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

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Python · 361 lines · 15 KB · GPL-3.0

  1. import pandas as pd
  2. from pathlib import Path
  3. import os
  4. import glob
  5. import tifffile as tif
  6. import PAZ_Processing as paz
  7. import numpy as np
  8. import seaborn as sns
  9. import matplotlib.pyplot as plt
  10. from scipy.stats import kstest, kruskal, f_oneway as anova, ttest_ind as ttest, mannwhitneyu as mwu
  11. import scikit_posthocs as ph
  13. ''' Classes to aggregate across experiments the various types of PAZ analysis data, including:
  14. Mesh intensity/localization/colocalization data stored in [X}-ALLTHECELLS.csv files
  15. Radial profile data stored in [X]-ALLTHEPROFILES.csv files
  16. Centroid distribution data stored per [img name]-centroids.csv file in [experiment]_centroids folders'''
  18. # An obvious alternative to these relatively more complex/specific structures would be to simply have a dataframe with experiment as a column value...
  19. # Indeed this is easier. Maybe make a class but just subclass df?
  21. #root_folder = "Z:\\Current members\\DelSignore\\Coding_Projects\\Analyze_PAZ_Data\\TestData"
  22. #version_num = 214
  23. # glob to find all specific version of analysis folders across all experiments
  24. #target_dirs = [path for path in Path(root_folder).rglob(f"Analysis-V{version_num}*")]
  25. #experiment_labels = [dir.name.split("_")[1] for dir in target_dirs]
  26. #for dir in target_dirs:
  27. # print(dir)
  29. #for lab in experiment_labels:
  30. # print(lab)
  32. class DataAggregate:
  33. def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
  34. assert datasets or data_path, "Must provide either a dataset or path etc to construct DataAggregate"
  35. if(datasets == None):
  36. self.datasets = self.load_data(data_path, version_num, file_name)
  37. else:
  38. self.datasets = datasets
  40. def load_data(self, data_path, version_num, file_name):
  41. '''
  42. Load .csv datasets and store as dictionary with experiment number as keys
  43. Note: Tried using glob and Path.rglob to recursively search dirs but
  44. this was *very* slow bc of large number of subdirs.
  45. '''
  46. exp_dirs = glob.glob(data_path+"*/")
  47. analysis_dirs = glob.glob(os.path.join(data_path,"*", f"*V{version_num}*/"))
  48. exp_labels = [dir.split("_")[0] for dir in exp_dirs]
  49. datasets = {}
  50. for exp, exp_dir in zip(exp_labels, exp_dirs):
  51. datasets[exp] = pd.read_csv(os.path.join(exp_dir, file_name))
  52. return datasets
  54. def filter(self, filter_cols, filter_vals):
  55. ''' filter all experiments by indicated columns.
  56. filter_cols is a list of strings containing column label(s) eg ['AZ_count']
  57. filter_vals is a list of strings containing filter expressions eg ['>0']'''
  58. filtered = {}
  59. filter_strings = [f"['{filter_col}']{filter_val}" for filter_col, filter_val in zip(filter_cols, filter_vals)]
  60. for experiment in self.datasets:
  61. filter_expression = [f"(self.datasets['{experiment}']{filter_string})" for filter_string in filter_strings]
  62. filter_expression = " & ".join(filter_expression)
  63. bool_filter = eval(filter_expression)
  64. filtered[experiment] = self.datasets[experiment][bool_filter]
  65. return filtered
  67. def average_by_image(self):
  68. grouped = {}
  69. for experiment in self.datasets:
  70. grouped[experiment] = self.datasets[experiment].groupby(['Image']).mean()
  71. return grouped
  73. def aggregate_experiments(self):
  74. return pd.concat(self.datasets)
  77. class MeshData(DataAggregate):
  78. def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
  80. super().__init__(datasets, data_path, version_num, file_name, *args, **kwargs)
  82. def average_by_image(self):
  83. averaged = super().average_by_image()
  84. return MeshData(datasets = averaged)
  86. def filter(self, filter_cols, filter_vals):
  87. filtered = super().filter(filter_cols, filter_vals)
  88. return MeshData(datasets = filtered)
  90. class ProfileData(DataAggregate):
  91. def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
  92. super().__init__(datasets, data_path, version_num, file_name, *args, **kwargs)
  94. def average_by_image(self):
  95. averaged = super().average_by_image()
  96. return ProfileData(datasets = averaged)
  98. def filter(self, filter_cols, filter_vals):
  99. filtered = super().filter(filter_cols, filter_vals)
  100. return ProfileData(datasets = filtered)
  102. class CentroidData(DataAggregate):
  103. def __init__(self, datasets=None, data_path=None, version_num=None, file_name=None, *args, **kwargs):
  104. super().__init__(datasets, data_path, version_num, file_name, *args, **kwargs)
  106. def average_by_image(self):
  107. averaged = super().average_by_image()
  108. return ProfileData(datasets = averaged)
  110. def filter(self, filter_cols, filter_vals):
  111. filtered = super().filter(filter_cols, filter_vals)
  112. return ProfileData(datasets = filtered)
  115. def superplot_df(df, measurements):
  116. ''' Take an aggregated dataframe and organize specific measurements
  117. into 'superplot' fashion by channels and measurements
  118. Row indices are hierarchical - Experiment - image'''
  119. if type(measurements) is not type([]):
  120. measurements = [measurements]
  121. splits = [col.split("_") for col in df.columns.values]
  122. unique_channels = list(set([colsplit[1] for colsplit in splits if len(colsplit)>1]))
  123. retrieve_cols = [f'{measure}_{channel}' for measure in measurements for channel in unique_channels]
  124. # figure out how to handle PCCs. Maybe have those without channels (ie don't underscore)?
  126. superplot = df[[*retrieve_cols]]
  127. return superplot
  129. def aggregate_experiment_pixels(imgdir):
  130. '''
  131. Iterate through images in one experiment folder:
  132. Make mask and signed distance tranform
  133. Normalize pixel intensities per channel based on masked region
  134. create aggregated dataframe listing edm and channel intensities per pixel
  136. imdir - path of format [ExperimentLabel]_[c1-label]_[cn-label]...
  137. returns pd.DataFrame containing Experiment, Image, EDM, and normalized
  138. channel intensity columns for all pixels in all images
  140. '''
  142. if(imgdir[-1] != os.path.sep):
  143. imgdir += os.path.sep
  144. exp = (imgdir.split(os.path.sep)[-2]).split("_")[0]
  145. chans = (imgdir.split(os.path.sep)[-2]).split("_")[1:4]
  146. alldata=pd.DataFrame(columns = ["Experiment", "Image", "EDM"] + chans)
  147. imgs = glob.glob(os.path.join(imgdir, "*.tif"))
  149. for img in imgs:
  150. imgn = tif.imread(img)
  151. mask = paz.segment(imgn, 1)
  152. sdm = paz.sdt(mask)
  153. imgn_norm = paz.norm_by_mask(imgn, mask, 1)
  154. fg_norm = imgn_norm[mask>0]
  155. fg_edm = sdm[sdm>-1]
  156. fg_norm = fg_norm.reshape(-1, imgn_norm.shape[-1])
  157. fg_edm = fg_edm.flatten()
  158. imgdf = pd.DataFrame(fg_norm)
  159. imgdf = imgdf.set_axis(chans, axis=1)
  160. imgdf["EDM"] = fg_edm
  161. imgdf["Image"]=os.path.basename(img)
  162. alldata = pd.concat([alldata, imgdf])
  163. alldata["Experiment"] = exp
  164. return alldata
  166. def aggregate_all_pixels(rootpath):
  167. exp_dirs = glob.glob(rootpath+"*/")
  168. allexpdata = pd.DataFrame()
  169. for expdir in exp_dirs:
  170. print(expdir)
  171. tempdf = process_folder(expdir)
  172. allexpdata = pd.concat([allexpdata, tempdf])
  173. return allexpdata
  175. def aggregate_csvs(root_dir, version, file_name, verbose=False):
  176. '''
  177. Open csv file and reorganize into regualrly formatted DataFrame ready to aggregate
  178. '''
  180. if(root_dir[-1] != os.path.sep):
  181. root_dir += os.path.sep
  182. exp_dirs = glob.glob(root_dir+"*/")
  183. analysis_dirs = glob.glob(os.path.join(root_dir,"*", f"*V{version}*/"))
  184. alldata=pd.DataFrame(columns = ["Experiment"])
  185. for analysis_dir in analysis_dirs:
  186. file = os.path.join(analysis_dir, file_name)
  187. if os.path.exists(file):
  188. exp = (analysis_dir.split(os.path.sep)[-2]).split("_")[-1]
  189. df = pd.read_csv(file)
  190. df["Experiment"] = exp
  191. alldata = pd.concat([alldata, df])
  192. if verbose:
  193. print(f"Loaded {df.shape[0]} meshes from experiment {exp}")
  194. return alldata
  196. def subset_pairwiseData(df, usecols=None, dropchannels=None):
  197. '''
  198. Take a PAZ dataframe and return a subset of columns [usecols].
  199. Optional exclude channels in [dropchannels]
  200. '''
  202. if usecols:
  203. if type(usecols) is not type([]):
  204. usecols=[usecols]
  206. measure_cols = [col for col in df.columns.values if
  207. any([check in col for check in usecols])]
  208. else:
  209. measure_cols = df.columns.values
  210. measure_cols.remove("Experiment")
  211. measure_cols.remove("Image")
  213. # Start with Experiment and Image columns necessary for sorting later
  214. dfsub = df.loc[:, ["Experiment"]]
  215. dfsub[measure_cols] = df.loc[:, measure_cols]
  217. if dropchannels:
  218. if type(dropchannels) is not type([]):
  219. dropchannels=[dropchannels]
  221. drop_cols = [col for col in dfsub.columns.values if
  222. any([check in col for check in dropchannels])]
  223. dfsub = dfsub.drop(drop_cols, axis = 1)
  224. return dfsub
  226. def subset_data(df, usecols=None, dropchannels=None):
  227. '''
  228. Take a PAZ dataframe and return a subset of columns [usecols].
  229. Optional exclude channels in [dropchannels]
  230. '''
  232. if usecols:
  233. if type(usecols) is not type([]):
  234. usecols=[usecols]
  236. measure_cols = [col for col in df.columns.values if
  237. any([check in col for check in usecols])]
  238. else:
  239. measure_cols = df.columns.values
  240. measure_cols.remove("Experiment")
  241. measure_cols.remove("Image")
  243. # Start with Experiment and Image columns necessary for sorting later
  244. dfsub = df.loc[:, ["Experiment", "Image"]]
  245. dfsub[measure_cols] = df.loc[:, measure_cols]
  247. if dropchannels:
  248. if type(dropchannels) is not type([]):
  249. dropchannels=[dropchannels]
  251. drop_cols = [col for col in dfsub.columns.values if
  252. any([check in col for check in dropchannels])]
  253. dfsub = dfsub.drop(drop_cols, axis = 1)
  254. return dfsub
  256. def norm_data(df, norm_col=None, melt=True, keep_scale=False):
  257. '''
  258. Given dataframe of PAZ data, return normalized dataframe
  259. Normalization is per row, so if rows are meshes, normalization will be at level of mesh.
  260. Default behavior is to normalize each column with values against each other column.
  261. Optionally, can set norm_col to one column against which to normalize all other columns.
  262. '''
  263. if norm_col:
  264. measure_cols = [norm_col]
  265. else:
  266. measure_cols = df.columns
  267. measure_cols = measure_cols.drop(["Experiment", "Image"])
  268. norm = df.loc[:, ["Experiment", "Image"]]
  269. for num in measure_cols:
  270. for den in measure_cols:
  271. if num is not den:
  272. norm[f"{num}-{den}"]=df.loc[:, num]/df.loc[:, den]
  273. if melt:
  274. melted = norm.melt(id_vars = ["Experiment", "Image"])
  275. melted[['C1', 'C2']] = melted['variable'].str.split('-', expand=True)
  276. melted = melted.drop("variable", axis=1)
  277. return melted
  278. else:
  279. return norm
  281. def superplot(df, data_order=None, box=True):
  282. melt = df.melt(id_vars = ["Experiment", "Image"])
  283. ImgMean = melt.groupby(["Experiment", "Image", "variable"]).mean().reset_index()
  284. ExpMean = melt.groupby(["Experiment", "variable"]).mean().reset_index()
  285. sp = sns.swarmplot(data=ImgMean, x="variable", y="value", hue="Experiment", size=5, order=data_order, zorder=0)
  286. sns.swarmplot(x="variable", y="value", hue="Experiment", size=12, edgecolor="k", linewidth=2, data=ExpMean, order=data_order, zorder=0)
  287. if box:
  288. sns.boxplot(data=ImgMean, x="variable", y="value",
  289. order=data_order,
  290. showfliers=False,
  291. whis=0,
  292. boxprops={'facecolor':'None', 'linewidth':2.5, 'edgecolor':'k'},
  293. medianprops = {'linewidth':5, 'color':'k'},
  294. whiskerprops={'linewidth':0},
  295. ax=sp)
  296. sp.legend_.remove()
  297. plt.xticks(rotation=45)
  299. def superplot_norm(df, data_order=None):
  300. ImgMean = df.groupby(["Experiment", "Image", "C1"]).mean().reset_index()
  301. ExpMean = df.groupby(["Experiment", "C1"]).mean().reset_index()
  302. sp = sns.swarmplot(data=ImgMean, x="C1", y="value", hue="Experiment", size=5, order=data_order)
  303. ax = sns.swarmplot(data=ExpMean, x="C1", y="value", hue="Experiment", size=12, edgecolor="k", linewidth=2, order=data_order)
  304. sp.legend_.remove()
  305. plt.xticks(rotation=45)
  307. def compareGroups(df, verbose=True):
  308. ImgPost = df.melt()
  309. ImgPost = ImgPost.loc[-np.isnan(ImgPost.value), :]
  311. # Check whether outcomes are normally distributed
  312. ks = [kstest(df.loc[df[col]>-1, col], 'norm') for col in df]
  313. kpass = True if np.min(np.array(ks)[:,1])>0.05 else False
  314. test_name = "ANOVA" if kpass else "Kruskal-Wallis"
  315. posthoc = pd.DataFrame()
  317. if kpass:
  318. #Do anova if vars normally distributed
  319. test = anova(*[df.loc[-np.isnan(df[col]), col] for col in df])
  320. if test[1] < 0.05:
  321. # Do posthoc ttest if model is significant
  322. posthoc = ph.posthoc_ttest(ImgPost, group_col='variable', val_col='value', p_adjust='sidak')
  323. else:
  324. # Do KW test to test if medians different
  325. test = kruskal(*[df.loc[:, col] for col in df], nan_policy='omit')
  326. if test[1] < 0.05:
  327. # Do posthoc dunns if model is significant
  328. posthoc = ph.posthoc_dunn(ImgPost, group_col='variable', val_col='value', p_adjust='sidak')
  330. if verbose:
  331. print(f"Performed {test_name}: test statistic = {test[0]} p-value:{test[1]}")
  333. return {'test':test_name, 'result':test, 'posthoc':posthoc}
  336. def ttestl(df, verbose=True):
  337. '''Assess distributions of groups to compare. If normal, do ttest. If not, do MWU test.'''
  338. ImgPost = df.melt()
  339. ImgPost = ImgPost.loc[-np.isnan(ImgPost.value), :]
  341. # Check whether outcomes are normally distributed
  342. ks = [kstest(df.loc[df[col]>-1, col], 'norm') for col in df]
  343. kpass = True if np.min(np.array(ks)[:,1])>0.05 else False
  344. test_name = "ttest" if kpass else "Mann-Whitney"
  345. posthoc = pd.DataFrame()
  347. if kpass:
  348. #Do ttest if vars normally distributed
  349. test = ttest(*[df.loc[-np.isnan(df[col]), col] for col in df])
  351. else:
  352. # Do KW test to test if medians different
  353. test = kruskal(*[df.loc[:, col] for col in df], nan_policy='omit')
  354. if test[1] < 0.05:
  355. # Do posthoc dunns if model is significant
  356. posthoc = ph.posthoc_dunn(ImgPost, group_col='variable', val_col='value', p_adjust='sidak')
  358. if verbose:
  359. print(f"Performed {test_name}: test statistic = {test[0]} p-value:{test[1]}")
  361. 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-Melero1,2, 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
  1. Department of Neurobiology, Harvard Medical School, Boston, United States
  2. Department of Neuroscience and Institute for Translational Neuroscience, New York University Grossman School of Medicine, New York, United States
  3. Department of Biology, Brandeis University, Waltham, United States
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
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.

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

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

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Data

Datasets cited

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.

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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.

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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

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"id": "10.7554/elife.107276",
"type": "article-journal",
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"author": [
{
"family": "Emperador-Melero",
"given": "Javier"
},
{
"family": "Del Signore",
"given": "Steven J"
},
{
"family": "De León González",
"given": "Kevin M"
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{
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{
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"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
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]
}
}

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