Jul 25, 2022

Public workspaceMembrane Tube Image Analysis

DOI
dx.doi.org/10.17504/protocols.io.5jyl891e9v2w/v1
  • 1Hurley Lab
Liv Jensen
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DOI: dx.doi.org/10.17504/protocols.io.5jyl891e9v2w/v1
Protocol Citation: Liv Jensen 2022. Membrane Tube Image Analysis. protocols.io https://dx.doi.org/10.17504/protocols.io.5jyl891e9v2w/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it’s working
Created: May 03, 2022
Last Modified: May 31, 2024
Protocol Integer ID: 61852
Keywords: Membrane Tube Image Analysis, FIJI, ASAPCRN
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Abstract
This protocol details Membrane Tube Image Analysis.
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416-899.pdf
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Materials
Materials:
  • Image stack of multichannel confocal fluorescence images
  • FIJI
  • Tube enrichment quantification script (Python)
Membrane Tube Image Analysis
Membrane Tube Image Analysis
In FIJI, create new image stacks from two rectangular selections: one containing a section of the membrane tube, and one containing the approximately horizontal section of the guv.
Save these new stacks as .tif files and paste paths into enrichment quantification script.
Run enrichment quantification, adjusting parameters such as ktub to fit your experimental conditions.