Jul 07, 2023

Public workspaceQuantifying the LAMP1 positive puncta

  • Narayana Yadavalli1,2,3,4,5,
  • Shawn M. Ferguson1,2,3,4,6,5
  • 1Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut 06510, USA;
  • 2Neuroscience, Yale University School of Medicine, New Haven, Connecticut 06510, USA;
  • 3Program in Cellular Neuroscience, Neurodegeneration and Repair;
  • 4Wu Tsai Institute Yale University School of Medicine, New Haven, Connecticut 06510, USA;
  • 5Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, USA;
  • 6Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, Connecticut 06510, USA
Open access
Protocol CitationNarayana Yadavalli, Shawn M. Ferguson 2023. Quantifying the LAMP1 positive puncta. protocols.io https://dx.doi.org/10.17504/protocols.io.14egn2pb6g5d/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 24, 2023
Last Modified: May 31, 2024
Protocol Integer ID: 82375
Keywords: LAMP1 positive puncta, ASAPCRN
Funders Acknowledgement:
ASAP
Grant ID: 00058
Abstract
This is describes quantifying the LAMP1 positive puncta.
Attachments
Materials
Tools required

  • ImageJ/Fiji
Quantifying the LAMP1 positive puncta
Quantifying the LAMP1 positive puncta
Select cells of interest with similar shapes and without saturation, and isolate single ROI.
Apply auto threshold to total stacks of the images.
Estimate the number of LAMP1 positive vesicles in each ROI by using “Analyze particle” in ImageJ/Fiji.
Plot the results by calculating the mean number of LAMP1 positive puncta per each cell.