OSCR

Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency.

Code ↔ Paper

3 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] § RESULTS › Single nucleus RNA-seq reveals specific cell types contributing to Grin2d up-regulation in the Kdm5b mutant neocortex ↔ Parse_snRNAseq_analysis.ipynb, lines 287–321 · score 0.89 · CPN L5, Cajal Retzius, intermediate progenitors, immature neurons, GABAergic, Grin2d
  2. [2] § RESULTS › Single nucleus RNA-seq reveals specific cell types contributing to Grin2d up-regulation in the Kdm5b mutant neocortex ↔ Parse_snRNAseq_analysis.ipynb, lines 244–278 · score 0.75 · intermediate progenitors, immature neurons, GABAergic, Cpne9, Mcm6, astrocytes
  3. [3] § MATERIALS AND METHODS › Parse sequencing ↔ Parse_snRNAseq_analysis.ipynb, lines 111–115 · score 0.50 · highly variable genes, log1p, pp, Scanpy

Paper

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The authors' code

Jupyter notebook · 349 lines · 11 KB · CC-BY-4.0 · 3 matches

  1. # %% [markdown]
  2. # # Directory and enviroment
  4. # %%
  5. import numpy as np
  6. import scanpy as sc
  7. import seaborn as sns
  8. import matplotlib.pyplot as plt
  9. from scipy.stats import median_abs_deviation
  10. import pandas as pd
  11. import pybiomart
  12. import anndata as ad
  13. import seaborn as sns
  14. from matplotlib.pyplot import rc_context
  16. sc.settings.verbosity = 0
  17. sc.settings.set_figure_params(
  18. dpi=80,
  19. facecolor="white",
  20. frameon=False,
  21. )
  23. # %% [markdown]
  24. # # Dataset loading and QC
  26. # %%
  27. adata = sc.read_csv(
  28. filename="counts.csv")
  30. # %%
  31. adata=adata.transpose()
  33. # %%
  34. metadata=pd.read_csv("meta.csv", index_col="Index")
  36. # %%
  37. adata.obs.index=adata.obs.index.str.slice(1)
  39. # %%
  40. adata.obs=metadata
  42. # %% [markdown]
  43. # ## Change names from Ensemble to gene names
  45. # %%
  46. annot=sc.queries.biomart_annotations(org="mmusculus", attrs=['ensembl_gene_id', 'external_gene_name']).set_index("ensembl_gene_id")
  48. # %%
  49. adata.var[annot.columns] = annot
  51. # %% [markdown]
  52. # ## Quality Control
  54. # %%
  55. # mitochondrial genes
  56. adata.var["mt"] = adata.var["external_gene_name"].str.startswith("mt-")
  57. # ribosomal genes
  58. adata.var["ribo"] = adata.var["external_gene_name"].str.startswith(("Rps", "Rpl"))
  59. # hemoglobin genes.
  60. adata.var["hb"] = adata.var["external_gene_name"].str.contains("^Hb[^(P)]")
  62. # %%
  63. # mitochondrial genes
  64. adata.var["mt"] = adata.var["mt"].astype('bool')
  65. # ribosomal genes
  66. adata.var["ribo"] = adata.var["ribo"].astype('bool')
  67. # hemoglobin genes.
  68. adata.var["hb"] = adata.var["hb"].astype('bool')
  70. # %%
  71. sc.pp.calculate_qc_metrics(
  72. adata, qc_vars=["mt", "ribo", "hb"], inplace=True, percent_top=[20], log1p=True
  73. )
  75. # %%
  76. def is_outlier(adata, metric: str, nmads: int):
  77. M = adata.obs[metric]
  78. outlier = (M < np.median(M) - nmads * median_abs_deviation(M)) | (
  79. np.median(M) + nmads * median_abs_deviation(M) < M
  80. )
  81. return outlier
  83. # %%
  84. adata.obs["outlier"] = (
  85. is_outlier(adata, "log1p_total_counts", 5)
  86. | is_outlier(adata, "log1p_n_genes_by_counts", 5)
  87. | is_outlier(adata, "pct_counts_in_top_20_genes", 5)
  88. )
  89. print(adata.obs.outlier.value_counts())
  91. # %%
  93. adata.obs["mt_outlier"] = is_outlier(adata, "pct_counts_mt", 5) | (
  94. adata.obs["pct_counts_mt"] > 1
  95. )
  96. print(adata.obs.mt_outlier.value_counts())
  98. # %%
  99. sc.pp.scrublet(adata)
  101. # %%
  102. adata.obs["predicted_doublet"].value_counts()
  104. # %% [markdown]
  105. # # Normalization
  107. # %%
  108. scales_counts = sc.pp.normalize_total(adata, target_sum=None, inplace=False)
  109. adata.layers["log1p_norm"] = sc.pp.log1p(scales_counts["X"], copy=True)
  111. # %% [markdown]
  112. # # Feature selection
  114. # %%
  115. sc.pp.highly_variable_genes(adata, layer="log1p_norm")
  117. # %% [markdown]
  118. # # Dimensionality reduction before integration
  120. # %%
  121. adata.X = adata.layers["log1p_norm"]
  123. # %%
  124. sc.pp.pca(adata, svd_solver="arpack", use_highly_variable=True)
  126. # %%
  127. sc.pl.pca_scatter(adata, color="total_counts")
  129. # %%
  130. sc.pp.neighbors(adata)
  131. sc.tl.umap(adata)
  133. # %% [markdown]
  134. # # Integration
  136. # %%
  137. import scanpy.external as sce
  138. sce.pp.harmony_integrate(adata, "samples", max_iter_harmony=25)
  140. # %% [markdown]
  141. # # Dimensionality reduction after integration
  143. # %%
  144. adata.obsm['X_pca'] = adata.obsm['X_pca_harmony']
  145. sc.pp.neighbors(adata, random_state=0)
  146. sc.tl.umap(adata)
  148. # %%
  149. sc.tl.leiden(adata, key_added="leiden_res0_25", resolution=0.25)
  150. sc.tl.leiden(adata, key_added="leiden_res0_5", resolution=0.5)
  151. sc.tl.leiden(adata, key_added="leiden_res1", resolution=1.0)
  153. # %%
  154. sc.pl.umap(
  155. adata,
  156. color=["leiden_res0_25", "leiden_res0_5", "leiden_res1", "samples"],
  157. #legend_loc="on data",
  158. )
  160. # %%
  161. adata.obs["cell_types"] = adata.obs["leiden_res0_5"].map(
  162. {
  163. "0": "Intermediate progenitors",
  164. "1": "Immature neurons",
  165. "2": "Stellate/Layer4",
  166. "3": "CPN L2/3",
  167. "4": "GABAergic SST",
  168. "5": "CorticoFugal N. L6b",
  169. "6": "Astrocytes",
  170. "7": "GABAergic",
  171. "8": "Glutamatergic N.",
  172. "9": "GABAergic",
  173. "10": "CPN L5/6",
  174. "11": "GABAergic VIP",
  175. "12": "Oligodendrocytes",
  176. "13": "Vascular",
  177. "14": "Glutamatergic N.",
  178. "15": "CorticoFugal N.",
  179. "16": "Cajal Retzius",
  180. }
  181. )
  183. # %%
  184. markers=sc.get.rank_genes_groups_df(adata, group=None, key="cell_types")
  186. # %% [markdown]
  187. # # Visualization
  189. # %%
  190. adata.var.index=adata.var["external_gene_name"]
  192. # %%
  193. #Figure 6A
  194. custom_palette=["#cc66ff", "#990033", "#ffcccc", "#66ff66", "#660033", "#cc0066", "#cccc00", "#999966", "#669900",
  195. "#334d00", "#6699ff", "#ff6600", "#0066ff", "#6600cc", "#ff3300"]
  197. sc.pl.umap(
  198. adata,
  199. color=["cell_types"],
  200. palette=custom_palette,
  201. size=10,
  202. #legend_loc="on data",
  203. save="UMAP_AnnotatedFinal.pdf"
  204. )
  206. # %%
  207. #Figure 6B
  208. sc.pl.umap(
  209. adata,
  210. color=["Genotype"],
  211. palette="Accent",
  212. groups="KDM5b HOM",
  213. size=10,
  214. #legend_loc="on data",
  215. save="UMAP_GenotypeHOM.pdf"
  216. )
  218. # %%
  219. #Figure 6B
  220. sc.pl.umap(
  221. adata,
  222. color=["Genotype"],
  223. palette="Accent",
  224. groups="KDM5b WT",
  225. size=10,
  226. #legend_loc="on data",
  227. save="UMAP_GenotypeWT.pdf"
  228. )
  230. # %%
  231. #Figure 6C
  232. adata.obs["Leiden_genotype"]=adata.obs["Genotype"].astype('str')+"_"+adata.obs["cell_types"].astype('str')
  233. val_counts = adata.obs['Leiden_genotype'].value_counts()
  234. # saving as csv file
  235. val_counts.to_csv('val_countsGenotype.csv', sep="\t")
  237. # %%
  238. #Figure 6D
  239. sc.pl.dotplot(adata,
  240. var_names= [
  241. 'Kdm5b'],
  242. groupby="cell_types", log=True)
  244. # %%
  245. #Figure 6E
  246. adatasubGaba=adata[adata.obs["cell_types"].isin(["GABAergic_SST", "GABAergic_VIP", "GABAergic"])]
  247. sc.pl.dotplot(adatasubGaba,
  248. var_names= ["Cpne9", "Mcm6"],
  249. groupby="Leiden_genotype", standard_scale="var",
  250. categories_order=["KDM5b WT_GABAergic VIP", "KDM5b HOM_GABAergic VIP",
  251. "KDM5b WT_GABAergic SST", "KDM5b HOM_GABAergic SST",
  252. "KDM5b WT_GABAergic", "KDM5b HOM_GABAergic"], save="DotplotTopGABA_v3.pdf")
  254. adatasubGlut=adata[adata.obs["cell_types"].isin(["CPN_L5_6", "CPN_L2_3", "CorticoFugal_N",
  255. "CorticoFugal_N_L6b", "Glutamatergic_N", "Stellate_Layer4"])]
  257. sc.pl.dotplot(adatasubGlut,
  258. var_names= ["Cpne9", "Mcm6"],
  259. groupby="Leiden_genotype", standard_scale="var",
  260. categories_order=["KDM5b WT_CPN L5/6", "KDM5b HOM_CPN L5/6",
  261. "KDM5b WT_CPN L2/3", "KDM5b HOM_CPN L2/3",
  262. "KDM5b WT_CorticoFugal N.", "KDM5b HOM_CorticoFugal N.",
  263. "KDM5b WT_CorticoFugal N. L6b", "KDM5b HOM_CorticoFugal N. L6b",
  264. "KDM5b WT_Glutamatergic N.", "KDM5b HOM_Glutamatergic N.",
  265. "KDM5b WT_Stellate/Layer4", "KDM5b HOM_Stellate/Layer4"], save="DotplotTopGlu_v3t.pdf")
  267. adatasubOthers=adata[adata.obs["cell_types"].isin(["Astrocytes", "Cajal_Retzius", "Immature_neurons", "Intermediate_progenitors",
  268. "Oligodendrocytes", "Vascular"])]
  270. sc.pl.dotplot(adatasubOthers,
  271. var_names= ["Cpne9", "Mcm6"],
  272. groupby="Leiden_genotype", standard_scale="var",
  273. categories_order=["KDM5b WT_Astrocytes", "KDM5b HOM_Astrocytes",
  274. "KDM5b WT_Oligodendrocytes", "KDM5b HOM_Oligodendrocytes",
  275. "KDM5b WT_Cajal Retzius", "KDM5b HOM_Cajal Retzius",
  276. "KDM5b WT_Vascular", "KDM5b HOM_Vascular",
  277. "KDM5b WT_Immature neurons", "KDM5b HOM_Immature neurons",
  278. "KDM5b WT_Intermediate progenitors", "KDM5b HOM_Intermediate progenitors"], save="DotplotTopOthercells_v3.pdf")
  280. # %%
  281. #Figure 7A
  282. sc.pl.dotplot(adata,
  283. var_names= [
  284. 'Grin2d'],
  285. groupby="cell_types", log=True)
  287. # %%
  288. #Figure 7B
  289. adatasubGaba=adata[adata.obs["cell_types"].isin(["GABAergic_SST", "GABAergic_VIP", "GABAergic"])]
  290. sc.pl.dotplot(adatasubGaba,
  291. var_names= ["Grin2d"],
  292. groupby="Leiden_genotype", standard_scale="var",
  293. categories_order=["KDM5b WT_GABAergic VIP", "KDM5b HOM_GABAergic VIP",
  294. "KDM5b WT_GABAergic SST", "KDM5b HOM_GABAergic SST",
  295. "KDM5b WT_GABAergic", "KDM5b HOM_GABAergic"], save="DotplotGrin2dGABA_v3.pdf")
  297. adatasubGlut=adata[adata.obs["cell_types"].isin(["CPN_L5_6", "CPN_L2_3", "CorticoFugal_N",
  298. "CorticoFugal_N_L6b", "Glutamatergic_N", "Stellate_Layer4"])]
  300. sc.pl.dotplot(adatasubGlut,
  301. var_names= ["Grin2d"],
  302. groupby="Leiden_genotype", standard_scale="var",
  303. categories_order=["KDM5b WT_CPN L5/6", "KDM5b HOM_CPN L5/6",
  304. "KDM5b WT_CPN L2/3", "KDM5b HOM_CPN L2/3",
  305. "KDM5b WT_CorticoFugal N.", "KDM5b HOM_CorticoFugal N.",
  306. "KDM5b WT_CorticoFugal N. L6b", "KDM5b HOM_CorticoFugal N. L6b",
  307. "KDM5b WT_Glutamatergic N.", "KDM5b HOM_Glutamatergic N.",
  308. "KDM5b WT_Stellate/Layer4", "KDM5b HOM_Stellate/Layer4"], save="DotplotGrin2d_v3t.pdf")
  310. adatasubOthers=adata[adata.obs["cell_types"].isin(["Astrocytes", "Cajal_Retzius", "Immature_neurons", "Intermediate_progenitors",
  311. "Oligodendrocytes", "Vascular"])]
  313. sc.pl.dotplot(adatasubOthers,
  314. var_names= ["Grin2d"],
  315. groupby="Leiden_genotype", standard_scale="var",
  316. categories_order=["KDM5b WT_Astrocytes", "KDM5b HOM_Astrocytes",
  317. "KDM5b WT_Oligodendrocytes", "KDM5b HOM_Oligodendrocytes",
  318. "KDM5b WT_Cajal Retzius", "KDM5b HOM_Cajal Retzius",
  319. "KDM5b WT_Vascular", "KDM5b HOM_Vascular",
  320. "KDM5b WT_Immature neurons", "KDM5b HOM_Immature neurons",
  321. "KDM5b WT_Intermediate progenitors", "KDM5b HOM_Intermediate progenitors"], save="DotplotGrin2dOthercells_v3.pdf")
  323. # %%
  324. #Figure S5A
  325. sc.pl.dotplot(adata,
  326. var_names= ['Apoe', 'Aldh1l1', # Astrocytes_6
  327. 'Cux1', 'Cux2', # L2/3
  328. 'Satb2', 'Lmo4', # L5/6
  329. 'Reln', 'Emx2',#CajalRetzius_16
  330. 'Bcl11b', 'Fezf2',#Corticofugal
  331. 'Sox5','Tle4',#Cortcofugal L6
  332. 'Erbb4', 'Adarb2', 'Gad1', 'Gad2', #GABAergic
  333. 'Sst', 'Vip',#GABA
  334. 'Slc17a7', 'Camk2a',#Glutamatergic
  335. 'Dcx','Stmn1',
  336. 'Neurod2', 'Neurod6', 'Gfap', 'Pax6',
  337. 'Sox6', 'Pdgfra', 'Olig2', 'Mbp', # Oligo_12
  338. 'Rorb', 'Lpl',#Stellate
  339. 'Cldn5', 'Igfbp7', 'Rgs5',#Vasculature
  340. 'Aif1', 'C1qb','Trem2',#Microglia non existent
  341. ], groupby="cell_types", standard_scale="var", cmap="Reds",
  342. save="DotPlotmarkers.pdf")
  344. # %%
  345. #Figure S5B
  346. adata.obs["Annot_Sample"]=adata.obs["samples"].astype('str')+"_"+adata.obs["cell_types"].astype('str')
  347. val_counts1 = adata.obs['Annot_Sample'].value_counts()
  348. # saving as csv file
  349. val_counts1.to_csv('val_counts_Annot_Sample.csv', sep="\t")

Parse_snRNAseq_analysis.ipynb, under CC-BY-4.0 · at the source

Overview

Authors: Leticia Pérez-Sisqués1,2, Shail U. Bhatt1, Angela Caruso3, Josephine L. Robb4, Alex P. A. Donovan5, Rosemary Bamford4, Alejo Torres-Cano6, Shoshana Spring7, Eleanor Hendy4, Talia E. Gileadi1, Martyna Panasiuk2, Jed Trengove4, Neeru Jindal4, Mohi U. Ahmed1, Mara Sabbioni3, Joyce Taylor-Papadimitriou8, Diana Cash9, Nicholas Clifton4, Jacob Ellegood7,10, Laura C. Andreae2, Jason P. Lerch7,11, Maria Luisa Scattoni3, K. Peter Giese12, Cathy Fernandes13, M. Albert Basson1,2,4
ORCID iDs: Leticia Pérez-Sisqués, Shail U. Bhatt, Josephine L. Robb, Alejo Torres-Cano, Shoshana Spring, Talia E. Gileadi, Martyna Panasiuk, Jed Trengove, Neeru Jindal, Mohi U. Ahmed, Mara Sabbioni, Diana Cash, Nicholas Clifton, Jacob Ellegood, Laura C. Andreae, Maria Luisa Scattoni, Cathy Fernandes, M. Albert Basson
13 affiliations
  1. Centre for Craniofacial and Regenerative Biology, Guy’s Hospital, King’s College London, London SE1 9RT, UK
  2. MRC Centre for Neurodevelopmental Disorders, New Hunt’s House, King’s College London, London SE1 1UL, UK
  3. Research Coordination and Support Service, Istituto Superiore di Sanità, 00161 Rome, Italy
  4. Clinical and Biomedical Sciences, University of Exeter Medical School, Hatherly Laboratories, Prince of Wales Road, Exeter EX4 4PS, UK
  5. MRC Laboratory of Molecular Biology (LMB), Cambridge Biomedical Campus, Francis Crick Avenue, Trumpington, Cambridge CB2 0QH, UK
  6. Centre for Gene Therapy and Regenerative Medicine, King’s College London, London SE1 9RT, UK
  7. Mouse Imaging Centre (MICe), Hospital for Sick Children, Toronto, Ontario M5T 3H7, Canada
  8. Comprehensive Cancer Centre, King’s College London, Great Maze Pond, London SE1 1UL, UK
  9. Biomarker Research And Imaging in Neuroscience (BRAIN) Centre, Department of Neuroimaging, King’s College London, London, SE5 9NU, UK
  10. Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario M4G 1R8, Canada
  11. Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
  12. Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, 5 Cutcombe Rd, London SE5 9RT, UK
  13. Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, 16 De Crespigny Park, London SE5 8AB, UK
Institutions: Guy's Hospital (United Kingdom); King's College London (United Kingdom); MRC Centre for Neurodevelopmental Disorders (United Kingdom); Istituto Superiore di Sanità (Italy); University of Exeter (United Kingdom); MRC Laboratory of Molecular Biology (United Kingdom); Hospital for Sick Children (Canada); Holland Bloorview Kids Rehabilitation Hospital (Canada); Bloorview Research Institute (Canada); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom)
Journal: Science advances, volume 12, issue 21, article eadq6577
Dates: received 24 May 2024; accepted 15 April 2026; published online 20 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.adq6577 · PMID 42160407 · PMCID PMC13189110 · OpenAlex W7161852640
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), autism (population)
Methods: Preprocessing, Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions
MeSH: Autism Spectrum Disorder*, Autistic Disorder*, Jumonji Domain-Containing Histone Demethylases*, Nuclear Proteins*, Receptors, N-Methyl-D-Aspartate*, Repressor Proteins*, Animals, Disease Models, Animal, DNA-Binding Proteins, Histones, Male, Memantine, Mice, Neocortex, Phenotype, Synaptosomes (* major topic)
Journal subjects: Biomedicine and Life Sciences, Developmental Neuroscience, Diseases and Disorders
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: RCUK | Medical Research Council (MRC) (MR/V013173/1, MR/Y008170/1, MR/X010481/1, MR/W017156/1)
Citations: not cited yet (Europe PMC); 115 references in the paper

Abstract

Loss-of-function mutations in genes encoding lysine demethylases specific for trimethylated lysine 4 of histone 3 (H3K4me3) are associated with neurodevelopmental conditions, including autism spectrum disorder (ASD) and intellectual disability (ID). To study the role of KDM5B (lysine demethylase 5B)–mediated H3K4me3 demethylation, we investigated neurodevelopmental phenotypes in mice without KDM5B demethylase activity. These mice exhibited autism-like behaviors and increased brain size. H3K4me3 levels and the expression of neurodevelopmental genes were increased in the developing Kdm5b mutant neocortex. Increased H3K4me3 levels at the promoter and associated expression of the Grin2d gene were associated with increased levels of N-methyl-d-aspartate receptor subunit 2D (NMDAR2D) protein in synaptosomes isolated from the early postnatal Kdm5b-deficient neocortex. Treating mice with the NMDAR antagonist memantine rescued deficits in ultrasonic vocalizations. These findings suggest that increased H3K4me3 levels and associated Grin2d gene up-regulation disrupt brain development and function, leading to socio-communication deficits and identify a potential therapeutic target for neurodevelopmental disorders associated with KDM5B deficiency.

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Zenodo 17466762

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
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Found in: “Data, code, and materials availability:”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: anndata (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), Scanpy (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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At the source: doi.org/10.5281/zenodo.17466762

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Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. RNA-seq, CUT&Tag-seq, and Parse snRNA-seq raw data have been deposited at the Gene Expression Omnibus (GEO) archive (GSE262555, GSE311802, and GSE316882) and will be made freely available upon publication. The snRNA-seq analysis code is available on Zenodo (https://doi.org/10.5281/zenodo.17466762). The Kdm5b mouse line can be provided by J.T.-P. pending scientific review and a completed material transfer agreement. Requests for the mouse line should be submitted to .

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Recorded: type, language, journal, volume, issue, pages, dates, 25 authors, 16 MeSH terms, 1 funder, 115 references.

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Pérez-Sisqués, L., Bhatt, S. U., Caruso, A., Robb, J. L., Donovan, A. P. A., Bamford, R., Torres-Cano, A., Spring, S., Hendy, E., Gileadi, T. E., Panasiuk, M., Trengove, J., Jindal, N., Ahmed, M. U., Sabbioni, M., Taylor-Papadimitriou, J., Cash, D., Clifton, N., Ellegood, J., . . . Basson, M. A. (2026). Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency. Science advances, 12(21), eadq6577. https://doi.org/10.1126/sciadv.adq6577

BibTeX

@article{perezsisques2026autism,
author = {Pérez-Sisqués, Leticia and Bhatt, Shail U. and Caruso, Angela and Robb, Josephine L. and Donovan, Alex P. A. and Bamford, Rosemary and Torres-Cano, Alejo and Spring, Shoshana and Hendy, Eleanor and Gileadi, Talia E. and Panasiuk, Martyna and Trengove, Jed and Jindal, Neeru and Ahmed, Mohi U. and Sabbioni, Mara and Taylor-Papadimitriou, Joyce and Cash, Diana and Clifton, Nicholas and Ellegood, Jacob and Andreae, Laura C. and Lerch, Jason P. and Scattoni, Maria Luisa and Giese, K. Peter and Fernandes, Cathy and Basson, M. Albert},
title = {{Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency}},
journal = {Science advances},
year = {2026},
month = may,
volume = {12},
number = {21},
pages = {eadq6577},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adq6577},
url = {https://doi.org/10.1126/sciadv.adq6577},
pmid = {42160407},
pmcid = {PMC13189110}
}

RIS

TY - JOUR
AU - Pérez-Sisqués, Leticia
AU - Bhatt, Shail U.
AU - Caruso, Angela
AU - Robb, Josephine L.
AU - Donovan, Alex P. A.
AU - Bamford, Rosemary
AU - Torres-Cano, Alejo
AU - Spring, Shoshana
AU - Hendy, Eleanor
AU - Gileadi, Talia E.
AU - Panasiuk, Martyna
AU - Trengove, Jed
AU - Jindal, Neeru
AU - Ahmed, Mohi U.
AU - Sabbioni, Mara
AU - Taylor-Papadimitriou, Joyce
AU - Cash, Diana
AU - Clifton, Nicholas
AU - Ellegood, Jacob
AU - Andreae, Laura C.
AU - Lerch, Jason P.
AU - Scattoni, Maria Luisa
AU - Giese, K. Peter
AU - Fernandes, Cathy
AU - Basson, M. Albert
TI - Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/05/20
VL - 12
IS - 21
SP - eadq6577
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adq6577
UR - https://doi.org/10.1126/sciadv.adq6577
LA - en
ER -

CSL-JSON

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"title": "Autism-like phenotypes and increased NMDAR2D expression in mice with KDM5B histone lysine demethylase deficiency",
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"author": [
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},
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{
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},
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{
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{
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"container-title-short": "Sci Adv",
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"PMID": "42160407",
"PMCID": "PMC13189110",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
20
]
]
}
}

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