OSCR

Astrocyte Proximity Protects Synapses From Human Amyloid-Beta Induced Degeneration in a Mouse Ex Vivo Model of Early Alzheimer's Disease.

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 › Data Analysis › Analysis of Calcium Activity and Event Extraction ↔ Synapse_Event_Detection_Runme.ipynb, lines 69–192 · score 0.92 · constrained_foopsi, optimize_g, s_min, PRE recordings, scored normalised, Event frequency
  2. [2] § Results › Multicolour Imaging of Synaptic Activity and Astrocyte–Neuron Interactions ↔ Single_event_calculation.ipynb, lines 511–536 · score 0.74 · GCaMP6s, jGCaMP8s, peak amplitude, jGCaMP7b, GECI, variants
  3. [3] § Materials and Methods › Structural and Calcium Imaging › Comparison of GCaMP Variants ↔ Single_event_calculation.ipynb, lines 511–536 · score 0.65 · GCaMP6s, jGCaMP8s, jGCaMP7b, Variants
  4. [4] § Materials and Methods › Data Analysis › Comparison of GCaMP Variants ↔ Synapse_Event_Detection_Runme.ipynb, lines 69–192 · score 0.62 · event detection, scored normalised, raw, optimize, OASIS, frame

Paper

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The authors' code

Jupyter notebook · 448 lines · 14 KB · Apache-2.0 · 2 matches

  1. # %%
  2. import pandas as pd
  3. import numpy as np
  4. import matplotlib.pyplot as plt
  5. import seaborn as sns
  6. import os
  7. import matplotlib as mpl
  8. import caiman as cm
  9. from caiman.source_extraction import cnmf
  10. from caiman.utils.utils import download_demo
  11. from caiman.utils.visualization import inspect_correlation_pnr
  12. from caiman.source_extraction.cnmf import params as params
  13. from caiman.source_extraction.cnmf.deconvolution import constrained_foopsi
  14. from caiman.source_extraction.cnmf.deconvolution import constrained_oasisAR2
  17. def Okada_filter(trace):
  18. """Trace is a list.
  19. This will apply Okada filter to it as described in Ishikawa et al, STAR Protocols 2020"""
  20. x=trace.copy()
  21. M=np.mean(x)
  22. SD=np.std(x)
  23. T=len(x)
  24. for t in range(2, T-1):
  25. Z=abs(x[t+1]-M)/SD
  26. if (x[t]-x[t+1])*(x[t]-x[t+1])>0:
  27. x[t]=(x[t-1]+Z*x[t]+x[t+1])/(2+Z)
  28. return x
  30. def subtract_baseline(trace, order):
  31. x=np.linspace(0,len(trace)*0.05,len(trace))
  32. p = np.poly1d(np.polyfit(x, trace, int(order)))
  33. return p
  35. def dFF(trace):
  36. """Trace is a list.
  37. This will calculate deltaF/F0"""
  38. x=trace.copy()
  39. M=np.mean(trace)
  40. T=len(trace)
  41. for t in range(0, T):
  42. x[t]=(trace[t]-M)/M
  43. return x
  46. def process(trace, polynomial_order=10, Okada_order=4):
  47. """Trace is a list.
  48. This will subtract the background with a nth order polynomial and apply Okada filter (m interactions)
  50. n=polynomial_order (default is 10)
  51. m=number of consecutive Okada iteraction (default is 4)
  53. """
  54. x=trace.tolist().copy()
  55. baseline=subtract_baseline(x,polynomial_order)
  57. t = np.linspace(0, len(x)*0.05,len(x))
  59. x = [a - b for a, b in zip(x, baseline(t))]
  61. for i in range(Okada_order):
  63. x=Okada_filter(x)
  65. return x
  67. framerate=20 #Hz for interframe time 50ms
  69. # %%
  70. #Select PRE ROIs
  73. folder=r'\\AA06299633\S2410250541\Results' # Input folder
  75. file="20241210_S2410250541_field01_day1_mScarletxCre_Ch1mScarlet-Ch2GCaMP7b_PRE_Results.csv" #File location of PRE recording
  78. df=pd.read_csv(os.path.join(folder, file))
  79. df=df.iloc[:, 1:]
  81. for column in df.columns:
  82. if column[:4]!='Mean' :
  83. df=df.drop(column, axis=1)
  84. elif column[:4]=='Mean' :
  85. df=df.rename(columns={column:'ROI'+column[4:]})
  87. df2=df.copy()
  89. for column in df2.columns:
  90. df2[column]=dFF(df2[column]) #calculates Delta F/F
  91. df2[column]=process(df2[column]) # subtracts background and filters noise
  93. cells=df.columns
  95. from matplotlib import gridspec
  96. from sklearn.preprocessing import normalize
  99. #plotting params
  100. mpl.rcParams['axes.facecolor'] = 'white'
  101. mpl.rcParams['axes.edgecolor'] = 'black'
  102. mpl.rcParams['axes.linewidth'] = '0.5'
  103. mpl.rcParams['axes.labelsize'] = '8'
  104. mpl.rcParams['axes.labelcolor'] = 'black'
  105. mpl.rcParams['xtick.color'] = 'black'
  106. mpl.rcParams['xtick.labelsize'] = '4'
  107. mpl.rcParams['ytick.labelsize'] = '4'
  108. mpl.rcParams['ytick.color'] = 'black'
  110. bl=None
  111. c1=None #initial calcium concentation (value)
  112. g=None
  113. sn=10 #noise SD
  114. p=1
  115. method_deconvolution='oasis'
  116. bas_nonneg=True #baseline estimation
  117. noise_range=[0.25, 0.75] #frquency range to estimate noise
  118. noise_method='logmexp' #method to estimate noise
  119. lags=5
  120. fudge_factor=.99
  121. verbosity=False
  122. solvers=None
  123. optimize_g=5
  124. s_min=2.5 #if z-scored use s_min=2.5 equivalent to 2.5SD ; elsse use s_min between .1 and .25 (usually .2)
  126. #--------------------------------------
  128. trace_dlc = np.array(df2.values.astype(float)).transpose()[:]
  129. cells_tv = cells[:]
  130. all_events = []
  131. all_events_count=[]
  132. all_events_frequency=[]
  133. for cell in range(len(trace_dlc)):
  135. raw_trace = trace_dlc[cell]
  136. raw_trace = np.array(raw_trace)#/max(np.array(raw_trace)) #If we want traces between 0 and 1 (OPTIONAL)
  137. raw_trace = (raw_trace - np.mean(raw_trace))/np.std(raw_trace) #z-score normalised
  139. #OASIS function run through
  140. events = constrained_foopsi(raw_trace,
  141. bl=bl,
  142. c1=c1,
  143. g=g,
  144. sn=sn,
  145. p=p,
  146. method_deconvolution=method_deconvolution,
  147. bas_nonneg=bas_nonneg,
  148. noise_range=noise_range,
  149. noise_method=noise_method,
  150. lags=lags,
  151. fudge_factor=fudge_factor,
  152. verbosity=verbosity,
  153. solvers=solvers,
  154. optimize_g=optimize_g,
  155. s_min=s_min)
  158. all_events.append(events[5])
  159. all_events_frequency.append(np.count_nonzero(events[5])/len(events[5])*framerate)
  160. all_events_count.append(np.count_nonzero(events[5]))
  164. #Visualising data
  165. fig, axs = plt.subplots(3, 1, figsize=(5,1), dpi=400, facecolor='w', edgecolor='k' )
  166. gs = gridspec.GridSpec(3, 1, height_ratios=[1,1,1], )
  167. fig.suptitle(cells_tv[cell], fontsize=10)
  169. for ax in fig.get_axes():
  170. ax.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)
  172. #Raw traces
  173. axs[0] = plt.subplot(gs[0])
  174. axs[0] = plt.plot( raw_trace, c='green', linewidth=.5)
  176. #OASIS noise consideration
  177. axs[1] = plt.subplot(gs[1])
  178. axs[1] = plt.plot(events[0], c='b', linewidth=.5)
  180. #Events plotted on raw trace
  181. axs[2] = plt.subplot(gs[2])
  182. axs[2] = plt.plot(raw_trace, c='k', alpha=0.1, linewidth=.5)
  183. axs[2] = plt.scatter( np.arange(0,len(events[5]),1),events[5], c='r', s=.5)
  185. fig.text(0.5, -0.1, 'Frame', ha='center', fontsize=5)
  186. fig.text(0.05, 0.5, u'Δ F/F (z-score)', va='center', rotation='vertical', fontsize=5)
  189. plt.subplots_adjust(wspace=0, hspace=0)
  190. plt.show()
  192. df_events=pd.DataFrame(data={'ROI':cells,'PRE_n':all_events_count,'PRE_Hz':all_events_frequency})
  195. # %%
  196. #Select POST ROIs
  198. file="20241210_S2410250541_field01_day1_mScarletxCre_Ch1mScarlet-Ch2GCaMP7b_POST_Results.csv" #File location of POST recording
  201. df=pd.read_csv(os.path.join(folder, file))
  202. df=df.iloc[:, 1:]
  204. for column in df.columns:
  205. if column[:4]!='Mean' :
  206. df=df.drop(column, axis=1)
  207. elif column[:4]=='Mean' :
  208. df=df.rename(columns={column:'ROI'+column[4:]})
  210. df2=df.copy()
  212. for column in df2.columns:
  213. df2[column]=dFF(df2[column]) #calculates Delta F/F
  214. df2[column]=process(df2[column]) # subtracts background and filters noise
  216. cells=df.columns
  218. from matplotlib import gridspec
  219. from sklearn.preprocessing import normalize
  222. #plotting params
  223. mpl.rcParams['axes.facecolor'] = 'white'
  224. mpl.rcParams['axes.edgecolor'] = 'black'
  225. mpl.rcParams['axes.linewidth'] = '0.5'
  226. mpl.rcParams['axes.labelsize'] = '8'
  227. mpl.rcParams['axes.labelcolor'] = 'black'
  228. mpl.rcParams['xtick.color'] = 'black'
  229. mpl.rcParams['xtick.labelsize'] = '4'
  230. mpl.rcParams['ytick.labelsize'] = '4'
  231. mpl.rcParams['ytick.color'] = 'black'
  233. bl=None
  234. c1=None #initial calcium concentation (value)
  235. g=None
  236. sn=10 #noise SD
  237. p=1
  238. method_deconvolution='oasis'
  239. bas_nonneg=True #baseline estimation
  240. noise_range=[0.25, 0.75] #frquency range to estimate noise
  241. noise_method='logmexp' #method to estimate noise
  242. lags=5
  243. fudge_factor=.99
  244. verbosity=False
  245. solvers=None
  246. optimize_g=5
  247. s_min=2.2 #if z-scored use s_min=2 equivalent to 2SD ; elsse use s_min between .1 and .25 (usually .2)
  249. #--------------------------------------
  251. trace_dlc = np.array(df2.values.astype(float)).transpose()[:]
  252. cells_tv = cells[:]
  253. all_events = []
  254. all_events_count=[]
  255. all_events_frequency=[]
  256. for cell in range(len(trace_dlc)):
  258. raw_trace = trace_dlc[cell]
  259. raw_trace = np.array(raw_trace)#/max(np.array(raw_trace)) #If we want traces between 0 and 1 (OPTIONAL)
  260. raw_trace = (raw_trace - np.mean(raw_trace))/np.std(raw_trace) #z-score normalised
  262. #OASIS function run through
  263. events = constrained_foopsi(raw_trace,
  264. bl=bl,
  265. c1=c1,
  266. g=g,
  267. sn=sn,
  268. p=p,
  269. method_deconvolution=method_deconvolution,
  270. bas_nonneg=bas_nonneg,
  271. noise_range=noise_range,
  272. noise_method=noise_method,
  273. lags=lags,
  274. fudge_factor=fudge_factor,
  275. verbosity=verbosity,
  276. solvers=solvers,
  277. optimize_g=optimize_g,
  278. s_min=s_min)
  281. all_events.append(events[5])
  282. all_events_frequency.append(np.count_nonzero(events[5])/len(events[5])*framerate)
  283. all_events_count.append(np.count_nonzero(events[5]))
  286. #Viualising data
  287. fig, axs = plt.subplots(3, 1, figsize=(5,1), dpi=400, facecolor='w', edgecolor='k' )
  288. gs = gridspec.GridSpec(3, 1, height_ratios=[1,1,1], )
  289. fig.suptitle(cells_tv[cell], fontsize=10)
  291. for ax in fig.get_axes():
  292. ax.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)
  294. #Raw traces
  295. axs[0] = plt.subplot(gs[0])
  296. axs[0] = plt.plot( raw_trace, c='green', linewidth=.5)
  298. #OASIS noise consideration
  299. axs[1] = plt.subplot(gs[1])
  300. axs[1] = plt.plot(events[0], c='b', linewidth=.5)
  302. #Events plotted on raw trace
  303. axs[2] = plt.subplot(gs[2])
  304. axs[2] = plt.plot(raw_trace, c='k', alpha=0.1, linewidth=.5)
  305. axs[2] = plt.scatter( np.arange(0,len(events[5]),1),events[5], c='r', s=.5)
  307. fig.text(0.5, -0.1, 'Frame', ha='center', fontsize=5)
  308. fig.text(0.05, 0.5, u'Δ F/F (z-score)', va='center', rotation='vertical', fontsize=5)
  311. plt.subplots_adjust(wspace=0, hspace=0)
  312. plt.show()
  314. df_events_post=pd.DataFrame(data={'ROI':cells,'POST_n':all_events_count,'POST_Hz':all_events_frequency})
  316. df_events=pd.merge(df_events, df_events_post, on='ROI')
  318. # %%
  319. #Select POST ROIs
  321. file="20241210_S2410250541_field01_day1_mScarletxCre_Ch1mScarlet-Ch2GCaMP7b_POST2_Results.csv" #Optional :File location of second POST recording
  324. df=pd.read_csv(os.path.join(folder, file))
  325. df=df.iloc[:, 1:]
  327. for column in df.columns:
  328. if column[:4]!='Mean' :
  329. df=df.drop(column, axis=1)
  330. elif column[:4]=='Mean' :
  331. df=df.rename(columns={column:'ROI'+column[4:]})
  333. df2=df.copy()
  335. for column in df2.columns:
  336. df2[column]=dFF(df2[column]) #calculates Delta F/F
  337. df2[column]=process(df2[column]) # subtracts background and filters noise
  339. cells=df.columns
  341. from matplotlib import gridspec
  342. from sklearn.preprocessing import normalize
  345. #plotting params
  346. mpl.rcParams['axes.facecolor'] = 'white'
  347. mpl.rcParams['axes.edgecolor'] = 'black'
  348. mpl.rcParams['axes.linewidth'] = '0.5'
  349. mpl.rcParams['axes.labelsize'] = '8'
  350. mpl.rcParams['axes.labelcolor'] = 'black'
  351. mpl.rcParams['xtick.color'] = 'black'
  352. mpl.rcParams['xtick.labelsize'] = '4'
  353. mpl.rcParams['ytick.labelsize'] = '4'
  354. mpl.rcParams['ytick.color'] = 'black'
  356. bl=None
  357. c1=None #initial calcium concentation (value)
  358. g=None
  359. sn=10 #noise SD
  360. p=1
  361. method_deconvolution='oasis'
  362. bas_nonneg=True #baseline estimation
  363. noise_range=[0.25, 0.75] #frquency range to estimate noise
  364. noise_method='logmexp' #method to estimate noise
  365. lags=5
  366. fudge_factor=.99
  367. verbosity=False
  368. solvers=None
  369. optimize_g=5
  370. s_min=2.2 #if z-scored use s_min=2 equivalent to 2SD ; elsse use s_min between .1 and .25 (usually .2)
  372. #--------------------------------------
  374. trace_dlc = np.array(df2.values.astype(float)).transpose()[:]
  375. cells_tv = cells[:]
  376. all_events = []
  377. all_events_count=[]
  378. all_events_frequency=[]
  379. for cell in range(len(trace_dlc)):
  381. raw_trace = trace_dlc[cell]
  382. raw_trace = np.array(raw_trace)#/max(np.array(raw_trace)) #If we want traces between 0 and 1 (OPTIONAL)
  383. raw_trace = (raw_trace - np.mean(raw_trace))/np.std(raw_trace) #z-score normalised
  385. #OASIS function run through
  386. events = constrained_foopsi(raw_trace,
  387. bl=bl,
  388. c1=c1,
  389. g=g,
  390. sn=sn,
  391. p=p,
  392. method_deconvolution=method_deconvolution,
  393. bas_nonneg=bas_nonneg,
  394. noise_range=noise_range,
  395. noise_method=noise_method,
  396. lags=lags,
  397. fudge_factor=fudge_factor,
  398. verbosity=verbosity,
  399. solvers=solvers,
  400. optimize_g=optimize_g,
  401. s_min=s_min)
  404. all_events.append(events[5])
  405. all_events_frequency.append(np.count_nonzero(events[5])/len(events[5])*framerate)
  406. all_events_count.append(np.count_nonzero(events[5]))
  409. #Viualising data
  410. fig, axs = plt.subplots(3, 1, figsize=(5,1), dpi=400, facecolor='w', edgecolor='k' )
  411. gs = gridspec.GridSpec(3, 1, height_ratios=[1,1,1], )
  412. fig.suptitle(cells_tv[cell], fontsize=10)
  414. for ax in fig.get_axes():
  415. ax.tick_params(bottom=False, labelbottom=False, left=False, labelleft=False)
  417. #Raw traces
  418. axs[0] = plt.subplot(gs[0])
  419. axs[0] = plt.plot( raw_trace, c='green', linewidth=.5)
  421. #OASIS noise consideration
  422. axs[1] = plt.subplot(gs[1])
  423. axs[1] = plt.plot(events[0], c='b', linewidth=.5)
  425. #Events plotted on raw trace
  426. axs[2] = plt.subplot(gs[2])
  427. axs[2] = plt.plot(raw_trace, c='k', alpha=0.1, linewidth=.5)
  428. axs[2] = plt.scatter( np.arange(0,len(events[5]),1),events[5], c='r', s=.5)
  430. fig.text(0.5, -0.1, 'Frame', ha='center', fontsize=5)
  431. fig.text(0.05, 0.5, u'Δ F/F (z-score)', va='center', rotation='vertical', fontsize=5)
  434. plt.subplots_adjust(wspace=0, hspace=0)
  435. plt.show()
  437. df_events_post=pd.DataFrame(data={'ROI':cells,'POST2_n':all_events_count,'POST2_Hz':all_events_frequency})
  439. df_events=pd.merge(df_events, df_events_post, on='ROI')
  441. # %%
  442. df_events
  444. # %%
  445. df_events.to_csv(os.path.join(folder, file[:21]+'EVE.csv'))
  447. # %%

Synapse_Event_Detection_Runme.ipynb at commit 48fd558, under Apache-2.0 · at the source

Overview

Authors: Francesco Gobbo1, Declan King1, Jane Tulloch1, Davide Gobbo2, Calum Bonthron1, Soraya Meftah1, Caleb Stoddart‐Campbelton1, Arisa Tamura3, Jamie Rose1, Colin Smith1, Claire Durrant1, Tara L Spires‐Jones1
ORCID iDs: Francesco Gobbo, Declan King, Davide Gobbo, Calum Bonthron, Soraya Meftah, Claire Durrant, Tara L Spires‐Jones
  1. Institute for Neuroscience and Cardiovascular Research and UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK
  2. Department of Molecular Physiology, Centre for Integrative Physiology and Molecular Medicine (CIPMM), University of Saarland, Homburg, Germany
  3. Department of Biological Sciences, Tokyo Metropolitan University, Tokyo, Japan
Institutions: UK Dementia Research Institute (United Kingdom); University of Edinburgh (United Kingdom); Saarland University (Germany); Tokyo Metropolitan University (Japan)
Journal: The European journal of neuroscience, volume 63, issue 7, article e70480
Dates: received 3 September 2025; accepted 9 March 2026; published online 28 March 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ejn.70480 · PMID 41902755 · PMCID PMC13032744 · OpenAlex W7142270710
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Evoked potentials, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Alzheimer's disease, astrocytes, Aβ, synapse, synapse loss
MeSH: Alzheimer Disease*, Amyloid beta-Peptides*, Astrocytes*, Synapses*, Animals, Brain, Dendritic Spines, Disease Models, Animal, Humans, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Alzheimer's Society (AS-PG-21-006); James Dyson Foundation; The James Dyson Foundation; UK Dementia Research Institute (UK DRI-4204, UK DRI‐4204); Race Against Dementia (ARUK-RADF-2019a-001)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Synapse loss is the strongest pathological correlate of cognitive decline in Alzheimer's disease (ad) and is most pronounced around amyloid plaque pathology in the brain. Although mechanisms remain incompletely understood, hyperactivity downstream of soluble amyloid beta (Aβ) is strongly implicated in synapse degeneration. Engulfment of synapses by reactive astrocytes was observed in end‐stage disease tissue, particularly around plaques. Due to astrocytes' role in synaptic modulation, we hypothesised that astrocytes could modulate synapse degeneration downstream of soluble Aβ earlier in disease pathogenesis. To test this, we challenged organotypic mouse brain slices with human ad brain homogenates containing Aβ. Changes in synaptic activity were detected 2 h after Aβ challenge, and spine loss was seen after 24 h. We observe that Aβ‐containing homogenate induces a significant loss of spines compared with controls. Aβ‐containing homogenate also causes a significant increase in the frequency of synaptic calcium events, particularly in synapses lost at 24 h. Dendritic spines associated with astrocytic processes were significantly more likely to survive at 24 h after Aβ challenge and had reduced levels of externalised phosphatidyl serine despite no effect of astrocyte proximity on synaptic activity. Inhibiting astrocytic glutamate transporters prevented the protective effects of astrocytes on synapses, indicating that astrocytes are protective of synapses at least in part through removing excess glutamate from the synaptic microenvironment. Our findings suggest that an organotypic mouse brain slice model challenged with disease tissue homogenates effectively recapitulates key features of early AD, including synapse loss and hyperexcitability. Moreover, they indicate that astrocytes play a protective role in preserving synapses, particularly during short‐term exposure to low concentrations of toxic Aβ. Future work is needed to elucidate the role of astrocyte‐mediated synapse phagocytosis in response to chronic Aβ exposure.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

francesco-gobbo/astrocyte-proximity-protects-synapses-in-ad

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 48fd5583b2f733ef8b43398879ae458aa4b03ce1, 9 February 2026
Languages: Jupyter (3), R (1)
Size: 9 files, 4 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, 4 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), pandas (3 files), CaImAn (2 files), scikit-learn (2 files), SciPy (2 files), seaborn (2 files), emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), reshape2 (1 file), statsmodels (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files
At the source: github.com/francesco-gobbo/Astrocyte-proximity-protects-synapses-in-AD/tree/main

The paper's code and data availability statement is in the Data section.

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Data

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

Data Availability Statement

The code used in the paper is available at https://github.com/francesco‐gobbo/Astrocyte‐proximity‐protects‐synapses‐in‐AD/tree/main (https://github.com/francesco-gobbo/Astrocyte-proximity-protects-synapses-in-AD/tree/main). The data used in this paper are available at Edinburgh DataShare: https://doi.org/10.7488/ds/8077 (within the collection https://datashare.ed.ac.uk/handle/10283/3076). Original images are available from the corresponding author upon request.

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 12 MeSH terms, 5 funders, 85 references.

Cite

This paper

Gobbo, F., King, D., Tulloch, J., Gobbo, D., Bonthron, C., Meftah, S., Stoddart‐Campbelton, C., Tamura, A., Rose, J., Smith, C., Durrant, C., & Spires‐Jones, T. L. (2026). Astrocyte Proximity Protects Synapses From Human Amyloid-Beta Induced Degeneration in a Mouse Ex Vivo Model of Early Alzheimer's Disease. The European journal of neuroscience, 63(7), e70480. https://doi.org/10.1111/ejn.70480

BibTeX

@article{gobbo2026astrocyte,
author = {Gobbo, Francesco and King, Declan and Tulloch, Jane and Gobbo, Davide and Bonthron, Calum and Meftah, Soraya and Stoddart‐Campbelton, Caleb and Tamura, Arisa and Rose, Jamie and Smith, Colin and Durrant, Claire and Spires‐Jones, Tara L},
title = {{Astrocyte Proximity Protects Synapses From Human Amyloid-Beta Induced Degeneration in a Mouse Ex Vivo Model of Early Alzheimer's Disease}},
journal = {The European journal of neuroscience},
year = {2026},
month = apr,
volume = {63},
number = {7},
pages = {e70480},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70480},
url = {https://doi.org/10.1111/ejn.70480},
pmid = {41902755},
pmcid = {PMC13032744}
}

RIS

TY - JOUR
AU - Gobbo, Francesco
AU - King, Declan
AU - Tulloch, Jane
AU - Gobbo, Davide
AU - Bonthron, Calum
AU - Meftah, Soraya
AU - Stoddart‐Campbelton, Caleb
AU - Tamura, Arisa
AU - Rose, Jamie
AU - Smith, Colin
AU - Durrant, Claire
AU - Spires‐Jones, Tara L
TI - Astrocyte Proximity Protects Synapses From Human Amyloid-Beta Induced Degeneration in a Mouse Ex Vivo Model of Early Alzheimer's Disease
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/04/01
VL - 63
IS - 7
SP - e70480
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70480
UR - https://doi.org/10.1111/ejn.70480
LA - en
ER -

CSL-JSON

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"id": "10.1111/ejn.70480",
"type": "article-journal",
"title": "Astrocyte Proximity Protects Synapses From Human Amyloid-Beta Induced Degeneration in a Mouse Ex Vivo Model of Early Alzheimer's Disease",
"container-title": "The European journal of neuroscience",
"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": "Eur J Neurosci",
"volume": "63",
"issue": "7",
"page": "e70480",
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"PMID": "41902755",
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"language": "en",
"issued": {
"date-parts": [
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1
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]
}
}

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