OSCR

Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia.

Code ↔ Paper

4 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 4 matches
  1. [1] § MATERIALS AND METHODS › Immunostaining › Image processing and analysis ↔ KXA_V1_Morphomics.ipynb, lines 99–127 · score 0.57 · swc format, Morphology Converter, MATLAB, skeleton, exporting, ims
  2. [2] § MATERIALS AND METHODS › Immunostaining › Image processing and analysis ↔ morphomics/Analysis/reduction.py, lines 80–111 · score 0.55 · death distance, persistence image, barcode, morphOMICs
  3. [3] § MATERIALS AND METHODS › Single-nucleus multiome sequencing › Data analysis ↔ R/functions.R, lines 6–26 · score 0.54 · lme4, full model, ANOVA, fit, interaction, genes
  4. [4] § MATERIALS AND METHODS › Immunostaining › Image processing and analysis ↔ analysis_v1_layer/d_stat_test_vae_pi.ipynb, lines 200–269 · score 0.52 · Kruskal Wallis, Bonferroni, Dunn, VAE

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 463 lines · 13 KB · MIT · 1 match

  1. # %% [markdown]
  2. # # Microglia morphological adaptations in V1 upon anesthetic ketamine exposure using Morphomics
  3. #
  4. #
  5. # Notebook that takes you through Morphomics analysis of V1 microglia morphologies.
  7. # %%
  8. %load_ext autoreload
  9. %autoreload 2
  11. # see if you have nb-black installed
  12. # if not: pip install nb-black
  13. %load_ext lab_black
  15. # %%
  16. # change directory to where Morphomics is located
  17. import os
  19. os.chdir("/media/drive_siegert/RyCu/Projects/Git_codes/morphomics_v2")
  21. # %%
  22. import morphomics
  24. import numpy as np
  25. from scipy.linalg import svd
  26. from scipy.sparse import csr_matrix
  27. import time
  29. import pandas as pd
  31. # these are libraries that you need to install
  32. import tomli # pip install tomli
  33. import umap # conda install -c conda-forge umap-learn
  34. import ipyvolume as ipv # https://ipyvolume.readthedocs.io/en/latest/install.html
  35. import alphashape # pip install alphashape
  37. import matplotlib.pyplot as plt
  38. from matplotlib import cm
  40. %matplotlib inline
  42. # %% [markdown]
  43. # #
  45. # %% [markdown]
  46. # ### Load the parameters file
  47. #
  48. # The parameters file should be a TOML formatted file (https://github.com/toml-lang/toml). </br>
  49. # With the `params_ID`, you can index which set of parameters were used to generate succeeding figures/results.
  51. # %%
  52. parameters_folder = "/media/drive_siegert/RyCu/Projects/TMD/_Parameters"
  53. params_ID = 925
  55. save_filename = "%s/Morphomics.parameters%d.toml" % (parameters_folder, params_ID)
  56. with open(save_filename, mode="rb") as _parameter_file:
  57. parameters = tomli.load(_parameter_file)
  59. # %%
  60. input_filename = "%s/Morphomics.%s.InfoFrame.BarcodeFilter%s" % (
  61. parameters["Input"]["save_folder"],
  62. parameters["Tissue"],
  63. "".join([x.title() for x in parameters["Input"]["barcode_filter"].split("_")]),
  64. )
  66. infoframe_filename = (
  67. "%s/Morphomics.%s.InfoFrame.Parameter%d.BarcodeFilter%s.Cleaned"
  68. % (
  69. parameters["Input"]["save_folder"],
  70. parameters["Tissue"],
  71. params_ID,
  72. "".join([x.title() for x in parameters["Input"]["barcode_filter"].split("_")]),
  73. )
  74. )
  76. bootstrap_filename = "%s/Morphomics.%s.Parameter%d.Conds-%s.BarCutoff%d.Bootstrap%d" % (
  77. parameters["Bootstrapping"]["results_folder"],
  78. parameters["Tissue"],
  79. params_ID,
  80. "-".join(parameters["Bootstrapping"]["bootstrap_conditions"]),
  81. parameters["Bootstrapping"]["barcodesize_cutoff"],
  82. parameters["Bootstrapping"]["N_pop"],
  83. )
  85. images_filename = (
  86. "%s/Morphomics.%s.Parameter%d.Conds-%s.BarCutoff%d.Bootstrap%d.KernelWidth%s.NormMethod%s"
  87. % (
  88. parameters["Bootstrapping"]["results_folder"],
  89. parameters["Tissue"],
  90. params_ID,
  91. "-".join(parameters["Bootstrapping"]["bootstrap_conditions"]),
  92. parameters["Bootstrapping"]["barcodesize_cutoff"],
  93. parameters["Bootstrapping"]["N_pop"],
  94. str(parameters["Persistence_Images"]["bw_method"]).replace(".", "p"),
  95. parameters["Persistence_Images"]["norm_method"],
  96. )
  97. )
  99. # %% [markdown]
  100. # #
  102. # %% [markdown]
  103. # ### Loading the dataset
  104. #
  105. # `Morphomics` takes an input 3D reconstructions of microglia in .swc format.
  106. #
  107. # First, you need convert .ims images to .swc files. To do this, there are two routes: </br>
  108. # 1.) `MATLAB Imaris converter` </br>
  109. # This requires MATLAB installation and the NLMorphologyConverter (http://neuronland.net/NLMorphologyConverter/NLMorphologyConverter.html) </br>
  110. # Access the MATLAB scripts in `/media/drive_siegert/RyCu/Projects/TMD/_Codes/_Codes_from_collaborators/Christoph/Imaris` </br>
  111. # The script `folder_processing.m` will convert .ims files to corrected.swc files </br>
  112. # </br>
  113. #
  114. # 2.) `Python Extension in Imaris` </br>
  115. # In the ISTA workstations, you should be able to export your skeletons directly to .swc files.
  117. # %%
  118. from morphomics.io import io
  120. info_frame = io.load_data(
  121. folder_location=parameters["Input"]["folder_location"],
  122. extension=parameters["Input"]["extension"],
  123. barcode_filter=parameters["Input"]["barcode_filter"],
  124. save_filename=input_filename,
  125. conditions=parameters["Input"]["conditions"],
  126. separated_by=parameters["Input"]["separated_by"],
  127. )
  129. # %% [markdown]
  130. # #
  132. # %% [markdown]
  133. # ### Clean up the input files
  135. # %%
  136. load = True
  138. if load:
  139. info_frame = morphomics.utils.load_obj(
  140. input_filename,
  141. )
  143. # %%
  144. # filter unprocessed files
  145. info_frame = info_frame.loc[~info_frame.Barcodes.isna()].reset_index(drop=True)
  147. # filter for barcode size
  148. barlength_cutoff = parameters["Clean_input"]["barlength_cutoff"]
  149. info_frame["Barcode_length"] = info_frame.Barcodes.apply(lambda x: len(x))
  150. info_frame = info_frame.query("Barcode_length >= @barlength_cutoff").reset_index(
  151. drop=True
  152. )
  154. # filter for regions
  155. parameters["Clean_input"]["excluded_regions"] = ["All_Layers"]
  156. for _excluded in parameters["Clean_input"]["excluded_regions"]:
  157. info_frame = info_frame.query("Region != @_excluded").reset_index(drop=True)
  159. # %%
  160. save = True
  162. if save:
  163. morphomics.utils.save_obj(
  164. info_frame,
  165. infoframe_filename,
  166. )
  168. else:
  169. print("Nothing to do...")
  171. # %% [markdown]
  172. # #
  174. # %% [markdown]
  175. # ### Bootstrap and create subsamples
  177. # %%
  178. load = True
  180. if load:
  181. info_frame = morphomics.utils.load_obj(
  182. infoframe_filename,
  183. )
  184. else:
  185. print("Nothing to do here...")
  187. # %%
  188. bootstrapped_frame = (
  189. morphomics.Analysis.bootstrapping.get_subsampled_population_from_infoframe(
  190. info_frame,
  191. bootstrap_conditions=parameters["Bootstrapping"]["bootstrap_conditions"],
  192. bootstrap_resolution=parameters["Bootstrapping"]["bootstrap_resolution"],
  193. N_pop=parameters["Bootstrapping"]["N_pop"],
  194. N_samples=parameters["Bootstrapping"]["N_samples"],
  195. rand_seed=parameters["Bootstrapping"]["rand_seed"],
  196. save_filename=bootstrap_filename,
  197. )
  198. )
  200. # %%
  201. bootstrap_info = (
  202. bootstrapped_frame[parameters["Bootstrapping"]["bootstrap_resolution"]]
  203. .reset_index(drop=True)
  204. .astype("category")
  205. )
  206. print(bootstrap_info)
  208. # %%
  209. morphomics.utils.save_obj(
  210. bootstrap_info,
  211. "%s.BootstrapInfo" % (bootstrap_filename),
  212. )
  214. # %% [markdown]
  215. # #
  217. # %% [markdown]
  218. # ### Calculate persistence images
  220. # %%
  221. X_mat = morphomics.Analysis.reduction.get_images_array_from_infoframe(
  222. bootstrapped_frame,
  223. xlims=parameters["Persistence_Images"]["xlims"],
  224. ylims=parameters["Persistence_Images"]["ylims"],
  225. bw_method=parameters["Persistence_Images"]["bw_method"],
  226. norm_method=parameters["Persistence_Images"]["norm_method"],
  227. barcode_size_cutoff=parameters["Persistence_Images"]["barcodesize_cutoff"],
  228. save_filename=images_filename, # save the persistence images
  229. )
  231. # %%
  232. # I want to do this to free up some RAM
  233. # DataFrame demands much more memory, unfortunately
  234. del bootstrapped_frame
  236. # %% [markdown]
  237. # #
  239. # %% [markdown]
  240. # ### Dimensionality reduction using UMAP
  242. # %%
  243. load = True
  245. if load:
  246. X_mat = morphomics.utils.load_obj(
  247. "%s.%s" % (images_filename, parameters["Persistence_Images"]["object_name"])
  248. )
  249. print(X_mat.shape)
  250. else:
  251. print("Nothing to do here...")
  253. # %%
  254. filter_pixels = parameters["Persistence_Images"]["filter_pixels"]
  256. # filter X_mat, throwing out pixels that have negligible variations
  257. # uncomment the next lines to run this analysis
  259. if filter_pixels:
  260. _tokeep = np.where(
  261. np.std(X_mat, axis=0) >= parameters["Persistence_Images"]["pixel_std_cutoff"]
  262. )[0]
  263. X_mat = np.array([np.array(X_mat[_i][_tokeep]) for _i in np.arange(len(X_mat))])
  265. print(len(_tokeep), X_mat.shape)
  267. morphomics.utils.save_obj(
  268. X_mat,
  269. "%s.%s.Filtered"
  270. % (images_filename, parameters["Persistence_Images"]["object_name"]),
  271. )
  273. morphomics.utils.save_obj(
  274. _tokeep,
  275. "%s.%s.FilteredIndex"
  276. % (images_filename, parameters["Persistence_Images"]["object_name"]),
  277. )
  279. else:
  280. print("Nothing to do here...")
  282. # %%
  283. parameters["UMAP_parameters"]["n_components"] = 2
  285. F_umap = umap.UMAP(
  286. n_neighbors=parameters["UMAP_parameters"]["n_neighbors"],
  287. min_dist=parameters["UMAP_parameters"]["min_dist"],
  288. spread=parameters["UMAP_parameters"]["spread"],
  289. random_state=parameters["UMAP_parameters"]["random_state"],
  290. n_components=parameters["UMAP_parameters"]["n_components"],
  291. metric=parameters["UMAP_parameters"]["metric"],
  292. densmap=bool(parameters["UMAP_parameters"]["densmap"]),
  293. )
  295. X_umap = F_umap.fit_transform(X_mat)
  297. morphomics.utils.save_obj(
  298. F_umap,
  299. "%s.%s.UMAP.%dD"
  300. % (
  301. images_filename,
  302. parameters["Persistence_Images"]["object_name"],
  303. parameters["UMAP_parameters"]["n_components"],
  304. ),
  305. )
  306. morphomics.utils.save_obj(
  307. X_umap,
  308. "%s.%s.UMAP.%dD.Coordinates"
  309. % (
  310. images_filename,
  311. parameters["Persistence_Images"]["object_name"],
  312. parameters["UMAP_parameters"]["n_components"],
  313. ),
  314. )
  316. # %% [markdown]
  317. # #
  319. # %% [markdown]
  320. # ### Plotting UMAP representation
  322. # %%
  323. load = True
  325. if load:
  326. bootstrap_info = morphomics.utils.save_obj(
  327. "%s.BootstrapInfo" % (bootstrap_filename),
  328. )
  330. X_umap = morphomics.utils.load_obj(
  331. "%s.%s.UMAP.%dD.Coordinates"
  332. % (
  333. images_filename,
  334. parameters["Persistence_Images"]["object_name"],
  335. parameters["UMAP_parameters"]["n_components"],
  336. ),
  337. )
  339. else:
  340. print("Nothing to do here...")
  342. # %%
  343. from scipy.spatial.distance import pdist, squareform
  344. from scipy.sparse import csr_matrix
  345. from scipy.sparse.csgraph import connected_components
  346. from scipy.spatial import ConvexHull, convex_hull_plot_2d
  348. # do this for 2D plots
  349. color = parameters["colormaps"]
  350. regions = ["L1", "L2-3", "L4", "L5-6"]
  352. markers = {}
  353. markers["KXA"] = "o"
  354. markers["Saline"] = "o"
  355. markers["KXA+SAFIT2"] = "$\u2295$"
  356. markers["Saline+SAFIT2"] = "$\u2295$"
  359. linestyle = {}
  360. linestyle["KXA"] = (0, (3, 3))
  361. linestyle["Saline"] = (0, (3, 3))
  362. linestyle["KXA+SAFIT2"] = (0, (1, 1))
  363. linestyle["Saline+SAFIT2"] = (0, (1, 1))
  366. fig, ax = plt.subplots(1, 2, dpi=200)
  367. fig.set_size_inches(25, 10)
  369. sex = ["M", "F"]
  371. xmax, xmin = np.amax(X_umap[:, 0]), np.amin(X_umap[:, 0])
  372. ymax, ymin = np.amax(X_umap[:, 1]), np.amin(X_umap[:, 1])
  374. for i in [0, 1]:
  375. ax[i].scatter(X_umap[:, 0], X_umap[:, 1], s=10, c="whitesmoke", alpha=0.7)
  377. for _region in regions:
  378. for _conds in bootstrap_info.Model.unique():
  379. _inds = np.where(
  380. (bootstrap_info["Region"] == _region)
  381. * (bootstrap_info["Model"] == _conds)
  382. * (bootstrap_info["Sex"] == sex[i])
  383. )[0]
  385. if "SAFIT" in _conds:
  386. edgecolor = color[_region][_conds]
  387. lw = 0.1
  388. else:
  389. edgecolor = "k"
  390. lw = 0.3
  392. ax[i].scatter(
  393. X_umap[:, 0][_inds],
  394. X_umap[:, 1][_inds],
  395. s=30,
  396. c=color[_region][_conds],
  397. marker=markers[_conds],
  398. alpha=1.0,
  399. lw=lw,
  400. edgecolor=edgecolor,
  401. label="%s: %s (%s)" % (_region, _conds, sex[i]),
  402. zorder=10,
  403. rasterized=True,
  404. )
  406. _indsx = np.where(
  407. (bootstrap_info["Region"] == _region)
  408. * (bootstrap_info["Model"] == _conds)
  409. * (bootstrap_info["Sex"] != sex[i])
  410. )[0]
  412. if len(_indsx) > 1:
  413. distances = pdist(X_umap[_indsx])
  414. distances = squareform(distances)
  415. graph = (distances <= 0.4).astype("int")
  416. graph = csr_matrix(graph)
  417. n_components, labels = connected_components(
  418. csgraph=graph, directed=False, return_labels=True
  419. )
  421. largest_component = np.argmax(
  422. [len(np.where(labels == ii)[0]) for ii in np.unique(labels)]
  423. )
  424. _indsx = _indsx[np.where(labels == largest_component)[0]]
  425. hull = ConvexHull(X_umap[_indsx])
  427. for simplex in hull.simplices:
  428. ax[i].plot(
  429. X_umap[_indsx][simplex, 0],
  430. X_umap[_indsx][simplex, 1],
  431. color=color[_region][_conds],
  432. ls=linestyle[_conds],
  433. lw=2,
  434. zorder=2,
  435. rasterized=True,
  436. )
  438. ax[i].legend(loc="upper right", fontsize=8)
  440. ax[i].set_xlabel("UMAP 1", fontsize=14)
  441. ax[i].set_ylabel("UMAP 2", fontsize=14)
  442. ax[i].set_xlim(left=xmin * (1.1), right=xmax * (1.1))
  443. ax[i].set_ylim(bottom=ymin * (1.1), top=ymax * (1.1))
  444. ax[i].xaxis.set_ticklabels([])
  445. ax[i].yaxis.set_ticklabels([])
  447. save_filename = "%s.%s.UMAP.2D.Region-%s.Conds-%s" % (
  448. images_filename,
  449. parameters["Persistence_Images"]["object_name"],
  450. "-".join(regions),
  451. "-".join(bootstrap_info.Model.unique()),
  452. )
  454. fig.savefig(
  455. "%s.pdf" % save_filename,
  456. bbox_inches="tight",
  457. dpi=300,
  458. )
  460. plt.show()
  462. # %%

KXA_V1_Morphomics.ipynb at commit 05d79d8, under MIT · at the source

Overview

Authors: Alessandro Venturino1, MohammadAmin Alamalhoda1, Thomas Negrello1, Kelly Jin2, Cindy T J van Velthoven2, Ryan John A Cubero1, Jake Yeung1, Peter Koppensteiner1, Bosiljka Tasic2, Sandra Siegert1
ORCID iDs: Alessandro Venturino, MohammadAmin Alamalhoda, Thomas Negrello, Kelly Jin, Cindy T J van Velthoven, Ryan John A Cubero, Jake Yeung, Peter Koppensteiner, Bosiljka Tasic, Sandra Siegert
  1. Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
  2. Allen Institute, Brain Science, 615 Westlake Ave. N, Seattle, WA 90109, USA
Institutions: Institute of Science and Technology Austria (Austria); Allen Institute (United States); Allen Institute for Brain Science (United States)
Journal: Science advances, volume 12, issue 31, article eadz6517
Dates: received 9 June 2025; accepted 26 June 2026; published online 31 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.adz6517 · PMID 42536755 · PMCID PMC13426458 · OpenAlex W4415352889
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.19.683299
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Machine learning
MeSH: Corticosterone*, Ketamine*, Microglia*, Neuronal Plasticity*, Sex Characteristics*, Anesthesia, Animals, Female, Male, Mice, Neurons, Tacrolimus Binding Proteins (* major topic)
Topic: Cancer, Stress, Anesthesia, and Immune Response (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Allen Institute for Brain Science; Institute of Science and Technology Austria
Citations: cited by 1 paper (Europe PMC); 114 references in the paper

Abstract

Anesthesia recovery is critical for resuming normal physiological and neuronal functions; however, the mechanisms involved remain elusive. Here, we identify a female-selective corticosterone-mediated microglia-neuron interaction during ketamine anesthesia recovery, absent in males. This microglia-neuron interaction induces plastic and functional neuronal changes, as evidenced by increased mEPSC frequency, which was occluded upon microglia depletion. We showed that this process is driven through up-regulation of the stress-responsive co-chaperone Fkbp5 mRNA and its protein, Fkbp51, in female microglia. Fkbp5/Fkbp51 is a key intermediary in a corticosteroid-induced stress response, and its involvement points toward a critical interface between endocrine signaling and microglia. To counteract the observed ketamine anesthesia-mediated increase in blood corticosterone during recovery, we removed the primary source of corticosterone by adrenalectomy. Close microglia-neuron interaction was reduced and increased again following corticosterone injection. Our findings identify a sex-specific microglia-mediated mechanism of neuronal plasticity during anesthesia recovery, driven by corticosterone, thereby enhancing our understanding of sex differences in brain function.

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 4 matches between paragraphs and lines of code.

siegert-lab/V1_morphOMICs

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 05d79d8c9e7d166f072455bbd3c2db38bbce2901, 8 May 2023
Languages: Jupyter (1)
Size: 5 files, 1 script
Software Heritage: not archived
Found in: the text, “Image processing and analysis”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/siegert-lab/V1_morphOMICs

Zenodo 19057068

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (21 files), pandas (15 files), Matplotlib (9 files), Plotly (9 files), PyTorch (8 files), SciPy (6 files), scikit-learn (2 files), scikit-posthocs (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
38 files
At the source: doi.org/10.5281/zenodo.19057068

Zenodo 19019013

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (16 files), SciPy (8 files), scikit-learn (2 files), anndata (1 file), Matplotlib (1 file), pandas (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
28 files
At the source: dx.doi.org/10.5281/zenodo.19019013

Zenodo 19071467

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials 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: dx.doi.org/10.5281/zenodo.19071467

rcubero/morphomics

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cabeb1ef45d48399fe2329569cb303e414730e7a, 14 March 2026
Languages: Python (26)
Size: 33 files, 26 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (setup.py)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (16 files), SciPy (8 files), scikit-learn (2 files), anndata (1 file), Matplotlib (1 file), pandas (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/rcubero/MorphOMICs/tree/v1.0.0

thomasngl/morphomics_venturino_2025

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f95e5bf9bc4cb331eb421cd864e1f19cbc3c3102, 16 March 2026
Languages: Python (22), Jupyter (15)
Size: 49 files, 37 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, CITATION.cff, environment (setup.py), 15 notebooks
Not found: license file, tests, continuous integration, documentation
Tools: NumPy (21 files), pandas (15 files), Matplotlib (9 files), Plotly (9 files), PyTorch (8 files), SciPy (6 files), scikit-learn (2 files), scikit-posthocs (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
38 files
At the source: github.com/ThomasNgl/morphOMICs_Venturino_2025/tree/morphOMICs_Venturino_2025_release_tag

jakeyeung/gliamultiomics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 14cd56ec976533587d9bc141487e75e26a6efb57, 17 December 2023
Languages: R (4)
Size: 17 files, 4 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (DESCRIPTION)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: data.table (3 files), ggplot2 (3 files), tidyverse (3 files), lme4 (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/jakeyeung/GliaMultiomics/tree/1.0

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:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 131 scripts, each with its path and the digest of its content;
  • 4 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, 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. Transcriptional data can be accessed at the NCBI GEO accession GSE298669 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE298669). The code for the morphological analysis is available under DOI: https://doi.org/10.5281/zenodo.19057068. The codes for the single-nucleus analysis are accessible under DOI: 10.5281/zenodo.19019013 (http://dx.doi.org/10.5281/zenodo.19019013) and DOI: 10.5281/zenodo.19071467 (http://dx.doi.org/10.5281/zenodo.19071467).

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, issue, pages, dates, 10 authors, 12 MeSH terms, 2 funders, 107 references.

Cite

This paper

Venturino, A., Alamalhoda, M., Negrello, T., Jin, K., van Velthoven, C. T. J., Cubero, R. J. A., Yeung, J., Koppensteiner, P., Tasic, B., & Siegert, S. (2026). Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia. Science advances, 12(31), eadz6517. https://doi.org/10.1126/sciadv.adz6517

BibTeX

@article{venturino2026corticosterone,
author = {Venturino, Alessandro and Alamalhoda, MohammadAmin and Negrello, Thomas and Jin, Kelly and van Velthoven, Cindy T J and Cubero, Ryan John A and Yeung, Jake and Koppensteiner, Peter and Tasic, Bosiljka and Siegert, Sandra},
title = {{Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia}},
journal = {Science advances},
year = {2026},
month = jul,
volume = {12},
number = {31},
pages = {eadz6517},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adz6517},
url = {https://doi.org/10.1126/sciadv.adz6517},
pmid = {42536755},
pmcid = {PMC13426458}
}

RIS

TY - JOUR
AU - Venturino, Alessandro
AU - Alamalhoda, MohammadAmin
AU - Negrello, Thomas
AU - Jin, Kelly
AU - van Velthoven, Cindy T J
AU - Cubero, Ryan John A
AU - Yeung, Jake
AU - Koppensteiner, Peter
AU - Tasic, Bosiljka
AU - Siegert, Sandra
TI - Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/07/31
VL - 12
IS - 31
SP - eadz6517
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adz6517
UR - https://doi.org/10.1126/sciadv.adz6517
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.adz6517",
"type": "article-journal",
"title": "Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia",
"container-title": "Science advances",
"author": [
{
"family": "Venturino",
"given": "Alessandro"
},
{
"family": "Alamalhoda",
"given": "MohammadAmin"
},
{
"family": "Negrello",
"given": "Thomas"
},
{
"family": "Jin",
"given": "Kelly"
},
{
"family": "van Velthoven",
"given": "Cindy T J"
},
{
"family": "Cubero",
"given": "Ryan John A"
},
{
"family": "Yeung",
"given": "Jake"
},
{
"family": "Koppensteiner",
"given": "Peter"
},
{
"family": "Tasic",
"given": "Bosiljka"
},
{
"family": "Siegert",
"given": "Sandra"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "31",
"page": "eadz6517",
"DOI": "10.1126/sciadv.adz6517",
"PMID": "42536755",
"PMCID": "PMC13426458",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.adz6517",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
31
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41514-026-00391-9 [code]
Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level.
Journal: npj aging
In common: anndata, Plotly, data.table, 10 other tools, cellular / molecular, 2 references
[2] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: UMAP, anndata, Plotly, 10 other tools, cellular / molecular, 1 reference
[3] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: UMAP, Plotly, lme4, 11 other tools, mouse, cellular / molecular
[4] doi:10.1016/j.isci.2026.116055 [code]
Mapping the transcriptional diversity of calcium signaling in the mouse and human brain.
Journal: iScience
In common: UMAP, anndata, Plotly, 11 other tools, mouse
[5] doi:10.1186/s13059-026-04177-w [code]
Genomic sequence evolution underlying human neocortical interareal diversification.
Journal: Genome biology
In common: UMAP, anndata, Plotly, 9 other tools, mouse, cellular / molecular
[6] doi:10.1038/s44318-026-00818-9 [code]
FAM134B-mediated ER-phagy degrades APP and suppresses Alzheimer's disease pathology.
Journal: The EMBO journal
In common: UMAP, anndata, Plotly, 9 other tools, mouse, cellular / molecular
[7] doi:10.1093/nar/gkag706 [code]
scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.
Journal: Nucleic acids research
In common: UMAP, anndata, statsmodels, 8 other tools, 2 references
[8] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: UMAP, anndata, lme4, 9 other tools, mouse, cellular / molecular
[9] doi:10.1371/journal.pcbi.1014327 [code]
Supervised deep learning with gene functional annotation for cell classification.
Journal: PLoS computational biology
In common: anndata, data.table, statsmodels, 9 other tools, cellular / molecular, 1 reference
[10] doi:10.1016/j.xcrm.2026.102651 [code]
Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.
Journal: Cell reports. Medicine
In common: UMAP, anndata, Plotly, 9 other tools, cellular / molecular

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.