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The actomyosin cortex controls t-tubule remodeling in skeletal muscle.

Code ↔ Paper

2 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 2 matches
  1. [1] § MATERIALS AND METHODS › Image analysis ↔ BAR/src/main/resources/scripts/BAR/Analysis/LoG-DoG_Spot_Counter.py, lines 80–138 · score 0.68 · TrackMate, LoG detector, threshold, diameter, detection, ROI
  2. [2] § MATERIALS AND METHODS › Image analysis ↔ BAR/src/main/resources/scripts/BAR/Analysis/LoG-DoG_Spot_Counter.py, lines 80–138 · score 0.66 · LoG detector, spots, Trackmate, threshold, quality, diameter

Paper

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

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The authors' code

Python · 210 lines · 8.4 KB · GPL-3.0 · 2 matches

  1. # @Integer(label="First channel (Ch1)", description="Target channel of first detector",min=1,max=10,style="scroll bar",value="1") channel_1
  2. # @String(label="Ch1 Detector", description="Detection algorithm", choices={"LoG", "DoG"}, style="radioButtonHorizontal") detector_ch1
  3. # @Double(label="Ch1 Estimated spot size",description="Estimated diameter in physical units",min=0.001,max=100,style="scroll bar",value=7.200) diameter_ch1
  4. # @Double(label="Ch1 Quality cutoff",description="Spots with lower quality than this are ignored",min=1,max=100,style="scroll bar",value=3.5) threshold_ch1
  5. # @ColorRGB(label="Ch1 Marker color",value="magenta") color_ch1
  7. # @String(value=" ", visibility="MESSAGE") spacer
  8. # @Integer(label="Second channel (Ch2)", description="Target channel of second detector, if present",min=2,max=10,style="scroll bar",value="2") channel_2
  9. # @String(label="Ch2 Detector", description="Detection algorithm", choices={"LoG", "DoG"}, style="radioButtonHorizontal") detector_ch2
  10. # @Double(label="Ch2 Estimated spot size",description="Estimated diameter in physical units",min=0.001,max=1000,style="scroll bar",value=1.080) diameter_ch2
  11. # @Double(label="Ch2 Quality cutoff",description="Spots with lower quality than this are ignored",min=1,max=100,style="scroll bar",value=70.5) threshold_ch2
  12. # @ColorRGB(label="Ch2 Marker color",value="yellow") color_ch2
  14. # @String(value=" ", visibility="MESSAGE") spacer
  15. # @String(label="Analysis label",description="Used to group data in Results table", value="Control image") group
  16. # @Boolean(label="3D stacks: Analyze projection", value=false) project_image
  17. # @Boolean(label="Display console log", value=false) open_console
  19. # @ImagePlus image
  20. # @LogService lservice
  21. # @UIService uiservice
  23. '''
  24. LoG-DoG_Spot_Counter.py
  25. https://github.com/tferr/Scripts/
  27. Detects particles in a multichannel image using TrackMate LoG/DoG (Laplacian/
  28. Difference of Gaussian) segmentation[1,2]. Detected centroids are displayed in
  29. the non-destructive image overlay and total counts shown in the Results table.
  30. The script was written for counting PLA (Proximity ligation Assay) foci in
  31. tissue counterstained for DAPI and WGA, but can be applied to similar images.
  32. It also exemplifies how to script TrackMate[3].
  34. NB:
  35. - If an area ROI exists, it will be used to confine detection
  36. - Toggling Color mode ('Image>Color>Channels Tools...') allows you to display
  37. only the spots detected for the active channel
  38. - The 'Group' field can be used to generate box plots of the data using 'BAR>
  39. Data Analysis>Create Boxplot'
  41. TF 201611
  42. [1] http://imagej.net/TrackMate
  43. [2] http://imagej.net/TrackMate_Algorithms#Spot_detectors
  44. [3] http://imagej.net/Scripting_TrackMate
  45. '''
  47. from fiji.plugin.trackmate import Model, Logger, Settings, TrackMate
  48. from fiji.plugin.trackmate.detection import DetectorKeys as DK, \
  49. LogDetectorFactory, DogDetectorFactory
  50. from org.scijava.util import ColorRGB
  51. from java.awt import Color
  52. from ij import IJ, ImagePlus
  53. from ij.gui import Overlay, PointRoi
  54. from ij.measure import Calibration, ResultsTable
  55. from bar import Utils
  58. def colorRGBtoColor(colorRGB):
  59. """Converts a org.scijava.util.ColorRGB into a java.awt.Color"""
  60. return Color(colorRGB.getRed(), colorRGB.getGreen(), colorRGB.getBlue())
  63. def error(msg):
  64. """ Displays an error message """
  65. uiservice.showDialog(msg, "Error")
  68. def getOverlay(imp):
  69. """ Returns an image overlay cleansed of spot ROIs from previous runs """
  70. overlay = imp.getOverlay()
  71. if overlay is None:
  72. return Overlay()
  73. for i in range(0, overlay.size()-1):
  74. roi_name = overlay.get(i).getName()
  75. if roi_name is not None and "Spots" in roi_name:
  76. overlay.remove(i)
  77. return overlay
  80. def getSpots(imp, channel, detector_type, radius, threshold, overlay,
  81. roi_type="large", roi_color=ColorRGB("blue")):
  82. """ Performs the detection, adding spots to the image overlay
  83. :imp: The image (ImagePlus) being analyzed
  84. :channel: The target channel
  85. :detector_type: A string describing the detector: "LoG" or "DoG"
  86. :radius: Spot radius (NB: trackmate GUI accepts diameter)
  87. :threshold: Quality cutoff value
  88. :overlay: The image overlay to store spot (MultiPoint) ROIs
  89. :roi_type: A string describing how spot ROIs should be displayed
  90. :returns: The n. of detected spots
  91. """
  92. settings = Settings()
  93. settings.setFrom(imp)
  94. settings.detectorFactory = (LogDetectorFactory() if "LoG" in detector_type
  95. else DogDetectorFactory())
  96. settings.detectorSettings = {
  97. DK.KEY_DO_SUBPIXEL_LOCALIZATION: False,
  98. DK.KEY_DO_MEDIAN_FILTERING: True,
  99. DK.KEY_TARGET_CHANNEL: channel,
  100. DK.KEY_RADIUS: radius,
  101. DK.KEY_THRESHOLD: threshold,
  102. }
  103. trackmate = TrackMate(settings)
  104. if not trackmate.execDetection():
  105. lservice.error(str(trackmate.getErrorMessage()))
  106. return 0
  107. model = trackmate.model
  108. spots = model.getSpots()
  109. count = spots.getNSpots(False)
  110. ch_id = "Spots Ch%d" % channel
  111. if count > 0:
  112. roi = None
  113. cal = imp.getCalibration()
  114. t_pos = imp.getT()
  115. if (t_pos > 1):
  116. lservice.warn("Only frame %d was considered..." % t_pos)
  117. for spot in spots.iterable(False):
  118. x = cal.getRawX(spot.getFeature(spot.POSITION_X))
  119. y = cal.getRawY(spot.getFeature(spot.POSITION_Y))
  120. z = spot.getFeature(spot.POSITION_Z)
  121. if z == 0 or not cal.pixelDepth or cal.pixelDepth == 0:
  122. z = 1
  123. else:
  124. z = int(z // cal.pixelDepth)
  125. imp.setPosition(channel, z, t_pos)
  126. if roi is None:
  127. roi = PointRoi(int(x), int(y), imp)
  128. else:
  129. roi.addPoint(imp, x, y)
  130. roi.setStrokeColor(colorRGBtoColor(roi_color))
  131. if "large" in roi_type:
  132. roi.setPointType(3)
  133. roi.setSize(4)
  134. else:
  135. roi.setPointType(2)
  136. roi.setSize(1)
  137. overlay.add(roi, ch_id)
  138. return count
  141. def projectionImage(imp):
  142. """Returns the MIP of the specified ImagePlus (a composite stack)"""
  143. from ij.plugin import ZProjector
  144. roi_exists = imp.getRoi() is not None
  145. imp.deleteRoi()
  146. zp = ZProjector(imp)
  147. zp.setMethod(ZProjector.MAX_METHOD)
  148. zp.setStartSlice(1)
  149. zp.setStopSlice(imp.getNSlices())
  150. zp.doHyperStackProjection(True)
  151. mip_imp = zp.getProjection()
  152. mip_imp.setCalibration(imp.getCalibration())
  153. if roi_exists:
  154. mip_imp.restoreRoi()
  155. return mip_imp
  158. def main():
  160. image = IJ.getImage() # ???This is to solve a mysterious UnboundLocalError:
  161. # local variable 'image' referenced before assignment
  162. # Script parameter @ImagePlus not being recognized???
  164. n_channels = image.getNChannels()
  165. if channel_1 > n_channels and channel_2 > n_channels:
  166. error("Image does not contain specified channel(s)")
  167. return
  169. if open_console:
  170. uiservice.getDefaultUI().getConsolePane().show()
  171. lservice.info("Analyzing " + image.getTitle())
  173. # 2D / 3D analysis?
  174. if project_image and image.getNSlices() > 1:
  175. lservice.info("Retrieving MIP")
  176. image = projectionImage(image)
  177. image.show()
  179. # Prepare overlay and Results table
  180. overlay = getOverlay(image)
  181. table = Utils.getTable("LoG-DoG Spots")
  182. table.incrementCounter()
  183. table.setLabel(image.getTitle(), table.getCounter()-1)
  185. # Perform detection
  186. spots_ch1 = spots_ch2 = float('nan')
  187. if channel_1 <= n_channels:
  188. lservice.info("Processing Ch%d" % channel_1)
  189. spots_ch1 = getSpots(image, channel_1, detector_ch1, diameter_ch1/2,
  190. threshold_ch1, overlay, "large", color_ch1)
  192. if channel_2 <= n_channels:
  193. lservice.info("Processing Ch%d" % channel_2)
  194. spots_ch2 = getSpots(image, channel_2, detector_ch2, diameter_ch2/2,
  195. threshold_ch2, overlay, "small", color_ch2)
  197. # Show results
  198. lservice.info("Displaying spot ROIs and results...")
  199. image.setOverlay(overlay)
  200. table.addValue("# " + "Spots Ch%d" % channel_1, spots_ch1)
  201. table.addValue("# " + "Spots Ch%d" % channel_2, spots_ch2)
  202. table.addValue("Ratio Ch%d/Ch%d" % (channel_1, channel_2),
  203. (float('nan') if spots_ch1 == 0 else spots_ch2 / float(spots_ch1)))
  204. table.addValue("Group", group)
  205. table.show("LoG-DoG Spots")
  206. lservice.info("Analysis concluded")
  209. if __name__ == '__main__':
  210. main()

LoG-DoG_Spot_Counter.py at commit dc0801d, under GPL-3.0 · at the source

Overview

Authors: Ana Raquel Pereira1,2, Silvia Di Francescantonio1,2, Ana da Rosa Soares1,2,3, Tianyang Liu4, Filomena A Carvalho1,2, Josie Liane Ferreira4,5, Graciano Leal1,2, Inês Faleiro1,2, Naoko Kogata6, Beatrice Labella7, Teresinha Evangelista7,8,9, Nuno C Santos1,2, Michael Way6,10, Carolyn A Moores4, Edgar R Gomes1,2
ORCID iDs: Ana Raquel Pereira, Silvia Di Francescantonio, Ana da Rosa Soares, Tianyang Liu, Filomena A Carvalho, Josie Liane Ferreira, Inês Faleiro, Naoko Kogata, Beatrice Labella, Teresinha Evangelista, Nuno C Santos, Michael Way, Carolyn A Moores, Edgar R Gomes
  1. GIMM–Gulbenkian Institute for Molecular Medicine, Avenida Prof. Egas Moniz, 1649-028 Lisboa, Portugal
  2. Faculdade de Medicina, Universidade de Lisboa, Av. Prof. Egas Moniz, 1649-028 Lisboa, Portugal
  3. iNOVA4Health, NOVA Medical School|Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisbon, Portugal
  4. Institute of Structural and Molecular Biology, Birkbeck College, London WC1E 7HX, UK
  5. Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
  6. Cellular Signalling and Cytoskeletal Function Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
  7. Myology Institute, Groupe Hospitalier Universitaire Pitié-Salpêtrière, Paris, France
  8. Functional Unit of Neuromuscular Pathology, Department of Neuropathology, Groupe Hospitalier Universitaire Pitié-Salpêtrière, APHP Sorbonne University, Paris, France
  9. Sorbonne University, Myology Research Center, UMRS 974, Paris, France
  10. Department of Infectious Disease, Imperial College, London SW7 2AZ, UK
Institutions: University of Lisbon (Portugal); Gulbenkian Institute for Molecular Medicine (Portugal); Universidade Nova de Lisboa (Portugal); Institute of Structural and Molecular Biology (United Kingdom); Birkbeck, University of London (United Kingdom); University College London (United Kingdom); The Francis Crick Institute (United Kingdom); Institut de Myologie (France); Pitié-Salpêtrière Hospital (France); Sorbonne Université (France); Imperial College London (United Kingdom)
Journal: Science advances, volume 12, issue 36, article eaeb3209
Dates: received 10 August 2025; accepted 22 July 2026; published online 2 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aeb3209 · PMID 42685205 · PMCID PMC13537240 · OpenAlex W7206217040
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Connectivity, Evoked potentials, fMRI & imaging
MeSH: Actomyosin*, Muscle, Skeletal*, Actin-Related Protein 2-3 Complex, Animals, Calcium, Cell Membrane, Humans, Mice, Mice, Knockout, Muscle Contraction (* major topic)
Topic: Ion channel regulation and function (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Wellcome Trust (202679/Z/16/Z, 206166/Z/17/Z, CC2096); European Research Council (810207)
Citations: not cited yet (Europe PMC); 81 references in the paper
Research resources: RRID:Addgene_98823

Abstract

A network of plasma membrane invaginations called t-tubules plays an essential role in controlling calcium release from the endoplasmic reticulum at the triads during muscle contraction. Although the importance of t-tubules for muscle physiology is well established, and abnormalities are found in muscle disorders, the mechanisms that mediate t-tubule growth are unknown. We show that the actomyosin cortex beneath the plasma membrane, regulated by Arp2/3 complexes containing Arpc5, acts as a gatekeeper for the membrane availability during t-tubule growth. Enlarged t-tubules are formed upon disruption of Arpc5, impairing the synchronization between plasma membrane depolarization and calcium release. Knockout of Arpc5 in mouse skeletal muscle results in impaired locomotion and posture. Furthermore, we show that human triadopathy patients and Arpc5 knockout mice accumulate enlarged t-tubules. We propose that cortex-dependent membrane availability affects muscle function, offering a potential pathophysiological mechanism for muscle disorders.

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 2 matches between paragraphs and lines of code.

Zenodo 28838

License: other-open
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ImageJ / Fiji (5 files)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
17 files
At the source: zenodo.org/records/28838

tferr/scripts

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: dc0801d6288fb63d5ddabc7c552b86b5d74459d0, 3 June 2022
Languages: Python (18), Java (16), JavaScript (3), Shell (2)
Size: 136 files, 39 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, continuous integration
Not found: CITATION.cff, environment file, tests, documentation
Tools: ImageJ / Fiji (12 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
41 files
At the source: github.com/tferr/Scripts/tree/1.1.6

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;
  • 55 scripts, each with its path and the digest of its content;
  • 2 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.

Data

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. AAV1-CAG-GFP-CAAX, GFP-Bin1 (VB220728-1365nbr) plasmids and the C2C12 GFP-Bin1 cell line are available from the corresponding author E.R.G. (). The Arpc5 and Arpc5l KO mice on a C57Bl/6 background that are described in this study are available from M.W. () under a material transfer agreement with The Francis Crick Institute.

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, 15 authors, 10 MeSH terms, 2 funders, 78 references, 1 RRID.

Cite

This paper

Pereira, A. R., Di Francescantonio, S., da Rosa Soares, A., Liu, T., Carvalho, F. A., Ferreira, J. L., Leal, G., Faleiro, I., Kogata, N., Labella, B., Evangelista, T., Santos, N. C., Way, M., Moores, C. A., & Gomes, E. R. (2026). The actomyosin cortex controls t-tubule remodeling in skeletal muscle. Science advances, 12(36), eaeb3209. https://doi.org/10.1126/sciadv.aeb3209

BibTeX

@article{pereira2026actomyosin,
author = {Pereira, Ana Raquel and Di Francescantonio, Silvia and da Rosa Soares, Ana and Liu, Tianyang and Carvalho, Filomena A and Ferreira, Josie Liane and Leal, Graciano and Faleiro, Inês and Kogata, Naoko and Labella, Beatrice and Evangelista, Teresinha and Santos, Nuno C and Way, Michael and Moores, Carolyn A and Gomes, Edgar R},
title = {{The actomyosin cortex controls t-tubule remodeling in skeletal muscle}},
journal = {Science advances},
year = {2026},
month = sep,
volume = {12},
number = {36},
pages = {eaeb3209},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aeb3209},
url = {https://doi.org/10.1126/sciadv.aeb3209},
pmid = {42685205},
pmcid = {PMC13537240}
}

RIS

TY - JOUR
AU - Pereira, Ana Raquel
AU - Di Francescantonio, Silvia
AU - da Rosa Soares, Ana
AU - Liu, Tianyang
AU - Carvalho, Filomena A
AU - Ferreira, Josie Liane
AU - Leal, Graciano
AU - Faleiro, Inês
AU - Kogata, Naoko
AU - Labella, Beatrice
AU - Evangelista, Teresinha
AU - Santos, Nuno C
AU - Way, Michael
AU - Moores, Carolyn A
AU - Gomes, Edgar R
TI - The actomyosin cortex controls t-tubule remodeling in skeletal muscle
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/09/02
VL - 12
IS - 36
SP - eaeb3209
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aeb3209
UR - https://doi.org/10.1126/sciadv.aeb3209
LA - en
ER -

CSL-JSON

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"id": "10.1126/sciadv.aeb3209",
"type": "article-journal",
"title": "The actomyosin cortex controls t-tubule remodeling in skeletal muscle",
"container-title": "Science advances",
"author": [
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"family": "Pereira",
"given": "Ana Raquel"
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{
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"given": "Filomena A"
},
{
"family": "Ferreira",
"given": "Josie Liane"
},
{
"family": "Leal",
"given": "Graciano"
},
{
"family": "Faleiro",
"given": "Inês"
},
{
"family": "Kogata",
"given": "Naoko"
},
{
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{
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{
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{
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{
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{
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],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "36",
"page": "eaeb3209",
"DOI": "10.1126/sciadv.aeb3209",
"PMID": "42685205",
"PMCID": "PMC13537240",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aeb3209",
"language": "en",
"issued": {
"date-parts": [
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2
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}

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