OSCR

Arrayed single-gene perturbations identify drivers of human anterior neural tube closure.

Code ↔ Paper

8 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 8 matches
  1. [1] § Results › A reproducible in vitro model of human anterior neurulation ↔ GA_analysis_2_clustering_day2_week4_ectoderm.ipynb, lines 253–257 · score 0.71 · TFAP2A, ZEB1, SIX1, GATA3, ZEB2, DLX5
  2. [2] § Methods › Quantification of transduction efficiency ↔ CA_transduction_efficiency_analysis.py, lines 200–348 · score 0.60 · Transduction efficiency, Ilastik, pixel, channel, Circle, background
  3. [3] § Methods › scRNA-seq analysis of Day 2 and 4 organoids and Week 3 and 4 human embryos ↔ RH291_analysis_5a_phenotypes_on_saved_PCA.ipynb, lines 348–381 · score 0.58 · principal component, pca, ARPACK, solver, Louvain, neighbors
  4. [4] § Results › A high-throughput genetic screen of anterior neurulation reveals three transcription factors necessary for proper neural tube closure ↔ RH291_analysis_1_merging_guide_calls.ipynb, lines 107–186 · score 0.58 · POU3F1, TOX3, TGIF1, ZFHX4, GLI3, LHX2
  5. [5] § Results › Identifying transcription factor candidates for regulation of anterior neurulation ↔ RH296_analysis_7_choosing candidates.ipynb, lines 287–329 · score 0.56 · downregulated genes, fold change, candidate, log2, threshold, regulatory
  6. [6] § Results › A high-throughput genetic screen of anterior neurulation reveals three transcription factors necessary for proper neural tube closure ↔ JC_hdWGCNA.R, lines 86–139 · score 0.52 · Module eigengene, neural cell, WGCNA, neural ectoderm, dendrogram, knockdown
  7. [7] § Results › A high-throughput genetic screen of anterior neurulation reveals three transcription factors necessary for proper neural tube closure ↔ JC_3_ClusterProfiler_Annotation.R, lines 166–248 · score 0.51 · co expressed, neural cell, WGCNA, modules, Scatterplots, bar
  8. [8] § Results › A high-throughput genetic screen of anterior neurulation reveals three transcription factors necessary for proper neural tube closure ↔ RH291_analysis_1_merging_guide_calls.ipynb, lines 107–186 · score 0.51 · POU3F2, SP5, TGIF1, ZFHX4, MSX1, PAX3

Paper

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The authors' code

Jupyter notebook · 355 lines · 13 KB · MIT · 2 matches

  1. # %% [markdown]
  2. # # Merging "protospacer_calls_per_cell" with "filtered_matrix" hdf5 file
  4. # %% [markdown]
  5. # ## 1. Import packages
  7. # %%
  8. # Import all the packages
  10. import h5py
  11. from pathlib import Path
  13. import numpy as np
  14. import pandas as pd
  15. from scipy.io import mmread, mmwrite
  16. from scipy.sparse import load_npz, save_npz, coo_matrix, csr_matrix, vstack
  17. from scipy.ndimage import gaussian_filter1d
  18. from scipy.stats import binned_statistic
  19. import matplotlib.pyplot as plt
  20. import matplotlib as mpl
  21. import ray
  22. import seaborn as sns
  23. #import fastcluster
  24. import seaborn as sns
  25. #import hdf5plugin
  26. COLOR = 'black'
  27. mpl.rcParams['text.color'] = COLOR
  28. mpl.rcParams['axes.labelcolor'] = COLOR
  29. mpl.rcParams['xtick.color'] = COLOR
  30. mpl.rcParams['ytick.color'] = COLOR
  32. import scanpy as sc
  33. #import louvain
  34. #import leidenalg
  35. #import anndata as ad
  37. import sys
  38. sys.setrecursionlimit(100000)
  40. # Set scanpy save settings
  42. sc._settings.ScanpyConfig.dpi_save = 300
  43. sc._settings.ScanpyConfig.file_format_figs = 'png'
  45. # %% [markdown]
  46. # ## 2. Prepare guide call dataframe
  48. # %%
  49. # Load CRISPR guide and count data into dataframes
  50. barcode_282_data = pd.read_csv('../data/RH282_protospacer_calls_per_cell.csv')
  51. print('RH282 protospacer calls shape:', barcode_282_data.shape[0])
  52. barcode_284_data = pd.read_csv('../data/RH284_protospacer_calls_per_cell.csv')
  53. print('RH284 protospacer calls shape:', barcode_284_data.shape[0])
  55. # Delete any rows with more than two features
  56. barcode_282_data = barcode_282_data[barcode_282_data['num_features'] < 3]
  57. print('RH282 protospacer calls, < 3 features:', barcode_282_data.shape[0])
  58. barcode_284_data = barcode_284_data[barcode_284_data['num_features'] < 3]
  59. print('RH284 protospacer calls, < 3 features:', barcode_284_data.shape[0])
  60. print(barcode_284_data.shape)
  62. # %%
  63. # Add together counts from cells with two guide calls
  65. #RH282
  66. barcode_282_data['first_count'] = np.nan
  67. barcode_282_data['second_count'] = np.nan
  68. barcode_282_data['final_count'] = np.nan
  70. # Do NOT loop through a range here. That will call the wrong indices for the truncated dataframe.
  71. for i in barcode_282_data.index:
  72. # Do not use iloc here. That is for integer indexing. Here we want to use the label indexing.
  73. s = barcode_282_data['num_umis'].loc[i]
  74. # If there are no bars, the final count is the single guide count
  75. if s.count('|') == 0:
  76. barcode_282_data.at[i,'final_count'] = float(s)
  77. # If there is one bar, the final count is the dual guide count
  78. if s.count('|') == 1:
  79. bar_idx = int(s.find('|'))
  80. first_count = float(s[0:bar_idx])
  81. second_count = float(s[bar_idx+1:len(s)])
  82. barcode_282_data.at[i,'first_count'] = first_count
  83. barcode_282_data.at[i, 'second_count'] = second_count
  84. barcode_282_data.at[i,'final_count']= first_count + second_count
  86. #RH284
  87. barcode_284_data['first_count'] = np.nan
  88. barcode_284_data['second_count'] = np.nan
  89. barcode_284_data['final_count'] = np.nan
  91. # Do NOT loop through a range here. That will call the wrong indices for the truncated dataframe.
  92. for i in barcode_284_data.index:
  93. # Do not use iloc here. That is for integer indexing. Here we want to use the label indexing.
  94. s = barcode_284_data['num_umis'].loc[i]
  95. # If there are no bars, the final count is the single guide count
  96. if s.count('|') == 0:
  97. barcode_284_data.at[i,'final_count'] = float(s)
  98. # If there is one bar, the final count is the dual guide count
  99. if s.count('|') == 1:
  100. bar_idx = int(s.find('|'))
  101. first_count = float(s[0:bar_idx])
  102. second_count = float(s[bar_idx+1:len(s)])
  103. barcode_284_data.at[i,'first_count'] = first_count
  104. barcode_284_data.at[i,'second_count'] = second_count
  105. barcode_284_data.at[i,'final_count']= first_count + second_count
  107. # %%
  108. # Merge guide calls
  110. #RH282
  111. barcode_282_data['guide'] = np.nan
  113. for i in barcode_282_data.index:
  114. s = barcode_282_data['feature_call'].loc[i]
  115. if s == 'SOX11-1' or s == 'SOX11-2' or s == 'SOX11-1|SOX11-2' or s == 'SOX11-2|SOX11-1':
  116. barcode_282_data.at[i,'guide'] = 'SOX11'
  117. if s == 'ZIC5-1' or s == 'ZIC5-2' or s == 'ZIC5-1|ZIC5-2' or s == 'ZIC5-2|ZIC5-1':
  118. barcode_282_data.at[i,'guide'] = 'ZIC5'
  119. if s == 'ZNF521-1' or s == 'ZNF521-2' or s == 'ZNF521-1|ZNF521-2' or s == 'ZNF521-2|ZNF521-1':
  120. barcode_282_data.at[i,'guide'] = 'ZNF521'
  121. if s == 'SCRAMBLE2-1' or s == 'SCRAMBLE2-2' or s == 'SCRAMBLE2-1|SCRAMBLE2-2' or s == 'SCRAMBLE2-2|SCRAMBLE2-1':
  122. barcode_282_data.at[i,'guide'] = 'SCRAMBLE2a'
  124. #RH284
  125. barcode_284_data['guide'] = np.nan
  127. for i in barcode_284_data.index:
  128. s = barcode_284_data['feature_call'].loc[i]
  129. if s == 'SCRAMBLE2-1' or s == 'SCRAMBLE2-2' or s == 'SCRAMBLE2-1|SCRAMBLE2-2' or s == 'SCRAMBLE2-2|SCRAMBLE2-1':
  130. barcode_284_data.at[i,'guide'] = 'SCRAMBLE2b'
  131. if s == 'ZIC2-1' or s == 'ZIC2-2' or s == 'ZIC2-1|ZIC2-2' or s == 'ZIC2-2|ZIC2-1':
  132. barcode_284_data.at[i,'guide'] = 'ZIC2'
  133. if s == 'HES4-1' or s == 'HES4-2' or s == 'HES4-1|HES4-2' or s == 'HES4-2|HES4-1':
  134. barcode_284_data.at[i,'guide'] = 'HES4'
  135. if s == 'HES5-1' or s == 'HES5-2' or s == 'HES5-1|HES5-2' or s == 'HES5-2|HES5-1':
  136. barcode_284_data.at[i,'guide'] = 'HES5'
  137. if s == 'GLI3-1' or s == 'GLI3-2' or s == 'GLI3-1|GLI3-2' or s == 'GLI3-2|GLI3-1':
  138. barcode_284_data.at[i,'guide'] = 'GLI3'
  139. if s == 'LHX2-1' or s == 'LHX2-2' or s == 'LHX2-1|LHX2-2' or s == 'LHX2-2|LHX2-1':
  140. barcode_284_data.at[i,'guide'] = 'LHX2'
  141. if s == 'MSX1-1' or s == 'MSX1-2' or s == 'MSX1-1|MSX1-2' or s == 'MSX1-2|MSX1-1':
  142. barcode_284_data.at[i,'guide'] = 'MSX1'
  143. if s == 'NR6A1-1' or s == 'NR6A1-2' or s == 'NR6A1-1|NR6A1-2' or s == 'NR6A1-2|NR6A1-1':
  144. barcode_284_data.at[i,'guide'] = 'NR6A1'
  145. if s == 'OTX1-1' or s == 'OTX1-2' or s == 'OTX1-1|OTX1-2' or s == 'OTX1-2|OTX1-1':
  146. barcode_284_data.at[i,'guide'] = 'OTX1'
  147. if s == 'OTX2-1' or s == 'OTX2-2' or s == 'OTX2-1|OTX2-2' or s == 'OTX2-2|OTX2-1':
  148. barcode_284_data.at[i,'guide'] = 'OTX2'
  149. if s == 'PAX3-1' or s == 'PAX3-2' or s == 'PAX3-1|PAX3-2' or s == 'PAX3-2|PAX3-1':
  150. barcode_284_data.at[i,'guide'] = 'PAX3'
  151. if s == 'PAX6-1' or s == 'PAX6-2' or s == 'PAX6-1|PAX6-2' or s == 'PAX6-2|PAX6-1':
  152. barcode_284_data.at[i,'guide'] = 'PAX6'
  153. if s == 'PAX7-1' or s == 'PAX7-2' or s == 'PAX7-1|PAX7-2' or s == 'PAX7-2|PAX7-1':
  154. barcode_284_data.at[i,'guide'] = 'PAX7'
  155. if s == 'POU3F1-1' or s == 'POU3F1-2' or s == 'POU3F1-1|POU3F1-2' or s == 'POU3F1-2|POU3F1-1':
  156. barcode_284_data.at[i,'guide'] = 'POU3F1'
  157. if s == 'POU3F2-1' or s == 'POU3F2-2' or s == 'POU3F2-1|POU3F2-2' or s == 'POU3F2-2|POU3F2-1':
  158. barcode_284_data.at[i,'guide'] = 'POU3F2'
  159. if s == 'RAX1-1' or s == 'RAX1-2' or s == 'RAX1-1|RAX1-2' or s == 'RAX1-2|RAX1-1':
  160. barcode_284_data.at[i,'guide'] = 'RAX1'
  161. if s == 'SCRAMBLE1-1' or s == 'SCRAMBLE1-2' or s == 'SCRAMBLE1-1|SCRAMBLE1-2' or s == 'SCRAMBLE1-2|SCRAMBLE1-1':
  162. barcode_284_data.at[i,'guide'] = 'SCRAMBLE1'
  163. if s == 'SOX2-1' or s == 'SOX2-2' or s == 'SOX2-1|SOX2-2' or s == 'SOX2-2|SOX2-1':
  164. barcode_284_data.at[i,'guide'] = 'SOX2'
  165. if s == 'SOX3-1' or s == 'SOX3-2' or s == 'SOX3-1|SOX3-2' or s == 'SOX3-2|SOX3-1':
  166. barcode_284_data.at[i,'guide'] = 'SOX3'
  167. if s == 'SP5-1' or s == 'SP5-2' or s == 'SP5-1|SP5-2' or s == 'SP5-2|SP5-1':
  168. barcode_284_data.at[i,'guide'] = 'SP5'
  169. if s == 'TGIF1-1' or s == 'TGIF1-2' or s == 'TGIF1-1|TGIF1-2' or s == 'TGIF1-2|TGIF1-1':
  170. barcode_284_data.at[i,'guide'] = 'TGIF1'
  171. if s == 'TOX3-1' or s == 'TOX3-2' or s == 'TOX3-1|TOX3-2' or s == 'TOX3-2|TOX3-1':
  172. barcode_284_data.at[i,'guide'] = 'TOX3'
  173. if s == 'ZBTB16-1' or s == 'ZBTB16-2' or s == 'ZBTB16-1|ZBTB16-2' or s == 'ZBTB16-2|ZBTB16-1':
  174. barcode_284_data.at[i,'guide'] = 'ZBTB16'
  175. if s == 'ZEB2-1' or s == 'ZEB2-2' or s == 'ZEB2-1|ZEB2-2' or s == 'ZEB2-2|ZEB2-1':
  176. barcode_284_data.at[i,'guide'] = 'ZEB2'
  177. if s == 'ZFHX4-1' or s == 'ZFHX4-2' or s == 'ZFHX4-1|ZFHX4-2' or s == 'ZFHX4-2|ZFHX4-1':
  178. barcode_284_data.at[i,'guide'] = 'ZFHX4'
  180. # Drop mixed guide calls and cells with Tianlei's guides
  181. print(barcode_282_data.shape)
  182. print(barcode_284_data.shape)
  183. barcode_282_data.dropna(subset=['guide'], inplace=True)
  184. barcode_284_data.dropna(subset=['guide'], inplace=True)
  185. print(barcode_282_data.shape)
  186. print(barcode_284_data.shape)
  188. # %%
  189. # Store minimum dataframe
  190. barcode_282_data_min = barcode_282_data[['cell_barcode', 'guide', 'final_count']]
  191. barcode_284_data_min = barcode_284_data[['cell_barcode', 'guide', 'final_count']]
  193. # %%
  194. # Check that cell barcodes are all unique
  195. unique_values = barcode_282_data_min['cell_barcode'].unique()
  196. print(len(unique_values))
  198. unique_values = barcode_284_data_min['cell_barcode'].unique()
  199. print(len(unique_values))
  201. # %% [markdown]
  202. # ## 3. Merge guide calls and counts with cells_genes matrix; save as new AnnData
  204. # %%
  205. # Load the cells_by_genes data into scanpy as Anndata
  207. sample_282 = sc.read_10x_h5('../data/RH282_filtered_matrix.h5')
  208. sample_282.var_names_make_unique()
  209. print(sample_282)
  211. sample_284 = sc.read_10x_h5('../data/RH284_filtered_matrix.h5')
  212. sample_284.var_names_make_unique()
  213. print(sample_284)
  215. # %%
  216. # Extract cell barcodes from the cells_by_genes Anndata
  218. cell_ids_282 = pd.DataFrame()
  219. cell_ids_282['cell_barcode'] = sample_282.obs.index
  221. cell_ids_284 = pd.DataFrame()
  222. cell_ids_284['cell_barcode'] = sample_284.obs.index
  224. # Merge the guide calls and counts to the cell barcodes, keeping the order from Anndata
  225. merger_df_282 = cell_ids_282.merge(barcode_282_data_min, on='cell_barcode', how='left')
  226. merger_df_284 = cell_ids_284.merge(barcode_284_data_min, on='cell_barcode', how='left')
  228. # Re-index merger dataframes by cell barcode
  229. merger_df_282_reindexed = merger_df_282.set_index('cell_barcode')
  230. merger_df_284_reindexed = merger_df_284.set_index('cell_barcode')
  232. # Assign new observations to the Anndata
  233. sample_282.obs['guide'] = merger_df_282_reindexed['guide']
  234. sample_282.obs['guide_count'] = merger_df_282_reindexed['final_count']
  236. sample_284.obs['guide'] = merger_df_284_reindexed['guide']
  237. sample_284.obs['guide_count'] = merger_df_284_reindexed['final_count']
  239. print(sample_282)
  240. print(sample_284)
  242. # Write data to new anndata files
  243. #sample_282.write('../data/RH282_merged_matrix.h5')
  244. #sample_284.write('../data/RH284_merged_matrix.h5')
  246. # %%
  247. # Check number of cells with guides called
  249. RH282_num_guide_calls = pd.Series(sample_282.obs.guide)
  250. RH282_num_guide_calls = RH282_num_guide_calls.dropna()
  252. RH284_num_guide_calls = pd.Series(sample_284.obs.guide)
  253. RH284_num_guide_calls = RH284_num_guide_calls.dropna()
  255. print(len(RH282_num_guide_calls))
  256. print(len(RH284_num_guide_calls))
  258. # %%
  259. scramble_282 = sample_282[sample_282.obs['guide'].isin(['SCRAMBLE2a']),:]
  260. print('Number of scramble cells in RH282:', scramble_282.shape[0])
  262. # %% [markdown]
  263. # ## X. Count number of guide calls from sequence one or sequence 2 (no saving to dataframe here)
  265. # %%
  266. # Load CRISPR guide and count data into dataframes
  267. barcode_282_data = pd.read_csv('../data/RH282_protospacer_calls_per_cell.csv')
  268. print('RH282 protospacer calls shape:', barcode_282_data.shape[0])
  270. # Delete any rows with more than 2 features
  271. barcode_282_data = barcode_282_data[barcode_282_data['num_features'] < 3]
  272. print('RH282 protospacer calls, < 3 features:', barcode_282_data.shape[0])
  274. print(barcode_282_data['num_umis'].iloc[200])
  276. # %%
  277. # SOX11
  278. pos1_count = 0
  279. pos2_count = 0
  281. #pos1_only_calls = 0
  282. #pos2_only_calls = 0
  283. #pos12_calls = 0
  286. for i in barcode_282_data.index:
  287. calls = barcode_282_data['feature_call'].loc[i]
  288. counts = barcode_282_data['num_umis'].loc[i]
  290. if calls == 'SOX11-1':
  291. #pos1_only_calls = pos1_only_calls+1
  292. pos1_counts = pos1_count + float(counts)
  293. if calls == 'SOX11-2':
  294. #pos2_only_calls = pos2_only_calls+2
  295. pos2_counts = pos2_count + float(counts)
  297. if calls == 'SOX11-1|SOX11-2':
  298. #pos12_calls = pos12_calls+1
  299. #pos2_calls = pos2_calls+2
  301. bar_idx = int(counts.find('|'))
  302. first_count = float(counts[0:bar_idx])
  303. second_count = float(counts[bar_idx+1:len(counts)])
  304. pos1_count = pos1_count + first_count
  305. pos2_count = pos2_count + second_count
  307. if calls == 'SOX11-2|SOX11-1':
  308. #pos12_calls = pos12_calls+1
  309. #pos2_calls = pos2_calls+2
  311. bar_idx = int(counts.find('|'))
  312. first_count = float(counts[0:bar_idx])
  313. second_count = float(counts[bar_idx+1:len(counts)])
  314. pos1_count = pos1_count + second_count
  315. pos2_count = pos2_count + first_count
  317. #print(pos1_only_calls)
  318. #print(pos2_only_calls)
  319. #print(pos12_calls)
  320. print(pos1_count)
  321. print(pos2_count)
  323. # %%
  324. pos1_count = 0
  325. pos2_count = 0
  327. i = 200
  328. calls = barcode_282_data['feature_call'].loc[i]
  329. counts = barcode_282_data['num_umis'].loc[i]
  331. if calls == 'SCRAMBLE2-1':
  332. pos1_counts = pos1_count + float(counts)
  333. if calls == 'SCRAMBLE2-2':
  334. #pos2_only_calls = pos2_only_calls+2
  335. pos2_counts = pos2_count + float(counts)
  337. if calls == 'SCRAMBLE2-1|SCRAMBLE2-2':
  338. bar_idx = int(counts.find('|'))
  339. first_count = float(counts[0:bar_idx])
  340. second_count = float(counts[bar_idx+1:len(counts)])
  341. pos1_count = pos1_count + first_count
  342. pos2_count = pos2_count + second_count
  344. if calls == 'SCRAMBLE2-2|SCRAMBLE2-1':
  345. bar_idx = int(counts.find('|'))
  346. first_count = float(counts[0:bar_idx])
  347. second_count = float(counts[bar_idx+1:len(counts)])
  348. pos1_count = pos1_count + second_count
  349. pos2_count = pos2_count + first_count
  351. print(pos1_count)
  352. print(pos2_count)
  354. # %%

RH291_analysis_1_merging_guide_calls.ipynb at commit dd0c80b, under MIT · at the source

Overview

Authors: Roya E Huang1,2,3, Giridhar M Anand1,2,3, Heitor C Megale1,2,3, Jason Chen1,3, Chudi Abraham-Igwe3, Sharad Ramanathan1,2,3
ORCID iDs: Roya E Huang, Giridhar M Anand, Sharad Ramanathan
  1. Department of Stem Cell and Regenerative Biology, Harvard University Cambridge United States
  2. School of Engineering and Applied Sciences, Harvard University Cambridge United States
  3. Department of Molecular and Cellular Biology, Harvard University Cambridge United States
Institutions: Harvard University (United States)
Journal: eLife, volume 14, article RP108224
Dates: published online 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.108224 · PMID 42411605 · PMCID PMC13341112 · OpenAlex W4414004418
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Smoothing, state filtering, decompositions, Machine learning
Keywords: stem cell, morphogenesis, neural tube, CRISPR, screen, Human
MeSH: Morphogenesis*, Neural Tube*, Neurulation*, Gene Expression Profiling, Gene Expression Regulation, Developmental, Humans, Organoids, Pluripotent Stem Cells, Transcription Factors (* major topic)
Journal subjects: Developmental Biology, Stem Cells and Regenerative Medicine
Topic: Pluripotent Stem Cells Research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institutes of Health (R01MH136014, R01GM131105); National Science Foundation (Graduate Research Fellowship Program)
Citations: cited by 1 paper (Europe PMC); 73 references in the paper
Research resources: Alexa-488-conjugated ECAD antibody RRID:AB_10691457, secondary antibody Invitrogen A31571 RRID:AB_162542, RRID:AB_2167691, TFAP2A was stained with primary antibody RRID:AB_2313948, RRID:AB_2533148, secondary antibody Invitrogen A31573 RRID:AB_2536183, RRID:AB_2800316, pMD2.G were gifts from Didier Trono RRID:Addgene_12251, pMD2.G were gifts from Didier Trono RRID:Addgene_12253, pMD2.G were gifts from Didier Trono RRID:Addgene_12259, RRID:Addgene_73497

Abstract

Genetic studies of human embryonic morphogenesis are constrained by ethical and practical challenges, restricting insights into developmental mechanisms and disorders. Human pluripotent stem cell (hPSC)-derived organoids provide a powerful alternative for the study of embryonic morphogenesis. However, screening for genetic drivers of morphogenesis in vitro has been infeasible due to organoid variability and the high costs of performing scaled tissue-wide single-gene perturbations. By overcoming both these limitations, we developed a platform that integrates reproducible organoid morphogenesis with uniform single-gene perturbations, enabling high-throughput arrayed CRISPR interference screening in hPSC-derived organoids. To demonstrate the power of this platform, we screened 77 transcription factors in an organoid model of anterior neurulation to identify ZIC2, SOX11, and ZNF521 as essential regulators of neural tube closure. We discovered that ZIC2 and SOX11 are required for closure, while ZNF521 prevents ectopic closure points. Single-cell transcriptomic analysis of perturbed organoids revealed co-regulated gene targets of ZIC2 and SOX11 and an opposing role for ZNF521, suggesting that these transcription factors jointly govern a gene regulatory program driving neural tube closure in the anterior forebrain region. Our single-gene perturbation platform enables high-throughput genetic screening of in vitro models of human embryonic morphogenesis.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

royahuang/2026_HuangAnand

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dd0c80ba3a29947a4e7f7d040f75d568f40936c6, 26 January 2026
Languages: Jupyter (14), R (3), Python (1)
Size: 20 files, 18 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 14 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (15 files), NumPy (15 files), pandas (14 files), SciPy (13 files), Scanpy (12 files), seaborn (12 files), reticulate (3 files), Seurat (3 files), statsmodels (3 files), cowplot (2 files), ggpubr (2 files), patchwork (2 files), tidyverse (2 files), WGCNA (2 files), anndata (1 file), clusterProfiler (1 file), ggplot2 (1 file), h5py (1 file), Pillow (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
20 files
At the source: github.com/royahuang/2026_HuangAnand

The paper's code and data availability statement is in the Data section.

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;
  • 18 scripts, each with its path and the digest of its content;
  • 8 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

Raw scRNA-seq data will remain unpublished to protect donor patient privacy, in accordance with Institutional Review Board (IRB) protocol and updated WiCell contract restrictions on publishing RNA sequences derived from human embryonic stem cells. Cell-by-gene count matrices from this paper are available at Figshare dataset 2026_HuangAnand (https://doi.org/10.6084/m9.figshare.31151509). Code from this paper is available at GitHub repository royahuang/2026_HuangAnand (https://github.com/royahuang/2026_HuangAnand), copy archived at Huang and Anand, 2026. Requests for detailed protocols will be fulfilled by corresponding authors Roya Huang and Giridhar Anand. Requests for plasmids and cell lines will be fulfilled by the lead contact, Sharad Ramanathan. Materials will be provided upon completion of a Material Transfer Agreement. Ex vivo scRNA-seq data from Zeng et al. 2023, can be found at GEO accession number GSE155121.

The following dataset was generated:

HuangRE AnandGM MegaleHC ChenJ Abraham-IgweC RamanathanS 20262026_HuangAnandfigshare10.6084/m9.figshare.31151509PMC1334111242411605

The following previously published dataset was used:

LiuZ ZengB ZhongS WangX WuQ 2023The single-cell and spatial transcriptional landscape of human developmentNCBI Gene Expression OmnibusGSE155121

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 6 keywords, 9 MeSH terms, 2 funders, 71 references, 11 RRIDs.

Cite

This paper

Huang, R. E., Anand, G. M., Megale, H. C., Chen, J., Abraham-Igwe, C., & Ramanathan, S. (2026). Arrayed single-gene perturbations identify drivers of human anterior neural tube closure. eLife, 14, RP108224. https://doi.org/10.7554/elife.108224

BibTeX

@article{huang2026arrayed,
author = {Huang, Roya E and Anand, Giridhar M and Megale, Heitor C and Chen, Jason and Abraham-Igwe, Chudi and Ramanathan, Sharad},
title = {{Arrayed single-gene perturbations identify drivers of human anterior neural tube closure}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP108224},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.108224},
url = {https://doi.org/10.7554/elife.108224},
pmid = {42411605},
pmcid = {PMC13341112}
}

RIS

TY - JOUR
AU - Huang, Roya E
AU - Anand, Giridhar M
AU - Megale, Heitor C
AU - Chen, Jason
AU - Abraham-Igwe, Chudi
AU - Ramanathan, Sharad
TI - Arrayed single-gene perturbations identify drivers of human anterior neural tube closure
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/07/07
VL - 14
SP - RP108224
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.108224
UR - https://doi.org/10.7554/elife.108224
LA - en
ER -

CSL-JSON

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