Jun 16, 2026

Co-localization Analysis

DOI
https://dx.doi.org/10.17504/protocols.io.bp2l6z98zgqe/v1
  • Elisabeth Holzer1
  • 1Laboratory of Sascha Martens, Max Perutz Labs, University of Vienna, Austria
Elisabeth Holzer
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DOI: https://dx.doi.org/10.17504/protocols.io.bp2l6z98zgqe/v1
Protocol Citation: Elisabeth Holzer 2026. Co-localization Analysis. protocols.io https://dx.doi.org/10.17504/protocols.io.bp2l6z98zgqe/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
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Created: August 06, 2025
Last Modified: June 16, 2026
Protocol  Integer ID: 224296
Keywords: ASAPCRN, protocol detail, protocol, description of co
Funders Acknowledgements:
Aligning Science Across Parkinson’s (ASAP)
Grant ID: ASAP-000350
DOC Fellowship (Austrian Academy of Sciences)
Abstract
This protocol details the description of Co-localization Analysis.
Co-localization Analysis

Note
Co-localization analysis was performed to assess the spatial overlap between two fluorescent channels in immunofluorescence (IF) images, using Pearson’s correlation coefficient (PCC) as a quantitative metric (Bolte et al., 2006).

Open images in Fiji (ImageJ).
Use the Just Another Co-localization Plugin (JaCoP) to calculate the PCC for each image.
Compute PCC values across all pixels to evaluate the linear correlation between fluorescence intensities in the two channels.

  • A PCC near +1 indicates strong positive co-localization.
  • A PCC near 0 indicates random distribution.
  • A PCC near –1 indicates anti-correlation.
Plot the resulting PCC values and subject to statistical evaluation using GraphPad PRISM software.