OSCR

Molecularly defined auditory neuron subtypes show different vulnerabilities to noise- and age-related synaptopathy in mice.

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] § Methods › Synapse analysis › SynAnalyzer overview ↔ SourceCode/3_SynAnalyzer_CompileXYZData.ipynb, lines 138–261 · score 0.67 · DataFrame, animal ID, Paired Synapse, compiled, metadata, reconstruction
  2. [2] § Results › Age and noise have different effects on PSD volumes ↔ SourceCode/3_SynAnalyzer_CompileXYZData.ipynb, lines 131–136 · score 0.60 · normalized PSD volumes, median volume, sham
  3. [3] § Methods › Synapse analysis › Quantification of synaptic properties ↔ SourceCode/3_SynAnalyzer_CompileXYZData.ipynb, lines 131–136 · score 0.54 · Control median normalized, median volume, um3, sham

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Jupyter notebook · 264 lines · 11 KB · MIT · 3 matches

  1. # %% [markdown]
  2. # # 3_SynAnalyzer_CompileXYZData.ipynb
  3. #
  4. # This Python notebook reads in the CSV files generated in 2_SynAnalyzer_AnalyzeXYZs.ijm that were annotated by the user during
  5. # manual array scoring. These files were saved in SAR.Analysis. This code pulls these files and the associated Metadata files
  6. # from the Batch directory.
  8. # %%
  9. # *** IMPORT PACKAGES **
  10. import datetime
  11. import numpy as np
  12. import os
  13. import matplotlib.pyplot as plt
  14. import pandas as pd
  15. import glob
  16. import seaborn as sns
  17. import statistics
  18. import math
  20. # %%
  21. # *** GET TIME OF ANALYSIS START ***
  22. # could turn this into a module?
  23. toa = str(datetime.datetime.today()).split() #Time of analysis
  24. today = toa[0]
  25. now = toa[1]
  26. timestamp = today.replace('-','')+'-'+now.replace(':','')[:6]
  28. # %%
  29. # *** WHAT TO ANALYZE // WHERE TO GET/STORE **
  30. # Batch analysis directory (where the new metadata sheet will be stored)
  31. batchID = 'SynAnalyzer_DemoBatch'
  32. # Cutoff for automated classificaiton of subtypes
  33. # This value was determined empirically (see Methods)
  34. threshAutoTdT = .22
  36. # Directories
  37. # existing ones
  38. dirMain = '/Users/joyfranco/Partners HealthCare Dropbox/Joy Franco/JF_Shared/Data/WSS/'
  39. dirBA = dirMain+"BatchAnalysis/"+batchID+"/"
  40. dirMD = dirBA+"Metadata/"
  41. dirSA = dirBA+"SAR.Analysis/"
  43. # ones that need to be made
  44. dirRes = dirBA+"SAR.Results/"
  45. dirTR = dirRes+timestamp+'/'
  47. # Files to access
  48. fnMDSamp = batchID+".Metadata.Samples.csv"
  49. fnMDIms = batchID+".Metadata.Imaging.csv"
  51. # Files to make
  52. fnXYZ = batchID+".XYZSummary.csv"
  53. fnBS = batchID+".BatchSummary.csv"
  55. # %%
  56. # *** INITIALIZE RUN SPECIFIC DIRECTORY ETC FOR STORING RESULTS **
  57. # Create directory for storing spreadsheets and summary plots for this run
  58. if not os.path.exists(dirRes): os.mkdir(dirRes)
  59. if not os.path.exists(dirTR): os.mkdir(dirTR)
  61. # %%
  62. # *** LOAD MD SHEETS **
  63. # Sample metadata sheet that include animal and condition info
  64. dfSamps = pd.read_csv(dirMD+fnMDSamp)
  65. #dfSamps.drop('Unnamed: 0', axis=1, inplace=True)
  66. # Imaging metadata sheet that includes frequency and NoHC info
  67. dfIms = pd.read_csv(dirMD+fnMDIms)
  69. # %%
  70. # *** GET LIST OF ALL XYZ FILES TO COMPILE ***
  71. allFiles = os.listdir(dirSA)
  72. xyzFiles = []
  73. for f in allFiles:
  74. if "XYZ" in f:
  75. xyzFiles.append(f)
  76. xyzFiles.sort()
  77. xyzFiles
  79. # %%
  80. # *** COMPILE XYZ DATA ***
  81. # Initialize df for compiling all xyz data
  82. dfXYZAll = pd.DataFrame()
  83. for f in xyzFiles:
  84. # Get the metadata from the filename
  85. samp = f[0:10]
  86. imName = f[0:22]
  87. surfType = f[27:(len(f)-4)]
  88. # Get the metadata for this im from md dataframes
  89. inSampMD = dfSamps.index[dfSamps["SampleID"]==samp].tolist()[0]
  90. anID = dfSamps.loc[inSampMD]["AnimalID"]
  91. group = dfSamps.loc[inSampMD]["Group"]
  92. inImMD = dfIms.index[dfIms["ImageName"]==imName].tolist()[0]
  93. freq = dfIms.loc[inImMD]["Frequency"]
  94. noHC = dfIms.loc[inImMD]["HairCellsReconstructed"]
  95. # Load the file
  96. df = pd.read_csv(dirSA+f)
  97. df["SurfType"] = surfType
  98. df["SampleID"] = samp
  99. df["ImageName"] = imName
  100. df["AnimalID"] = anID
  101. df["Group"] = group
  102. df["Frequency"] = freq
  103. df["NoHCRecon"] = noHC
  105. #Calculate the image maximum normalized tdT intensity
  106. maxCh4Int = df["uIntCh_4"].max()
  107. df["MaxNormTdTom"] = df["uIntCh_4"]/maxCh4Int
  109. # Setup Autoclassified Subtype based on image maximum normalized tdTOM intensity
  110. if surfType == "PostSyn":
  111. df["AutoTdTStatus"] = "TBD"
  112. for index, row in df.iterrows():
  113. tdTomVal = df.loc[index]["MaxNormTdTom"]
  114. if (tdTomVal >= threshAutoTdT):
  115. df.at[index,"AutoTdTStatus"] = "Positive"
  116. else:
  117. df.at[index,"AutoTdTStatus"] = "Negative"
  118. else:
  119. df["AutoTdTStatus"] = "NA"
  121. # Clear up any empty entries
  122. df = df.fillna('')
  124. # Add it to the main df
  125. dfXYZAll = pd.concat([dfXYZAll,df])
  127. dfXYZAll.reset_index(inplace=True)
  128. dfXYZAll.drop(["index"], axis=1, inplace=True)
  129. dfXYZAll.to_csv(dirTR+fnXYZ)
  131. # %%
  132. # *** CALCULATE THE CONTROL MEDIAN NORMALIZED VOLUMES ***
  133. # This calculates a normalized PSD volume using the median volume from all control PSDs
  134. ctrlMedianVol = statistics.median(dfXYZAll[dfXYZAll["Group"]=="Sham"]["Volume_um3"])
  135. dfXYZAll["CtrlMedNormVol"] = dfXYZAll["Volume_um3"]/ctrlMedianVol
  136. dfXYZAll.to_csv(dirTR+fnXYZ)
  138. # %%
  139. # *** GENERATE IMAGE SUMMARY SHEET ***
  140. # Initialize df for compiling all image data
  141. dfImsAll = pd.DataFrame()
  142. for im in dfXYZAll["ImageName"].unique():
  143. samp = im[0:10]
  144. # Get metadata associated with each image
  145. inSampMD = dfSamps.index[dfSamps["SampleID"]==samp].tolist()[0]
  146. anID = dfSamps.loc[inSampMD]["AnimalID"]
  147. group = dfSamps.loc[inSampMD]["Group"]
  148. inImMD = dfIms.index[dfIms["ImageName"]==im].tolist()[0]
  149. freq = dfIms.loc[inImMD]["Frequency"]
  150. noHC = float(dfIms.loc[inImMD]["HairCellsReconstructed"])
  151. # Setup entry in df for this image
  152. df = pd.DataFrame({'ImageName': im,
  153. 'SampleID':samp,
  154. 'AnimalID':anID,
  155. 'Group':group,
  156. 'Freq':[freq],
  157. 'NoHCRecon':[noHC]
  158. })
  159. # Get surface types associated with this im
  160. surfs = dfXYZAll[dfXYZAll["ImageName"] == im]["SurfType"].unique()
  161. df["SurfacesAvailable"] = str(surfs)
  163. # Get surface types associated with this im
  164. surfs = dfXYZAll[dfXYZAll["ImageName"] == im]["SurfType"].unique()
  165. df["SurfacesAvailable"] = str(surfs)
  166. # Iterate through the available surface types
  167. for surf in surfs:
  168. # CALCULATE SYNAPSE INFORMATION
  169. df[surf+"_PairedSynapseIndex"] = len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  170. (dfXYZAll["SurfType"] == surf) &
  171. (dfXYZAll["SynapseStatus"] == "Synapse")])/noHC
  172. df[surf+"_UnpairedIndex"] = len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  173. (dfXYZAll["SurfType"] == surf) &
  174. (dfXYZAll["SynapseStatus"] == "Unpaired")])/noHC
  176. if surf=="PostSyn":
  177. # CALCULATE INFORMATION ON TERMINAL SCORING
  178. df[surf+"_TdTPosPairedSynIndex"] = len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  179. (dfXYZAll["SurfType"] == surf) &
  180. (dfXYZAll["SynapseStatus"] == "Synapse")&
  181. (dfXYZAll["TerminalStatus"] == "Positive")])/noHC
  182. df[surf+"_TdTPosUnpairedSynIndex"] = len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  183. (dfXYZAll["SurfType"] == surf) &
  184. (dfXYZAll["SynapseStatus"] == "Unpaired")&
  185. (dfXYZAll["TerminalStatus"] == "Positive")])/noHC
  186. try:
  187. df[surf+"_TdTPosPairedSynProp"] = len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  188. (dfXYZAll["SurfType"] == surf) &
  189. (dfXYZAll["SynapseStatus"] == "Synapse")&
  190. (dfXYZAll["TerminalStatus"] == "Positive")])/len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  191. (dfXYZAll["SurfType"] == surf) &
  192. (dfXYZAll["SynapseStatus"] == "Synapse")])
  193. except:
  194. df[surf+"_TdTPosPairedSynprop"] = 0
  196. try:
  197. df[surf+"_TdTPosUnpairedSynProp"] = (len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  198. (dfXYZAll["SurfType"] == surf) &
  199. (dfXYZAll["SynapseStatus"] == "Unpaired")&
  200. (dfXYZAll["TerminalStatus"] == "Positive")])/len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  201. (dfXYZAll["SurfType"] == surf) &
  202. (dfXYZAll["SynapseStatus"] == "Unpaired")]))
  203. except:
  204. df[surf+"_TdTPosUnpairedSynProp"] = 0
  207. try:
  208. df[surf+"_AutoTdTPosProp"] = (len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  209. (dfXYZAll["SurfType"] == surf) &
  210. (dfXYZAll["SynapseStatus"] == "Synapse")&
  211. (dfXYZAll["AutoTdTStatus"] == "Positive")])/len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  212. (dfXYZAll["SurfType"] == surf) &
  213. (dfXYZAll["SynapseStatus"] == "Synapse")]))
  214. except:
  215. df[surf+"_AutoTdTPosProp"] = 0
  217. try:
  218. df[surf+"_PropTermUncSyns"] = len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  219. (dfXYZAll["SurfType"] == surf) &
  220. (dfXYZAll["SynapseStatus"] == "Synapse")&
  221. (dfXYZAll["TerminalStatus"] == "Uncertain")])/len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  222. (dfXYZAll["SurfType"] == surf) &
  223. (dfXYZAll["SynapseStatus"] == "Synapse")])
  224. except:
  225. df[surf+"_PropTermUncSyns"] = 0
  227. try:
  228. df[surf+"_PropTermPosAndUncSyns"] = (len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  229. (dfXYZAll["SurfType"] == surf) &
  230. (dfXYZAll["SynapseStatus"] == "Synapse")&
  231. (dfXYZAll["TerminalStatus"] == "Positive")])+
  232. len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  233. (dfXYZAll["SurfType"] == surf) &
  234. (dfXYZAll["SynapseStatus"] == "Synapse")&
  235. (dfXYZAll["TerminalStatus"] == "Uncertain")]))/len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  236. (dfXYZAll["SurfType"] == surf) &
  237. (dfXYZAll["SynapseStatus"] == "Synapse")])
  238. except:
  239. df[surf+"_PropTermPosAndUncSyns"] = 0
  241. try:
  242. df[surf+"_PropTermNegAndUncSyns"] = (len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  243. (dfXYZAll["SurfType"] == surf) &
  244. (dfXYZAll["SynapseStatus"] == "Synapse")&
  245. (dfXYZAll["TerminalStatus"] == "Negative")])+
  246. len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  247. (dfXYZAll["SurfType"] == surf) &
  248. (dfXYZAll["SynapseStatus"] == "Synapse")&
  249. (dfXYZAll["TerminalStatus"] == "Uncertain")]))/len(dfXYZAll[(dfXYZAll["ImageName"] == im) &
  250. (dfXYZAll["SurfType"] == surf) &
  251. (dfXYZAll["SynapseStatus"] == "Synapse")])
  252. except:
  253. df[surf+"_PropTermNegAndUncSyns"] = 0
  256. # Add it to the main df
  257. dfImsAll = pd.concat([dfImsAll,df])
  259. dfImsAll.reset_index(inplace=True)
  260. dfImsAll.drop(["index"], axis=1, inplace=True)
  261. dfImsAll.to_csv(dirTR+fnBS)
  263. # %%

3_SynAnalyzer_CompileXYZData.ipynb at commit f17feec, under MIT · at the source

Overview

Authors: Joy A Franco1,2, Taylor G Copeland1,3, Ryan D Merrow1, Lisa V Goodrich1
ORCID iDs: Joy A Franco, Ryan D Merrow, Lisa V Goodrich
  1. Department of Neurobiology, Harvard Medical School, Boston, MA USA
  2. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA USA
  3. Speech and Hearing Bioscience and Technology PhD Program, Harvard University, Cambridge, MA USA
Institutions: Harvard University (United States); Dana-Farber Cancer Institute (United States)
Journal: Nature communications, volume 17, issue 1, article 8538
Dates: received 22 August 2025; accepted 23 June 2026; published online 10 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75275-3 · PMID 42431902 · PMCID PMC13483461 · OpenAlex W4413884189
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.27.672747
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Cochlea, Peripheral nervous system
MeSH: Aging*, Neurons*, Noise*, Spiral Ganglion*, Synapses*, Animals, Female, Hair Cells, Auditory, Inner, Male, Mice, Post-Synaptic Density (* major topic)
Topic: Hearing, Cochlea, Tinnitus, Genetics (Sensory Systems, Neuroscience), according to OpenAlex
Funding: NIA NIH HHS (K00 AG078230); U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (R01 DC009223); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (K00 AG078230); Blavatnik Family Foundation (Blavatnik Sensory Disorders Research Grant); NIDCD NIH HHS (R01 DC009223); U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) (R01 DC009223); U.S. Department of Health & Human Services | NIH | National Institute on Aging (K00 AG078230)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

GoodrichLab/SynAnalyzer

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f17feec5e309a8b1468f23b8d75d193a3b82c215, 24 March 2026
Languages: Jupyter (2)
Size: 3,832 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/GoodrichLab/SynAnalyzer

Zenodo 20517243

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-75275-3.

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  • 4 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.

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Data

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-75275-3.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 11 MeSH terms, 7 funders, 61 references.

Cite

This paper

Franco, J. A., Copeland, T. G., Merrow, R. D., & Goodrich, L. V. (2026). Molecularly defined auditory neuron subtypes show different vulnerabilities to noise- and age-related synaptopathy in mice. Nature communications, 17(1), 8538. https://doi.org/10.1038/s41467-026-75275-3

BibTeX

@article{franco2026molecularly,
author = {Franco, Joy A and Copeland, Taylor G and Merrow, Ryan D and Goodrich, Lisa V},
title = {{Molecularly defined auditory neuron subtypes show different vulnerabilities to noise- and age-related synaptopathy in mice}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8538},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75275-3},
url = {https://doi.org/10.1038/s41467-026-75275-3},
pmid = {42431902},
pmcid = {PMC13483461}
}

RIS

TY - JOUR
AU - Franco, Joy A
AU - Copeland, Taylor G
AU - Merrow, Ryan D
AU - Goodrich, Lisa V
TI - Molecularly defined auditory neuron subtypes show different vulnerabilities to noise- and age-related synaptopathy in mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/10
VL - 17
IS - 1
SP - 8538
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75275-3
UR - https://doi.org/10.1038/s41467-026-75275-3
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-75275-3",
"type": "article-journal",
"title": "Molecularly defined auditory neuron subtypes show different vulnerabilities to noise- and age-related synaptopathy in mice",
"container-title": "Nature communications",
"author": [
{
"family": "Franco",
"given": "Joy A"
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{
"family": "Copeland",
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{
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"given": "Lisa V"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8538",
"DOI": "10.1038/s41467-026-75275-3",
"PMID": "42431902",
"PMCID": "PMC13483461",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
10
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]
}
}

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