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BIN1 gain-of-function in the presynaptic compartment leads to isoform-specific synaptotoxicity.

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Paper

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The authors' code

Jupyter notebook · 123 lines · 4.7 KB · no license

  1. # %%
  2. %load_ext autoreload
  3. %autoreload 2
  4. %matplotlib inline
  7. # %%
  8. import spikeinterface.full as si
  9. import numpy as np
  10. import pylab as plt
  11. from pathlib import Path
  13. base_folder = Path('.')
  14. job_kwargs = {'n_jobs': -1, 'progress_bar' :True, 'chunk_duration' : '1s', 'verbose': True}
  17. # %% [markdown]
  18. # ## We load all the recordings
  20. # %%
  21. import os, h5py
  22. import pandas as pd
  23. from tools import load_experiment, infer_boundaries
  25. recordings = {}
  26. remove_center = True
  28. for folder in os.listdir(base_folder / "experiments"):
  29. datapath = base_folder / "experiments" / folder
  31. for file in os.listdir(datapath):
  32. if file.endswith(".xlsx"):
  33. data = pd.read_excel(datapath / file)
  34. data.to_csv(str(datapath / file).replace('.xlsx', '.csv'))
  36. for file in os.listdir(datapath):
  37. if file.endswith(".h5"):
  38. key, ext = os.path.splitext(file)
  39. try:
  40. recordings[key] = load_experiment(file, datapath, remove_center)
  41. except Exception:
  42. print('Problem while loading', datapath, file)
  43. print('We have loaded', len(recordings), 'recordings')
  44. for key in recordings.keys():
  45. recordings[key]['filtered'] = si.bandpass_filter(recordings[key]['raw'], freq_min= 150, freq_max= 7000, ftype= "bessel", filter_order= 2)
  46. recordings[key]['filtered'] = si.common_reference(recordings[key]['filtered'])
  47. # recordings[key]['filtered'] = si.zscore(recordings[key]['filtered'], dtype='float32')
  49. # %%
  52. # %% [markdown]
  53. # ## We perform (or load) all the spike sortings
  55. # %%
  56. job_kwargs = {'n_jobs': -1, 'progress_bar' :True, 'chunk_memory' : '100M'}
  57. si.set_global_job_kwargs(**job_kwargs)
  58. erase = True
  59. for key in recordings.keys():
  60. folder = base_folder / "sortings"
  61. folder.mkdir(parents=True, exist_ok=True)
  62. folder = base_folder / "sortings" / key
  63. if key == '2024-03-20_CG_BIN1exp3_MEA22338_v270224_DIV20_MOCK_basal':
  64. if folder.exists() and not erase:
  65. recordings[key]['sorting'] = si.read_sorter_folder(folder)
  66. else:
  67. recordings[key]['sorting'] = si.run_sorter('spykingcircus2', recordings[key]['filtered'],
  68. folder=folder, verbose=True, apply_preprocessing=False, remove_existing_folder=True)
  70. # %% [markdown]
  71. # ## We compute (or load) all the waveforms extracted from the spike sortings
  73. # %%
  74. erase = True
  75. for key in recordings.keys():
  76. folder = base_folder / "analyzers"
  77. folder.mkdir(parents=True, exist_ok=True)
  78. folder = base_folder / "analyzers" / key
  79. if key == '2024-03-20_CG_BIN1exp3_MEA22338_v270224_DIV20_MOCK_basal':
  80. if folder.exists() and not erase:
  81. recordings[key]['analyzer'] = si.load_sorting_analyzer(folder)
  82. else:
  83. recordings[key]['analyzer'] = si.create_sorting_analyzer(recordings[key]['sorting'],
  84. recordings[key]['filtered'], format='binary_folder',
  85. folder=folder, return_scaled=True, overwrite=True, sparse=True)
  86. recordings[key]['analyzer'].compute(['random_spikes', 'templates', 'noise_levels',
  87. 'quality_metrics', 'template_similarity', 'spike_amplitudes'])
  88. recordings[key]['analyzer'].compute('correlograms', window_ms=40, bin_ms=2)
  89. recordings[key]['analyzer'].save_as(folder=folder)
  91. # %% [markdown]
  92. # ## We compute the boundaries of the source/target population for every recording
  94. # %%
  95. from tools import infer_boundaries
  96. for key in recordings.keys():
  97. recordings[key]['boundaries'] = infer_boundaries(recordings[key]['mapping'])
  99. # %% [markdown]
  100. # ## We need to define a quality criteria that will be used in all the following operations
  102. # %%
  103. quality_criteria = 'snr > 3 & isi_violations_ratio < 0.1'
  105. # %% [markdown]
  106. # ## We compute (or load) the quality metrics for all the recordings
  108. # %%
  109. from tools import get_positions
  110. for key in recordings.keys():
  111. if key == '2024-03-20_CG_BIN1exp3_MEA22338_v270224_DIV20_MOCK_basal':
  112. sa = recordings[key]['analyzer']
  113. if sa.get_extension('quality_metrics') is None:
  114. sa.compute(['quality_metrics'])
  115. recordings[key]['metrics'] = sa.get_extension('quality_metrics').get_data()
  116. positions, x, y = get_positions(recordings[key])
  117. #recordings[key]['metrics'].insert(0, "position", list(positions))
  118. #recordings[key]['metrics'].insert(1, "x", list(x))
  119. #recordings[key]['metrics'].insert(2, "y", list(y))
  120. path = Path('plots') / "statistics"
  121. path.mkdir(parents=True, exist_ok=True)
  122. recordings[key]['metrics'].to_excel(path / f"{key}.xlsx")
  123. recordings[key]['metrics'].query(quality_criteria).to_excel(path / f"quality_only_{key}.xlsx")

population_analysis-checkpoint.ipynb at commit bce7f3a, no license · at the source

Overview

Authors: Erwan Lambert1, Carla Gelle1, Valentin Leclerc1, Alejandra Freire-Regatillo1, Lucie Liefooghe1, Nicolas Barois2,3, Tommy Malfoi1, Xavier Hermant1, Florie Demiautte1, Inès Gallienne1, Frank Lafont3, Philippe Amouyel1, Karine Blary4, Sabine Kuenen5,6, Chloé Najdek1, Patrik Verstreken5,6, Dolores Siedlecki-Wullich1, Pierre Yger7, Jean-Charles Lambert1, Devrim Kilinc1, Pierre Dourlen1
ORCID iDs: Sabine Kuenen, Patrik Verstreken
  1. Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 – RID – AGE – Facteurs de risque et déterminants moléculaires liés au vieillissement, LabEx DISTALZ,Lille, France
  2. Université de Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille,UAR CNRS 2014 - US Inserm 41 - PLBS, Lille, France
  3. University of Lille, CNRS, Inserm, CHU Lille, Institut Pasteur Lille, U1019-UMR 9017-CIIL-Center for Infection and Immunity of Lille,Lille, France
  4. Université de Lille, CNRS, Centrale Lille, Université de Polytechnique Hauts-de-France, UMR 8520 - IEMN - Institut d’Electronique de Microélectronique et de Nanotechnologie,Lille, F-59000 France
  5. VIB Center for Neuroscience, Leuven, Belgium
  6. Department of Neurosciences, Leuven Brain Institute, KU Leuven,Leuven, Belgium
  7. Lille Neurosciences & Cognition (LilNCog) – U1172 (INSERM, Lille), University of Lille, CHU Lille,Lille, 59045 France
Institutions: Institut Pasteur de Lille (France); Center for Infection and Immunity of Lille (France); Institut d'Electronique, de Microélectronique et de Nanotechnologie (France); KU Leuven (Belgium); Université de Lille (France)
Journal: Alzheimer's research & therapy, volume 18, issue 1, article 185
Dates: received 18 September 2025; accepted 31 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13195-026-02103-7 · PMID 42237143 · PMCID PMC13466144 · OpenAlex W7163426247
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), human (organism), rat (organism), drosophila (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Statistics, Single-unit activity, calcium imaging
Keywords: Alzheimer’s disease, BIN1 isoforms, Synapse, Drosophila, primary neuronal culture, Rab11, Electroretinography, MEA
MeSH: Adaptor Proteins, Signal Transducing*, Presynaptic Terminals*, Synapses*, Tumor Suppressor Proteins*, Animals, Animals, Genetically Modified, Cells, Cultured, Drosophila, Drosophila Proteins, Hippocampus, Humans, Motor Neurons, Neuromuscular Junction, Neurons, Nuclear Proteins, Protein Isoforms, rab GTP-Binding Proteins, rab11 GTP-Binding Proteins, Rats, Synaptic Transmission (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Région Hauts-de-France Regional Council; Association France Alzheimer (#6473, #328 ADhesion); Agence Nationale de la Recherche (French National Research Agency) (ANR-11-LABX-01 LabEx DISTALZ, ANR-23-CE14-0064 TAUFUNALZ); Neurodegenerative Disease Research (3DMiniBrain); Alzheimer’s Association (AARG-22-926152); French Renatech Network (P-21-03626); Fondation Vaincre Alzheimer (The BIN1-Tau neurotoxic link in Drosophila)
Citations: not cited yet (Europe PMC); 72 references in the paper
Research resources: RRID:AB_10015289, Homer1 RRID:AB_10549720, RRID:AB_10890165, RRID:AB_2313567, anti-Bruchpilot RRID:AB_2314866, goat-anti-chicken RRID:AB_2337381, goat-anti-guinea pig RRID:AB_2337402, anti-HRP488 RRID:AB_2338965, Dye Light 405 Donkey anti-rabbit RRID:AB_2340616, Alexa 633 Goat anti-mouse RRID:AB_2535719, guinea pig anti-PSD95 RRID:AB_2619800, MAP2 RRID:AB_2619881, chicken anti-Synaptophysin1 RRID:AB_2622239, anti-BIN1 primary antibodies RRID:AB_309738, rabbit monoclonal anti-BIN1 [EPR13463 RRID:AB_3669062, anti-GFP primary antibody RRID:AB_439690, 1/1000) and mouse anti-β-actin RRID:AB_476692, anti-actin primary antibody RRID:AB_476693, anti-α-tubulin primary antibody RRID:AB_477593, anti-BIN1 primary antibodies RRID:AB_725699, RRID:Addgene_51009, LifeAct-GFP RRID:Addgene_51010

Abstract

Background: Alzheimer’s disease (AD) is associated with strong genetic predisposition and early synaptic loss that correlates with cognitive decline. While genetic determinants are thought to contribute to synaptic vulnerability, their precise role in AD pathogenesis at the synaptic level remains unclear. BIN1, a major AD susceptibility gene, is expressed in multiple isoforms, but isoform-specific effects at synapses have not been well defined.

Methods: We investigated the impact of human BIN1 isoforms on synaptic structure and function using Drosophila and mammalian models. In flies, we overexpressed human BIN1 isoforms in retinal photoreceptor neurons and motoneurons, assessing synaptic function by electrophysiology and ultrastructural analyses. Morphological changes at neuromuscular junctions were also quantified. For both readouts, Rab11 modulation was tested as a potential rescue strategy. To determine conservation in mammals and distinguish presynaptic versus postsynaptic roles, we overexpressed BIN1 isoform 1 selectively in presynaptic or postsynaptic compartments of rat hippocampal neurons cultured in microfluidic devices. We assessed structural and functional connectivity using immunofluorescence and microelectrode arrays.

Results: Gain-of-function of BIN1 isoform 1, but not isoforms 8 or 9, induced early loss of synaptic transmission in Drosophila photoreceptor neurons indicating BIN1iso1 synaptotoxicity. Structural analyses revealed accumulation of abnormally large vesicles in photoreceptor terminals, resembling BIN1-induced endosomal defects in cell bodies. Moreover, Rab11 gain-of-function prevented BIN1iso1 synaptotoxicity, suggesting that it originates from endosomal trafficking defects. In motoneurons, BIN1iso1 overexpression induced synapse remodelling, altering bouton morphology, including increased bouton number, reduced bouton size, and formation of satellite boutons. Rab11 modulation was additive to BIN1 isoform 1 effects, suggesting a distinct mechanism. In rat hippocampal neurons, BIN1 isoform 1 decreased synaptic connectivity only when overexpressed presynaptically, a finding confirmed by microelectrode array recordings.

Conclusions: Our findings demonstrate that BIN1iso1 exerts isoform-specific, presynaptic disruption during synapse development and maintenance that compromises synaptic integrity across species. BIN1iso1 synaptotoxicity may contribute to early synapse loss observed in AD and provides mechanistic evidence that genetic determinants such as BIN1 predispose synapses to failure. These results highlight BIN1iso1 as a potential target for therapeutic strategies aimed at preserving synaptic function in AD.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13195-026-02103-7.

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

Repository

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

yger/pre-post-drive

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bce7f3acc50b085fdc2b6fa49e6d649b0c6c75be, 10 September 2025
Languages: Jupyter (3), Python (2)
Size: 6 files, 5 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (5 files), SpikeInterface (5 files), h5py (4 files), Matplotlib (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/yger/pre-post-drive

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 5 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

Data and material from the current study are available from the corresponding authors upon request. The raw code of the novel algorithm used to calculate the pre-post-drive parameter is available at https://github.com/yger/pre-post-drive.

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, 21 authors, 8 keywords, 20 MeSH terms, 7 funders, 71 references, 22 RRIDs.

Cite

This paper

Lambert, E., Gelle, C., Leclerc, V., Freire-Regatillo, A., Liefooghe, L., Barois, N., Malfoi, T., Hermant, X., Demiautte, F., Gallienne, I., Lafont, F., Amouyel, P., Blary, K., Kuenen, S., Najdek, C., Verstreken, P., Siedlecki-Wullich, D., Yger, P., Lambert, J.-C., . . . Dourlen, P. (2026). BIN1 gain-of-function in the presynaptic compartment leads to isoform-specific synaptotoxicity. Alzheimer's research & therapy, 18(1), 185. https://doi.org/10.1186/s13195-026-02103-7

BibTeX

@article{lambert2026bin1,
author = {Lambert, Erwan and Gelle, Carla and Leclerc, Valentin and Freire-Regatillo, Alejandra and Liefooghe, Lucie and Barois, Nicolas and Malfoi, Tommy and Hermant, Xavier and Demiautte, Florie and Gallienne, Inès and Lafont, Frank and Amouyel, Philippe and Blary, Karine and Kuenen, Sabine and Najdek, Chloé and Verstreken, Patrik and Siedlecki-Wullich, Dolores and Yger, Pierre and Lambert, Jean-Charles and Kilinc, Devrim and Dourlen, Pierre},
title = {{BIN1 gain-of-function in the presynaptic compartment leads to isoform-specific synaptotoxicity}},
journal = {Alzheimer's research \& therapy},
year = {2026},
month = jun,
volume = {18},
number = {1},
pages = {185},
publisher = {BMC},
issn = {1758-9193},
doi = {10.1186/s13195-026-02103-7},
url = {https://doi.org/10.1186/s13195-026-02103-7},
pmid = {42237143},
pmcid = {PMC13466144}
}

RIS

TY - JOUR
AU - Lambert, Erwan
AU - Gelle, Carla
AU - Leclerc, Valentin
AU - Freire-Regatillo, Alejandra
AU - Liefooghe, Lucie
AU - Barois, Nicolas
AU - Malfoi, Tommy
AU - Hermant, Xavier
AU - Demiautte, Florie
AU - Gallienne, Inès
AU - Lafont, Frank
AU - Amouyel, Philippe
AU - Blary, Karine
AU - Kuenen, Sabine
AU - Najdek, Chloé
AU - Verstreken, Patrik
AU - Siedlecki-Wullich, Dolores
AU - Yger, Pierre
AU - Lambert, Jean-Charles
AU - Kilinc, Devrim
AU - Dourlen, Pierre
TI - BIN1 gain-of-function in the presynaptic compartment leads to isoform-specific synaptotoxicity
T2 - Alzheimer's research & therapy
J2 - Alzheimers Res Ther
PY - 2026
DA - 2026/06/03
VL - 18
IS - 1
SP - 185
SN - 1758-9193
PB - BMC
DO - 10.1186/s13195-026-02103-7
UR - https://doi.org/10.1186/s13195-026-02103-7
LA - en
ER -

CSL-JSON

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