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Single-cell multi-omic atlas and morphogen screening informs midbrain and hindbrain organoid engineering.

Code ↔ Paper

15 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 15 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data analysis methods › Estimation of AP and DV scores ↔ morphogen_screen/cell_composition/04_calculateDV_R.ipynb, lines 67–81 · score 0.97 · LMX1B, PHOX2B, POU3F1, module scores, BNC2, DMRT3
  2. [2] § Methods › Data analysis methods › Processing and analysis of morphogen perturbation screen scRNA-seq data ↔ morphogen_screen/cell_composition/02_Glycolysis.ipynb, lines 45–48 · score 0.81 · aerobic electron transport, canonical glycolysis, glycolysis score, chain, Leiden, morphogen
  3. [3] § Methods › Data analysis methods › Trajectory inference based on transcriptome similarity ↔ morphogen_screen/cell_composition/06_UMAP_cell_types.ipynb, lines 40–84 · score 0.80 · RELN high, PAX2 high, POU6F2 high, glycinergic neuron, glutamatergic neuron, medulla
  4. [4] § Results › Differential abundance-informed trajectory inference provides inroads to understanding human brain development ↔ morphogen_screen/cell_composition/06_UMAP_cell_types.ipynb, lines 40–84 · score 0.78 · RELN high, PAX2 high, POU6F2 high, glycinergic neurons, glutamatergic neurons, medulla
  5. [5] § Methods › Data analysis methods › Processing and analysis of morphogen perturbation screen scRNA-seq data ↔ morphogen_screen/cell_composition/06_UMAP_cell_types.ipynb, lines 181–208 · score 0.77 · motor neurons, neural crest, glycinergic neurons, dopaminergic neurons, glutamatergic neurons, Scanpy
  6. [6] § Methods › Data analysis methods › Mapping scATAC-seq data to the primary reference using developed ATAC-mapper package ↔ src/atac_mapper/reference_mapping/mapping_atac.py, lines 95–155 · score 0.75 · trained scPoli, query model, encoder, layer, batch, latent
  7. [7] § Methods › Data analysis methods › Mapping scATAC-seq data to the primary reference using developed ATAC-mapper package ↔ src/atac_mapper/topic_matching/utils.py, the whole file · a weak match · score 0.73 · term frequency vector, lda, algorithm, iteration, inferred, Inference
  8. [8] § Methods › Data analysis methods › Cell type annotation of the scMultiome data ↔ morphogen_screen/cell_composition/06_UMAP_cell_types.ipynb, lines 181–208 · score 0.67 · neural crest, glycinergic neurons, dopaminergic neurons, glutamatergic neurons, NPC
  9. [9] § Methods › Data analysis methods › Processing and analysis of morphogen perturbation screen scRNA-seq data ↔ morphogen_screen/cell_composition/01_read_raw.ipynb, lines 96–99 · score 0.57 · n_neighbors, n_pcs, Scanpy, pp, tl, morphogen
  10. [10] § Methods › Data analysis methods › Processing and analysis of morphogen perturbation screen scRNA-seq data ↔ morphogen_screen/cell_composition/03_Filter_adata.ipynb, lines 52–55 · score 0.57 · n_neighbors, n_pcs, Scanpy, pp, tl, morphogen
  11. [11] § Methods › Data analysis methods › Cell type annotation of the scMultiome data ↔ morphogen_screen/cell_composition/07_01_Ext_data_fig7.ipynb, lines 321–326 · score 0.57 · NR4A2, LMX1A, EN1, FOXA2, UMAP
  12. [12] § Methods › Experimental methods › Organoid culture for the morphogen screen experiment ↔ morphogen_screen/cell_composition/07_01_Ext_data_fig7.ipynb, lines 31–93 · score 0.57 · FGF19, FGF17, FGF2, CHIR, FGF8, insulin
  13. [13] § Results › Combinatorial morphogen screen introduces a spectrum of organoid models of the posterior human brain ↔ morphogen_screen/cell_composition/06_UMAP_cell_types.ipynb, lines 239–305 · score 0.55 · glycinergic neurons, glutamatergic neurons, LHX9, PAX2, medulla, morphogen screen
  14. [14] § Methods › Data analysis methods › Mapping scATAC-seq data to the primary reference using developed ATAC-mapper package ↔ src/atac_mapper/topic_matching/topic_match.py, lines 16–75 · score 0.55 · iteration, cisTopic, vector, profiles, query, inferred
  15. [15] § Results › Chromatin accessibility dynamics during human posterior brain organoid development ↔ src/atac_mapper/__init__.py, the whole file · a weak match · score 0.51 · single cell chromatin, chromatin accessibility, matched

Paper

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The authors' code

Jupyter notebook · 305 lines · 10 KB · MIT · 5 matches

  1. # %%
  2. import scanpy as sc
  3. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=250, format='pdf')
  4. import numpy as np
  5. import pandas as pd
  6. from scipy import io
  7. from matplotlib.pyplot import rc_context
  8. import matplotlib.pyplot as plt
  9. import matplotlib.patches as patches
  10. import warnings
  11. warnings.filterwarnings(action='ignore')
  12. import seaborn as sns
  13. %matplotlib inline
  14. import os
  16. import matplotlib as mpl
  17. from matplotlib.colors import ListedColormap, LinearSegmentedColormap
  18. from matplotlib import cm
  21. viridis = cm.get_cmap('YlGnBu', 256)
  22. newcolors = viridis(np.linspace(0, 1, 256))
  24. pink = np.array([211/256, 211/256, 211/256, 1])
  26. newcolors[:25, :] = pink
  27. newcmp = ListedColormap(newcolors)
  30. from pathlib import Path
  32. # %%
  33. path_data = ''
  34. adata = sc.read_h5ad(Path(path_data)/'cleaned_adata_processed_celltypes_v7.h5ad')
  35. adata.obs.fullname_v7.unique().tolist()
  37. # %%
  40. # %%
  41. full_palette = {
  42. 'NBL_Midbrain': '#74a9cf', # Light blue
  43. 'Dopaminergic neuron_Midbrain': '#5DADE2', # Light blue
  44. 'NPC_Midbrain_V': '#0570b0',
  45. 'NPC-G2M_Midbrain': '#00436d', # Darker blue
  46. 'Glutamatergic neuron_Midbrain': '#023fa5', # Darkest blue
  47. 'NPC_Midbrain_D': '#4a6fe3',
  49. 'Motor neuron_Hindbrain': '#f9d14a', # Medium orange
  50. 'Glycenergic neuron_Hindbrain': '#f7aa58', # Light orange
  52. 'Glycinergic neuron (POU6F2 high)_Medulla': '#f7aa58',
  53. 'Glycinergic neuron (PAX2 high)_Medulla': '#edce79',
  55. 'NBL_Medulla': '#f0b98d',
  56. 'Glutamatergic neuron (RELN high)_Medulla': '#ffb178', # Orange
  57. 'NPC_Medulla_D':'#a0522d' ,
  58. 'Glutamatergic neuron (LHX1, LHX5 high)_Medulla':'#654321' ,
  60. 'NPC_Hindbrain': '#b24422', # Dark orange
  62. 'NPC_Floor-plate': '#9e9ac8', # Purple for Floor-plate
  63. 'NPC_Medulla_V': '#e1c59a', # Light yellow for Medulla
  65. 'NPC_Cerebellum': '#cb181d', # Red
  67. 'NBL_Cerebellum': '#8e063b', # Darkest red
  68. 'Glutamatergic neuron (LHX9 high)_Cerebellum': '#a40000',
  69. 'Glutamatergic neuron (ETV1 high)_Cerebellum': '#c93f55',
  70. #
  71. 'NPC_Roof-plate': '#749e89', # Greenish for Roof-plate
  72. 'NBL_Spinal Cord': '#e6afb9', # Medium pink
  73. 'NPC_Spinal cord': '#bb7784', # Darker pink
  74. 'Glutamatergic neuron (GRIK1, LHX2 high)_Pons':"#e76254",
  76. 'Melanocyte_NS': '#303030', # Black
  77. 'Neural Crest_NS': '#969696' # Grey
  78. }
  83. with rc_context({'figure.figsize': (6, 6)}):
  84. sc.pl.umap(adata, color=['fullname_v7'], frameon=False,legend_loc='right margin', legend_fontsize=13,s=5, palette =full_palette)
  89. # %%
  90. path_fig = ''
  91. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=350, format='.png')
  92. sc.settings.figdir = path_fig
  95. with rc_context({'figure.figsize': (6, 6)}):
  96. sc.pl.umap(adata, color=['fullname_v7'],
  97. frameon=False,legend_loc=None, legend_fontsize=13,s=4, save='v7_fullname.png',
  98. palette = full_palette) #'right margin'
  100. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=350, format='.pdf')
  101. with rc_context({'figure.figsize': (6, 6)}):
  102. sc.pl.umap(adata, color=['fullname_v7'],
  103. frameon=False,legend_loc='right margin', legend_fontsize=13,s=4,
  104. save='v7_fullname.pdf',
  105. palette = full_palette)
  107. # %%
  108. region_type_dict = {
  109. 'Midbrain':'#3690c0',
  110. 'Hindbrain':'#fd8d3c',
  111. 'Floor-plate':'#9e9ac8',
  112. 'Medulla':"#f5c34d",
  113. 'NS':'#dedede',
  114. 'Cerebellum':'#cb181d',
  115. 'Roof-plate':"#749e89",
  116. 'Spinal cord':'#f768a1',
  117. 'Pons':"#e76254"
  118. }
  121. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=350, format='.png')
  122. sc.settings.figdir = path_fig
  125. with rc_context({'figure.figsize': (6, 6)}):
  126. sc.pl.umap(adata, color=['regions_v7'],
  127. frameon=False,legend_loc=None, legend_fontsize=13,s=4, save='_v7_annotation_reg.png',
  128. palette = region_type_dict) #'right margin'
  131. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=350, format='.pdf')
  132. with rc_context({'figure.figsize': (6, 6)}):
  133. sc.pl.umap(adata, color=['regions_v7'],
  134. frameon=False,legend_loc='right margin', legend_fontsize=13,s=4,
  135. save='_v7_annotation_reg_legend.pdf',
  136. palette = region_type_dict)
  139. # %%
  140. adata.obs['cell_types_v7'].unique().tolist()
  142. # %%
  143. adata.obs['fullname_v7'].unique().tolist()
  145. # %%
  146. def harmonise_name(fullname):
  148. if fullname in ['NBL_Midbrain', 'NBL_Medulla', 'NBL_Spinal Cord', 'NBL_Cerebellum']:
  149. return 'NBL'
  150. elif fullname in ['NPC_Floor-plate', 'NPC_Midbrain_V', 'NPC_Medulla_V', 'NPC_Hindbrain', 'NPC_Cerebellum',
  151. 'NPC_Medulla_D', 'NPC_Roof-plate', 'NPC_Spinal cord','NPC_Midbrain_D']:
  152. return 'NPC'
  153. elif fullname in ['Melanocyte_NS', 'Neural Crest_NS']:
  154. return fullname.split('_')[0]
  155. elif fullname == 'Motor neuron_Hindbrain':
  156. return 'Motor neuron'
  157. elif fullname in ['Glutamatergic neuron (RELN high)_Medulla',
  158. 'Glutamatergic neuron (ETV1 high)_Cerebellum',
  160. 'Glutamatergic neuron (LHX1, LHX5 high)_Medulla',
  161. 'Glutamatergic neuron (LHX9 high)_Cerebellum',
  163. 'Glutamatergic neuron (GRIK1, LHX2 high)_Pons',
  165. 'Glutamatergic neuron_Midbrain']:
  166. return 'Glutamatergic neuron'
  167. elif fullname in ['Glycinergic neuron (POU6F2 high)_Medulla', 'Glycinergic neuron (PAX2 high)_Medulla']:
  168. return 'Glycinergic neuron'
  170. else:
  171. return 'Dopaminergic neuron'
  175. # %%
  176. adata.obs['cell_types_v7'] = adata.obs['fullname_v7'].apply(harmonise_name)
  178. # %%
  179. del adata.obs['fullname_v7_1']
  181. # %%
  182. cell_type_dict = {
  183. "NPC" : '#d7bde2', #"#C39BD3",
  184. "NBL" : "#884ea0",# "#9B59B6",
  185. "Glutamatergic neuron": '#5DADE2',
  186. "Glycinergic neuron": '#EB984E',
  187. "Dopaminergic neuron" : '#28B463',
  188. 'Motor neuron':"#f9d14a",
  189. 'NPC-G2M':"#7c4b73",
  190. 'Melanocyte':'#303030',
  191. 'Neural Crest': '#969696'}
  193. path_fig = ''
  194. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=350, format='.png')
  195. sc.settings.figdir = path_fig
  198. with rc_context({'figure.figsize': (6, 6)}):
  199. sc.pl.umap(adata, color=['cell_types_v7'],
  200. frameon=False,legend_loc=None, legend_fontsize=13,s=4, save='v7_annotation_ct.png',
  201. palette = cell_type_dict) #'right margin'
  203. sc.settings.set_figure_params(dpi=100, fontsize=10, dpi_save=350, format='.pdf')
  204. with rc_context({'figure.figsize': (6, 6)}):
  205. sc.pl.umap(adata, color=['cell_types_v7'],
  206. frameon=False,legend_loc='right margin', legend_fontsize=13,s=4,
  207. save='v7_annotation_ct_legend.pdf',
  208. palette = cell_type_dict)
  210. # %% [markdown]
  211. # # region double check
  213. # %%
  214. adata.obs['regions_v7'] = adata.obs['regions_v6'].copy()
  215. adata.obs['regions_v7'] = adata.obs['regions_v7'].astype(str)
  216. adata.obs.loc[adata.obs['fullname_v7']=='NBL_Medulla', 'regions_v7'] = 'Medulla'
  217. adata.obs.loc[adata.obs['fullname_v7']=='NPC_Medulla_D', 'regions_v7'] = 'Medulla'
  218. adata.obs.loc[adata.obs['fullname_v7']=='Glutamatergic neuron (GRIK1, LHX2 high)_Pons', 'regions_v7'] = 'Pons'
  219. adata.obs.loc[adata.obs['fullname_v7']=='NBL_Spinal Cord', 'regions_v7'] = 'Spinal cord'
  220. adata.obs.loc[adata.obs['fullname_v7']=='Glutamatergic neuron (LHX1, LHX5 high)_Medulla', 'regions_v7'] = 'Medulla'
  221. adata.obs.loc[adata.obs['fullname_v7']=='NPC_Cerebellum', 'regions_v7'] = 'Cerebellum'
  222. adata.obs.loc[adata.obs['fullname_v7']=='Glutamatergic neuron (ETV1 high)_Cerebellum', 'regions_v7'] = 'Cerebellum'
  223. adata.obs.loc[adata.obs['fullname_v7']=='Glutamatergic neuron (LHX9 high)_Cerebellum', 'regions_v7'] = 'Cerebellum'
  224. adata.obs.loc[adata.obs['fullname_v7']=='NBL_Cerebellum', 'regions_v7'] = 'Cerebellum'
  226. adata.obs['regions_detailed_v7'] = adata.obs['regions_v7'].copy()
  227. adata.obs['regions_detailed_v7'] = adata.obs['regions_detailed_v7'].astype(str)
  229. adata.obs.loc[adata.obs['fullname_v7']=='NPC_Medulla_D', 'regions_detailed_v7'] = 'Medulla_D'
  230. adata.obs.loc[adata.obs['fullname_v7']=='NPC_Medulla_V', 'regions_detailed_v7'] = 'Medulla_V'
  231. adata.obs.loc[adata.obs['fullname_v7']=='NPC_Midbrain_D', 'regions_detailed_v7'] = 'Midbrain_D'
  232. adata.obs.loc[adata.obs['fullname_v7']=='NPC_Midbrain_V', 'regions_detailed_v7'] = 'Midbrain_V'
  235. # %%
  236. path_data = ''
  237. adata.write_h5ad(path_data+'cleaned_adata_processed_celltypes_v7_1.h5ad')
  239. # %% [markdown]
  240. # # Barplot
  242. # %%
  243. dfc = pd.crosstab(adata.obs.Condition, adata.obs.fullname_v7, normalize='index').mul(100).round(100)
  245. cell_types_palette = dict(zip(adata.obs.fullname_v7.cat.categories.to_list(),
  246. adata.uns['fullname_v7_colors']))
  248. cond_order = ["1",
  249. "6", "44", "46", "47", "43", "14", "45", "48", "23", "40", "22", "25",
  250. "24", "15", "21", "42", "17", "16", "27", "41", "20", "26", "4", "37",
  251. "28", "34", "35", "29", "38", "39", "32", "10", "36", "11", "33", "30",
  252. "31", "8", "7", "9", "12", '13'
  253. ]
  254. dfc = dfc.loc[cond_order]
  255. cell_types_order = [
  256. "Glutamatergic neuron (LHX1, LHX5 high)_Medulla",
  257. "Neural Crest_NS",
  258. "Motor neuron_Hindbrain",
  259. "NPC_Hindbrain",
  260. "NBL_Medulla",
  261. "NPC_Medulla_V",
  262. "Glycinergic neuron (POU6F2 high)_Medulla",
  263. "Glutamatergic neuron (RELN high)_Medulla",
  264. "Glycinergic neuron (PAX2 high)_Medulla",
  265. "Melanocyte_NS",
  266. 'NPC_Floor-plate',
  267. "NPC_Midbrain_V",
  268. "Dopaminergic neuron_Midbrain",
  269. "NBL_Midbrain",
  270. "NPC_Spinal cord",
  271. "NPC_Medulla_D",
  272. "Glutamatergic neuron (ETV1 high)_Cerebellum",
  273. "NPC_Cerebellum",
  274. "Glutamatergic neuron (LHX9 high)_Cerebellum",
  275. "NPC_Midbrain_D",
  276. "Glutamatergic neuron_Midbrain",
  277. "NBL_Cerebellum",
  278. 'NPC_Roof-plate',
  279. "Glutamatergic neuron (GRIK1, LHX2 high)_Pons",
  280. "NBL_Spinal Cord"
  281. ]
  283. cell_types_order.reverse()
  284. dfc = dfc.loc[:,cell_types_order]
  285. cell_types_order.reverse()
  287. legend_order = [i for i in range(len(cell_types_order))]
  288. legend_order.reverse()
  291. sns.set_style("white")
  293. ax = dfc.plot(kind='bar', ylabel='Percent(%)', stacked=True, rot=0,
  294. figsize=(30, 8), color = full_palette,width=0.95)
  295. sns.despine(ax=ax, left=True, bottom=True)
  297. handles, labels = plt.gca().get_legend_handles_labels()
  299. plt.legend([handles[i] for i in legend_order], [labels[i] for i in legend_order],loc='right', ncol=1, fancybox=False, shadow=False, fontsize=16, bbox_to_anchor=(1.3, 0.5))
  301. plt.xticks(weight = 'bold')
  302. plt.tight_layout()
  303. plt.savefig(Path(path_fig)/'stackplot_conditions_cell_types_v7.pdf', dpi=500,
  304. format='pdf', bbox_inches='tight')
  305. plt.show()

06_UMAP_cell_types.ipynb at commit d74b10c, under MIT · at the source

Overview

Authors: Nadezhda Azbukina1, Zhisong He1, Hsiu-Chuan Lin1, Malgorzata Santel1, Bijan Kashanian1, Ashley Maynard1, Tivadar Török1, Ryoko Okamoto1, Marina T. Nikolova1, Makiko Seimiya1, Sabina Kanton2, Valentin Brösamle1, Rene Holtackers1, J. Gray Camp3,4, Barbara Treutlein1
ORCID iDs: Zhisong He, Hsiu-Chuan Lin, Malgorzata Santel, Ashley Maynard, Ryoko Okamoto, Marina T. Nikolova, Valentin Brösamle, Rene Holtackers, J. Gray Camp, Barbara Treutlein
  1. Department of Biosystems Science and Engineering, ETH Zürich,Basel, Switzerland
  2. Max Planck Institute for Evolutionary Anthropology,Leipzig, Germany
  3. Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel,Basel, Switzerland
  4. Biozentrum, University of Basel,Basel, Switzerland
Institutions: ETH Zurich (Switzerland); Max Planck Institute for Evolutionary Anthropology (Germany); Roche (Switzerland) (Switzerland); University of Basel (Switzerland)
Journal: Nature neuroscience, volume 29, issue 7, pages 1548-1558
Dates: received 15 March 2025; accepted 23 April 2026; published online 3 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02316-x · PMID 42237030 · PMCID PMC13337493 · OpenAlex W7163319065
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Gene regulatory networks, Multicellular systems, Developmental neurogenesis, Functional genomics
MeSH: Mesencephalon*, Organoids*, Rhombencephalon*, Animals, Cell Differentiation, Gene Expression Regulation, Developmental, Mice, Multiomics, Neurodevelopment, Neurons, Single-Cell Analysis, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) (Braintime-874606); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (310030_192604, National Center of Competence in Research Molecular Systems Engineering); Roche Institute of Human Biology Project Funding; Human Frontier Science Program (HFSP) (LT000399/2020-L); European Molecular Biology Organization (EMBO) (ALTF 1190-2019)
Citations: not cited yet (Europe PMC); 100 references in the paper

Abstract

Patterning of the neural tube establishes midbrain and hindbrain structures that coordinate motor movement, process sensory input and integrate cognitive functions. Cellular impairment within these structures underlies diverse neurological disorders, and in vitro organoid models promise inroads to understanding development and modeling disease. Here, we use paired single-cell transcriptome and accessible chromatin sequencing to map cell composition and regulatory mechanisms in organoid models of midbrain and hindbrain. We find that existing midbrain organoid protocols generate ventral and dorsal cell types, covering regions including floor plate, dorsal and ventral midbrain and adjacent hindbrain regions. Gene regulatory network inference and transcription factor perturbation resolve mechanisms underlying neuronal differentiation. A single-cell multiplexed patterning screen identifies morphogen concentrations that expand existing organoid models, including conditions generating medulla glycinergic neurons and cerebellum glutamatergic subtypes. Together, the multi-omic atlas and morphogen screen reveal morphogen–regulon relationships guiding region-specific progenitor differentiation towards diverse neuron types of the posterior brain.

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 15 matches between paragraphs and lines of code.

quadbio/atac_mapper

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8cd1399123d02f18f918d4024510a76168032cf8, 17 June 2025
Languages: Python (13), Jupyter (2)
Size: 53 files, 15 scripts
Software Heritage: not archived
Found in: the text, “Mapping scATAC-seq data to the primary reference”
Holds: README, license file, environment (pyproject.toml, setup.py, docs/requirements.txt), tests, continuous integration, documentation, 2 notebooks
Not found: CITATION.cff
Tools: NumPy (8 files), pandas (5 files), SciPy (4 files), anndata (3 files), Scanpy (2 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
17 files
At the source: github.com/quadbio/atac_mapper

quadbio/posterior_multiome

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d74b10c10681b4038b26ff4029870a5f8a32f5ae, 12 May 2026
Languages: Jupyter (28), R (8), Shell (2)
Size: 52 files, 38 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 30 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (6 files), NumPy (6 files), pandas (6 files), Scanpy (6 files), SciPy (6 files), seaborn (6 files), Seurat (5 files), tidyverse (5 files), ggplot2 (4 files), patchwork (2 files), cowplot (1 file), glmnet (1 file), Harmony (1 file), reshape2 (1 file), reticulate (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/quadbio/posterior_multiome/tree/main

Code availability

All code generated in the study, including analysis parameters, is available at https://github.com/quadbio/posterior_multiome (https://github.com/quadbio/posterior_multiome/tree/main). The Python package ATAC-mapper for mapping scATAC data of organoids to primary atlases is available at GitHub https://github.com/quadbio/atac_mapper and PyPi https://pypi.org/project/atac-mapper/.

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

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 15 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 count matrices and metadata for the RNA-seq portion of the time-course single-cell multi-omic dataset are part of the previously published integrated human neural organoid cell atlas, available on Zenodo (10.5281/zenodo.11203684) and the CellxGene Discover Census (https://cellxgene.cziscience.com/collections/de379e5f-52d0-498c-9801-0f850823c847). The raw and processed data of the scATAC-seq portion of the single-cell multi-omic data and the scRNA-seq data are deposited at Array Express with the following accession numbers: E-MTAB-15660 (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-15660/), E-MTAB-15826 (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-15826/) and E-MTAB-15659 (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-15659/).

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, 15 authors, 4 keywords, 12 MeSH terms, 5 funders, 100 references.

Cite

This paper

Azbukina, N., He, Z., Lin, H.-C., Santel, M., Kashanian, B., Maynard, A., Török, T., Okamoto, R., Nikolova, M. T., Seimiya, M., Kanton, S., Brösamle, V., Holtackers, R., Camp, J. G., & Treutlein, B. (2026). Single-cell multi-omic atlas and morphogen screening informs midbrain and hindbrain organoid engineering. Nature neuroscience, 29(7), 1548-1558. https://doi.org/10.1038/s41593-026-02316-x

BibTeX

@article{azbukina2026single,
author = {Azbukina, Nadezhda and He, Zhisong and Lin, Hsiu-Chuan and Santel, Malgorzata and Kashanian, Bijan and Maynard, Ashley and Török, Tivadar and Okamoto, Ryoko and Nikolova, Marina T. and Seimiya, Makiko and Kanton, Sabina and Brösamle, Valentin and Holtackers, Rene and Camp, J. Gray and Treutlein, Barbara},
title = {{Single-cell multi-omic atlas and morphogen screening informs midbrain and hindbrain organoid engineering}},
journal = {Nature neuroscience},
year = {2026},
month = jun,
volume = {29},
number = {7},
pages = {1548--1558},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02316-x},
url = {https://doi.org/10.1038/s41593-026-02316-x},
pmid = {42237030},
pmcid = {PMC13337493}
}

RIS

TY - JOUR
AU - Azbukina, Nadezhda
AU - He, Zhisong
AU - Lin, Hsiu-Chuan
AU - Santel, Malgorzata
AU - Kashanian, Bijan
AU - Maynard, Ashley
AU - Török, Tivadar
AU - Okamoto, Ryoko
AU - Nikolova, Marina T.
AU - Seimiya, Makiko
AU - Kanton, Sabina
AU - Brösamle, Valentin
AU - Holtackers, Rene
AU - Camp, J. Gray
AU - Treutlein, Barbara
TI - Single-cell multi-omic atlas and morphogen screening informs midbrain and hindbrain organoid engineering
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/06/03
VL - 29
IS - 7
SP - 1548
EP - 1558
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02316-x
UR - https://doi.org/10.1038/s41593-026-02316-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02316-x",
"type": "article-journal",
"title": "Single-cell multi-omic atlas and morphogen screening informs midbrain and hindbrain organoid engineering",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Azbukina",
"given": "Nadezhda"
},
{
"family": "He",
"given": "Zhisong"
},
{
"family": "Lin",
"given": "Hsiu-Chuan"
},
{
"family": "Santel",
"given": "Malgorzata"
},
{
"family": "Kashanian",
"given": "Bijan"
},
{
"family": "Maynard",
"given": "Ashley"
},
{
"family": "Török",
"given": "Tivadar"
},
{
"family": "Okamoto",
"given": "Ryoko"
},
{
"family": "Nikolova",
"given": "Marina T."
},
{
"family": "Seimiya",
"given": "Makiko"
},
{
"family": "Kanton",
"given": "Sabina"
},
{
"family": "Brösamle",
"given": "Valentin"
},
{
"family": "Holtackers",
"given": "Rene"
},
{
"family": "Camp",
"given": "J. Gray"
},
{
"family": "Treutlein",
"given": "Barbara"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "7",
"page": "1548-1558",
"DOI": "10.1038/s41593-026-02316-x",
"PMID": "42237030",
"PMCID": "PMC13337493",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02316-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
3
]
]
}
}

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

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