OSCR

Cellular and subcellular heterogeneity of astrocytic Na⁺ homeostasis tuning astrocytes into functionally distinct subgroups in the mouse brain.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

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

Python · 271 lines · 13 KB · MIT

  1. import numpy as np
  2. import time
  3. from utils import *
  4. from numba import njit
  5. from numba.typed import Dict
  6. from numba.core import types
  7. from pathlib import Path
  9. class Astrocyte:
  10. def __init__(self, dt, N, Ra, Cm1, astrocyte_data, stim_start_nt, stim_end_nt, glut_stim, pot_stim, stim_comp_glut, stim_comp_pot, stim_dur=0.0, n_stim=0, n_stimulated=0):
  11. self.dt = dt
  12. self.N = N
  13. self.Ra = Ra # intracellular resistivity
  14. self.Cm1 = Cm1
  15. self.stim_start_nt = stim_start_nt
  16. self.stim_end_nt = stim_end_nt
  17. self.stim_dur = stim_dur
  18. self.stim_comp_glut = stim_comp_glut
  19. self.stim_comp_pot = stim_comp_pot
  20. self.glut_stim = glut_stim
  21. self.pot_stim = pot_stim
  22. self.n_stim = n_stim
  23. self.n_stimulated = n_stimulated
  25. # Load astrocyte data
  26. self.astrocyte = np.loadtxt(astrocyte_data)
  27. self.parents = self.astrocyte[:, 6].astype(int)
  28. self.parents[0] = 0 # Root of tree
  29. self.xaxis, self.yaxis, self.zaxis, self.radius, self.identifier = self.astrocyte[:, 2], self.astrocyte[:, 3], self.astrocyte[:, 4], self.astrocyte[:, 5], self.astrocyte[:, 1]
  30. self.ids = self.astrocyte[:, 0].astype(int)
  32. # Initialize variables
  33. self.init_variables()
  36. def init_variables(self):
  37. #self.tau1, self.tau2, self.tau3, self.tau4, self.tau5 = 30.0, 35.0, 40.0, 45.0, 50.0
  38. # Initialize geometry-related variables
  39. self.distance_from_soma = np.zeros(self.N)
  40. self.length = np.zeros(self.N)
  41. self.area = np.zeros(self.N)
  42. self.area_cross = np.zeros(self.N)
  43. self.volume = np.zeros(self.N)
  44. self.rho = np.zeros(self.N)
  45. self.Rm = np.zeros(self.N)
  46. self.Cm = np.zeros(self.N)
  47. self.g_l = np.zeros(self.N)
  49. self.DiffK = np.zeros(self.N)
  50. self.DiffNa = np.zeros(self.N)
  51. self.DiffCa = np.zeros(self.N)
  52. self.gamma = np.zeros(self.N)
  53. self.rho2 = np.zeros(self.N)
  55. # Calculate geometric properties and initialize electrical parameters
  56. for i in range(self.N):
  57. self.calculate_geo_res(i)
  59. self.distance_from_soma[:] = 0.0
  60. for i in range(self.N):
  61. j = self.parents[i] # parent ID
  62. if j > 0: # parent row index
  63. self.distance_from_soma[i] = self.distance_from_soma[j-1] + self.length[i]
  64. else:
  65. # roots / soma samples start at 0
  66. self.distance_from_soma[i] = 0.0
  68. for i in range(self.N):
  69. self.calculate_gamma(i)
  71. self.branch_to_comps , self.branch_max_dict = self.compute_branch_groups()
  72. self.nb_branch_to_comps, self.nb_branch_max = self.convert_to_numba_dict()
  74. def compute_branch_groups(self):
  75. # """
  76. # Returns:
  77. # - branch_to_indices: {"branch_1": [row_idx0, row_idx1, ...], ...}
  78. # - branch_to_ids : {"branch_1": [id0, id1, ...], ...} (SWC IDs)
  79. # - branch_heads_id : {"branch_1": head_id, ...} (SWC ID of head)
  80. # - branch_max_dict : {"branch_1": max_distance, ...}
  81. # - branch_label : np.ndarray (N,) with the head row-index label for each node (-1 for soma/root)
  82. # Notes:
  83. # * A "primary branch" is the subtree whose root is any node whose parent <= 0.
  84. # * Uses parent-row = parent_id - 1 (j-1).
  85. # """
  87. # 1) Label each node by its primary branch head (child of soma)
  88. labels = np.full(self.N, -1, dtype=int)
  89. for i in range(self.N):
  90. if self.parents[i] <= 0:
  91. continue # soma/root nodes are not inside any branch
  92. k = i
  93. while self.parents[k] > 0 and self.parents[self.parents[k] - 1] > 0:
  94. k = self.parents[k] - 1
  95. labels[i] = k # k is the row index of the branch head
  97. # 2) Keep heads in order of first appearance (stable branch_1, branch_2, ...)
  98. heads, seen = [], set()
  99. for k in labels:
  100. if k >= 0 and k not in seen:
  101. heads.append(k); seen.add(k)
  103. # 3) Build groups and compute max per branch (include the head itself)
  104. branch_to_indices = {}
  105. branch_to_comps = {}
  106. branch_heads_id = {}
  107. branch_max_dict = {}
  109. for idx, h in enumerate(heads, start=1):
  110. name = f"branch_{idx}"
  111. mask = (labels == h) | (np.arange(self.N) == h) # include head
  112. idxs = np.nonzero(mask)[0] # all row indices in this branch
  113. branch_to_indices[name] = idxs.tolist()
  114. branch_to_comps [name] = [int(self.ids[j])-1 for j in idxs]
  115. branch_heads_id[name] = int(self.ids[h])
  116. branch_max_dict[name] = float(np.max(self.distance_from_soma[idxs]))
  118. return branch_to_comps, branch_max_dict
  120. # Convert to Numba typed dictionary
  121. def convert_to_numba_dict(self):
  122. # Create typed dicts
  123. nb_branch_to_comps = Dict.empty(
  124. key_type=types.int64,
  125. value_type=types.int64[:]
  126. )
  127. nb_branch_max = Dict.empty(
  128. key_type=types.int64,
  129. value_type=types.float64
  130. )
  132. # Map branch names to integers and fill dicts
  133. for idx, (bname, comps) in enumerate(self.branch_to_comps.items()):
  134. nb_branch_to_comps[idx] = np.array(comps, dtype=np.int64)
  135. nb_branch_max[idx] = self.branch_max_dict[bname]
  137. return nb_branch_to_comps, nb_branch_max
  139. def calculate_geo_res(self, i):
  141. # self.distance_from_soma[i] = np.sqrt((self.xaxis[i] - self.xaxis[0])**2 + (self.yaxis[i] - self.yaxis[0])**2 + (self.zaxis[i] - self.zaxis[0])**2)
  142. # self.distance_from_soma, _, _ = self.cumulative_distance_from_soma(self.ids, self.parents, self.x, self.y, self.z)
  143. j = self.parents[i]
  144. if i == 0:
  145. self.length[i] = self.radius[i]
  146. self.area[i] = 4.0 * np.pi * self.radius[i]**2
  147. self.volume[i] = 4.0/3.0 * np.pi * self.radius[i]**3
  148. else:
  149. self.length[i] = np.sqrt((self.xaxis[i] - self.xaxis[j-1])**2 + (self.yaxis[i] - self.yaxis[j-1])**2 + (self.zaxis[i] - self.zaxis[j-1])**2)
  150. self.area[i] = 2.0 * np.pi * self.radius[i] * self.length[i]
  151. self.volume[i] = np.pi * self.radius[i]**2 * self.length[i]
  153. self.area_cross[i] = np.pi * self.radius[i]**2
  154. self.rho[i] = self.Ra * 1.0e4 * self.length[i] / (2.0 * np.pi * self.radius[i]**2) #ohms
  155. if self.identifier[i] not in [1, 2]:
  156. self.Rm[i] = 50000.0
  157. self.Cm[i] = self.Cm1 * 2.0
  158. else:
  159. self.Rm[i] = 50000.0
  160. self.Cm[i] = self.Cm1
  161. self.g_l[i] = 1000.0 / self.Rm[i]
  163. def calculate_gamma(self, i):
  164. for i in range(self.N):
  165. j = self.parents[i]
  166. if i == 0:
  167. if self.N == 1:
  168. self.rho2[i] = 0
  169. self.gamma[i] = 0
  170. else:
  171. self.rho2[i] = 1.0 / (self.rho[i] + self.rho[i+1]) # 1/ohms i+1 last
  172. self.gamma[i] = 1.0e11 * self.rho2[i] / (2.0 * np.pi * self.radius[i+1] * self.length[i+1])* 1.0e-5 #mS/cm^2 i+1 both
  174. else:
  175. self.rho2[i] = 1.0 / (self.rho[i] + self.rho[j-1])
  176. self.gamma[i] = 1.0e11 * self.rho2[i] / (2.0 * np.pi * self.radius[i] * self.length[i]) * 1.0e-5 #mS/cm^2
  178. self.DiffK[i] = 250.0 * self.area_cross[i]/ (self.length[i] * self.volume[i]) # /s. 250 is the diffusion coefficent in um^2/s
  179. self.DiffNa[i] = 600.0 * self.area_cross[i]/ (self.length[i] * self.volume[i]) # /s #600
  180. # self.DiffCa[i] = 5.0 * self.area_cross[i]/ (self.length[i] * self.volume[i])
  183. def astrocyte_dynamics_diffusion(self, iskip, last):
  184. v, Kos, Naks, Clks, Kks, Nass, time_array, JNaKks, phis, phos, Icoups, IdiffKs, IdiffNas, IdiffCas, Caks, JNaks, JTRPVks, Iks, JNBCks, JNHEs, JNKCC1ks, JNCXs, JNaKks_alpha2beta1, JNaKks_alpha2beta2, JGluTs = update_astrocyte_dynamics(self.dt, self.identifier, self.gamma, self.parents, self.DiffNa, self.DiffK, self.DiffCa, self.g_l, self.distance_from_soma, self.radius, self.area, self.area_cross, self.volume, self.Cm, self.stim_start_nt, self.stim_end_nt, self.stim_dur, self.glut_stim, self.pot_stim, self.stim_comp_glut, self.stim_comp_pot, self.n_stim, self.n_stimulated, self.N, last, iskip, self.nb_branch_max, self.nb_branch_to_comps)
  185. return v, Kos, Naks, Clks, Kks, Nass, time_array, JNaKks, phis, phos, Icoups, IdiffKs, IdiffNas, IdiffCas, Caks, JNaks, JTRPVks, Iks, JNBCks, JNHEs, JNKCC1ks, JNCXs, JNaKks_alpha2beta1, JNaKks_alpha2beta2, JGluTs
  188. if __name__ == "__main__":
  189. start_time = time.time()
  190. select_cell = "Astrocyte"
  192. if select_cell == "Astrocyte":
  193. astrocyte_data_file = Path("NMO_282188.txt") # edit path. SHould be good as long as the file is in the same dir as this script
  194. if not astrocyte_data_file.is_file():
  195. raise FileNotFoundError(
  196. f"[ERROR] File not found: {astrocyte_data_file.resolve()}\n"
  197. "Please make sure a valid astrocytic morphology .txt file path is provided."
  198. )
  199. else:
  200. df = np.loadtxt(astrocyte_data_file)
  201. xaxis, yaxis, zaxis, parents, identifier = df[:, 2], df[:, 3], df[:, 4] , df[:,6].astype(int), df[:, 1]
  202. parents[0] = 0
  204. ids = df[:, 0].astype(int)
  206. # Uncomment for parent-child visualization purpose
  207. # # Build segments (parent -> child, then None to break)
  208. # id2idx = {nid: i for i, nid in enumerate(ids)}
  209. # Xs, Ys, Zs = [], [], []
  210. # for i, pid in enumerate(parents):
  211. # if pid > 0 and pid in id2idx:
  212. # p = id2idx[pid]
  213. # Xs += [xaxis[p], xaxis[i], None]
  214. # Ys += [yaxis[p], yaxis[i], None]
  215. # Zs += [zaxis[p], zaxis[i], None]
  217. data_size = df.shape[0]
  219. N = 3 # number of compartment to simulate. 1 if modeling a single cell with whole cell attributes else "data_size" for all compartments
  220. N_1 = 0
  221. N_2 = 3
  223. # save mutiple csv's for distance and branches
  224. astrocyte_init = Astrocyte(dt=0.00000010, N=N, Ra=400.0, Cm1=1.0, astrocyte_data=astrocyte_data_file, stim_start_nt=None, stim_end_nt=None, glut_stim=None, pot_stim=None, stim_comp_glut=None, stim_comp_pot=None)
  226. df_dist_soma = pd.DataFrame({
  227. 'Compartment_Index': range(len(astrocyte_init.distance_from_soma)),
  228. 'Distance_from_Soma_um': astrocyte_init.distance_from_soma
  229. })
  230. df_dist_soma.to_csv("distance_from_soma.csv", index=False)
  232. df_branch_to_comps = pd.DataFrame(list(astrocyte_init.branch_to_comps.items()), columns=['branch', 'compartments'])
  233. df_branch_to_comps.to_csv("branch_to_comps.csv", index=False)
  235. df_branch_max_dict = pd.DataFrame.from_dict(astrocyte_init.branch_max_dict, orient='index', columns=['max_value'])
  236. df_branch_max_dict.to_csv("branch_to_max_distance.csv")
  239. ################################################ Adjust for stimulation timing modifications ##################################################################
  241. stim_start_nt = 220000000 #220000000 #22nd second 1000000000
  242. stim_end_nt = 230000000 #230000000 #23rd second
  243. stim_type = "Potassium"
  245. if stim_type == "Potassium":
  246. pot_stim_freq = "Single"
  247. if pot_stim_freq == "Single":
  248. pot_stim_comps = {}
  249. pot_stims = {}
  250. pot_stim_comps["stim_comp_0"] = 0
  251. pot_stims["pot_stim_0"] = 7000
  253. pot_stim_comps_array = np.array(list(pot_stim_comps.values()), dtype=np.int64)
  254. pot_stims_array = np.array(list(pot_stims.values()), dtype=np.float64)
  256. astrocyte = Astrocyte(dt=0.00000010, N=N, Ra=400.0, Cm1=1.0, astrocyte_data=astrocyte_data_file, stim_start_nt=stim_start_nt, stim_end_nt=stim_end_nt, glut_stim=None, pot_stim=pot_stims_array, stim_comp_glut=None, stim_comp_pot=pot_stim_comps_array)
  258. iskip = 1000 #for testing/identifying only the base and the peak if needed, use 1000; else 16000
  259. last = 300000000 # for testing/identifying only the base and the peak if needed, use 300000000; else 10000000000 for 1000s
  261. vs, Kos, Naks, Clks, Kks, Nass, time_array, JNaKks, phis, phos, Icoups, IdiffKs, IdiffNas, IdiffCas, Caks, JNaks, JTRPVks, Iks, JNBCks, JNHEs, JNKCC1ks, JNCXs, JNaKks_alpha2beta1, JNaKks_alpha2beta2, JGluTs = astrocyte.astrocyte_dynamics_diffusion(iskip, last)
  263. comb_df = concat_ast_param(vs, Naks, Kos, Kks, Caks, phos, phis, IdiffNas, JNaks, JNaKks, Iks, time_array, JNBCks, JNHEs, JNKCC1ks, JNCXs, JNaKks_alpha2beta1, JNaKks_alpha2beta2, JGluTs, N_1, N_2)
  265. output_path = Path(__file__).with_name("comb_astro_data.csv")
  266. comb_df.to_csv(output_path, index=False)
  268. end_time = time.time()
  269. elapsed_time = end_time - start_time
  270. print(f"Simulation Completed! Total time elapsed: {elapsed_time} seconds")

ast_modeling.py at commit f315644, under MIT · at the source

Overview

Authors: Jan Meyer1, Viola Bornemann1, Alok Bhattarai2, Sara Eitelmann1, Petr Unichenko3, Simone Durry1, Karl W. Kafitz1, Nicholas Chalmers4, Jianfeng Fan1, Ruth Beckervordersandforth4, Christian Henneberger3,5, Ghanim Ullah2, Christine R. Rose1
ORCID iDs: Jan Meyer, Viola Bornemann, Alok Bhattarai, Sara Eitelmann, Nicholas Chalmers, Jianfeng Fan, Ruth Beckervordersandforth, Christian Henneberger, Ghanim Ullah, Christine R. Rose
  1. Institute of Neurobiology, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf,Düsseldorf, Germany
  2. Department of Physics, University of South Florida,Tampa, FL USA
  3. Institute of Cellular Neurosciences I, Medical Faculty, University of Bonn,Bonn, Germany
  4. Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität,Erlangen, Germany
  5. German Center for Neurodegenerative Diseases,Bonn, Germany
Institutions: Heinrich Heine University Düsseldorf (Germany); University of South Florida (United States); University of Bonn (Germany); Universitätsklinikum Erlangen (Germany); German Center for Neurodegenerative Diseases (Germany)
Journal: Nature communications, volume 17, issue 1, article 4515
Dates: received 8 August 2025; accepted 4 May 2026; published online 20 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73435-z · PMID 42161940 · PMCID PMC13190688 · OpenAlex W7161781727
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging, Physiology & signal measures
Keywords: Astrocyte, Neurophysiology
MeSH: Astrocytes*, Brain*, Homeostasis*, Sodium*, Animals, Male, Mice, Mice, Inbred C57BL, Potassium, Sodium-Potassium-Exchanging ATPase (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (Project #461542557, RO 2327/13-2,14-2); Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Project SynGluCross); National Institutes of Health (R01R01NS130916, R21AG087910); China Scholarship Council (202306370039)
Citations: cited by 1 paper (Europe PMC); 73 references in the paper
Research resources: We modeled a reconstructed astrocyte35 RRID:SCR_002145

Abstract

Astrocytes maintain extracellular ion and transmitter homeostasis, with the Na⁺ inward gradient playing a crucial role. Earlier studies suggested a rather low, uniform Na⁺ distribution in astrocytes, consistent with the view that these basic homeostatic properties are well-protected. Here, we employed multi-photon fluorescence lifetime imaging to quantitatively determine astrocytic [Na+] in mouse brain tissue slices and in vivo. Our data reveals a significant subcellular and cellular heterogeneity in astrocytic [Na+], accompanied by differences in the capacity for Na+/K+-ATPase (NKA)-mediated uptake of extracellular K+. RNAscope and immunohistochemistry indicate differential spatial expression patterns of NKA ß1 and ß2 subunits in astrocytes. Biophysical modeling of differential NKA expression together with varying strength of Na+ influx replicate the experimentally observed heterogeneity in astrocytic [Na+]. Altogether, our results suggest the existence of functionally distinct astrocytes and astrocyte subdomains in which Na+ homeostasis is locally adapted to the specific requirements of surrounding neural networks.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

Zenodo 19487413

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
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 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

banalok/modeling

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f315644b6ccc320d7d655f8b21e1136a84ab6f54, 17 February 2026
Languages: Python (3)
Size: 9 files, 3 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (3 files), Numba (2 files), pandas (2 files), Matplotlib (1 file), Plotly (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/banalok/Modeling/tree/v1.0.0

Code availability

The code used to compute the results and statistics, as well as generate visualizations of biophysical simulations, is deposited in a GitHub public repository at 10.5281/zenodo.19487413.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

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

Data availability

The source data generated in this study are provided in the Source Data file. Further data are available from the lead contact upon request. Source data are provided in this paper.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 keywords, 10 MeSH terms, 4 funders, 72 references, 1 RRID.

Cite

This paper

Meyer, J., Bornemann, V., Bhattarai, A., Eitelmann, S., Unichenko, P., Durry, S., Kafitz, K. W., Chalmers, N., Fan, J., Beckervordersandforth, R., Henneberger, C., Ullah, G., & Rose, C. R. (2026). Cellular and subcellular heterogeneity of astrocytic Na⁺ homeostasis tuning astrocytes into functionally distinct subgroups in the mouse brain. Nature communications, 17(1), 4515. https://doi.org/10.1038/s41467-026-73435-z

BibTeX

@article{meyer2026cellular,
author = {Meyer, Jan and Bornemann, Viola and Bhattarai, Alok and Eitelmann, Sara and Unichenko, Petr and Durry, Simone and Kafitz, Karl W. and Chalmers, Nicholas and Fan, Jianfeng and Beckervordersandforth, Ruth and Henneberger, Christian and Ullah, Ghanim and Rose, Christine R.},
title = {{Cellular and subcellular heterogeneity of astrocytic Na⁺ homeostasis tuning astrocytes into functionally distinct subgroups in the mouse brain}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {4515},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73435-z},
url = {https://doi.org/10.1038/s41467-026-73435-z},
pmid = {42161940},
pmcid = {PMC13190688}
}

RIS

TY - JOUR
AU - Meyer, Jan
AU - Bornemann, Viola
AU - Bhattarai, Alok
AU - Eitelmann, Sara
AU - Unichenko, Petr
AU - Durry, Simone
AU - Kafitz, Karl W.
AU - Chalmers, Nicholas
AU - Fan, Jianfeng
AU - Beckervordersandforth, Ruth
AU - Henneberger, Christian
AU - Ullah, Ghanim
AU - Rose, Christine R.
TI - Cellular and subcellular heterogeneity of astrocytic Na⁺ homeostasis tuning astrocytes into functionally distinct subgroups in the mouse brain
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/20
VL - 17
IS - 1
SP - 4515
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73435-z
UR - https://doi.org/10.1038/s41467-026-73435-z
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-73435-z",
"type": "article-journal",
"title": "Cellular and subcellular heterogeneity of astrocytic Na⁺ homeostasis tuning astrocytes into functionally distinct subgroups in the mouse brain",
"container-title": "Nature communications",
"author": [
{
"family": "Meyer",
"given": "Jan"
},
{
"family": "Bornemann",
"given": "Viola"
},
{
"family": "Bhattarai",
"given": "Alok"
},
{
"family": "Eitelmann",
"given": "Sara"
},
{
"family": "Unichenko",
"given": "Petr"
},
{
"family": "Durry",
"given": "Simone"
},
{
"family": "Kafitz",
"given": "Karl W."
},
{
"family": "Chalmers",
"given": "Nicholas"
},
{
"family": "Fan",
"given": "Jianfeng"
},
{
"family": "Beckervordersandforth",
"given": "Ruth"
},
{
"family": "Henneberger",
"given": "Christian"
},
{
"family": "Ullah",
"given": "Ghanim"
},
{
"family": "Rose",
"given": "Christine R."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4515",
"DOI": "10.1038/s41467-026-73435-z",
"PMID": "42161940",
"PMCID": "PMC13190688",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-73435-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
20
]
]
}
}

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:10.1186/s40478-026-02399-4
Overactive EAAT1 Cl&lt;sup&gt;-&lt;/sup&gt; channels impair GABAergic tonic inhibition in SLC1A3-associated episodic ataxia.
Journal: Acta neuropathologica communications
In common: mouse, cellular / molecular, 2 references, 2 authors
[2] doi:10.1038/s41467-026-71391-2 [code]
Accelerating Leigh syndrome drug discovery through deep learning screening in brain organoids.
Journal: Nature communications
In common: pandas, SciPy, Matplotlib, 1 other tool, 1 reference, author Christine R Rose
[3] doi:10.1038/s41593-026-02267-3 [code]
Spatial proteomic analysis in human Alzheimer's disease brains enables identification of microenvironment-dependent microglial cell states.
Journal: Nature neuroscience
In common: Numba, Plotly, seaborn, 4 other tools, cellular / molecular, 1 reference
[4] doi:10.1111/jnc.70470 [code]
Calcium Buffering in Astrocytes and Its Relevance for Experimental Data Interpretation and Computational Modeling.
Journal: Journal of neurochemistry
In common: cellular / molecular, 5 references
[5] doi:10.1186/s10020-026-01450-3 [code]
LPS-induced inflammation differentially affects endogenous Ca&lt;sup&gt;2&lt;/sup&gt;⁺ activity in mouse and human iPSC-derived astrocytes.
Journal: Molecular medicine (Cambridge, Mass.)
In common: seaborn, pandas, Matplotlib, 1 other tool, mouse, cellular / molecular, 3 references
[6] doi:10.3390/ijms27135902
Functional Expression of Nicotinic Receptors on iPSC-Derived Astrocytes and Signalling Disturbances by a Panel of Neonicotinoid Pesticides and Their Metabolites.
Journal: International journal of molecular sciences
In common: cellular / molecular, 5 references
[7] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Numba, Plotly, seaborn, 4 other tools, mouse, cellular / molecular
[8] 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: Numba, Plotly, seaborn, 4 other tools, mouse, cellular / molecular
[9] doi:10.1038/s41467-026-71803-3 [code]
Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.
Journal: Nature communications
In common: Numba, Plotly, seaborn, 4 other tools, mouse, cellular / molecular
[10] doi:10.1002/glia.70201 [code]
Dorso-Ventral and Night-Day Regulation of Extracellular K&lt;sup&gt;+&lt;/sup&gt; Dynamics in Mouse Hippocampal Astrocytes.
Journal: Glia
In common: mouse, cellular / molecular, 4 references

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.