
  46. cell_type_counts = cell_type_counts[cell_type_counts.columns[0:4]]
  47. cell_type_counts
  49. # %%
  50. cell_type_counts['total_cells'] = cell_type_counts['sample'].map(num_tot_cells).astype(int)
  53. cell_type_counts['frequency'] = cell_type_counts['experiment'] / cell_type_counts['total_cells']
  55. # Now, you can print the updated cell_type_counts DataFrame
  56. print(cell_type_counts)
  58. # %%
  59. import matplotlib.pyplot as plt
  60. import seaborn as sns
  61. plt.figure(figsize = (10,4))
  63. # Create a custom palette
  64. palette = {'vCTRL':'#6cb052','vEPM1':'#9652b0'}
  67. ax = sns.boxplot(data = cell_type_counts, x = 'leiden_R1', y = 'frequency', hue = 'type', palette=palette)
  69. plt.xticks(rotation = 35, rotation_mode = 'anchor', ha = 'right')
  71. # Save the figure as a PDF file
  72. plt.savefig('figures/FIG2_H.pdf', format='pdf', bbox_inches='tight')
  74. plt.show()
  76. # %%
  77. # Convert columns to string type and create a new column merging without underscore
  78. adata_Neuro_clusters.obs['leiden_R1'] = adata_Neuro_clusters.obs['leiden_R1'].astype(str) + ' ' + adata_Neuro_clusters.obs['type'].astype(str)
  80. # %%
  81. sc.pl.umap(adata_Neuro_clusters, color='leiden_R1', legend_loc='right margin' , legend_fontsize = 12, frameon=False,title="Neurons Subclusters",
  82. palette = { 'Neurons,0 vCTRL': '#00979f', # Original CTRL color
  83. 'Neurons,0 vEPM1': '#66b2b4', # Lightened shade for Neurons,0
  84. 'Neurons,1 vCTRL': '#00479f', # Original CTRL color
  85. 'Neurons,1 vEPM1': '#4da6ff', # Lightened shade for Neurons,1
  86. 'Neurons,2 vCTRL': '#007c9f', # Original CTRL color
  87. 'Neurons,2 vEPM1': '#33b5cc', # Lightened shade for Neurons,2
  88. },
  89. save="FIG2_G.pdf" )
  91. # %%
  92. sc.pl.umap(adata_Neuro_clusters,
  93. color='THSD7A',
  94. legend_loc='right margin',
  95. legend_fontsize = 12,
  96. frameon=False,
  97. save="FIG2_I_1.pdf")
  99. # %%
  100. sc.pl.umap(adata_Neuro_clusters,
  101. color='DLX1',
  102. legend_loc='right margin',
  103. legend_fontsize = 12,
  104. frameon=False,
  105. save="FIG2_I_2.pdf")
  107. # %%
  108. sc.pl.umap(adata_Neuro_clusters,
  109. color='TBR1',
  110. legend_loc='right margin',
  111. legend_fontsize = 12,
  112. frameon=False,
  113. save="FIG2_I_3.pdf")
  115. # %%
  116. sc.pl.umap(adata_Neuro_clusters, color='type', legend_loc='right margin' , legend_fontsize = 12, frameon=False,title="Neurons Subclusters - Genotype",
  117. palette={'vCTRL':'#6cb052','vEPM1':'#9652b0'}, save="Neurons_Subcluster_Genotype.pdf")
  119. # %%
  120. sc.pp.log1p(adata)
  122. sc.tl.rank_genes_groups(adata = adata_Neuro_clusters, groupby='leiden_R1', groups= ['Neurons,0','Neurons,1'],
  123. reference='Neurons,1', rankby_abs=True)
  125. # %%
  126. sc.pp.log1p(adata)
  128. sc.tl.rank_genes_groups(adata = adata_Neuro_clusters, groupby='leiden_R1', groups= ['Neurons,0','Neurons,2'],
  129. reference='Neurons,2', rankby_abs=True)
  131. # %%
  132. rcParams['figure.figsize']=(10,5)
  133. sc.pl.rank_genes_groups(adata_Neuro_clusters, size=10, n_genes=100)
  135. # %%
  136. adata_Neuro_clusters_0vs1=adata_Neuro_clusters[adata_Neuro_clusters.obs["leiden_R1"] != "Neurons,2"]
  138. # %%
  139. sc.pl.umap(
  140. adata_Neuro_clusters,
  141. color=['THSD7A', 'DLX1', 'TBR1'],
  142. show=False,
  143. frameon=False,
  144. legend_loc='right margin',
  145. legend_fontsize=12,
  146. wspace=0.1,
  147. ncols=3, # This will create 3 plots per row
  148. save="FIG2_I"
  149. )
  151. # %%
  152. sc.pl.umap(
  153. adata_Neuro_clusters,
  154. color=['CACNA2D1', 'NEUROD1',
  155. 'DLX1', 'GAD1',
  156. 'NEUROG2','EOMES'],
  157. show=False,
  158. frameon=False,
  159. legend_loc='right margin',
  160. legend_fontsize=12,
  161. wspace=0.1,
  162. ncols=2, # This will create 3 plots per row
  163. save="supplFIG2_F.pdf"
  164. )
  166. # %%
  167. sc.set_figure_params(dpi=150, dpi_save=300, fontsize=12)
  168. markers = ['NEUROD1','THSD7A', 'CACNA2D1','DLX1', 'DLX2', 'GAD1', 'NEUROG2', 'EOMES', 'TBR1']
  169. sc.pl.heatmap(adata_Neuro_clusters, markers, groupby='leiden_R1',swap_axes=True,
  170. figsize=[10,5],save="FIG2_J.pdf")
  172. # %%
  173. adata_Neuro_clusters_0vs1=adata_Neuro_clusters[adata_Neuro_clusters.obs["leiden_R1"] != "Neurons,2"]
  175. #rcParams['figure.figsize']=(9,5)
  177. df = sc.get.obs_df(adata_Neuro_clusters_0vs1, ['DLX1','DLX2', 'DLX5', 'THSD7A','CACNA2D1','NEUROD1','NEUROG2','leiden_R1'])
  178. df = df.set_index('leiden_R1').stack().reset_index()
  179. df.columns = ['leiden_R1', 'gene', 'value']
  180. import seaborn as sns
  181. g=sns.violinplot(data=df, x='gene', y='value', hue="leiden_R1", gap=.5,
  182. split=True, inner="quart", linewidth=.8, palette={'Neurons,0':'#00979f','Neurons,1':'#00479f'})
  183. g.set_xlabel("Genes")
  184. g.set_ylabel("Expression Levels")
  185. plt.setp(g.get_xticklabels(), rotation=45)
  187. # Put the legend out of the figure
  188. plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
  191. plt.savefig('figures/Neurons Subclusters splitplot.pdf', bbox_inches='tight')
  195. # %%
  196. sc.pl.rank_genes_groups_violin(adata_Neuro_clusters, groups='Neurons,0', n_genes=20)
  198. # %%
  199. #Same for Progenitors
  201. # %%
  202. #Subcluster
  203. #a12006
  204. adata_Prog = adata[adata.obs['annotated']=='Progenitors'].copy()
  205. sc.pl.umap(adata_Prog, color='annotated', legend_loc='right margin' , legend_fontsize = 8, frameon=False)
  207. # %%
  208. sc.pl.umap(adata_Prog,
  209. color=['GSX2','ASCL1','PAX6','MEIS2'],
  210. legend_loc='right margin',
  211. legend_fontsize = 12,
  212. frameon=False)
  214. # %%
  215. adata.obs
  217. # %%
  218. print(type(adata_Prog.obs['type']))
  221. # %%
  222. genes = ['NKX2-1', 'SIX3', 'PAX6', 'GSX2','ASCL1']
  223. sc.pl.dotplot(adata_Prog, genes, groupby='type', dendrogram=True)
  226. # %%
  227. sc.tl.leiden(adata_Prog, resolution=0.4, restrict_to = ['annotated',['Progenitors']], key_added='leiden_Prog')
  229. # %%
  230. from pylab import rcParams
  231. rcParams['figure.figsize']=(5,5)
  232. sc.pl.umap(adata_Prog, color='leiden_Prog', legend_loc='right margin' , legend_fontsize = 8, frameon=False)
  234. # %%
  235. cluster_sizes = adata_Prog.obs['leiden_Prog'].value_counts()
  236. print(cluster_sizes)
  238. # %%
  239. # Define your categories of interest
  241. progenitor_categories = ['Progenitors,0', 'Progenitors,1', 'Progenitors,2', 'Progenitors,3', 'Progenitors,4']
  243. # Subset the adata to only include cells where 'annotated' is one of the progenitor categories
  245. adata_Prog_clusters = adata_Prog[adata_Prog.obs['leiden_Prog'].isin(progenitor_categories)].copy()
  248. # %%
  249. adata_Prog_clusters
  251. # %%
  252. num_tot_cells = adata_Prog_clusters.obs.groupby(['sample']).count()
  253. num_tot_cells = dict(zip(num_tot_cells.index, num_tot_cells.scDblFinder_class))
  254. num_tot_cells
  256. # %%
  257. cell_type_counts = adata_Prog_clusters.obs.groupby(['type','sample', 'leiden_Prog']).count()
  258. cell_type_counts = cell_type_counts[cell_type_counts.sum(axis = 1) > 0].reset_index()
  259. cell_type_counts = cell_type_counts[cell_type_counts.columns[0:4]]
  260. cell_type_counts
  262. # %%
  263. cell_type_counts['total_cells'] = cell_type_counts['sample'].map(num_tot_cells).astype(int)
  266. cell_type_counts['frequency'] = cell_type_counts['experiment'] / cell_type_counts['total_cells']
  268. # Now, you can print the updated cell_type_counts DataFrame
  269. print(cell_type_counts)
  271. # %%
  272. import matplotlib.pyplot as plt
  273. import seaborn as sns
  275. plt.figure(figsize=(10, 4))
  277. # Create a custom palette
  278. palette = {'vCTRL': '#6cb052', 'vEPM1': '#9652b0'}
  280. # Create the boxplot
  281. ax = sns.boxplot(data=cell_type_counts, x='leiden_Prog', y='frequency', hue='type', palette=palette)
  283. # Set x-axis title with extra padding
  284. plt.xlabel("Progenitors Subclusters", labelpad=15) # Adjust 'labelpad' for spacing
  286. # Rotate x-axis labels
  287. plt.xticks(rotation=35, rotation_mode='anchor', ha='right')
  289. # Save the figure as a PDF file
  290. plt.savefig('figures/FIG2_C.pdf', format='pdf', bbox_inches='tight')
  292. plt.show()
  295. # %%
  296. sc.pl.umap(adata_Prog_clusters, color='type', legend_loc='right margin' , legend_fontsize = 8, frameon=False,title="Progenitors Subclusters - Genotype",
  297. palette={'vCTRL':'#6cb052','vEPM1':'#9652b0'}, save="Progenitors_Subcluster_Genotype.pdf")
  299. # %%
  300. sc.pl.umap(adata_Prog_clusters, color='leiden_Prog', legend_loc='right margin' , legend_fontsize = 8, frameon=False,title="Progenitors Subcluster",
  301. palette={'Progenitors,0':'#eb0954','Progenitors,1':'#eb2f09','Progenitors,2':'#eba009','Progenitors,3':'#8e358b','Progenitors,4':'#d40062'}, save="Progenitors Subclusters.pdf")
  303. # %%
  304. sc.pl.umap(adata_Prog_clusters, color=['MEIS2','NKX2-1','PAX6'], show=False, frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.5, save="FIG2_D.pdf")
  306. # %%
  307. # Convert columns to string type and create a new column merging without underscore
  308. adata_Prog_clusters.obs['leiden_type'] = adata_Prog_clusters.obs['leiden_Prog'].astype(str) + ' ' + adata_Prog_clusters.obs['type'].astype(str)
  310. # %%
  311. sc.pl.umap(adata_Prog_clusters, color='leiden_type', legend_loc='right margin' , legend_fontsize = 12, frameon=False,title="Progenitors Subclusters",
  312. palette = {
  313. 'Progenitors,0 vCTRL': '#eb0954', # Original color with slight modification
  314. 'Progenitors,0 vEPM1': '#ff8b94', # Lightened shade of original color
  315. 'Progenitors,1 vCTRL': '#eb2f09', # Original color with slight modification
  316. 'Progenitors,1 vEPM1': '#ff7e3b', # Lightened shade of original color
  317. 'Progenitors,2 vCTRL': '#eba009', # Original color with slight modification
  318. 'Progenitors,2 vEPM1': '#ffba33', # Lightened shade of original color
  319. 'Progenitors,3 vCTRL': '#8e358b', # Original color with slight modification
  320. 'Progenitors,3 vEPM1': '#ab6c9a', # Lightened shade of original color
  321. 'Progenitors,4 vCTRL': '#d40062', # Original color with slight modification
  322. 'Progenitors,4 vEPM1': '#f46b94', # Lightened shade of original color
  323. # Add more colors as needed
  324. },
  325. save="FIG2_B.pdf")
  327. # %%
  328. sc.pl.umap(adata_Prog_clusters,
  329. color='MEIS2',
  330. legend_loc='right margin',
  331. legend_fontsize = 12,
  332. frameon=False,
  333. save="FIG2_D_1")
  335. # %%
  336. sc.pl.umap(adata_Prog_clusters,
  337. color='PAX6',
  338. legend_loc='right margin',
  339. legend_fontsize = 12,
  340. frameon=False,
  341. save="FIG2_D_2")
  343. # %%
  344. sc.pl.umap(adata_Prog_clusters,
  345. color='NKX2-1',
  346. legend_loc='right margin',
  347. legend_fontsize = 12,
  348. frameon=False,
  349. save="FIG2_D_3")
  351. # %%
  352. sc.pl.umap(adata_Prog_clusters,
  353. color='SIX3',
  354. legend_loc='right margin',
  355. legend_fontsize = 12,
  356. frameon=False,
  357. save="FIG2_D_4")
  359. # %%
  360. sc.set_figure_params(dpi=300, dpi_save=300, fontsize=12)
  362. markers = ['MEIS2','PAX6','NKX2-1','SIX3']
  364. # Plot the heatmap using the new column with custom color mapping
  365. sc.pl.heatmap(
  366. adata_Prog_clusters,
  367. markers,
  368. figsize=[10,5],
  369. groupby='leiden_type', # Use the new combined column
  370. swap_axes=True,
  371. save="FIG2_E.pdf"
  372. )
  374. # %%
  375. adata_Prog_clusters_0vs2 = adata_Prog_clusters[~adata_Prog_clusters.obs["leiden_Prog"].isin(["Progenitors,1", "Progenitors,3", "Progenitors,4"])]
  377. adata_Prog_clusters_0vs2
  379. # %%
  380. rcParams['figure.figsize']=(9,5)
  382. df = sc.get.obs_df(adata_Prog_clusters_0vs2, ['NKX2-1','SIX3', 'PAX6', 'SOX2','leiden_Prog'])
  383. df = df.set_index('leiden_Prog').stack().reset_index()
  384. df.columns = ['leiden_Prog', 'gene', 'value']
  385. import seaborn as sns
  386. g=sns.violinplot(data=df, x='gene', y='value', hue="leiden_Prog", gap=.5,
  387. split=True, inner="quart", linewidth=.8, palette={'Progenitors,0':'#eb0954','Progenitors,2':'#eba009'})
  388. g.set_xlabel("Genes")
  389. g.set_ylabel("Expression Levels")
  390. plt.setp(g.get_xticklabels(), rotation=45)
  392. # Put the legend out of the figure
  393. plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
  395. plt.savefig('figures/FIG2_C.pdf', bbox_inches='tight')
  397. # %%
  398. sc.pp.log1p(adata_Prog_clusters)
  401. sc.tl.rank_genes_groups(adata = adata_Prog_clusters, groupby='leiden_Prog', groups= ['Progenitors,0','Progenitors,2'],
  402. reference='Progenitors,2', rankby_abs=True)
  404. # %%
  405. rcParams['figure.figsize']=(5,5)
  406. sc.pl.rank_genes_groups(adata_Prog_clusters, size=10, n_genes=50)
  408. # %%
  409. sc.pl.rank_genes_groups_violin(adata_Prog_clusters, n_genes=15)
  411. # %%
  412. sc.pl.umap(adata_Prog_clusters, color=['MEIS2','PAX6','NKX2-1','FOXG1'], show=False, frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.1)
  414. # %%
  415. sc.pl.umap(adata, color=['MEIS2','PAX6','NKX2-1','FOXG1'], show=False, frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.1)
  417. # %%
  418. ax = sc.pl.umap(adata, size=25, show=False)
  421. sc.pl.umap(
  422. adata[(adata.obs["annotated"] == "Progenitors") | (adata.obs["annotated"] == "Newborn Neurons")| (adata.obs["annotated"] == "Progenitors/NKX2-1+")|
  423. (adata.obs["annotated"] == "Neurons")|(adata.obs["annotated"] == "Progenitors/MEIS2+")| (adata.obs["annotated"] == "Striatum Progenitors")|
  424. (adata.obs["annotated"] == "Striatum Neurons")],
  425. size=25,
  426. show=False,
  427. frameon=False,
  428. color=['CACNA2D1'],
  429. ax=ax
  430. )
  432. # Adjust figure size and layout
  433. fig = plt.gcf()
  434. fig.set_size_inches(8, 6) # Adjust the size as needed
  435. fig.tight_layout() # Adjust layout
  437. # Save the figure
  438. plt.savefig('03_Figures/Fig2_B_2.png') # Adjust the filename and dpi as needed
  439. plt.show() # Optional: Show the plot if you want to display it in the notebook
  441. # %%
  442. rcParams['figure.figsize']=(5,5)
  444. # %%
  445. sc.pl.umap(adata, color=['DLX1','DLX2','GAD1'], show=False, frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.1, save="Inhibitory_Markers.pdf")
  447. # %%
  448. sc.pl.umap(adata, color=['NEUROD1','THSD7A','CACNA2D1'], show=False, frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.1,save="Excitatory_Markers.pdf")
  450. # %%
  451. sc.pl.umap(adata, color=['NEUROG2','INSM1','TBR1'], show=False, frameon=False, legend_loc='right margin', legend_fontsize=12, wspace=0.1,save="Dorsal_Markers.pdf")
  453. # %%
  454. adata.obs['sample'].value_counts()
  456. # %%
  457. adata.write("02_Results/CSTB_annotated_09.h5ad")
  459. # %%
  460. adata = sc.read_h5ad('02_Results/CSTB_annotated_09.h5ad')
  462. # %%
  463. sc.pl.umap(adata, color=['type'], show=False, frameon=False, legend_loc='right margin', title="Genotype",
  464. legend_fontsize=12, save="Sfig2_A.pdf")
  466. # %%
  467. sc.pl.umap(adata, color=['SOX2'], show=False, frameon=False, legend_loc='right margin',
  468. legend_fontsize=12, save="Sfig2_B1.pdf")
  470. # %%
  471. sc.pl.umap(adata, color=['VIM'], show=False, frameon=False, legend_loc='right margin',
  472. vmax=0.8,
  473. legend_fontsize=12, save="Sfig2_B2.pdf")
  475. # %%
  476. sc.pl.umap(adata, color=['NES'], show=False, frameon=False, legend_loc='right margin',
  477. legend_fontsize=12, save="Sfig2_B3.pdf")
  479. # %%
  480. sc.pl.umap(adata_Prog_clusters, color=['type'], show=False, frameon=False, legend_loc='right margin', title="Progenitors Subclusters - Genotype",
  481. legend_fontsize=12, save="Sfig2_C.pdf")
  483. # %%
  484. sc.pl.umap(adata, color=['DCX'], show=False, frameon=False, legend_loc='right margin',
  485. legend_fontsize=12, save="Sfig2_E1.pdf")
  487. # %%
  488. sc.pl.umap(adata, color=['MAP2'], show=False, frameon=False, legend_loc='right margin',
  489. legend_fontsize=12, save="Sfig2_E2.pdf")
  491. # %%
  492. sc.pl.umap(adata_Neuro_clusters, color=['type'], show=False, frameon=False, legend_loc='right margin', title="Neurons Subclusters - Genotype",
  493. legend_fontsize=12, save="Sfig2_F.pdf")
  495. # %%
  496. sc.pl.umap(adata_Neuro_clusters, color=['CACNA2D1'], show=False, frameon=False, legend_loc='right margin',
  497. legend_fontsize=12, save="Sfig2_G1.pdf")
  498. sc.pl.umap(adata_Neuro_clusters, color=['NEUROD1'], show=False, frameon=False, legend_loc='right margin',
  499. legend_fontsize=12, save="Sfig2_G2.pdf")
  500. sc.pl.umap(adata_Neuro_clusters, color=['DLX2'], show=False, frameon=False, legend_loc='right margin',
  501. legend_fontsize=12, save="Sfig2_G3.pdf")
  502. sc.pl.umap(adata_Neuro_clusters, color=['GAD1'], show=False, frameon=False, legend_loc='right margin',
  503. legend_fontsize=12, save="Sfig2_G4.pdf")
  504. sc.pl.umap(adata_Neuro_clusters, color=['NEUROG2'], show=False, frameon=False, legend_loc='right margin',
  505. legend_fontsize=12, save="Sfig2_G5.pdf")
  506. sc.pl.umap(adata_Neuro_clusters, color=['EOMES'], show=False, frameon=False, legend_loc='right margin',
  507. legend_fontsize=12, save="Sfig2_G6.pdf")

vCO_08_nomenclature_post_integration-PT2.ipynb at commit 4c22f86, under MIT · at the source

Overview

Authors: Andrea Forero1, Veronica Pravata1, Fabrizia Pipicelli2, Elisa Frenna1,2,3,4, Alessandro Soloperto2, Marta Ianni1,5, Natalia Abate1,5, Francesco Di Matteo2, Zagorka Bekjarova2, Laura Canafoglia6, Francesca Ragona6, Giuseppina Maccarrone2, Mariano Gonzalez Pisfil7, Christian Wahl-Schott8, Filippo M Cernilogar9,10, Matthias Eder2, Rossella Di Giaimo1,2,5, Silvia Cappello1,2,11
ORCID iDs: Andrea Forero, Veronica Pravata, Fabrizia Pipicelli, Elisa Frenna, Alessandro Soloperto, Marta Ianni, Natalia Abate, Laura Canafoglia, Francesca Ragona, Mariano Gonzalez Pisfil, Filippo M Cernilogar, Matthias Eder, Rossella Di Giaimo, Silvia Cappello
  1. Physiological Genomics, Biomedical Center (BMC), LMU Medizin, LMU Munich, Munich, Germany
  2. Max Planck Institute of Psychiatry, Munich, Germany
  3. International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
  4. Graduate School of Systemic Neurosciences (GSN), Ludwig-Maximilians-University, Munich, Germany
  5. Department of Biology, University of Naples Federico II, Naples, Italy
  6. Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
  7. Core Facility Bioimaging and Walter-Brendel-Centre of Experimental Medicine, Biomedical Center, Ludwig Maximilian University, Munich, Germany
  8. Division of Cardiovascular Physiology and Pathophysiology, Biomedical Center (BMC), LMU Medizin, LMU Munich, Munich, Germany
  9. Division of Molecular Biology, Biomedical Center (BMC), LMU Medizin, LMU Munich, Munich, Germany
  10. Department of Science and Technological Innovation, University of Piemonte Orientale, Alessandria, Italy
  11. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
Institutions: Ludwig-Maximilians-Universität München (Germany); Max Planck Institute of Psychiatry (Germany); University of Naples Federico II (Italy); Fondazione IRCCS Istituto Neurologico Carlo Besta (Italy); Institut für Chirurgische Forschung (Germany); Università degli Studi del Piemonte Orientale “Amedeo Avogadro” (Italy); Munich Cluster for Systems Neurology (Germany)
Journal: Science advances, volume 12, issue 36, article eadu3955
Dates: received 5 November 2024; accepted 23 July 2026; published online 2 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.adu3955 · PMID 42685196 · PMCID PMC13537273 · OpenAlex W7206171561
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials
MeSH: Epilepsies, Myoclonic*, Hedgehog Proteins*, Neurons*, Animals, Humans, Mutation, Organoids, Protein Transport, Signal Transduction (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (101043959)
Citations: not cited yet (Europe PMC); 81 references in the paper
Research resources: Pegtel RRID:Addgene_130901, RRID:SCR_025745

Abstract

The extracellular milieu, including extracellular vesicles (EVs), plays a pivotal role in brain development. In this study, we sought to elucidate the pathogenesis of progressive myoclonus epilepsy type 1 (EPM1), a disease caused by mutations in the CSTB gene, using cerebral organoids (COs) derived from patient cells. The results demonstrate that EPM1 COs display increased electrophysiological activity and disrupted excitatory/inhibitory (E/I) balance. Single-cell RNA sequencing analysis of ventral EPM1-COs revealed an abnormal specification of progenitor fate, with a shift toward dorsal neuron identities. We demonstrated that this misspecification is driven by a functional alteration of the ventral signaling niche, resulting from impaired EV dynamics and altered protein cargo. Mechanistically, we identified Sonic Hedgehog (SHH) as a direct physical interactor of CSTB and demonstrated that CSTB deficiency leads to reduced SHH content and secretion. Our findings establish CSTB as a safeguard of ventral patterning and identify the CSTB-SHH-EV axis as a potential therapeutic target for mitigating the E/I imbalance associated with EPM1.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

coolbutuseless/ggpattern

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 14e1355d38f35566c21a23b76bf2417285beb9ac, 24 June 2026
Languages: R (93)
Size: 238 files, 93 scripts
Software Heritage: archived
Found in: the text, “Bulk RNA-seq analysis”
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 12 notebooks
Not found: CITATION.cff
Tools: ggplot2 (19 files), tidyverse (7 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
96 files
At the source: github.com/coolbutuseless/ggpattern

VeronicaMP/Defective-EV-Mediated-Transport-of-SHH-Alters-Neural-Fate-Specification-in-EPM1-epilepsy

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 4c22f863f350fa0a1844f76f2d9d723f46a6c011, 19 July 2026
Languages: Jupyter (14), R (1)
Size: 18 files, 15 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file, 14 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Scanpy (7 files), Matplotlib (5 files), NumPy (5 files), seaborn (5 files), rpy2 (4 files), SciPy (3 files), pandas (2 files), DESeq2 (1 file), ggplot2 (1 file), Numba (1 file), reshape2 (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
11 files
At the source: github.com/VeronicaMP/Defective-EV-Mediated-Transport-of-SHH-Alters-Neural-Fate-Specification-in-EPM1-epilepsy

Zenodo 21453507

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Scanpy (7 files), Matplotlib (5 files), NumPy (5 files), seaborn (5 files), rpy2 (4 files), SciPy (3 files), pandas (2 files), DESeq2 (1 file), ggplot2 (1 file), Numba (1 file), reshape2 (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
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At the source: doi.org/10.5281/zenodo.21453507

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Data

Datasets cited

Data, code, and materials availability

Raw sequencing data have been deposited in the Gene Expression Omnibus (GEO) database under accession codes GSE188219 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE188219) and GSE282281 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282281), and proteomics data have been deposited in PRIDE under the accession code PXD072599. All custom code used in this study is available via Zenodo at https://doi.org/10.5281/zenodo.21453507 (archived from the GitHub repository https://github.com/VeronicaMP/Defective-EV-Mediated-Transport-of-SHH-Alters-Neural-Fate-Specification-in-EPM1-epilepsy). All other data needed to evaluate and reproduce the results are present in the paper and/or the Supplementary Materials. This study did not generate new materials.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 9 MeSH terms, 1 funder, 78 references, 2 RRIDs.

Cite

This paper

Forero, A., Pravata, V., Pipicelli, F., Frenna, E., Soloperto, A., Ianni, M., Abate, N., Di Matteo, F., Bekjarova, Z., Canafoglia, L., Ragona, F., Maccarrone, G., Pisfil, M. G., Wahl-Schott, C., Cernilogar, F. M., Eder, M., Di Giaimo, R., & Cappello, S. (2026). Defective EV-mediated transport of SHH alters neural fate specification in EPM1 epilepsy. Science advances, 12(36), eadu3955. https://doi.org/10.1126/sciadv.adu3955

BibTeX

@article{forero2026defective,
author = {Forero, Andrea and Pravata, Veronica and Pipicelli, Fabrizia and Frenna, Elisa and Soloperto, Alessandro and Ianni, Marta and Abate, Natalia and Di Matteo, Francesco and Bekjarova, Zagorka and Canafoglia, Laura and Ragona, Francesca and Maccarrone, Giuseppina and Pisfil, Mariano Gonzalez and Wahl-Schott, Christian and Cernilogar, Filippo M and Eder, Matthias and Di Giaimo, Rossella and Cappello, Silvia},
title = {{Defective EV-mediated transport of SHH alters neural fate specification in EPM1 epilepsy}},
journal = {Science advances},
year = {2026},
month = sep,
volume = {12},
number = {36},
pages = {eadu3955},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adu3955},
url = {https://doi.org/10.1126/sciadv.adu3955},
pmid = {42685196},
pmcid = {PMC13537273}
}

RIS

TY - JOUR
AU - Forero, Andrea
AU - Pravata, Veronica
AU - Pipicelli, Fabrizia
AU - Frenna, Elisa
AU - Soloperto, Alessandro
AU - Ianni, Marta
AU - Abate, Natalia
AU - Di Matteo, Francesco
AU - Bekjarova, Zagorka
AU - Canafoglia, Laura
AU - Ragona, Francesca
AU - Maccarrone, Giuseppina
AU - Pisfil, Mariano Gonzalez
AU - Wahl-Schott, Christian
AU - Cernilogar, Filippo M
AU - Eder, Matthias
AU - Di Giaimo, Rossella
AU - Cappello, Silvia
TI - Defective EV-mediated transport of SHH alters neural fate specification in EPM1 epilepsy
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/09/02
VL - 12
IS - 36
SP - eadu3955
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adu3955
UR - https://doi.org/10.1126/sciadv.adu3955
LA - en
ER -

CSL-JSON

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