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Distinct differentiation trajectories leave lasting impacts on gene regulation and function of V2a neurons.

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.

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  1. [1] § Methods › Cell oracle ↔ config.py, lines 76–88 · score 0.68 · variable genes, scRNA, ComBat, seq, GRN, clusters
  2. [2] § Methods › Cell oracle ↔ 04_scrna_preprocess_integrate.py, lines 54–81 · score 0.65 · CellOracle, ComBat, Scanpy, preprocessing, scRNA, filtered
  3. [3] § Methods › Cell oracle ↔ config.py, lines 181–191 · score 0.64 · post perturbation, reactome, metric, noise, permutation, Pre
  4. [4] § Methods › Cell oracle ↔ 01_atac_cicero.R, the whole file · a weak match · score 0.64 · scATAC, co accessible, Cicero, connections, CellOracle, filtered
  5. [5] § Methods › Cell oracle ↔ 02_atac_peak_processing.py, lines 12–34 · score 0.57 · scATAC, co accessible, connections, Cicero, peaks
  6. [6] § Methods › Cell oracle ↔ 08_gsea.py, lines 57–76 · score 0.56 · gp.gsea, GSEApy, noise, permutation, signal, gene
  7. [7] § Methods › Multiome data analysis ↔ 01_atac_cicero.R, the whole file · a weak match · score 0.54 · detected genes, UMAP, ATAC, root, RNA, filtered
  8. [8] § Results › Lineage shapes epigenetic and transcriptional V2a identity ↔ config.py, lines 104–121 · score 0.53 · BHLHE22, NEUROD1, NEUROG2, RARA, RXRA, RXRB

Paper

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The authors' code

Python · 191 lines · 8.6 KB · no license · 3 matches

  1. """
  2. Shared configuration for the CellOracle pipeline.
  4. The data are scRNA-seq / scATAC-seq multiome of four human samples:
  5. sample 1 WTC-HB sample 2 FC-HB sample 3 WTC-NMP sample 4 FC-NMP
  6. "hindbrain" (HB) lineage = samples 1 & 2, "NMP" lineage = samples 3 & 4.
  7. """
  9. import os
  11. # ---------------------------------------------------------------------------
  12. # Root locations
  13. # ---------------------------------------------------------------------------
  14. DATA_ROOT = "/sadra/fattahilab/alireza"
  16. # 10x multiome output (Cell Ranger filtered_feature_bc_matrix) for each sample.
  17. RAW_MULTIOME_ROOT = "/sadra/fattahilab/nicke/projects/multiome2022/processed_data"
  19. PIPELINE_ROOT = os.path.join(
  20. DATA_ROOT, "rna_seq_data/nick/converted_scanpy_cleaned/nick_pipeline"
  21. )
  22. COMBINED_OUTPUT_DIR = os.path.join(
  23. PIPELINE_ROOT, "combined_outputs/combined_all_four_from_beginning"
  24. )
  26. # ---------------------------------------------------------------------------
  27. # Reference genome
  28. # ---------------------------------------------------------------------------
  29. REF_GENOME = "hg38"
  31. # ---------------------------------------------------------------------------
  32. # Per-sample inputs
  33. # ---------------------------------------------------------------------------
  34. SAMPLES = [1, 2, 3, 4]
  36. # Cell Ranger filtered matrices (used by the scRNA-seq integration, module 04).
  37. SAMPLE_MATRIX_H5 = {
  38. 1: f"{RAW_MULTIOME_ROOT}/1-WTC-HB/outs/filtered_feature_bc_matrix.h5",
  39. 2: f"{RAW_MULTIOME_ROOT}/2-FC-HB/outs/filtered_feature_bc_matrix.h5",
  40. 3: f"{RAW_MULTIOME_ROOT}/3-WTC-NMP/outs/filtered_feature_bc_matrix.h5",
  41. 4: f"{RAW_MULTIOME_ROOT}/4-FC-NMP/outs/filtered_feature_bc_matrix.h5",
  42. }
  44. # Seurat barcode / cluster annotation tables (one row per cell that passed
  45. # Seurat QC, with the cluster it was assigned to). Used by module 04.
  46. SAMPLE_BARCODE_CSV = {
  47. 1: f"{DATA_ROOT}/sc_rna/celloracle/scripts/Nick_UMAP/1-WTC-HB_res08_barcodes.csv",
  48. 2: f"{DATA_ROOT}/sc_rna/celloracle/scripts/Nick_UMAP/2-FC-HB_res08_barcodes.csv",
  49. 3: f"{DATA_ROOT}/sc_rna/celloracle/scripts/Nick_UMAP/3-WTC-NMP_res08_barcodes.csv",
  50. 4: f"{DATA_ROOT}/sc_rna/celloracle/scripts/Nick_UMAP/4-FC-NMP_res08_barcodes.csv",
  51. }
  54. def sample_cicero_dir(sample):
  55. """Per-sample Cicero / peak-processing working directory (modules 01-03)."""
  56. return f"{DATA_ROOT}/rna_seq_data/sample{sample}/filtered_feature_bc_matrix/cicero_output"
  59. # ---------------------------------------------------------------------------
  60. # ATAC processing (modules 01-03)
  61. # ---------------------------------------------------------------------------
  62. # Cicero CDS filtering thresholds (peaks per cell).
  63. ATAC_MIN_PEAK_COUNT = 2000
  64. ATAC_MAX_PEAK_COUNT = 20000
  66. # Cicero co-accessibility cutoff when filtering TSS-connected peaks (module 02).
  67. COACCESS_THRESHOLD = 0.8
  69. # Motif scan (module 03).
  70. MOTIF_SCAN_FPR = 0.02
  71. MOTIF_SCORE_THRESHOLD = 10
  73. # Final base GRN written by module 03 and consumed by module 05.
  74. BASE_GRN_PATH = os.path.join(COMBINED_OUTPUT_DIR, "base_GRN_dataframe.parquet")
  76. # ---------------------------------------------------------------------------
  77. # scRNA-seq integration (module 04)
  78. # ---------------------------------------------------------------------------
  79. # Cluster labels kept for the integrated object, grouped by sample pair. The
  80. # samples are combined as WTC = (1, 3) and FC = (2, 4); only the p2 and V2a
  81. # populations are carried into the GRN analysis.
  82. CLUSTERS_TO_KEEP = {
  83. "sample_1_3": ["1-p2", "1-V2a", "3-p2", "3-V2a"],
  84. "sample_2_4": ["2-p2", "2-V2a", "4-p2", "4-V2a"],
  85. }
  87. HVG_N_TOP_GENES = 3000 # highly variable genes retained
  88. USE_COMBAT_INTEGRATION = True # batch-correct lineages with ComBat
  90. # Genes of interest forced to survive HVG filtering and used as perturbation
  91. # targets downstream (module 07).
  92. NMP_INSILICO_TARGETS = [
  93. "PBX3", "NR3C1", "TCF7L2", "EBF1", "RFX8", "CREB5", "ZBTB7C", "NPAS3",
  94. "RFX4", "PBX1", "FOXO1", "MYBL1", "HOXB9", "E2F1", "E2F7", "KLF12",
  95. "KLF4", "PLAGL1", "ZBTB7B", "SP9", "MEF2C", "ZIC1", "GATA2", "ELF1", "HES5",
  96. ]
  97. HB_INSILICO_TARGETS = [
  98. "PBX3", "NR3C1", "ELF1", "TCF7L2", "REST", "EBF1", "GATA2", "HES5",
  99. "TFDP2", "RFX8", "GLIS3", "NPAS3", "FOXO1", "AR", "RFX4", "PAX6", "PAX5",
  100. "CREB5", "E2F7", "HES1", "HNF4G",
  101. ]
  102. GENES_OF_INTEREST = sorted(set(NMP_INSILICO_TARGETS + HB_INSILICO_TARGETS))
  104. # Additional TF targets taken from the figure, perturbed in module 07 on top of
  105. OTHER_TARGETS_FROM_FIGURE = [
  106. "EBF1", "EBF3", "EBF2", "NEUROD1", "NEUROG2", "NHLH", "NHLH2", "BHLHE22",
  107. "MYF5", "ATOH1", "FERD3L", "PTF1A", "ASCL1", "PLAGL2", "TCF12", "ASCL2",
  108. "BHLHA15", "PLAG1", "ZNF148", "MYOG", "PBX1", "TGIF2LY", "HAND2", "TGIF2LX",
  109. "PKNOX2", "POU5F1B", "POU3F4", "POU2F2", "TFAP2B", "POU5F1", "TGIF1",
  110. "TCF21", "TGIF2", "MEIS1", "CTCFL", "MEIS3", "ZNF263", "SOX13", "MAZ",
  111. "PLAGL1", "SHOX", "ALX3", "MYF6", "TFAP2C", "POU2F3", "TFAP2A", "ZNF281",
  112. "ALX4", "DLX6", "CDX4", "CDX1", "MEIS2", "NR2C2", "PBX2", "THRB", "NR2F1",
  113. "KLF15", "KLF4", "ONECUT2", "RXRA", "KLF5", "RXRB", "ONECUT3", "ZBTB14",
  114. "NR2C1", "PPARD", "WT1", "PPARG", "E2F6", "NR2F6", "VEZF1", "RXRG",
  115. "ZNF740", "TAP2A", "ONECUT1", "SP9", "CDX2", "SP2", "KLF16", "NR1H2",
  116. "HOXA13", "EGR1", "ZNF682", "HOXD9", "RARA", "RARB", "NRF1",
  117. ]
  118. # Full perturbation target set (module 07).
  119. PERTURBATION_TARGETS = sorted(
  120. set(NMP_INSILICO_TARGETS + HB_INSILICO_TARGETS + OTHER_TARGETS_FROM_FIGURE)
  121. )
  123. # Integrated AnnData written by module 04 and read by module 05.
  124. INTEGRATED_ADATA_PATH = os.path.join(
  125. PIPELINE_ROOT, "combined_from_beginning_v2a_sample_all_four_combined_integrated.h5ad"
  126. )
  128. # ---------------------------------------------------------------------------
  129. # GRN / network analysis (module 05)
  130. # ---------------------------------------------------------------------------
  131. # Cluster column used to build cluster-wise GRNs. We label cells by lineage
  132. # (HB / NMP) crossed with cell type (p2 / V2a), e.g. "HB-V2a".
  133. CLUSTER_COLUMN = "cell_cluster"
  134. EMBEDDING_NAME = "X_draw_graph_fa"
  136. N_CELLS_DOWNSAMPLE = 30000
  137. GRN_ALPHA = 10 # ridge regularisation for get_links / fit_GRN
  138. LINKS_FILTER_P = 0.001 # p-value cutoff for filter_links
  139. LINKS_FILTER_THRESHOLD_NUMBER = 2000
  141. ORACLE_PATH = os.path.join(COMBINED_OUTPUT_DIR, "sample_based_on_nmp_hb.celloracle.oracle")
  142. LINKS_PATH = os.path.join(COMBINED_OUTPUT_DIR, "links_based_on_nmp_hb.celloracle.links")
  143. NODE_SCORES_DIR = os.path.join(
  144. DATA_ROOT, "v2a_network_output_for_nick/node_scores/all_samples_combined"
  145. )
  147. # ---------------------------------------------------------------------------
  148. # Pseudotime (module 06)
  149. # ---------------------------------------------------------------------------
  150. # Lineages expressed in terms of the CLUSTER_COLUMN labels.
  151. HB_LINEAGE_CLUSTERS = ["HB-V2a", "HB-p2"]
  152. NMP_LINEAGE_CLUSTERS = ["NMP-V2a", "NMP-p2"]
  154. # Root cells selected interactively from the embedding (one per lineage).
  155. HB_ROOT_CELL = "AGGGTTGCATTATGGT-1-0-1"
  156. NMP_ROOT_CELL = "TCAGTGAGTCGTAATG-1-1-0"
  158. # Oracle object with pseudotime added, written by module 06, read by module 07.
  159. ORACLE_WITH_PSEUDOTIME_PATH = os.path.join(
  160. COMBINED_OUTPUT_DIR, "sample_based_on_nmp_hb_with_Psudotime.celloracle.oracle"
  161. )
  163. # ---------------------------------------------------------------------------
  164. # Perturbation simulation (module 07)
  165. # ---------------------------------------------------------------------------
  166. PERTURBATION_OUTPUT_DIR = os.path.join(COMBINED_OUTPUT_DIR, "perturbations_all_samples_based_on_nmp_hb")
  168. SIM_N_PROPAGATION = 3 # signal propagation rounds in simulate_shift
  169. SIM_N_NEIGHBORS = 200 # neighbours for transition-prob / p-mass
  170. SIM_SIGMA_CORR = 0.05 # correlation kernel width for embedding shift
  171. SIM_N_GRID = 40 # grid resolution for vector fields
  172. SIM_MIN_MASS = 0.0047 # probability-mass filter threshold
  173. SIM_SCALE = 35 # quiver plot scale
  174. SIM_SCALE_DEV = 35 # developmental-flow scale
  175. SIM_SCALE_SIMULATION = 0.4 # simulation-flow scale
  177. # Lineage membership used when scoring perturbation vs differentiation.
  178. SIM_HB_LINEAGE = ["1-V2a", "1-p2", "2-V2a", "2-p2"]
  179. SIM_NMP_LINEAGE = ["3-V2a", "3-p2", "4-V2a", "4-p2"]
  181. # ---------------------------------------------------------------------------
  182. # GSEA (module 08)
  183. # ---------------------------------------------------------------------------
  184. GSEA_GENE_SETS = "Reactome_2022"
  185. GSEA_PERMUTATION_NUM = 1000
  186. GSEA_PERMUTATION_TYPE = "phenotype"
  187. GSEA_METHOD = "s2n" # signal-to-noise ranking metric
  188. GSEA_THREADS = 16
  189. GSEA_CELL_TYPES = ["HB-V2a", "NMP-V2a"] # cell types compared pre/post perturbation
  190. GSEA_NOM_PVAL_CUTOFF = 0.05
  191. GSEA_FDR_QVAL_CUTOFF = 0.2

config.py at commit 5868e94, no license · at the source

Overview

Authors: Nicholas H. Elder1,2,3,4, Alireza Majd1,2, Andrius Cesiulis1,2,3, Spencer Nyarady1,2, Kalyan Sankar1,2, Emily A. Bulger3,5,6, Ryan M. Samuel1,2, Lyandysha V. Zholudeva5, Todd C. McDevitt5,7, Faranak Fattahi1,2,8
ORCID iDs: Alireza Majd, Emily A. Bulger, Faranak Fattahi
  1. Department of Cellular and Molecular Pharmacology, University of California, San Francisco,San Francisco, CA USA
  2. Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco,San Francisco, CA USA
  3. Developmental and Stem Cell Biology Graduate Program, University of California,San Francisco, CA USA
  4. Present Address: Cellanome, Foster City, CA USA
  5. Gladstone Institute of Cardiovascular Disease, Gladstone Institutes,San Francisco, CA USA
  6. Present Address: Genentech,South San Francisco, CA USA
  7. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco,San Francisco, CA USA
  8. Program in Craniofacial Biology, University of California, San Francisco,San Francisco, CA USA
Institutions: University of California, San Francisco (United States); Gladstone Institutes (United States); Present Address: Genentech,South San Francisco, CA USA
Journal: Nature communications, volume 17, issue 1, article 9187
Dates: received 6 January 2025; accepted 16 July 2026; published online 29 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-76054-w · PMID 42660945 · PMCID PMC13522347 · OpenAlex W7171672184
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Developmental neurogenesis, Induced pluripotent stem cells, Neural patterning
MeSH: Cell Differentiation*, Interneurons*, Neurons*, Animals, Cell Lineage, Gene Expression Regulation, Developmental, Humans, Neural Stem Cells, Rhombencephalon, Spinal Cord, Transcription Factor 7-Like 2 Protein, Transcription Factors (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: California Institute for Regenerative Medicine (CIRM) (DISC0-14521, DISC2-15119, DISC2-16715); NIDDK (R01DK142437)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

V2a interneurons are excitatory neurons found throughout the hindbrain and spinal cord, two regions that arise from distinct progenitors during embryonic development. Whether this lineage difference shapes mature gene regulation and function is unknown. V2a neurons show plasticity after spinal cord injury and are candidates for cell therapy. We differentiated human stem cells into V2a neurons through hindbrain- and spinal cord-like progenitor routes and profiled them by single nucleus multiomic sequencing. The two lineages showed distinct transcription factor motif enrichment and differentially expressed genes governing axon growth and calcium handling. Inducing V2a transcription factors directly, bypassing developmental patterning, produced a population unlike either lineage, confirming progenitor history is not interchangeable. Using CellOracle and lentiviral knockdown, we identified CREB5 and TCF7L2 as regulators specific to the spinal-like lineage. These results show that progenitor origin shapes V2a identity and reveal new regulators of neural diversity along the anterior-posterior axis.

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

Repository

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Alireza-Majd/celloracle_pipeline

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5868e942a1823b1dc8eb2fa47e7577b6ca51df62, 26 June 2026
Languages: Python (8), R (1)
Size: 10 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (6 files), Matplotlib (4 files), NumPy (4 files), Scanpy (4 files), Monocle 3 (1 file), Plotly (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/Alireza-Majd/celloracle_pipeline

Code availability

Analysis of sequencing data utilized publicly available tools and packages as referenced in the “Methods” section. CellOracle analysis scripts are available at [https://github.com/Alireza-Majd/celloracle_pipeline]. Custom R, Python, and command-line scripts used to analyze calcium imaging data are available from the authors upon request.

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

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Data

Datasets cited

Data availability

Data supporting findings of this study are available within the article and its Supplementary Information and Source Data files. The sequencing data generated in this study (snRNA-seq, snATAC-seq, and bulk RNA-seq for knockdown studies) have been deposited in the Gene Expression Omnibus under accession number GSE283005 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE283005). Source data are provided with this paper.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 12 MeSH terms, 2 funders, 63 references.

Cite

This paper

Elder, N. H., Majd, A., Cesiulis, A., Nyarady, S., Sankar, K., Bulger, E. A., Samuel, R. M., Zholudeva, L. V., McDevitt, T. C., & Fattahi, F. (2026). Distinct differentiation trajectories leave lasting impacts on gene regulation and function of V2a neurons. Nature communications, 17(1), 9187. https://doi.org/10.1038/s41467-026-76054-w

BibTeX

@article{elder2026distinct,
author = {Elder, Nicholas H. and Majd, Alireza and Cesiulis, Andrius and Nyarady, Spencer and Sankar, Kalyan and Bulger, Emily A. and Samuel, Ryan M. and Zholudeva, Lyandysha V. and McDevitt, Todd C. and Fattahi, Faranak},
title = {{Distinct differentiation trajectories leave lasting impacts on gene regulation and function of V2a neurons}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {9187},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76054-w},
url = {https://doi.org/10.1038/s41467-026-76054-w},
pmid = {42660945},
pmcid = {PMC13522347}
}

RIS

TY - JOUR
AU - Elder, Nicholas H.
AU - Majd, Alireza
AU - Cesiulis, Andrius
AU - Nyarady, Spencer
AU - Sankar, Kalyan
AU - Bulger, Emily A.
AU - Samuel, Ryan M.
AU - Zholudeva, Lyandysha V.
AU - McDevitt, Todd C.
AU - Fattahi, Faranak
TI - Distinct differentiation trajectories leave lasting impacts on gene regulation and function of V2a neurons
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/29
VL - 17
IS - 1
SP - 9187
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76054-w
UR - https://doi.org/10.1038/s41467-026-76054-w
LA - en
ER -

CSL-JSON

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