Like any microscopy experiment co-localization requires to follow [[https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/good_practices/ | Good Practices ]].
= Co-localization =
(IMPORTANT) You would like to assess if two components are the "place" in the image.
(WARNING) You should then compare two conditions to show a difference between two states **Together** VS **Independent**.
(NOTE) Controls are required to assess the staining quality (proove signal is not due to //bleedthrough// or //crosstalk//).
= Qualitative Co-Localisation=
(IMPORTANT) You visually assess if two components look like in the "place" in the image.
(WARNING) It is recommended to compare two conditions to show a difference between two states **Together** VS **Independent**.
(NOTE) Controls are required to assess staining quality (proove signal is not due to //bleedthrough// or //crosstalk//).
From the images set below, some people could try to argue that **Protein X** can be found in **Organel A**.
{F718309995478 ,size = full}
**BUT**
From the images set below, one could easily argue that the **Protein X** is found more with Organel B than with Organel A.
{F718309595486 ,size = full}
// Even if **Protein X** is (by chance?) found with **Organel A** at some places, it looks like that **Organel B** is much more co-present with **Protein X**//. It looks like every organels define by Organel A staining is similarly defined by PrtoProtein X staining.
On the contrary, Onallmost all the contrary,Organel A are devoid of Protein X. all most all the Organel A are devoid of Protein X.//
= Quantitative Co-Localisation=
(IMPORTANT) You **measure a parameter**, or some parameters, to quantify the degree of co-Localisation of two components.
(WARNING) It is recommended to compare two conditions to show a difference between two states **Together** VS **Independent**.
(NOTE) Controls are required to assess staining quality (proove that signal is not due to //bleedthrough// or //crosstalk//) and (depending of the paramater(s) you can use) are required to do the analysis.
= Pearson's correlation coefficient (PCC) =
(WARNING) It works well for signals with equal (or very similar) Histogram (distribution of intensities) and Area coverage within the image.
(IMPORTANT) In EVERY other cases it could lead to a misleading conclusion!
In the figure below, the analysed images (ch.1 VS ch.2) have an equal number of spots.
In these cases, one can see that PCC reflects well the co-localization status of spots.
{F7182820, size=full}
**BUT** if the total number of spots are different between the two channels, PCC coudl lead to mis-interpretation.
One can compare cases AF and AI , which have similar PCC while exhibit very different scenari .
{F7183034, size=full}
So we could use it to look at PCC or ProteinX and Organel B.
BUT
Organel-A and Protein-X have area coverages are reallly too different and can't be analysed by PCC.
So one couldn't use PCC to juge of co-localisation of Protein-X with Organel A or B.
= Mander's coefficient =
A way overcome this issue it we can use Mander's coefficient
=some refs=
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074624/