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} // 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 Protein X staining. On the contrary, allmost 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 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/

= 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/

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} // 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 Protein X staining. On the contrary, allmost 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 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== 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/