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Protocol for fluorescent false neurotransmitter live imaging of dopamine release dynamics from individual synapses in acute brain slices.

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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 · 139 lines · 3.4 KB · CC-BY-4.0

  1. # %%
  2. """
  3. ************************************* READ ME *************************************
  4. This code is for averaging 3 frames with the highest Pearson's correlation to the mid slice
  6. The Pearson file looks like this
  8. **************************************************
  9. Image A: mid
  10. Image B: reg-1-1
  12. Pearson's Coefficient:
  13. r=0.208
  14. **************************************************
  15. Image A: mid
  16. Image B: reg-1-2
  18. Pearson's Coefficient:
  19. r=0.219
  20. **************************************************
  22. I want to extract for every frame which slice is best fitted with the mid one.
  24. The Dimension file looks like this
  25. width, height, channels, slices, frames
  26. 256 256 1 5 35
  28. Enjoy.
  29. Patrick Cottilli
  30. """
  31. import sys
  32. import os
  33. import heapq
  34. import shutil
  36. direct = input("Please provide the full path of the directory you want to analyse.\nIt should look like this: User/username/location-of-the-image/Data\n")
  37. fls = []
  40. #Select the files from the directory
  41. for files in os.listdir(direct):
  42. path = f"{direct}/"
  44. #Copy the first file, creating a new file to merge the two Pearson files
  45. shutil.copy(path+"/Pearson-mid1.txt", path+"/Pearson-mid.txt")
  47. if 'Dimensions.txt' in files:
  48. fls.append(path + files)
  49. if 'Pearson' in files:
  50. fls.append(path + files)
  52. i=1 #boolean first line
  53. for file in fls:
  54. if 'Dimensions' not in file:
  55. continue
  56. file = open(file)
  57. for line in file:
  58. if i:
  59. i=0
  60. continue
  61. line = line.split(' ')
  62. slices = int(line[-2])
  63. frames = int(line[-1])
  65. for file in fls:
  66. if "mid." in file:
  67. merge = open(file, "a")
  68. if "mid2" in file:
  69. src = open(file, "r")
  71. #This creates the new file with all the Pearson values
  72. merge.write("\n")
  73. merge.write(src.read())
  76. wr = "" #string to store the values and then write them at once
  77. dic1 = {} #Dictionary storing the values for the r
  78. for file in fls:
  79. if "mid." not in file:
  80. continue
  81. file = open(file)
  82. slc = 0
  83. frm = 1
  84. temp = {}
  85. for line in file:
  86. if "*" in line:
  87. slc+=1
  88. if slc == slices+1:
  89. vals = [] #get maximum r value
  90. for key in temp:
  91. vals.append(temp[key])
  92. if len(vals) != slices:
  93. sys.exit("Line 70: You are not taking all the values on every frame.")
  94. selected = heapq.nlargest(3,vals)
  95. ind1 = vals.index(selected[0])
  96. ind2 = vals.index(selected[1])
  97. ind3 = vals.index(selected[2])
  98. key1 = f"{frm}-{ind1+1}-{ind2+1}-{ind3+1}"
  99. dic1[key1] = selected
  100. wr += f"{key1},"
  101. slc = 1
  102. frm += 1
  103. temp = {}
  105. if "Image B" in line:
  106. line = line.split(" ")
  107. image = line[-1]
  108. temp[image] = 0
  109. continue
  111. elif "r=" in line:
  112. r = line.split("=")
  113. r = r[-1]
  114. r = r.split("\n")[0]
  115. temp[image] = float(r)
  117. #get maximum r value from the last frame
  118. vals = []
  119. for key in temp:
  120. vals.append(temp[key])
  122. selected = heapq.nlargest(3,vals)
  123. ind1 = vals.index(selected[0])
  124. ind2 = vals.index(selected[1])
  125. ind3 = vals.index(selected[2])
  126. key1 = f"{frm}-{ind1+1}-{ind2+1}-{ind3+1}"
  127. dic1[key1] = selected
  128. wr += f"{key1}"
  130. direct = direct.split("/")
  131. direct = "/".join(direct[:-1])
  132. out = open(direct + "/selected-frames.csv", "w")
  133. wr+="\n"
  134. out.write(wr)
  135. out.close()
  136. print("Command finished!")
  138. # %%

2-Pearson-FIJI-time.ipynb at commit bd7f2e5, under CC-BY-4.0 · at the source

Overview

Authors: Patrick Cottilli1, Eugene V. Mosharov2,3, Michael J. Devine1,4, David L. Sulzer2,3
ORCID iDs: Patrick Cottilli, Michael J. Devine
  1. Mitochondrial Neurobiology Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
  2. Columbia University, New York, New York, NY 10027, USA
  3. New York State Psychiatric Institute, New York, New York, NY 10032, USA
  4. Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
Institutions: The Francis Crick Institute (United Kingdom); New York State Psychiatric Institute; Columbia University (United States); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom)
Journal: STAR protocols, volume 7, issue 3, article 104773
Dates: published online 6 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.xpro.2026.104773 · PMID 42566307 · PMCID PMC13476366 · OpenAlex W7196977614
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, fMRI & imaging
Keywords: Microscopy, Neuroscience, Molecular/Chemical Probes
Topic: Cellular transport and secretion (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Francis Crick Institute; Cancer Research UK (CRUK) (CC2206); Medical Research Council (CC2206); Wellcome Trust (CC2206); FTF Foundation; NIH; U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA) (07418); Bogue Fellowship; University College London; Harold Hyam Wingate Foundation Medical Research
Citations: not cited yet (Europe PMC); 18 references in the paper

Abstract

Striatal dopaminergic (DA) axons have many en passant boutons with synaptic vesicles, but only a fraction display exocytosis. Here, we present a protocol for live imaging of murine DA axons using fluorescent false neurotransmitter 200 (FFN200), a vesicular monoamine transporter 2 (VMAT2) substrate accumulated by and released from DA synaptic vesicles, providing spatial and temporal kinetics of exocytosis. We describe steps for preparing mouse acute brain slices, loading slices with the FFN, acquiring images with 2-photon microscopy, and analyzing the data.

For complete details on the use and execution of this protocol, please refer to Hwu et al.1

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

Repository

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

MDevineLab/FFN200-live-imaging

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: bd7f2e5a3e5a36a21dca1eaa805b63d7cd8752e0, 14 July 2026
Languages: Jupyter (2)
Size: 8 files, 2 scripts
Software Heritage: not archived
Found in: DataCite
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file), Plotly (1 file), SciPy (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/MDevineLab/FFN200-live-imaging

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;
  • 2 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 and code availability

All original code has been deposited at GitHub and Figshare. This is publicly available at Github: https://github.com/MDevineLab/FFN200-live-imaging and Figshare: https://doi.org/10.25418/crick.32731767. We also provide an example of original raw data for users.

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, 4 authors, 3 keywords, 10 funders, 18 references.

Cite

This paper

Cottilli, P., Mosharov, E. V., Devine, M. J., & Sulzer, D. L. (2026). Protocol for fluorescent false neurotransmitter live imaging of dopamine release dynamics from individual synapses in acute brain slices. STAR protocols, 7(3), 104773. https://doi.org/10.1016/j.xpro.2026.104773

BibTeX

@article{cottilli2026protocol,
author = {Cottilli, Patrick and Mosharov, Eugene V. and Devine, Michael J. and Sulzer, David L.},
title = {{Protocol for fluorescent false neurotransmitter live imaging of dopamine release dynamics from individual synapses in acute brain slices}},
journal = {STAR protocols},
year = {2026},
month = aug,
volume = {7},
number = {3},
pages = {104773},
publisher = {Elsevier},
issn = {2666-1667},
doi = {10.1016/j.xpro.2026.104773},
url = {https://doi.org/10.1016/j.xpro.2026.104773},
pmid = {42566307},
pmcid = {PMC13476366}
}

RIS

TY - JOUR
AU - Cottilli, Patrick
AU - Mosharov, Eugene V.
AU - Devine, Michael J.
AU - Sulzer, David L.
TI - Protocol for fluorescent false neurotransmitter live imaging of dopamine release dynamics from individual synapses in acute brain slices
T2 - STAR protocols
J2 - STAR Protoc
PY - 2026
DA - 2026/08/06
VL - 7
IS - 3
SP - 104773
SN - 2666-1667
PB - Elsevier
DO - 10.1016/j.xpro.2026.104773
UR - https://doi.org/10.1016/j.xpro.2026.104773
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Cottilli",
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"PMCID": "PMC13476366",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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