Hi, JACoP is a good idea, so is Coloc 2. Both can give you various colocalisation metrics such as Person and Manders, you can try which one you'd like better. They don't work on ROIs if I'm not mistaken, but that is not an issue in Image J environment, where you can easily select an ROI, duplicate it and then run the colocalisation analysis on it.Both are well documented Image J plugins used in many publications. In a way using an open-source plugin makes your published results more transparent than using a black box from Leica.

Hi there,

The main difference of using freeware vs built-in colocalization tools in expensive software is the price :)

Basically, these software incorporate algorithms to express colocalization and pack them into an user-friendly interface, but they're in no way superior to any other (free) software or plugin.

Additionally, you can gain insight into what these software are doing, so you will be able to know which analysis suits better your experiment by using freeware and reading carefully the guides and the manuals (Thresholds, background subtraction, ROIs, and so on...)

I have always used ICA with pretty good results (it operates in a similar manner to JACoP) and you can define ROIs in it. However there are many others. Check the attached documents and links below for practical working examples and explanations. The two attached documents are for ICA, but they provide good working examples and differences for the coefficients

Overall interesting review:

Article A practical guide to evaluating colocalization in biological...

JACop Plugin original paper:

Article A guided tour into subcellular colocalization analysis in li...

ICA Pugin original paper:

https://pubmed.ncbi.nlm.nih.gov/15102922/

ImageJ forum about differneces in JACoP and ICA:

http://imagej.1557.x6.nabble.com/Colocalization-analysis-w-ICA-and-JACoP-td3687953.html

Why to choose one or another coefficient?

https://pubmed.ncbi.nlm.nih.gov/20653013/

Manders' Original paper:

Article Measurement Of Colocalization Of Objects In Dual-Color Confocal Images

Guide to ICA:

https://imagej.net/mbf/colour_analysis.htm

Another free plugin:

https://www.nature.com/articles/s41598-018-33592-8

And, for the second question:

"Is there any requirements on the software I use for co-localisation analysis if I am planning to publish my data?"

No, the requirement is not for the software, but for the explanation/justification and interpretation that you give to the analysis and results. Usually, all the Plugins incorporated in ImageJ have been published if that's what you mean

Why you have chosen this analysis? Have you corrected the background? Are you using ROIs? Is this analysis powerful enough? Why you provide one coefficient and not another?

In this case, understanding what you are doing and using proper controls should be enough to validate the analysis tools that you are using for colocalization.

As positive controls, using two secondary Abs against the same 1ary Ab is usually a really good positive control (if you have time and can spend Ab's doing so). You can also perform randomization to see how accurate your coefficients are, but all these things are explained in the attached documents,

Cheers,

J

Thanks for all of your help !!

Do any of you know what method of colocalization LAS X uses to calculate colocalization? It calculates colocalization rate by taking overlapping area and dividing it by the foreground area (foreground=image area[where there is fluorescence]/total area). Is this based on MOC (Mander's overlap coefficient)?

Hi All,

Recently, I've used BIOP JACoP plugin to analyse the protein protein colocalization during mitosis. I struggle to understand the output of the fluorogram. Please need help if anyone know how can I read it.

BW,

Duaa

Hi there,

Can you provide a picture or an extended explanation of what you don't understand?

The original paper of JaCoP is this one

Article Bolte, S. & Cordelières, F.P. A guided tour into subcellular...

you will find valuable info about the fluorograms in there.

Also in these other papers:

Article Automatic and Quantitative Measurement of Protein-Protein Co...

Article A Syntaxin 1, G o, and N-Type Calcium Channel Complex at a P...

To try to understand this fluorogams and what they tell you about co-localization this paper is very useful

Article A Syntaxin 1, G o, and N-Type Calcium Channel Complex at a P...

PS: Although the fluorograms are related, what is more important are the different coefficients that the plugin yields.

Hope this helps,

Cheers,

J

Hi there,

These what got fomr the BIOP JACop outcomes. you can see the distribution look the same for both images but the two cross line look different. Here in pic3, i used the default threshold but pic4 used manual threshold, got the same Pearson's Coefficient. my q is how can illustrate these in my thesis as i wanna to compare between two mutant form of one of POI and how this mutant in one protein affect the localization of the other protein.

Hope my explanation is clear enough.

BW,

Duaa

Q1- ...you can see the distribution look the same for both images but the two cross line look different. Here in pic3, i used the default threshold but pic4 used manual threshold...

So that's the reason why. Cross-hair represent the threshold. In pic 3 is more astringent than in pic 4.

Q2-...got the same Pearson's Coefficient...

Because JaCoP is calculating PCC in the entire image (all pixels) independently of the threshold...

Article A practical guide to evaluating colocalization in biological...

from this paper:

"In many image analysis software packages, PCC is measured for entire images by default. However, PCC should generally be measured for individual cells, which can be accomplished by hand-drawing a “region of interest” (ROI) over the image."

If you want something more meaningful for your PCC, you should use the "Crop ROis" function of JaCoP. For this you need either a user defined selection over the image or a ROI manager window open (labels unchecked or it crashes).

In that case you measure the PCC only in those ROIs.

Indeed, you can use one of the source images in which you are measuring colocalization,

1)duplicate

2)threshold (to a given value or threshold type)

3)analyze particles, and

4)use that ROI manager as the ROI to measure colocalization in JaCoP.

This would be somehow as to measure PCC only in those pixels above a given threshold.

However, what it is actually affected by the threshold is the Manders Overlapping Coefficient. You can use MOC instead, although PCC is superior. Read this:

Article Quantifying Colocalization by Correlation: The Pearson Corre...

Q3-my q is how can illustrate these in my thesis as i wanna to compare between two mutant form of one of POI and how this mutant in one protein affect the localization of the other protein.

As to illustrate your work, use the same thresholds to represent fluorograms of control and the other conditions and let the data speak by itself.

IF it's presented along a proper statistical analysis, it should work.

In the original paper of ICA (mentioned in one of my previous posts in this threads) they use a plot system that, in my opinion, is more representative than the raw fluorogram. Maybe you want to give it a try for representing, although the important thing, again, is the stats for the coefficient you choose.

(Figure 2 here Article A Syntaxin 1, G o, and N-Type Calcium Channel Complex at a P...

Hope this helps,

Cheers,

J

this really helpful, thanks a lot J. Ramirez-Franco.

Cheers,

Duaa