= 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**. {F7183099,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. {F7183095,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 Prtoein X staining. On the contrary, 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/

= Keywords = Colocalization, Colocalisation, Co-Localization, Co-Localisation, Coloc, Manders, Pearsons, JACOP = Co-Localization = Like any microscopy experiment, **co-localization** requires experimenters to follow [[https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/good_practices/ | Good Practices ]]. We usually distinguish two major schools when it comes to co-localization: **Qualitative vs. Quantitative** While the confidence of your results can vary wildly between the two methods, the methodology and approach are the same: (IMPORTANT) **Biological Question:** 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 (eg. **Together** VS **Independent**). (NOTE) You must produce controls to assess the staining quality (prove that the signal is not due to [[ TBD | bleed-through, cross-talk]] or unspecific staining). = Qualitative Co-Localisation= (IMPORTANT) Means **visually** assessing if two components **look like** they are in the same "place" in the image. From the image set below, some people could try to argue that **Protein X** can be found in **Organelle A**. Others (like the BIOP) will be more //skeptical// and will ask to compare to a different situation. For example, by adding a drug to increase (or decrease) the co-localization levels or doing another staining (another organelle). {F7198994, size = full} **Indeed**, from the image set below, one could easily argue that the **Protein X** is localizing more in Organelle B with respect to Organelle A. {F7199000 ,size = full} // It looks like the image we can observe from the //Organelle B// staining is very similar to the image from the //Protein X// staining. On the contrary, it seems as though almost all the //Organelle A// are devoid of //Protein X//. Even if //Protein X// is (by chance?) found within //Organelle A// at some places, it looks like that //Organelle B// is much more co-present with //Protein X//. Quantitative Co-Localization analysis can help you to decrease the //"by chance?"// factor as well as measure it. = Quantitative Co-Localization= (IMPORTANT) You **measure a parameter**, or some parameters, to **quantify** the degree of co-localization 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 (prove that signal is not due to [[ TBD | bleed-through or cross-talk]] ) and are required to do the analysis (depending of the parameter(s) you can use) . == Pixel Based == === 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 analyzed images (ch.1 VS ch.2) have an equal number of spots. {F7198970, size = full} In these cases, one can see that PCC reflects very well the co-localization status of the spots. For a 100 % co-localization PCC is equal to 1. The fewer of spots co-localizing and the closer to 0. Note that, if no spots are co-localizing PCC is below 0, spots are excluded (they are anti-correlated). {F7198942, 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 scenario. For me, having 3/4 spots co-localizing with 36 other spots , is a very different situation than having 9/27 still with 36 , but the PCC can not reflect. {F7198948, size=full} So, even if we could use PCC to look at Protein-X and Organelle-B co-localization (area coverage are similar) the Organelle-A and Protein-X have area coverage are really too different and can't be analyzed by PCC. So one couldn't use PCC to measure Protein-X co-localize more with Organelle-A VS Organelle-B. A way to overcome this issue is to use the Mander's coefficients . === Mander's coefficient === (IMPORTANT) Original article : https://doi.org/10.1111/j.1365-2818.1993.tb03313.x In the figure below, the analyzed 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 ( M1 and M2, respectively for ch.1 and ch.2). {F7198978, size = full} **BUT** when 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-localization. Compare AF, AG, AH {F7198984, size = full} =Mander's coefficient Drawback= With "Biological" images (having background, a bit of [[ TBD | bleed-through or cross-talk]] ), you have to define threshold value for each channels to limit analysis to "True Positive" pixels. Defining this threshold is not necessary easy but should be doable , and better than using PCC. == Object Based == The object based is interesting, because we can get a lot of information , look at object clustering, ... **BUT** we need to define objects, accurately !!! Some criteria/parameters like "Center to Center Distance" can be used in a limited number of cases (ie distance between 2 population of vesicles ). Otherwise it will be biased by presence of clump (even if it's due to resolution limit of the acquisition system) == JACOP == [[https://imagej.nih.gov/ij/plugins/track/jacop2.html | link JACoP ]] === Center to Particles surface === {F7199390, size=full} === Center to Center Distance === Looks at the distance from one center to Corresponding one in a certain range. The current implementation generates result depending of image calibration , see figures below. {F7199402, size=full} == Ripley's K-functions == An [[ http://icy.bioimageanalysis.org/ | ICY ]] plugin implements use of Ripley's K-functions , that can be summarized by the figure below {F7199206, size=full} from [[https://www.ncbi.nlm.nih.gov/pubmed/24349021| Lagache T ]] //et al.// , PLoS One. 2013. NOTE : it requires a large number of object to analyze. =some refs= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074624/

= 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**. {F7183099,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. {F7183095,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 Prtoein X staining. On the contrary, 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== Keywords = Colocalization, Colocalisation, Co-Localization, Co-Localisation, Coloc, Manders, Pearsons, JACOP = Co-Localization = Like any microscopy experiment, **co-localization** requires experimenters to follow [[https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/good_practices/ | Good Practices ]]. We usually distinguish two major schools when it comes to co-localization: **Qualitative vs. Quantitative** While the confidence of your results can vary wildly between the two methods, the methodology and approach are the same: (IMPORTANT) **Biological Question:** 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 (eg. **Together** VS **Independent**). (NOTE) You must produce controls to assess the staining quality (prove that the signal is not due to [[ TBD | bleed-through, cross-talk]] or unspecific staining). = Qualitative Co-Localisation= (IMPORTANT) Means **visually** assessing if two components **look like** they are in the same "place" in the image. From the image set below, some people could try to argue that **Protein X** can be found in **Organelle A**. Others (like the BIOP) will be more //skeptical// and will ask to compare to a different situation. For example, by adding a drug to increase (or decrease) the co-localization levels or doing another staining (another organelle). {F7198994, size = full} **Indeed**, from the image set below, one could easily argue that the **Protein X** is localizing more in Organelle B with respect to Organelle A. {F7199000 ,size = full} // It looks like the image we can observe from the //Organelle B// staining is very similar to the image from the //Protein X// staining. On the contrary, it seems as though almost all the //Organelle A// are devoid of //Protein X//. Even if //Protein X// is (by chance?) found within //Organelle A// at some places, it looks like that //Organelle B// is much more co-present with //Protein X//. Quantitative Co-Localization analysis can help you to decrease the //"by chance?"// factor as well as measure it. = Quantitative Co-Localization= (IMPORTANT) You **measure a parameter**, or some parameters, to **quantify** the degree of co-localization 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 (prove that signal is not due to [[ TBD | bleed-through or cross-talk]] ) and are required to do the analysis (depending of the parameter(s) you can use) . == Pixel Based == === 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 analyzed images (ch.1 VS ch.2) have an equal number of spots. {F7198970, size = full} In these cases, one can see that PCC reflects very well the co-localization status of the spots. For a 100 % co-localization PCC is equal to 1. The fewer of spots co-localizing and the closer to 0. Note that, if no spots are co-localizing PCC is below 0, spots are excluded (they are anti-correlated). {F7198942, 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 scenario. For me, having 3/4 spots co-localizing with 36 other spots , is a very different situation than having 9/27 still with 36 , but the PCC can not reflect. {F7198948, size=full} So, even if we could use PCC to look at Protein-X and Organelle-B co-localization (area coverage are similar) the Organelle-A and Protein-X have area coverage are really too different and can't be analyzed by PCC. So one couldn't use PCC to measure Protein-X co-localize more with Organelle-A VS Organelle-B. A way to overcome this issue is to use the Mander's coefficients . === Mander's coefficient === (IMPORTANT) Original article : https://doi.org/10.1111/j.1365-2818.1993.tb03313.x In the figure below, the analyzed 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 ( M1 and M2, respectively for ch.1 and ch.2). {F7198978, size = full} **BUT** when 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-localization. Compare AF, AG, AH {F7198984, size = full} =Mander's coefficient Drawback= With "Biological" images (having background, a bit of [[ TBD | bleed-through or cross-talk]] ), you have to define threshold value for each channels to limit analysis to "True Positive" pixels. Defining this threshold is not necessary easy but should be doable , and better than using PCC. == Object Based == The object based is interesting, because we can get a lot of information , look at object clustering, ... **BUT** we need to define objects, accurately !!! Some criteria/parameters like "Center to Center Distance" can be used in a limited number of cases (ie distance between 2 population of vesicles ). Otherwise it will be biased by presence of clump (even if it's due to resolution limit of the acquisition system) == JACOP == [[https://imagej.nih.gov/ij/plugins/track/jacop2.html | link JACoP ]] === Center to Particles surface === {F7199390, size=full} === Center to Center Distance === Looks at the distance from one center to Corresponding one in a certain range. The current implementation generates result depending of image calibration , see figures below. {F7199402, size=full} == Ripley's K-functions == An [[ http://icy.bioimageanalysis.org/ | ICY ]] plugin implements use of Ripley's K-functions , that can be summarized by the figure below {F7199206, size=full} from [[https://www.ncbi.nlm.nih.gov/pubmed/24349021| Lagache T ]] //et al.// , PLoS One. 2013. NOTE : it requires a large number of object to analyze. =some refs= https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074624/