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Presynaptic filopodia form kinapses and promote convective membrane flow to sustain synchronous neurotransmitter release.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § RESULTS › A convection-diffusion model for filopodial effects on synchronous versus asynchronous release › Stochastic release module ↔ stochastic_vesicle_release/stochastic_vesicle_release_simulation.py, lines 74 to 160 · score 0.63 · considered asynchronous, Release events, simulated, ms, RRP, probabilities · Report a mismatch
  2. [2] § MATERIALS AND METHODS › Analysis of membrane flow and uPAINT SPT analysis ↔ spt_data_analysis/spt_data_analysis.py, lines 35 to 104 · score 0.59 · squared displacement, curves, longer, frames, SPT, fitting · Report a mismatch
  3. [3] § MATERIALS AND METHODS › Live imaging of filopodia formation and colocalization analysis with SypmOr2, LifeAct, Bsn, and GluA ↔ stochastic_vesicle_release/stochastic_vesicle_release_simulation.py, lines 74 to 160 · score 0.56 · pulse width, 4 s, RC, ms, AP, stimulation · Report a mismatch

Paper

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The authors' code

Python · 160 lines · 6.1 KB · no license · 2 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Tue Apr 8 19:14:29 2025
  5. @author: Rico's Account
  6. """
  7. import numpy as np
  8. from matplotlib import pyplot as plt
  9. from functions import integral, inverse
  11. def spike_train(time, pulse_width, t1 = 5, t2 = 100, A1 = 0.95, A2 = 0.05, plot = False):
  13. if np.isscalar(time):
  14. if time>pulse_width:
  15. return (A1*np.exp(-(time-pulse_width)/t1) + A2*np.exp(-(time-pulse_width)/t2))
  16. elif time<=pulse_width:
  17. return 1
  18. else:
  19. y = time.copy()
  20. y[time > pulse_width] = (A1*np.exp(-(time-pulse_width)/t1) + A2*np.exp(-(time-pulse_width)/t2))[time > pulse_width]
  21. y[time <= pulse_width] = 1
  23. if plot == True:
  24. plt.figure(dpi=300)
  25. plt.title('Release probability over time')
  26. plt.xlabel('Time (ms)')
  27. plt.ylabel('Release Probability (P_r = 1)')
  28. plt.plot(time, y)
  29. plt.show()
  31. return y
  33. def compute_next_timestep(a1, a2, t0, pulse_width, max_dt, increment, plot = False, debug = False):
  35. n = 1
  36. min_dt = 200
  38. while True:
  39. try:
  40. t = np.linspace(t0, t0+min_dt*n, int((min_dt)/increment))
  41. z = a1 + a2* spike_train(t, pulse_width)
  42. result = integral(t, z)
  44. if plot == True:
  45. plt.plot(t, result, color = 'red')
  46. plt.show()
  48. x = np.random.uniform(0,1)
  49. tau = inverse(t-t0, result, -np.log(1-x))
  50. break
  52. except KeyboardInterrupt:
  53. print("Stopped by user")
  54. break
  56. except Exception as e:
  57. if debug:
  58. print(e)
  60. n += 1
  62. if min_dt*n > max_dt:
  63. raise Exception('Max dt exceeded')
  65. else:
  66. if debug:
  67. print(f"dt too small, new dt = {min_dt*n}")
  68. pass
  70. return tau
  72. #Parameters
  74. simlen = 50 #simulation length in ms
  75. pulse_width = 1 #pulse width of AP stimulation in millseconds
  76. k = 0.004 #units of 1/vesicle*ms
  77. R0 = 6 #total vesicles in RRP
  78. async_thresh = 10 #ms after ap upon which releases are considered asynchronous
  79. R_init = R0/2 #initial number of vesicles in RRP
  81. P_r_list = [0.048, 0.44] #Probability of Release (per vesicle) values to iterate through
  82. async_frac_list = [] #average async release fraction per P_r in P_r_list
  84. plt.figure(dpi=600)
  85. plt.rcParams.update({'font.size': 18}) #initialize a matplotlib figure
  87. for P_r in P_r_list: #Choose a P_r from P_r_list
  88. times = [] #initialize a list containing times where a vesicle was released, aggregated across all iterations
  89. iteration = 0 #initialize number of iterations run so far
  91. while iteration < 30000: #sample at least 30000 times before stopping
  93. R = R_init #initialize RRP with R_init vesicles
  94. S = 0 #initialize with 0 vesicles released
  95. time = 0 #initialize current time in simulation as 0
  97. R_rxn = [1, -1] #if reaction 0, then RRP gains vesicle, if reaction 1, RRP loses vesicle
  98. S_rxn = [0, 1] #if reaction 0, no vesicles are released, if reaction 1, a vesicle is released
  100. while(time<simlen): #run the simulation as long as the current time is less than simlen
  101. a1 = k*(R0-R)
  102. a2 = P_r*R
  104. dt = compute_next_timestep(a1, a2, t0 = time, #compute when the first reaction happens
  105. pulse_width = pulse_width,
  106. max_dt = 4000, increment = 1e-2)
  108. time += dt #move forward to when the reaction happens
  110. propensities = [a1, a2*spike_train(time, pulse_width)] #calculate propensities
  111. p_sum = np.sum(propensities)
  112. propensities_prob = propensities/p_sum
  113. rxn = np.random.choice([0, 1], p = propensities_prob) #pick the reaction to run
  115. if rxn == 1 and time <= simlen:
  116. times.append(time) #if a release event happens at this time, add it to the list of release events
  118. R = R + R_rxn[rxn] #update the number of vesicles in RRP
  119. S = S + S_rxn[rxn] #update the number of vesicles released
  121. iteration += 1
  122. if np.mod(iteration, 100) == 0:
  123. print(f'Iteration {iteration}') #output iteration number every 100 iterations
  125. times = np.array(times)#convert times to a numpy array
  127. def compute_async_frac(u_bound, times, async_thresh):
  128. #only compute the ratio within the simulation time interval
  129. data = times[times<=u_bound]
  131. syn = np.shape(data[data<=async_thresh])[0] #releases within async_thresh ms is synchronous
  132. asyn = np.shape(data[data>async_thresh])[0] #releases after async_thresh ms is asynchronous
  133. async_frac = asyn/(syn+asyn) #compute async release fraction
  135. plt.xlim(0,u_bound)
  136. bins = np.linspace(0, 50, 25)
  137. bin_centers = (bins[:-1]+bins[1:])/2
  138. plt.xlim(0,u_bound)
  139. counts, bins = np.histogram(data, bins = bins, density = True)
  140. plt.plot(bin_centers, counts, '.', markersize = 15, label = f'P_r={P_r}')
  141. plt.plot(bin_centers, counts, '--', linewidth = 2, color = 'black') #plot the data but do not show yet; we will save everything at the end
  143. return async_frac
  145. async_frac = compute_async_frac(simlen, times, async_thresh)
  146. async_frac_list.append(async_frac)
  148. plt.xlabel('Time (ms)')
  149. plt.ylabel('Normalized Frequency')
  150. plt.axvline(async_thresh, linestyle = '--', color = 'orangered')
  151. plt.yscale('log')
  152. plt.legend()
  153. plt.savefig('/results/vesicle_releases_over_time.jpg')
  154. #%%
  155. plt.figure(dpi=300)
  156. plt.scatter(np.array(P_r_list), async_frac_list)
  157. plt.xlabel('Probability of release (per vesicle)')
  158. plt.ylabel('Asynchronous release fraction')
  159. plt.title('Async. Frac. vs. Prob. of release')
  160. plt.savefig('/results/Async_frac_vs_P_r.jpg')

stochastic_vesicle_release_simulation.py at commit df8af3c, no license · at the source

Overview

Authors: Akanksha Jain1,2,3, Rico Dong4,5, Jana Kroll6, Jack F Webster1,2,3, Jon Moss7, Arthur Bikbaev8, Sila K Ultanir9, Alfredo Gonzalez-Sulser1,2,3, Christian Rosenmund6, Martin Heine8, Jian Liu4,5, Michael A Cousin1,2,3, Daniela Ivanova1,2,3
ORCID iDs: Akanksha Jain, Rico Dong, Jana Kroll, Jack F Webster, Sila K Ultanir, Alfredo Gonzalez-Sulser, Christian Rosenmund, Martin Heine, Jian Liu, Michael A Cousin, Daniela Ivanova
  1. Institute for Neuroscience and Cardiovascular Research, Hugh Robson Building, George Square, University of Edinburgh, Edinburgh, Scotland, UK
  2. Muir Maxwell Epilepsy Centre, Hugh Robson Building, George Square, University of Edinburgh, Edinburgh, Scotland, UK
  3. Simons Initiative for the Developing Brain, Hugh Robson Building, George Square, University of Edinburgh, Edinburgh, Scotland, UK
  4. Department of Cell Biology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
  5. Center for Cell Dynamics, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
  6. Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Neurophysiology, Charitéplatz 1, Berlin, Germany
  7. The Roslin Institute 3D Volume Electron Microscopy Capability, Proteomics and Metabolomics Facility, The Roslin Institute, Royal (Dick) School of Veterinary Studies, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, Scotland, UK
  8. Johannes Gutenberg University Mainz, Institute of Developmental Biology and Neurobiology, AG Functional Neurobiology, Biocenter 1, Hanns-Dieter Hüsch Weg 15, Mainz, Germany
  9. Kinases and Brain Development Lab, The Francis Crick Institute, London, UK
Institutions: Simons Initiative for the Developing Brain (United Kingdom); University of Edinburgh (United Kingdom); Johns Hopkins University (United States); Humboldt-Universität zu Berlin (Germany); Freie Universität Berlin (Germany); Charité – Universitätsmedizin Berlin (Germany); Roslin Institute (United Kingdom); Johannes Gutenberg University Mainz (Germany); The Francis Crick Institute (United Kingdom)
Journal: Science advances, volume 12, issue 44, article eaei2661
Dates: received 23 April 2026; accepted 25 September 2026; published online 28 October 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aei2661 · PMID 42950046 · PMCID PMC13715708 · OpenAlex W7224086438
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Connectivity, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Cell Membrane*, Neurotransmitter Agents*, Presynaptic Terminals*, Pseudopodia*, Synaptic Transmission*, Animals, Humans, Neuronal Plasticity, Synaptic Vesicles (* major topic)
Topic: Neuroscience and Neuropharmacology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Simons Foundation; Johns Hopkins University; Wellcome Trust; Federation of European Biochemical Societies; Cancer Research UK; Royal Society; Epilepsy Research UK; Carnegie Trust for the Universities of Scotland; University of Edinburgh; Deutsche Forschungsgemeinschaft; National Institutes of Health; Medical Research Council
Citations: not cited yet (Europe PMC); 80 references in the paper
Research resources: NHE1 RRID:AB_11153959, goat anti-rabbit Alexa Fluor 488 RRID:AB_143165, synaptophysin 1 RRID:AB_1961591, vGAT sulfocyanine 3 (vGAT-Cy3 RRID:AB_2189809, donkey anti-rabbit Alexa Fluor 568 RRID:AB_2534017, goat anti-rabbit Alexa Fluor 647 RRID:AB_2535812, synaptobrevin 2 RRID:AB_2619757, GluA Oyster550 RRID:AB_2619877, GFP-Booster_ATTO647N RRID:AB_2629215, RRID:AB_2715510, goat anti-mouse IgG IRDye 800 CW RRID:AB_621842, SV2A RRID:AB_778192, pAAV.hSyn.iGluSnFR3.v857.SGZ RRID:Addgene_178330, GPI-mApple RRID:Addgene_182868, pCAG:GPI-GFP RRID:Addgene_32601, GCaMP6f RRID:Addgene_40755, mRuby-Lifeact-7 RRID:Addgene_54560, RRID:IMSR_JAX:007788

Abstract

Synaptic plasticity is the principal mechanism by which neural circuits compute and adapt. Presynaptic plasticity is traditionally regarded as a transient adaptation of the synaptic vesicle fusion and recycling machinery, whereas the role of structural remodeling of synaptic boutons remains unclear. Here, we demonstrate a mechanical coupling between presynaptic structural remodeling and synaptic efficacy. We find that dynamic presynaptic filopodia emerge during active synaptic transmission and deform synaptic boutons while forming transient contacts with postsynaptic structures, which we term neuronal kinapses. Filopodia formation is activity dependent and occurs in intact circuits and human brain. Mechanistically, filopodial growth induces convective in-plane membrane flow toward the filopodium, accelerating release site clearance, a process associated with sustained release probability during repeated synaptic vesicle fusion. As a result, filopodia formation favors an increase in synchronous over asynchronous neurotransmitter release. These results indicate that presynaptic structural remodeling can alter the spatial organization of neurotransmission and, through the membrane flow it generates, modulate its temporal precision.

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 3 matches between paragraphs and lines of code.

Zenodo 19667730

License: CC-BY-4.0
State: the link answers, verified on 30 October 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: Matplotlib (3 files), NumPy (3 files), SciPy (2 files), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 30 October 2026: the link answers (HTTP 200)
  • 30 October 2026: the link answers (HTTP 200)
4 files
At the source: doi.org/10.5281/zenodo.19667730

jliu187/presynaptic-terminal-flow

License: none: the authors keep all their rights
State: the link answers, verified on 30 October 2026
Evidence: files inventoried
Commit: df8af3ca6885253b94babf81f87c3f085dd764e9, 20 April 2026
Languages: Python (3)
Size: 27 files, 3 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), SciPy (2 files), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 30 October 2026: the link answers
  • 30 October 2026: the link answers
4 files
At the source: github.com/jliu187/Presynaptic-terminal-flow/tree/presynaptic_terminal_flow

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

Tracing map

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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;
  • 6 scripts, each with its path and the digest of its content;
  • 3 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.

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

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. All custom code used for data analysis and mathematical modeling is archived on Zenodo at https://doi.org/10.5281/zenodo.19667730. The newly generated DNA constructs in this study are available from the corresponding author (D.I.) upon reasonable request without restrictions.

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Version 1, 30 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 9 MeSH terms, 12 funders, 79 references, 18 RRIDs.

Cite

This paper

Jain, A., Dong, R., Kroll, J., Webster, J. F., Moss, J., Bikbaev, A., Ultanir, S. K., Gonzalez-Sulser, A., Rosenmund, C., Heine, M., Liu, J., Cousin, M. A., & Ivanova, D. (2026). Presynaptic filopodia form kinapses and promote convective membrane flow to sustain synchronous neurotransmitter release. Science advances, 12(44), eaei2661. https://doi.org/10.1126/sciadv.aei2661

BibTeX

@article{jain2026presynaptic,
author = {Jain, Akanksha and Dong, Rico and Kroll, Jana and Webster, Jack F and Moss, Jon and Bikbaev, Arthur and Ultanir, Sila K and Gonzalez-Sulser, Alfredo and Rosenmund, Christian and Heine, Martin and Liu, Jian and Cousin, Michael A and Ivanova, Daniela},
title = {{Presynaptic filopodia form kinapses and promote convective membrane flow to sustain synchronous neurotransmitter release}},
journal = {Science advances},
year = {2026},
month = oct,
volume = {12},
number = {44},
pages = {eaei2661},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aei2661},
url = {https://doi.org/10.1126/sciadv.aei2661},
pmid = {42950046},
pmcid = {PMC13715708}
}

RIS

TY - JOUR
AU - Jain, Akanksha
AU - Dong, Rico
AU - Kroll, Jana
AU - Webster, Jack F
AU - Moss, Jon
AU - Bikbaev, Arthur
AU - Ultanir, Sila K
AU - Gonzalez-Sulser, Alfredo
AU - Rosenmund, Christian
AU - Heine, Martin
AU - Liu, Jian
AU - Cousin, Michael A
AU - Ivanova, Daniela
TI - Presynaptic filopodia form kinapses and promote convective membrane flow to sustain synchronous neurotransmitter release
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/10/28
VL - 12
IS - 44
SP - eaei2661
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aei2661
UR - https://doi.org/10.1126/sciadv.aei2661
LA - en
ER -

CSL-JSON

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"id": "10.1126/sciadv.aei2661",
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"container-title": "Science advances",
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{
"family": "Ultanir",
"given": "Sila K"
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{
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"volume": "12",
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"page": "eaei2661",
"DOI": "10.1126/sciadv.aei2661",
"PMID": "42950046",
"PMCID": "PMC13715708",
"ISSN": "2375-2548",
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"date-parts": [
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}

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