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 at the same 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 that the signal is not due to //bleedthrough// or //crosstalk//). = Qualitative Co-Localisation= (IMPORTANT) You **visually** assess if two components **look like** in the same "place" in the image. From the images set below, some people could try to argue that **Protein X** can be found in **Organel A**. Others (like me) will be more //sceptical// and will ask to compare to a different situation. For exemple, by adding a drug to increase (or decrease) the co-localisation levels or doing another staining (another organel). {F7195478 ,size = full} **Indeed**, from the images set below, one could easily argue that the **Protein X** is found more with Organel B than with Organel A. {F7195486 ,size = full} // It looks like every organels define by Organel B staining is similarly defined by Protein X staining. On the contrary, allmost all the Organel A are devoid of Protein X. 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**. // Quantitative Co-Localisation analysis can help you to decrease the //"by chance?"// factor. = 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 are required to do the analysis (depending of the paramater(s) you can use) . = Pearson's correlation coefficient (PCC) = (WARNING) It works well for signals with equal (or very similar) Histogram (the distribution of the 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 very well the co-localization status of the spots. For a 100 % co-localisation PCC is equal to 1. The fewer of spots co-localising and the close to 0. Note that, if no spots are co-localising PCC is below 0, spots are excluded (they are anti-correlated). {F7182820, size=full} **BUT** if the total number of spots are different between the two channels, PCC could lead to mis-interpretation. One can compare cases AF and AI , which have similar PCC while exhibiting very different spots number. {F7183034, size=full} So, even if we could use PCC to look at ProteinX and Organel B co-localisation the 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. A way to overcome this issue of PCC iis to use the Mander's coefficients. = Mander's coefficient = (IMPORTANT) TBD : link to Mander's article 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 very well the co-localization status of the spots so do the Mander's coefficients. {F7196763, size = full} **BUT** if the total number of spots are different between the two channels, Mander's coefficients appear much more meaningful because they highlight the contribution of each channel into the co-localisation. Compare AF, AG, AH {F7196768, size = full} =some refs= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074624/

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 at the same 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 that the signal is not due to //bleedthrough// or //crosstalk//). = Qualitative Co-Localisation= (IMPORTANT) You **visually** assess if two components **look like** in the same "place" in the image. From the images set below, some people could try to argue that **Protein X** can be found in **Organel A**. Others (like me) will be more //sceptical// and will ask to compare to a different situation. For exemple, by adding a drug to increase (or decrease) the co-localisation levels or doing another staining (another organel). {F7195478 ,size = full} **Indeed**, from the images set below, one could easily argue that the **Protein X** is found more with Organel B than with Organel A. {F7195486 ,size = full} // It looks like every organels define by Organel B staining is similarly defined by Protein X staining. On the contrary, allmost all the Organel A are devoid of Protein X. 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**. // Quantitative Co-Localisation analysis can help you to decrease the //"by chance?"// factor. = 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 are required to do the analysis (depending of the paramater(s) you can use) . = Pearson's correlation coefficient (PCC) = (WARNING) It works well for signals with equal (or very similar) Histogram (the distribution of the 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 very well the co-localization status of the spots. For a 100 % co-localisation PCC is equal to 1. The fewer of spots co-localising and the closer to 0. Note that, if no spots are co-localising PCC is below 0, spots are excluded (they are anti-correlated). {F7182820, size=full} **BUT** if the total number of spots are different between the two channels, PCC could lead to mis-interpretation. One can compare cases AF and AI , which have similar PCC while exhibiting very different scenari. For me, having 3/4 spots co-localising with 36 other spots , is a very different situtation than having 9/27 still with 36. {F7183034, size=full} So, even if we could use PCC to look at ProteinX and Organel B co-localisation the 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. A way to overcome this issue of PCC iis to use the Mander's coefficients. = Mander's coefficient = (IMPORTANT) TBD : link to Mander's article 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 very well the co-localization status of the spots so do the Mander's coefficients. {F7196763, size = full} **BUT** if the total number of spots are different between the two channels, Mander's coefficients appear much more meaningful because they highlight the contribution of each channel into the co-localisation. Compare AF, AG, AH {F7196768, size = full} =some refs= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074624/

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 at the same 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 that the signal is not due to //bleedthrough// or //crosstalk//). = Qualitative Co-Localisation= (IMPORTANT) You **visually** assess if two components **look like** in the same "place" in the image. From the images set below, some people could try to argue that **Protein X** can be found in **Organel A**. Others (like me) will be more //sceptical// and will ask to compare to a different situation. For exemple, by adding a drug to increase (or decrease) the co-localisation levels or doing another staining (another organel). {F7195478 ,size = full} **Indeed**, from the images set below, one could easily argue that the **Protein X** is found more with Organel B than with Organel A. {F7195486 ,size = full} // It looks like every organels define by Organel B staining is similarly defined by Protein X staining. On the contrary, allmost all the Organel A are devoid of Protein X. 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**. // Quantitative Co-Localisation analysis can help you to decrease the //"by chance?"// factor. = 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 are required to do the analysis (depending of the paramater(s) you can use) . = Pearson's correlation coefficient (PCC) = (WARNING) It works well for signals with equal (or very similar) Histogram (the distribution of the 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 very well the co-localization status of the spots. For a 100 % co-localisation PCC is equal to 1. The fewer of spots co-localising and the closer to 0. Note that, if no spots are co-localising PCC is below 0, spots are excluded (they are anti-correlated). {F7182820, size=full} **BUT** if the total number of spots are different between the two channels, PCC could lead to mis-interpretation. One can compare cases AF and AI , which have similar PCC while exhibiting very different spots numbercenari. For me, having 3/4 spots co-localising with 36 other spots , is a very different situtation than having 9/27 still with 36. {F7183034, size=full} So, even if we could use PCC to look at ProteinX and Organel B co-localisation the 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. A way to overcome this issue of PCC iis to use the Mander's coefficients. = Mander's coefficient = (IMPORTANT) TBD : link to Mander's article 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 very well the co-localization status of the spots so do the Mander's coefficients. {F7196763, size = full} **BUT** if the total number of spots are different between the two channels, Mander's coefficients appear much more meaningful because they highlight the contribution of each channel into the co-localisation. Compare AF, AG, AH {F7196768, size = full} =some refs= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074624/