Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release.

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; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 361 lines · 15 KB · GPL-3.0

import pandas as pd
from pathlib import Path
import os
import glob
import tifffile as tif
import PAZ_Processing as paz
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import kstest, kruskal, f_oneway as anova, ttest_ind as ttest, mannwhitneyu as mwu
import scikit_posthocs as ph
''' Classes to aggregate across experiments the various types of PAZ analysis data, including:
Mesh intensity/localization/colocalization data stored in [X}-ALLTHECELLS.csv files
Radial profile data stored in [X]-ALLTHEPROFILES.csv files
Centroid distribution data stored per [img name]-centroids.csv file in [experiment]_centroids folders'''
# An obvious alternative to these relatively more complex/specific structures would be to simply have a dataframe with experiment as a column value...
# Indeed this is easier. Maybe make a class but just subclass df?
#root_folder = "Z:\\Current members\\DelSignore\\Coding_Projects\\Analyze_PAZ_Data\\TestData"
#version_num = 214
# glob to find all specific version of analysis folders across all experiments
#target_dirs = [path for path in Path(root_folder).rglob(f"Analysis-V{version_num}*")]
#experiment_labels = [dir.name.split("_")[1] for dir in target_dirs]
#for dir in target_dirs:
# print(dir)
#for lab in experiment_labels:
# print(lab)
class DataAggregate:
def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
assert datasets or data_path, "Must provide either a dataset or path etc to construct DataAggregate"
if(datasets == None):
self.datasets = self.load_data(data_path, version_num, file_name)
else:
self.datasets = datasets
def load_data(self, data_path, version_num, file_name):
'''
Load .csv datasets and store as dictionary with experiment number as keys
Note: Tried using glob and Path.rglob to recursively search dirs but
this was *very* slow bc of large number of subdirs.
'''
exp_dirs = glob.glob(data_path+"*/")
analysis_dirs = glob.glob(os.path.join(data_path,"*", f"*V{version_num}*/"))
exp_labels = [dir.split("_")[0] for dir in exp_dirs]
datasets = {}
for exp, exp_dir in zip(exp_labels, exp_dirs):
datasets[exp] = pd.read_csv(os.path.join(exp_dir, file_name))
return datasets
def filter(self, filter_cols, filter_vals):
''' filter all experiments by indicated columns.
filter_cols is a list of strings containing column label(s) eg ['AZ_count']
filter_vals is a list of strings containing filter expressions eg ['>0']'''
filtered = {}
filter_strings = [f"['{filter_col}']{filter_val}" for filter_col, filter_val in zip(filter_cols, filter_vals)]
for experiment in self.datasets:
filter_expression = [f"(self.datasets['{experiment}']{filter_string})" for filter_string in filter_strings]
filter_expression = " & ".join(filter_expression)
bool_filter = eval(filter_expression)
filtered[experiment] = self.datasets[experiment][bool_filter]
return filtered
def average_by_image(self):
grouped = {}
for experiment in self.datasets:
grouped[experiment] = self.datasets[experiment].groupby(['Image']).mean()
return grouped
def aggregate_experiments(self):
return pd.concat(self.datasets)
class MeshData(DataAggregate):
def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
super().__init__(datasets, data_path, version_num, file_name, *args, **kwargs)
def average_by_image(self):
averaged = super().average_by_image()
return MeshData(datasets = averaged)
def filter(self, filter_cols, filter_vals):
filtered = super().filter(filter_cols, filter_vals)
return MeshData(datasets = filtered)
class ProfileData(DataAggregate):
def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
super().__init__(datasets, data_path, version_num, file_name, *args, **kwargs)
def average_by_image(self):
averaged = super().average_by_image()
return ProfileData(datasets = averaged)
def filter(self, filter_cols, filter_vals):
filtered = super().filter(filter_cols, filter_vals)
return ProfileData(datasets = filtered)
class CentroidData(DataAggregate):
def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
super().__init__(datasets, data_path, version_num, file_name, *args, **kwargs)
def average_by_image(self):
averaged = super().average_by_image()
return ProfileData(datasets = averaged)
def filter(self, filter_cols, filter_vals):
filtered = super().filter(filter_cols, filter_vals)
return ProfileData(datasets = filtered)
def superplot_df(df, measurements):
''' Take an aggregated dataframe and organize specific measurements
into 'superplot' fashion by channels and measurements
Row indices are hierarchical - Experiment - image'''
if type(measurements) is not type([]):
measurements = [measurements]
splits = [col.split("_") for col in df.columns.values]
unique_channels = list(set([colsplit[1] for colsplit in splits if len(colsplit)>1]))
retrieve_cols = [f'{measure}_{channel}' for measure in measurements for channel in unique_channels]
# figure out how to handle PCCs. Maybe have those without channels (ie don't underscore)?
superplot = df[[*retrieve_cols]]
return superplot
def aggregate_experiment_pixels(imgdir):
'''
Iterate through images in one experiment folder:
Make mask and signed distance tranform
Normalize pixel intensities per channel based on masked region
create aggregated dataframe listing edm and channel intensities per pixel
imdir - path of format [ExperimentLabel]_[c1-label]_[cn-label]...
returns pd.DataFrame containing Experiment, Image, EDM, and normalized
channel intensity columns for all pixels in all images
'''
if(imgdir[-1] != os.path.sep):
imgdir += os.path.sep
exp = (imgdir.split(os.path.sep)[-2]).split("_")[0]
chans = (imgdir.split(os.path.sep)[-2]).split("_")[1:4]
alldata=pd.DataFrame(columns = ["Experiment", "Image", "EDM"] + chans)
imgs = glob.glob(os.path.join(imgdir, "*.tif"))
for img in imgs:
imgn = tif.imread(img)
mask = paz.segment(imgn, 1)
sdm = paz.sdt(mask)
imgn_norm = paz.norm_by_mask(imgn, mask, 1)
fg_norm = imgn_norm[mask>0]
fg_edm = sdm[sdm>-1]
fg_norm = fg_norm.reshape(-1, imgn_norm.shape[-1])
fg_edm = fg_edm.flatten()
imgdf = pd.DataFrame(fg_norm)
imgdf = imgdf.set_axis(chans, axis=1)
imgdf["EDM"] = fg_edm
imgdf["Image"]=os.path.basename(img)
alldata = pd.concat([alldata, imgdf])
alldata["Experiment"] = exp
return alldata
def aggregate_all_pixels(rootpath):
exp_dirs = glob.glob(rootpath+"*/")
allexpdata = pd.DataFrame()
for expdir in exp_dirs:
print(expdir)
tempdf = process_folder(expdir)
allexpdata = pd.concat([allexpdata, tempdf])
return allexpdata
def aggregate_csvs(root_dir, version, file_name, verbose=False):
'''
Open csv file and reorganize into regualrly formatted DataFrame ready to aggregate
'''
if(root_dir[-1] != os.path.sep):
root_dir += os.path.sep
exp_dirs = glob.glob(root_dir+"*/")
analysis_dirs = glob.glob(os.path.join(root_dir,"*", f"*V{version}*/"))
alldata=pd.DataFrame(columns = ["Experiment"])
for analysis_dir in analysis_dirs:
file = os.path.join(analysis_dir, file_name)
if os.path.exists(file):
exp = (analysis_dir.split(os.path.sep)[-2]).split("_")[-1]
df = pd.read_csv(file)
df["Experiment"] = exp
alldata = pd.concat([alldata, df])
if verbose:
print(f"Loaded {df.shape[0]} meshes from experiment {exp}")
return alldata
def subset_pairwiseData(df, usecols=None, dropchannels=None):
'''
Take a PAZ dataframe and return a subset of columns [usecols].
Optional exclude channels in [dropchannels]
'''
if usecols:
if type(usecols) is not type([]):
usecols=[usecols]
measure_cols = [col for col in df.columns.values if
any([check in col for check in usecols])]
else:
measure_cols = df.columns.values
measure_cols.remove("Experiment")
measure_cols.remove("Image")
# Start with Experiment and Image columns necessary for sorting later
dfsub = df.loc[:, ["Experiment"]]
dfsub[measure_cols] = df.loc[:, measure_cols]
if dropchannels:
if type(dropchannels) is not type([]):
dropchannels=[dropchannels]
drop_cols = [col for col in dfsub.columns.values if
any([check in col for check in dropchannels])]
dfsub = dfsub.drop(drop_cols, axis = 1)
return dfsub
def subset_data(df, usecols=None, dropchannels=None):
'''
Take a PAZ dataframe and return a subset of columns [usecols].
Optional exclude channels in [dropchannels]
'''
if usecols:
if type(usecols) is not type([]):
usecols=[usecols]
measure_cols = [col for col in df.columns.values if
any([check in col for check in usecols])]
else:
measure_cols = df.columns.values
measure_cols.remove("Experiment")
measure_cols.remove("Image")
# Start with Experiment and Image columns necessary for sorting later
dfsub = df.loc[:, ["Experiment", "Image"]]
dfsub[measure_cols] = df.loc[:, measure_cols]
if dropchannels:
if type(dropchannels) is not type([]):
dropchannels=[dropchannels]
drop_cols = [col for col in dfsub.columns.values if
any([check in col for check in dropchannels])]
dfsub = dfsub.drop(drop_cols, axis = 1)
return dfsub
def norm_data(df, norm_col=None, melt=True, keep_scale=False):
'''
Given dataframe of PAZ data, return normalized dataframe
Normalization is per row, so if rows are meshes, normalization will be at level of mesh.
Default behavior is to normalize each column with values against each other column.
Optionally, can set norm_col to one column against which to normalize all other columns.
'''
if norm_col:
measure_cols = [norm_col]
else:
measure_cols = df.columns
measure_cols = measure_cols.drop(["Experiment", "Image"])
norm = df.loc[:, ["Experiment", "Image"]]
for num in measure_cols:
for den in measure_cols:
if num is not den:
norm[f"{num}-{den}"]=df.loc[:, num]/df.loc[:, den]
if melt:
melted = norm.melt(id_vars = ["Experiment", "Image"])
melted[['C1', 'C2']] = melted['variable'].str.split('-', expand=True)
melted = melted.drop("variable", axis=1)
return melted
else:
return norm
def superplot(df, data_order=None, box=True):
melt = df.melt(id_vars = ["Experiment", "Image"])
ImgMean = melt.groupby(["Experiment", "Image", "variable"]).mean().reset_index()
ExpMean = melt.groupby(["Experiment", "variable"]).mean().reset_index()
sp = sns.swarmplot(data=ImgMean, x="variable", y="value", hue="Experiment", size=5, order=data_order, zorder=0)
sns.swarmplot(x="variable", y="value", hue="Experiment", size=12, edgecolor="k", linewidth=2, data=ExpMean, order=data_order, zorder=0)
if box:
sns.boxplot(data=ImgMean, x="variable", y="value",
order=data_order,
showfliers=False,
whis=0,
boxprops={'facecolor':'None', 'linewidth':2.5, 'edgecolor':'k'},
medianprops = {'linewidth':5, 'color':'k'},
whiskerprops={'linewidth':0},
ax=sp)
sp.legend_.remove()
plt.xticks(rotation=45)
def superplot_norm(df, data_order=None):
ImgMean = df.groupby(["Experiment", "Image", "C1"]).mean().reset_index()
ExpMean = df.groupby(["Experiment", "C1"]).mean().reset_index()
sp = sns.swarmplot(data=ImgMean, x="C1", y="value", hue="Experiment", size=5, order=data_order)
ax = sns.swarmplot(data=ExpMean, x="C1", y="value", hue="Experiment", size=12, edgecolor="k", linewidth=2, order=data_order)
sp.legend_.remove()
plt.xticks(rotation=45)
def compareGroups(df, verbose=True):
ImgPost = df.melt()
ImgPost = ImgPost.loc[-np.isnan(ImgPost.value), :]
# Check whether outcomes are normally distributed
ks = [kstest(df.loc[df[col]>-1, col], 'norm') for col in df]
kpass = True if np.min(np.array(ks)[:,1])>0.05 else False
test_name = "ANOVA" if kpass else "Kruskal-Wallis"
posthoc = pd.DataFrame()
if kpass:
#Do anova if vars normally distributed
test = anova(*[df.loc[-np.isnan(df[col]), col] for col in df])
if test[1] < 0.05:
# Do posthoc ttest if model is significant
posthoc = ph.posthoc_ttest(ImgPost, group_col='variable', val_col='value', p_adjust='sidak')
else:
# Do KW test to test if medians different
test = kruskal(*[df.loc[:, col] for col in df], nan_policy='omit')
if test[1] < 0.05:
# Do posthoc dunns if model is significant
posthoc = ph.posthoc_dunn(ImgPost, group_col='variable', val_col='value', p_adjust='sidak')
if verbose:
print(f"Performed {test_name}: test statistic = {test[0]} p-value:{test[1]}")
return {'test':test_name, 'result':test, 'posthoc':posthoc}
def ttestl(df, verbose=True):
'''Assess distributions of groups to compare. If normal, do ttest. If not, do MWU test.'''
ImgPost = df.melt()
ImgPost = ImgPost.loc[-np.isnan(ImgPost.value), :]
# Check whether outcomes are normally distributed
ks = [kstest(df.loc[df[col]>-1, col], 'norm') for col in df]
kpass = True if np.min(np.array(ks)[:,1])>0.05 else False
test_name = "ttest" if kpass else "Mann-Whitney"
posthoc = pd.DataFrame()
if kpass:
#Do ttest if vars normally distributed
test = ttest(*[df.loc[-np.isnan(df[col]), col] for col in df])
else:
# Do KW test to test if medians different
test = kruskal(*[df.loc[:, col] for col in df], nan_policy='omit')
if test[1] < 0.05:
# Do posthoc dunns if model is significant
posthoc = ph.posthoc_dunn(ImgPost, group_col='variable', val_col='value', p_adjust='sidak')
if verbose:
print(f"Performed {test_name}: test statistic = {test[0]} p-value:{test[1]}")
return {'test':test_name, 'result':test, 'posthoc':posthoc}

Aggregate_PAZ_Data.py at commit 93478cf, under GPL-3.0 · at the source

Overview

Authors: Javier Emperador-Melero^1,2, Steven J Del Signore³, Kevin M De León González³, Pascal S Kaeser¹, Avital A Rodal³

ORCID iDs: Javier Emperador-Melero, Steven J Del Signore, Kevin M De León González, Pascal S Kaeser, Avital A Rodal

Institutions: Harvard University (United States); New York University (United States); Brandeis University (United States)

Journal: eLife, volume 14, article RP107276

Dates: published online 17 June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7554/elife.107276 · PMID 42307978 · PMCID PMC13275065 · OpenAlex W4411540712

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: mouse (organism), drosophila (organism), cellular / molecular (subfield)

Methods: Connectivity, Statistics, Evoked potentials

Keywords: D. melanogaster, Mouse

MeSH: Endocytosis*, Presynaptic Terminals*, Adaptor Proteins, Signal Transducing, Adaptor Proteins, Vesicular Transport, Animals, Drosophila, Drosophila melanogaster, Drosophila Proteins, Dynamins, Hippocampus, Mice, Nerve Tissue Proteins, Neuromuscular Junction, Synaptic Vesicles, Vesicular Transport Proteins (* major topic)

Topic: Cellular transport and secretion (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: NINDS NIH HHS (R01 NS083898); NIH HHS (R01MH113349, S10 OD034223, T32007292, R01NS116375, K99NS129959, R01NS083898); NIMH NIH HHS (R01 MH113349); U.S. National Science Foundation (NSF-DMR 2011846)

Citations: cited by 2 papers (Europe PMC); 122 references in the paper

Research resources: anti-PSD-95 (Mouse monoclonal) RRID:AB_10698024, anti-Bassoon (Mouse monoclonal) RRID:AB_11181058, anti-Synapsin-1 (Rabbit polyclonal) RRID:AB_2200097, anti-Gephyrin (Mouse monoclonal) RRID:AB_2232546, anti-Brp nc82 (Mouse monoclonal) RRID:AB_2314866, goat anti-mouse Alexa Fluor 488 RRID:AB_2534088, goat anti-rabbit Alexa Fluor 633 RRID:AB_2535731, goat anti-guinea pig Alexa Fluor 633 RRID:AB_2535757, goat anti-mouse IgG2a Alexa Fluor 555 RRID:AB_2535776, goat anti-rabbit Alexa Fluor 555 RRID:AB_2535849, goat anti-guinea pig Alexa Fluor 555 RRID:AB_2535856, RRID:AB_2567353, goat anti-rabbit Alexa Fluor 488 RRID:AB_2576217, anti-PSD-95 (Guinea pig monoclonal) RRID:AB_2619800, mouse anti-Dynamin RRID:AB_397640, anti-Synapsin (Mouse monoclonal) RRID:AB_528479, anti-AP180 (Rabbit polyclonal) RRID:AB_887691, anti-Munc13-1 (Rabbit polyclonal) RRID:AB_887733, anti-Synaptophysin (Mouse monoclonal) RRID:AB_887824, UAS-TeTxLC; UAS-TeNT RRID:BDSC_28838, GMR94G06-GAL4 RRID:BDSC_40701, rab3rup RRID:BDSC_78045, liprinR60 RRID:BDSC_8561, liprinF3ex15 RRID:BDSC_8563, HEK 293T cells RRID:CVCL_0063, Ppfia1 conditional knockout RRID:IMSR_EUMMCR:25506, Ppfia4 conditional knockout RRID:IMSR_EUMMCR:3103, Ppfia2 conditional knockout RRID:IMSR_HAR:6799, Erc1 conditional knockout RRID:IMSR_JAX:015830, Erc2 conditional knockout RRID:IMSR_JAX:015831, Rims1 conditional knockout RRID:IMSR_JAX:015832, Rims2 conditional knockout RRID:IMSR_JAX:015833, RRID:SCR_025892

Abstract

In presynaptic nerve terminals, the endocytic apparatus rapidly restores synaptic vesicles after neurotransmitter release. Many endocytic proteins localize to the periactive zone, a loosely defined area adjacent to active zones. A prevailing model posits that recruitment of these endocytic proteins to the periactive zone is activity-dependent. We show that periactive zone targeting of endocytic proteins is largely independent of active zone machinery and synaptic activity. At mouse hippocampal synapses and Drosophila neuromuscular junctions, pharmacological or genetic silencing resulted in unchanged or increased levels of endocytic proteins including Dynamin, Amphiphysin, Nervous Wreck, Endophilin A, Dap160/Intersectin, PIPK1γ, and AP-180. Similarly, disruption of active zone assembly via genetic ablation of active zone scaffolds at each synapse did not impair the localization of endocytic proteins. Overall, our work indicates that endocytic proteins are constitutively deployed to the periactive zone and supports the existence of independent assembly pathways for active zones and periactive zones.

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

Repositories

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

rodallab/nmj-measurement

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: c25ff74ccd0c20bd917189eb4f224d779859cd7f, 9 June 2026

Size: 2 files, 0 scripts

Software Heritage: archived

Found in: the text, “NMJ image acquisition and processing”

Holds: license file

Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

1 file

LICENSE, License, 674 lines

At the source: github.com/rodallab/nmj-measurement

rodallab/paz-analysis

License: GPL-3.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 93478cfa4b1449ebf54b6008f456116bb09d7340, 22 September 2022

Languages: Jupyter (4), Python (3)

Size: 15 files, 7 scripts

Software Heritage: archived

Found in: the text, “NMJ image acquisition and processing”

Holds: README, license file, 4 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (7 files), Matplotlib (6 files), SciPy (6 files), seaborn (6 files), pandas (5 files), scikit-posthocs (5 files), tifffile (5 files), scikit-learn (3 files), Numba (1 file), scikit-image (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

9 files

Analysis/Aggregate_PAZ_Data.py, Python, 361 lines
Analysis/CentroidAnalysis.py, Python, 368 lines
Analysis/Example_NMJ-level_analysesStats.ipynb, Jupyter, 142 lines
Analysis/Example_PAZ_Analyses.ipynb, Jupyter, 536 lines
Analysis/Example_PAZ_DescriptiveAnalyses.ipynb, Jupyter, 548 lines
Analysis/PAZ_Processing.py, Python, 35 lines
Analysis/paz-analysis_manuscript_analyses.ipynb, Jupyter, 1,515 lines
LICENSE, License, 674 lines
README.md, Text, 20 lines

At the source: github.com/rodallab/paz-analysis

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:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
7 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

Datasets cited

zenodo:20071165, at Zenodo; found in “Data availability”

Data availability

Data points generated for this study are included in the figures. Raw numerical data used to generate all figures are available at https://doi.org/10.5281/zenodo.20071165. Images, code, and raw data for Drosophila experiments are available at https://doi.org/10.5281/zenodo.17202731.

The following datasets were generated:

Emperador-Melero J. 2026. Data table for Emperador-Melero, Del Signore et al; "Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release". Zenodo.

Emperador Melero J, Del Signore S, De Leon Gonzalez K, Kaeser P, Rodal A. 2025. Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release: Drosophila Data. Zenodo.

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, pages, dates, 5 authors, 2 keywords, 15 MeSH terms, 4 funders, 119 references, 33 RRIDs.

Cite

This paper

Emperador-Melero, J., Del Signore, S. J., De León González, K. M., Kaeser, P. S., & Rodal, A. A. (2026). Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release. eLife, 14, RP107276. https://doi.org/10.7554/elife.107276

BibTeX

@article{emperadormelero2026deployment, author = {Emperador-Melero, Javier and Del Signore, Steven J and De León González, Kevin M and Kaeser, Pascal S and Rodal, Avital A}, title = {{Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release}}, journal = {eLife}, year = {2026}, month = jun, volume = {14}, pages = {RP107276}, publisher = {eLife Sciences Publications, Ltd}, issn = {2050-084X}, doi = {10.7554/elife.107276}, url = {https://doi.org/10.7554/elife.107276}, pmid = {42307978}, pmcid = {PMC13275065} }

RIS

TY - JOUR AU - Emperador-Melero, Javier AU - Del Signore, Steven J AU - De León González, Kevin M AU - Kaeser, Pascal S AU - Rodal, Avital A TI - Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release T2 - eLife J2 - eLife PY - 2026 DA - 2026/06/17 VL - 14 SP - RP107276 SN - 2050-084X PB - eLife Sciences Publications, Ltd DO - 10.7554/elife.107276 UR - https://doi.org/10.7554/elife.107276 LA - en ER -

CSL-JSON

{ "id": "10.7554/elife.107276", "type": "article-journal", "title": "Deployment of endocytic machinery to periactive zones of nerve terminals is independent of active zone assembly and evoked release", "container-title": "eLife", "author": [ { "family": "Emperador-Melero", "given": "Javier" }, { "family": "Del Signore", "given": "Steven J" }, { "family": "De León González", "given": "Kevin M" }, { "family": "Kaeser", "given": "Pascal S" }, { "family": "Rodal", "given": "Avital A" } ], "container-title-short": "eLife", "volume": "14", "page": "RP107276", "DOI": "10.7554/elife.107276", "PMID": "42307978", "PMCID": "PMC13275065", "ISSN": "2050-084X", "publisher": "eLife Sciences Publications, Ltd", "URL": "https://doi.org/10.7554/elife.107276", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 17 ] ] } }

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:10.7554/elife.79077 [code]: Munc13 supports fusogenicity of non-docked vesicles at synapses with disrupted active zones.
Journal: eLife
In common: mouse, cellular / molecular, 18 references, author Pascal S Kaeser
[2] doi:10.1038/s41593-024-01592-9: Liprin-α proteins are master regulators of human presynapse assembly.
Journal: Nature neuroscience
In common: cellular / molecular, 18 references
[3] doi:10.1038/s41467-024-55382-9 [code]: Blobby is a synaptic active zone assembly protein required for memory in Drosophila.
Journal: Nature communications
In common: drosophila, cellular / molecular, 13 references
[4] doi:10.1038/s41593-025-02002-4 [code]: Intersectin and endophilin condensates prime synaptic vesicles for release site replenishment.
Journal: Nature neuroscience
In common: mouse, cellular / molecular, 12 references
[5] doi:10.1038/s44318-024-00145-x [code]: Dynamin 1xA interacts with Endophilin A1 via its spliced long C-terminus for ultrafast endocytosis.
Journal: The EMBO journal
In common: mouse, cellular / molecular, 11 references
[6] doi:10.26508/lsa.202302021 [code]: Nanoscaled RIM clustering at presynaptic active zones revealed by endogenous tagging.
Journal: Life science alliance
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, cellular / molecular, 8 references
[7] doi:10.7554/elife.88412: Ca<sup>2+</sup> channel and active zone protein abundance intersects with input-specific synapse organization to shape functional synaptic diversity.
Journal: eLife
In common: drosophila, cellular / molecular, 10 references
[8] doi:10.1111/jnc.70551 [code]: Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway.
Journal: Journal of neurochemistry
In common: scikit-learn, SciPy, Matplotlib, 1 other tool, cellular / molecular, 7 references
[9] doi:10.1523/jneurosci.2320-25.2026: The Synaptic Vesicle Priming Protein Munc13 Mediates Evoked Somatodendritic Dopamine Release.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: mouse, cellular / molecular, 6 references, author Pascal S Kaeser
[10] doi:10.1038/s41467-025-62766-y [code]: Trans-synaptic molecular context of NMDA receptor nanodomains.
Journal: Nature communications
In common: tifffile, Numba, scikit-learn, 4 other tools, cellular / molecular, 5 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.

Add the badge to the code's README

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown

<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.7554/elife.107276.svg)](https://openscicode.org/badge/doi/10.7554/elife.107276)
<!-- oscr-badge:end -->

reStructuredText

.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.7554/elife.107276.svg
   :target: https://openscicode.org/badge/doi/10.7554/elife.107276
   :alt: OSCR

.. oscr-badge:end

HTML

<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.7554/elife.107276"><img src="https://openscicode.org/badge/doi/10.7554/elife.107276.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge 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: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

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