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Transcriptomic analysis of spinal V1 interneurons informs their multifunctional role in motor output.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Immunohistochemistry, RNAscope in situ hybridization, and confocal imaging ↔ 02_Data_Processing/Code_Replicate1.py, lines 264–275 · score 0.58 · Bcl11a, Pou6f2, Rnf220, Calb1, Sp8, St18
  2. [2] § Methods › Immunohistochemistry, RNAscope in situ hybridization, and confocal imaging ↔ 02_Data_Processing/Code_Replicate2.py, lines 242–253 · score 0.58 · Bcl11a, Pou6f2, Rnf220, Calb1, Sp8, St18
  3. [3] § Methods › Immunohistochemistry, RNAscope in situ hybridization, and confocal imaging ↔ 02_Data_Processing/Code_Replicate1.py, lines 264–275 · score 0.52 · Nr5a2, Pou6f2, Rnf220, Sp8, St18, Foxp2
  4. [4] § Methods › Immunohistochemistry, RNAscope in situ hybridization, and confocal imaging ↔ 02_Data_Processing/Code_Replicate2.py, lines 242–253 · score 0.52 · Nr5a2, Pou6f2, Rnf220, Sp8, St18, Foxp2
  5. [5] § Methods › snRNA-seq analysis › Data preprocessing and quality control ↔ 01_cellranger/CellRangerAutoScriptGFP.py, lines 5–17 · score 0.52 · force cells, CellRanger, mm10, RNA, transcripts

Paper

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The authors' code

Python · 543 lines · 16 KB · MIT · 2 matches

  1. #!/usr/bin/env python
  2. # coding: utf-8
  4. # # P0 Het and Ko analysis Replicate 1
  5. # AT Updated 8/8/24
  9. # ## Section 1: Load in data and concatenate datasets if applicable
  13. # Import libraries
  15. import numpy as np
  16. import pandas as pd
  17. import scanpy as sc
  18. from sklearn.cluster import KMeans
  19. from sklearn.metrics import silhouette_score
  20. import matplotlib.pyplot as plt
  21. from scipy import stats
  22. import warnings
  23. warnings.filterwarnings("ignore") # suppress warnings
  25. get_ipython().run_line_magic('matplotlib', 'inline')
  27. sc.settings.verbosity = 3
  28. sc.logging.print_header()
  30. sc.settings.set_figure_params(format = "pdf", vector_friendly = False, dpi_save = 80, transparent = True)
  31. sc.set_figure_params(vector_friendly = False, format = "eps", dpi = 80, dpi_save = 80, transparent = True)
  32. plt.rcParams['svg.fonttype'] = 'none'
  36. results_file = 'write/P0KOR1.h5ad'
  41. # Read in control data from replicate 1
  43. adata_het = sc.read_10x_mtx('/Users/atrevisa/Desktop/KO_sc/ForceCells/P0HetR1_filtered_feature_bc_matrix',
  44. var_names='gene_symbols',cache=True)
  45. adata_het.var_names_make_unique()
  46. adata_het
  52. # Read in Ko data from replicate 1
  53. adata_ko = sc.read_10x_mtx('/Users/atrevisa/Desktop/KO_sc/ForceCells/P0KoR1_filtered_feature_bc_matrix',
  54. var_names='gene_symbols',cache=True)
  55. adata_ko.var_names_make_unique()
  56. adata_ko
  62. # Merge the data (no batch correction)
  64. adata_all = adata_het.concatenate(adata_ko, batch_categories=['het', 'ko'])
  65. adata_all
  68. # ## Section 2: Define functions used for analaysis
  73. def QC_graphs (adata):
  75. # QC graphs generates graphs of basic QC data including the highest expressing genes, total counts, genes, % mt genes
  76. # example
  77. # Q_graphs(adata_all)
  78. # if you have not yet run preprocessing1 you will only see the top genes and then get an error
  79. # run preprocessing1 on the data to get the other QC graphs
  81. # Graph highest expressing genes overall
  82. sc.pl.highest_expr_genes (adata, n_top = 20)
  84. # If you have not yet run preprocessing1 the following will give an error because these values have not been calculated yet
  85. if 'batch' in adata.obs.columns:
  86. sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'], groupby = 'batch',jitter=0.4, multi_panel=True, size = 0)
  87. sc.pl.scatter(adata, x='log1p_total_counts', y='log1p_pct_counts_mt', color = 'batch')
  88. sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt', color = 'batch')
  89. sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts', color = 'batch')
  90. sc.pl.scatter(adata, x='log1p_total_counts', y='log1p_n_genes_by_counts', color = 'batch')
  92. else:
  93. sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'], jitter=0.4, multi_panel=True, size = 0)
  94. sc.pl.scatter(adata, x='log1p_total_counts', y='log1p_pct_counts_mt')
  95. sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt')
  96. sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts')
  97. sc.pl.scatter(adata, x='log1p_total_counts', y='log1p_n_genes_by_counts')
  103. def preprocessing1 (adata):
  105. # preprocessing 1 elimates genes with less than 3 counts and cells with less than 200 reads
  108. # preprocessing 1 also calculates QC stats
  109. # example
  110. # preprocessing(adata_all)
  112. # First compute the number of genes per cell and add to adata.obs in a column called 'n_genes'
  113. sc.pp.filter_cells(adata, min_genes=0, inplace = True)
  114. # Manually filter
  115. adata_copy = adata[adata.obs['n_genes'] >= 1000, :]
  117. # First compute the number of cells expressing each gene and add to adata.var in a column called 'n_cells'
  118. sc.pp.filter_genes(adata_copy, min_cells=0, inplace = True)
  119. # Manually filter
  120. adata_copy = adata_copy[:, adata_copy.var['n_cells'] >= 10]
  122. # Calculate qc statistics
  123. # note that log1p is the natural log
  124. adata_copy.var['mt'] = adata_copy.var_names.str.startswith('mt-') # annotate the group of mitochondrial genes as 'mt'
  125. sc.pp.calculate_qc_metrics(adata_copy, qc_vars=['mt'], percent_top=None, log1p=True, inplace=True)
  126. adata_copy.obs['log1p_pct_counts_mt'] = np.log1p(adata_copy.obs.pct_counts_mt)
  128. return(adata_copy)
  134. def preprocessing2 (adata):
  136. # preprocessing2 eleminates cells above and below 2 MADs from the median of the natiral log total counts and genes
  137. # preprocessing2 also gets rid of cells with > 5% mt genes expressed
  138. # example
  139. # adata_filtered = preprocessing2(adata_all)
  141. if 'batch' in adata.obs.columns:
  142. cell_ID2 = []
  144. for batch in adata.obs.batch.unique():
  145. print(batch)
  146. currentbatch = adata[adata.obs.batch == batch]
  147. print(currentbatch)
  149. upper_counts = currentbatch.obs.log1p_total_counts.median() + 2.5*stats.median_abs_deviation(currentbatch.obs.log1p_total_counts)
  150. lower_counts = currentbatch.obs.log1p_total_counts.median() - 2.5*stats.median_abs_deviation(currentbatch.obs.log1p_total_counts)
  151. upper_genes = currentbatch.obs.log1p_n_genes_by_counts.median() + 2.5*stats.median_abs_deviation(currentbatch.obs.log1p_n_genes_by_counts)
  152. lower_genes = currentbatch.obs.log1p_n_genes_by_counts.median() - 2.5*stats.median_abs_deviation(currentbatch.obs.log1p_n_genes_by_counts)
  154. currentbatch = currentbatch[currentbatch.obs.log1p_total_counts < upper_counts, :]
  155. currentbatch = currentbatch[currentbatch.obs.log1p_total_counts > lower_counts, :]
  156. currentbatch = currentbatch[currentbatch.obs.log1p_n_genes_by_counts > lower_genes, :]
  157. currentbatch = currentbatch[currentbatch.obs.log1p_n_genes_by_counts < upper_genes, :]
  159. currentbatch = currentbatch[currentbatch.obs.pct_counts_mt < 5, :]
  161. cell_ID2.extend(currentbatch.obs.index.tolist())
  163. #len(cell_ID2)
  164. cell_ID2_series = pd.Series(cell_ID2)
  165. adata = adata[cell_ID2_series]
  167. return(adata)
  169. # use the following code for only 1 batch - significantly simpler
  170. else:
  171. upper_counts = adata.obs.log1p_total_counts.median() + 2.5*stats.median_abs_deviation(adata.obs.log1p_total_counts)
  172. lower_counts = adata.obs.log1p_total_counts.median() - 2.5*stats.median_abs_deviation(adata.obs.log1p_total_counts)
  173. upper_genes = adata.obs.log1p_n_genes_by_counts.median() + 2.5*stats.median_abs_deviation(adata.obs.log1p_n_genes_by_counts)
  174. lower_genes = adata.obs.log1p_n_genes_by_counts.median() - 2.5*stats.median_abs_deviation(adata.obs.log1p_n_genes_by_counts)
  176. adata = adata[adata.obs.log1p_total_counts < upper_counts, :]
  177. adata = adata[adata.obs.log1p_total_counts > lower_counts, :]
  178. adata = adata[adata.obs.log1p_n_genes_by_counts > lower_genes, :]
  179. adata = adata[adata.obs.log1p_n_genes_by_counts < upper_genes, :]
  181. adata = adata[adata.obs.pct_counts_mt < 5, :]
  183. return(adata)
  189. def normalization (adata, regress=None):
  191. # normalization normalizes reads to total counts per cell, logarithmizes the data, identifies HVG, and scales the data
  192. # optionally, normalization can regress out variables of choice using the optional argument regress
  194. # options for regression
  195. # Continuous variables: ['total_counts', 'pct_counts_mt', 'n_genes_by_counts']
  196. # Categorical variable such as ['batch'] but this cannot be combined with continuous variables
  198. # you cannot run normalization twice on the same dataset
  200. # examples
  201. # adata_filtered = normalization(adata_filtered) # no regression
  202. # adata_filtered = normalization(adata_filtered, ['batch'])
  203. # adata_filtered = normalization(adata_filtered, ['total_counts', 'pct_counts_mt', 'n_genes_by_counts'])
  205. sc.pp.normalize_total(adata, target_sum=1e4)
  206. sc.pp.log1p(adata)
  207. sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
  209. # IF YOU HAVE ERRORS DO NOT RUN THE NEXT LINE
  210. adata.raw = adata # this may mess up some heatmaps later
  212. adata = adata[:, adata.var.highly_variable]
  214. if regress is not None:
  215. sc.pp.regress_out(adata, regress)
  217. sc.pp.scale(adata, max_value=10)
  219. return(adata)
  225. def pca (adata):
  227. # pca performs pca dimensionality reduction on the data and outputs several graphs to reflect this
  228. # example
  229. # pca(adata_filtered)
  231. sc.tl.pca(adata, svd_solver='arpack')
  232. sc.pl.pca(adata, color='total_counts')
  233. sc.pl.pca(adata, color='batch')
  234. sc.pl.pca_variance_ratio(adata, log=True)
  235. sc.pl.pca_loadings(adata) # genes contributing to each PC
  241. def cluster (adata, neighbors, pcs, res):
  243. # cluster performs non-linear dimensionality reduction and clustering
  244. # example
  245. # cluster(adata_filtered, neighbors = 30, pcs = 15, res = 1)
  247. sc.pp.neighbors(adata, n_neighbors=neighbors, n_pcs=pcs)
  249. # sc.tl.paga(adata_all_all)
  250. # sc.pl.paga(adata_all_all, plot=False) # remove `plot=False` if you want to see the coarse-grained graph
  251. # sc.tl.umap(adata_all_all, init_pos='paga')
  253. sc.tl.umap(adata)
  254. #sc.pl.umap(adata)
  256. sc.tl.leiden(adata, resolution = res) # Default resolution is 1
  257. sc.pl.umap(adata, color=['leiden'], legend_loc='on data')
  258. sc.pl.umap(adata, color=['leiden'])
  264. def filtering (adata):
  266. # filtering shows some basic info need for getting rid of non-V1 cells
  267. # example
  268. # filtering(adata)
  270. sc.pl.umap(adata, color=['total_counts', 'pct_counts_mt', 'n_genes_by_counts',
  271. 'log1p_total_counts', 'log1p_pct_counts_mt', 'log1p_n_genes_by_counts'], ncols=3)
  273. sc.pl.umap(adata, color=['Foxp2', 'St18', 'Calb1', 'Pou6f2', 'Sall3', 'Nr5a2', 'Rnf220', 'Bcl11a', 'Nos1', 'Piezo2', 'Ntn1', 'Sp8'], ncols=3)
  275. sc.pl.umap(adata, color=['leiden'], legend_loc='on data')
  278. # ## Section 3: Preliminary analysis -1
  283. adata_all
  286. adata_filtered = preprocessing1(adata_all)
  289. adata_filtered
  293. QC_graphs(adata_filtered)
  296. adata_filtered = preprocessing2(adata_filtered)
  299. adata_filtered
  302. QC_graphs(adata_filtered)
  305. adata_filtered = normalization(adata_filtered)
  308. adata_filtered
  314. # ## Negative Selection 1
  319. pca(adata_filtered)
  323. cluster(adata_filtered, neighbors = 30, pcs = 15, res = 1)
  327. sc.pl.umap(adata_filtered, color = 'batch')
  331. filtering(adata_filtered)
  335. # Glia
  337. # General
  338. # Astrocytes
  339. # Oligodendrocyte-lineage
  340. # Microglia
  341. sc.pl.umap(adata_filtered, color=["Sox6",
  342. "Aldh1l1", "Fgfr3", "Aqp4", "Gfap", "Glul", "Gja1", "Slc1a3", "Slc4a4", "Sox2", "Slc1a2", "S100b", "Ndrg2", "Nkx6-1", "Sox9",
  343. "Olig2", "Mbp", "Mog", "Mag", "Bcas1", "Pdgfra", "Mbp", "Plp1", "Pmp22", "Prx", "Cspg4", "Gpr17", "Cnp", "Sox10", "Olig1", "Cd9", "Zfp488", "Zfp536", "Nkx6-2", "Nkx2-2", "Cd82", "Mal", "Bmp4", "Aspa", "St18",
  344. "Aif1", "Trem2", "Inpp5d", "Ctss", "Itgam", "Ptprc", "Cx3cr1", "Cd68", "Adgre1", "Mertk", "Fcer1g", "Fcrls", "Hexb"], ncols = 3)
  350. # Other non-neuronal cells
  352. # Vasculature
  353. # CSF-contacting cells
  354. # Meninges
  355. # Ependymal cells
  356. sc.pl.umap(adata_filtered, color=["Cldn5", "Rgs5", "Flt1", 'Slco1c1', 'Fli1', "Sox17", "Fermt3", "Klf1", "Car2", "Pecam1", "Tek", "Egflam", "Dlc1", "Cald1", "Rapgef5", "Flt4",
  357. "Pkd2l1", "Pkd1l2", "Myo3b",
  358. "Dcn", "Col3a1", "Igf2",
  359. "Sox9", "Sox2",
  360. "Dnah12", "Spef2", "Ccdc114", "Ddo", "Cfap65", "Ak9", "Fam216b", "Zfp474", "Wdr63", "Ccdc180",
  361. "Lmx1a", "Msx1", "Pax3", "Wnt1"], ncols = 3)
  365. # Neurons
  367. # General excitatory markers
  368. # Cholinergic markers
  369. # Neural Crest derived
  370. sc.pl.umap(adata_filtered, color=["Slc17a6", "Lmx1b", "Ebf2", "Sox5", "Slc17a7", "Ebf1", "Ebf3", "Cacna2d1",
  371. "Chat", "Slc5a7", "Slc18a3", "Isl1", "Ret", "Slit3", "Prph", "Lhx3", "Isl2", "Mnx1", "Slc8a3",
  372. "Sox10", "Sox2",
  373. "Enc1", "Dlg4", "Eno2",
  374. "Lhx1", "Lhx5", "Pax8", "Lbx1", "Pax2", "Gbx1", "Bhlhe22", "Sall3",
  375. "Gad1", "Gad2", "Slc32a1",
  376. "Tal1", "Gata2", "Gata3",
  377. "Evx1", "Evx2",
  378. "Dmrt3", "Wt1", "En1"], ncols = 3)
  381. # dI4
  382. # dIlA
  383. sc.pl.umap(adata_filtered, color=["Lhx1", "Lhx5", "Pax8", "Lbx1", "Gbx1", "Bhlhe22",
  384. "Gbx1", "Pax2","Sall3", "Rorb", "Pdzd2"], ncols = 3)
  389. # remove clusters
  391. # First round contaminants
  392. adata_filtered = adata_filtered[~adata_filtered.obs.leiden.isin(["11", "6", "17", "15", "8", "16", "13", "10", "7", "12", "8", "18", "5", "9"])]
  395. adata_filtered
  398. filtering(adata_filtered)
  407. # ## Negative Selection 2
  412. pca(adata_filtered)
  415. cluster(adata_filtered, neighbors = 30, pcs = 15, res = 1)
  418. sc.pl.umap(adata_filtered, color = 'batch')
  421. filtering(adata_filtered)
  424. # Glia
  426. # General
  427. # Astrocytes
  428. # Oligodendrocyte-lineage
  429. # Microglia
  430. sc.pl.umap(adata_filtered, color=["Sox6",
  431. "Aldh1l1", "Fgfr3", "Aqp4", "Gfap", "Glul", "Gja1", "Slc1a3", "Slc4a4", "Sox2", "Slc1a2", "S100b", "Ndrg2", "Nkx6-1", "Sox9",
  432. "Olig2", "Mbp", "Mog", "Mag", "Bcas1", "Pdgfra", "Mbp", "Plp1", "Pmp22", "Prx", "Cspg4", "Gpr17", "Cnp", "Sox10", "Olig1", "Cd9", "Zfp488", "Zfp536", "Nkx6-2", "Nkx2-2", "Cd82", "Mal", "Bmp4", "Aspa", "St18",
  433. "Aif1", "Trem2", "Inpp5d", "Ctss", "Itgam", "Ptprc", "Cx3cr1", "Cd68", "Adgre1", "Mertk", "Fcer1g", "Fcrls", "Hexb"], ncols = 3)
  436. # other non-neuronal
  438. # Vasculature
  439. # CSF-contacting cells
  440. # Meninges
  441. # Ependymal cells
  442. sc.pl.umap(adata_filtered, color=["Cldn5", "Rgs5", "Flt1", 'Slco1c1', 'Fli1', "Sox17", "Fermt3", "Klf1", "Car2", "Pecam1", "Tek", "Egflam", "Dlc1", "Cald1", "Rapgef5", "Flt4",
  443. "Pkd2l1", "Pkd1l2", "Myo3b",
  444. "Dcn", "Col3a1", "Igf2",
  445. "Sox9", "Sox2",
  446. "Dnah12", "Spef2", "Ccdc114", "Ddo", "Cfap65", "Ak9", "Fam216b", "Zfp474", "Wdr63", "Ccdc180",
  447. "Lmx1a", "Msx1", "Pax3", "Wnt1"], ncols = 3)
  450. # neurons
  452. # General excitatory markers
  453. # Cholinergic markers
  454. # NC derived
  455. #
  456. sc.pl.umap(adata_filtered, color=["Slc17a6", "Lmx1b", "Ebf2", "Sox5", "Slc17a7", "Ebf1", "Ebf3", "Cacna2d1",
  457. "Chat", "Slc5a7", "Slc18a3", "Isl1", "Ret", "Slit3", "Prph", "Lhx3", "Isl2", "Mnx1", "Slc8a3",
  458. "Sox10", "Sox2",
  459. "Enc1", "Dlg4", "Eno2",
  460. "Lhx1", "Lhx5", "Pax8", "Lbx1", "Pax2", "Gbx1", "Bhlhe22", "Sall3",
  461. "Gad1", "Gad2", "Slc32a1",
  462. "Tal1", "Gata2", "Gata3",
  463. "Evx1", "Evx2",
  464. "Dmrt3", "Wt1", "En1"], ncols = 3)
  466. # dI4
  467. # dIlA
  468. sc.pl.umap(adata_filtered, color=["Lhx1", "Lhx5", "Pax8", "Lbx1", "Gbx1", "Bhlhe22",
  469. "Gbx1", "Pax2","Sall3", "Rorb", "Pdzd2"], ncols = 3)
  472. adata_filtered # before removing contaminates
  475. # remove clusters
  478. # Second round
  479. adata_filtered = adata_filtered[~adata_filtered.obs.leiden.isin(["9", "13", "0", "14"])]
  482. adata_filtered
  485. filtering(adata_filtered)
  490. # ## Select for V1's and perform analysis again
  495. adata_filtered
  498. V1 = pd.DataFrame(adata_filtered.obs.index)
  499. V1_ID = V1[0]
  500. len(V1_ID)
  503. adata_all
  506. adata_V1 = adata_all[V1_ID]
  509. adata_V1
  512. adata_V1 = preprocessing1(adata_V1)
  515. adata_V1
  518. QC_graphs(adata_V1)
  521. adata_V1 = normalization(adata_V1, ['batch']) # regress out batch effects`
  524. adata_V1
  527. pca(adata_V1)
  530. cluster(adata_V1, neighbors = 50, pcs = 12, res = 1)
  533. sc.pl.umap(adata_V1, color = 'batch') # optional
  537. for batch in ['het', 'ko']:
  538. sc.pl.umap(adata_V1, color='batch', groups=[batch])
  541. filtering(adata_V1)

Code_Replicate1.py at commit b6e8214, under MIT · at the source

Overview

Authors: Alexandra J. Trevisan1, Katie Han1, Phillip D. Chapman1, Anand S. Kulkarni1, Jennifer M. Hinton1, Ines Klein2, Cody Ramirez1, Alfonso Lavado3, Graziana Gatto2, Mariano I. Gabitto4,5, Vilas Menon6, Jay B. Bikoff1
ORCID iDs: Cody Ramirez, Graziana Gatto, Vilas Menon, Jay B. Bikoff
  1. Department of Developmental Neurobiology, St. Jude Children’s Research Hospital,Memphis, TN USA
  2. Department of Neurology, University Hospital of Cologne,Cologne, Germany
  3. Center for Pediatric Neurological Disease Research, St. Jude Children’s Research Hospital,Memphis, TN USA
  4. Allen Institute for Brain Science,Seattle, WA USA
  5. Department of Statistics, University of Washington,Seattle, WA USA
  6. Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University,New York, NY USA
Institutions: St. Jude Children's Research Hospital (United States); University Hospital Cologne (Germany); Allen Institute for Brain Science (United States); University of Washington (United States); Columbia University (United States)
Journal: Nature communications, volume 17, issue 1, article 9614
Dates: received 6 February 2025; accepted 31 July 2026; published online 10 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-76522-3 · PMID 42711323 · PMCID PMC13554063 · OpenAlex W7202083432
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging
Keywords: Spinal cord, Neural circuits, Cell type diversity
MeSH: Interneurons*, Spinal Cord*, Transcriptome*, Animals, Female, Gene Expression Profiling, Homeodomain Proteins, Locomotion, Male, Mice, Mice, Knockout (* major topic)
Topic: Zebrafish Biomedical Research Applications (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NINDS (R01NS123116); American Lebanese Syrian Associated Charities; Deutsche Forschungsgemeinschaft (DFG) (CRC1451 - 431549029-A09, CRC1451 - 431549029-Z02); National Cancer Institute (P30 CA021765); iBehave network from the Ministry of Culture and Science of the State of North Rhine-Westphalia
Citations: not cited yet (Europe PMC); 115 references in the paper

Abstract

Neural circuits in the spinal cord are composed of diverse populations of interneurons that play crucial roles in shaping motor output. However, the extent of interneuron heterogeneity and how this diversity relates to functional aspects of movement remain unclear. Here, through a focus on mouse spinal V1 interneurons, we show that loss of the V1 transcription factor En1 selectively disrupts the frequency of rhythmic locomotor output but does not disrupt flexion/extension limb movement, thereby decoupling two key functional roles ascribed to this neuronal population. To investigate the cellular basis of these deficits, we generated a single-nucleus transcriptomic atlas of V1 interneurons across postnatal development. Our analysis reveals age-dependent transcriptional changes while also demonstrating that their core molecular taxonomy perdures into adulthood. Notably, En1 deficiency selectively perturbed a single subset of V1Pou6f2 interneurons, thereby identifying a possible cellular substrate for influencing locomotor speed. Beyond serving as a molecular resource, our study highlights how deep neuronal profiling provides an entry point for understanding the multifunctional nature of heterogeneous interneuron populations.

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 5 matches between paragraphs and lines of code.

Bikoff-Lab

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: github.com/Bikoff-Lab

Bikoff-Lab/V1_KO_analysis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b6e8214879b477eaa7f784633160005cbb8a909b, 10 August 2026
Languages: Python (3)
Size: 6 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (3 files), Matplotlib (2 files), NumPy (2 files), Scanpy (2 files), scikit-learn (2 files), SciPy (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/Bikoff-Lab/V1_KO_analysis

Code availability

The code used for analysis of sequencing data is available at Github (https://github.com/Bikoff-Lab/V1_interneuron_snRNAseq_Age_Atlas and https://github.com/Bikoff-Lab/V1_KO_analysis).

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

Tracing map

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  • 3 scripts, each with its path and the digest of its content;
  • 5 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 sequencing data, counts tables, and associated metadata generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession number GEO: GSE275595 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse275595). In addition, an interactive, searchable web resource for exploration and visualization of these data is available at https://v1interneurons.stjude.org/shinyApp/. Source data are provided with this paper.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 11 MeSH terms, 5 funders, 114 references.

Cite

This paper

Trevisan, A. J., Han, K., Chapman, P. D., Kulkarni, A. S., Hinton, J. M., Klein, I., Ramirez, C., Lavado, A., Gatto, G., Gabitto, M. I., Menon, V., & Bikoff, J. B. (2026). Transcriptomic analysis of spinal V1 interneurons informs their multifunctional role in motor output. Nature communications, 17(1), 9614. https://doi.org/10.1038/s41467-026-76522-3

BibTeX

@article{trevisan2026transcriptomic,
author = {Trevisan, Alexandra J. and Han, Katie and Chapman, Phillip D. and Kulkarni, Anand S. and Hinton, Jennifer M. and Klein, Ines and Ramirez, Cody and Lavado, Alfonso and Gatto, Graziana and Gabitto, Mariano I. and Menon, Vilas and Bikoff, Jay B.},
title = {{Transcriptomic analysis of spinal V1 interneurons informs their multifunctional role in motor output}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9614},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76522-3},
url = {https://doi.org/10.1038/s41467-026-76522-3},
pmid = {42711323},
pmcid = {PMC13554063}
}

RIS

TY - JOUR
AU - Trevisan, Alexandra J.
AU - Han, Katie
AU - Chapman, Phillip D.
AU - Kulkarni, Anand S.
AU - Hinton, Jennifer M.
AU - Klein, Ines
AU - Ramirez, Cody
AU - Lavado, Alfonso
AU - Gatto, Graziana
AU - Gabitto, Mariano I.
AU - Menon, Vilas
AU - Bikoff, Jay B.
TI - Transcriptomic analysis of spinal V1 interneurons informs their multifunctional role in motor output
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/10
VL - 17
IS - 1
SP - 9614
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76522-3
UR - https://doi.org/10.1038/s41467-026-76522-3
LA - en
ER -

CSL-JSON

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