colocalization analysis
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal I have a triple labelled sample and would like to do colocalization analysis. What approaches are most people using? The plugins I have seen or used only handle double labelled specimen. A 3-D fluorogram perhaps? Thank you. Judy Trogadis Bio-Imaging Coordinator St. Michael's Hospital, 7Queen 30 Bond St. Toronto, ON M5B 1W8, Canada ph: 416-864-6060 x6337 pager: 416-685-9219 fax: 416-864-6043 [hidden email] |
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Search the CONFOCAL archive at
http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Judy, colocalization analysis is quite straight forward with ImageJ and the "Colocalisation Threshold" plus "Colocalization Test" plugin according to Costes. Regarding triple labeling, which type of questions do you plan to answer? I can think about a scenario with two marked structures and how they are colocalizing with the nuclei - then you do two runs: nuclei vs. staining 1, nuclei vs. staining 2. I am not aware of an established three-color colocalization equation. Or do I miss something here? Michael > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal > > I have a triple labelled sample and would like to do colocalization > analysis. What approaches are most people using? The plugins I have seen > or used only handle double labelled specimen. A 3-D fluorogram perhaps? > > Thank you. > > Judy Trogadis > Bio-Imaging Coordinator > St. Michael's Hospital, 7Queen > 30 Bond St. > Toronto, ON M5B 1W8, Canada > ph: 416-864-6060 x6337 > pager: 416-685-9219 > fax: 416-864-6043 > [hidden email] |
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Search the CONFOCAL archive at
http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Hi, Michael, You're right, 2 runs would do it - but one of the users of our imaging facility has triple labelled preps ready to analyze. He is looking for the presence of 3 proteins in a cell but I am not sure about their proximity to each other. Visual observation at high magnification could give a clue. I think the user wants some numerical value for a grant. Thanks. Judy >>> Michael Weber <[hidden email]> 08/27/08 9:45 AM >>> Search the CONFOCAL archive at http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Judy, colocalization analysis is quite straight forward with ImageJ and the "Colocalisation Threshold" plus "Colocalization Test" plugin according to Costes. Regarding triple labeling, which type of questions do you plan to answer? I can think about a scenario with two marked structures and how they are colocalizing with the nuclei - then you do two runs: nuclei vs. staining 1, nuclei vs. staining 2. I am not aware of an established three-color colocalization equation. Or do I miss something here? Michael > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal > > I have a triple labelled sample and would like to do colocalization > analysis. What approaches are most people using? The plugins I have seen > or used only handle double labelled specimen. A 3-D fluorogram perhaps? > > Thank you. > > Judy Trogadis > Bio-Imaging Coordinator > St. Michael's Hospital, 7Queen > 30 Bond St. > Toronto, ON M5B 1W8, Canada > ph: 416-864-6060 x6337 > pager: 416-685-9219 > fax: 416-864-6043 > [hidden email] |
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Search the CONFOCAL archive at
http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal
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Judy, "Colocalization Analysis" can mean everything and nothing.... You should ask the investigator to explain to you what his biological question is, preferably in plain English.... this will determine which method is most appropriate. Does (s)he want to know the proportion of cells that have all three proteins? the proportion of cells with protein A, that also have protein B and C? Does he want to know in any given cell how many foci are present containing all three proteins? or maybe what fraction of foci of Protein A are colocalized (within what distance?) with foci of B and C? The percentage of the cell's area/volume containing all three proteins (above what threshold?), and so on... Without a specific question you can't provide a specific answer... -- Julio Vazquez Fred Hutchinson Cancer Research Center Seattle, WA 98109-1024 On Aug 27, 2008, at 6:58 AM, Judy Trogadis wrote:
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In reply to this post by Judy Trogadis
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Judy, I'm going to stick my neck out an make a suggestion... Co-localization is a correlation. I suspected that statisticians can do a three variable correlation with minimal difficulty. I was interested, so I googled around this idea with the following (half decent) results; http://faculty.vassar.edu/lowry/ch3a.html http://books.google.ca/books? id=bmwhcJqq01cC&pg=PA1012&lpg=PA1012&dq=correlation+three+variables&so urce=web&ots=I9IYZSS-kj&sig=mItL7gIHEWt3RuQydR7XY4- PHsE&hl=en&sa=X&oi=book_result&resnum=4&ct=result I think the short answer is, talk to a statistician. As you mention, I have never seen this implemented. One caveat that I suspect you are aware of... In my experience people tend to relate visual colocalization with physical association. Very rarely do I see anyone consider a negative control, and then a comparisson of correlation coefficients between experimental and negative control to determine significance. I suspect that if you went this far, and then mixed in a third variable (protein), it would be hard to obtain significance simply due to resolution limitations. Of course, this would depend on abundance and distribution of protein(s) of interest. Would be interested to hear thoughts if anyone has further suggestions. Dan ------------------------ Dan Stevens, PhD Cellular Imaging Specialist Carl Zeiss Canada |
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In reply to this post by Judy Trogadis
Search the CONFOCAL archive at
http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal
Judy,
Just a few more thoughts: There have been a number of papers on colocalization. I just found a recent one in Current Protocols in Cell Biology that gives a working protocol so to say for doing colocalization analysis with the ImageJ colocalization plugins: Zinchuck and Zinchuk, Quantitative Colocalization Analysis of Confocal Fluorescence Microscopy Images. CPCB 4.19.1-4.19.16, June 2008. This can be obtained from the Journal's web site. The various colocalization coefficients are derived from the Correlation coefficients used in statistics. Wikipedia defines correlation as a measure of the relationship between two variables. Maybe the various correlation coefficients could be modified for use with three variables, but I am not even sure how the numbers could be interpreted, since different combinations of intensities could probably give similar results, and a lot of potentially useful information would be lost. So a workaround when using this type of approach would be to do pairwise colocalization comparisons (A vs B, B vs C and C vs A). If my concern were to evaluate the presence of three proteins (or other markers) inside a cell, my approach would be to select suitable thresholds for each signal, generate a mask for each channel with the selected thresholds, and then combine the three masks through Boolean or Image Arithmetic operations. This would generate a channel where all three markers are present (above set threshold). You could also create masks by combining two markers, and use that mask to analyze the distribution of the third marker. I would then quantitate the different markers through those masks (integrated intensity, surface area/volume, or whatever variable is the most relevant biologically), and either look at the absolute numbers, or the relative numbers as a function of the total cell area, or total intensity in any given channel, etc... To be even more accurate, I could make a mask for the cells (or nuclei, etc...) and restrict my analysis to those regions. As I mentioned in the previous post, it should be up to the investigator to let you know which of these numbers or pairwise comparisons are the most relevant biologically for their experiment. With the mask approach you can determine, for instance, that 20% of protein A is present in regions where proteins B and C are also present, and so on... this is very easy for me to grasp intuitively. I will also be able to know how large the colocalized patches are, how many there are, where they are located, and so on... In my opinion, this is much more informative that a Pearson's coefficient of, let's say, + 0.43. My main issue with the standard correlation approach is that it is pixel-based, as opposed to object-based, and that the results are highly dependent on how the images were collected (sampling) and processed, how much noise and background they have, how well registered the different channels are (always true, but even more so in this case, especially if looking at colocalization of very small objects) and which regions and thresholds are used for analysis. Used properly, these methods are quite powerful though, and will give very useful information such as colocalization coefficients, % of A colocalized, % of B colocalized, etc.... but I have seen so many results that didn't make sense just because someone was using a poorly sampled image, or a noisy image (such as a confocal image under limiting signal conditions), that I am very careful and tend to prefer a more visual approach where objects are identified based on thresholds, and then analyzed. Anyhow, the paper above discusses some of the issues to take into consideration when performing the pixel-based type of colocalization analysis. hope this helps, with kind regards, Julio. -- Julio Vazquez Fred Hutchinson Cancer Research Center Seattle, WA 98109-1024 - On Aug 27, 2008, at 6:58 AM, Judy Trogadis wrote:
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Search the CONFOCAL archive at
http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Dear Julio You are wonderful, I really appreciate your taking the time to think about this problem and then to discuss it in such depth. Your comments are very helpful and have given me fodder for thought. As is often the case with a seemingly simple question, the answer invariable begins with . . . . "it all depends on ". . . . Armed with this information and comments from others, I will probably give a presentation to the users about colocalization analysis. Thank you. Judy >>> Julio Vazquez <[hidden email]> 09/03/08 1:59 PM >>> Search the CONFOCAL archive at http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Judy, Just a few more thoughts: There have been a number of papers on colocalization. I just found a recent one in Current Protocols in Cell Biology that gives a working protocol so to say for doing colocalization analysis with the ImageJ colocalization plugins: Zinchuck and Zinchuk, Quantitative Colocalization Analysis of Confocal Fluorescence Microscopy Images. CPCB 4.19.1-4.19.16, June 2008. This can be obtained from the Journal's web site. The various colocalization coefficients are derived from the Correlation coefficients used in statistics. Wikipedia defines correlation as a measure of the relationship between two variables. Maybe the various correlation coefficients could be modified for use with three variables, but I am not even sure how the numbers could be interpreted, since different combinations of intensities could probably give similar results, and a lot of potentially useful information would be lost. So a workaround when using this type of approach would be to do pairwise colocalization comparisons (A vs B, B vs C and C vs A). If my concern were to evaluate the presence of three proteins (or other markers) inside a cell, my approach would be to select suitable thresholds for each signal, generate a mask for each channel with the selected thresholds, and then combine the three masks through Boolean or Image Arithmetic operations. This would generate a channel where all three markers are present (above set threshold). You could also create masks by combining two markers, and use that mask to analyze the distribution of the third marker. I would then quantitate the different markers through those masks (integrated intensity, surface area/volume, or whatever variable is the most relevant biologically), and either look at the absolute numbers, or the relative numbers as a function of the total cell area, or total intensity in any given channel, etc... To be even more accurate, I could make a mask for the cells (or nuclei, etc...) and restrict my analysis to those regions. As I mentioned in the previous post, it should be up to the investigator to let you know which of these numbers or pairwise comparisons are the most relevant biologically for their experiment. With the mask approach you can determine, for instance, that 20% of protein A is present in regions where proteins B and C are also present, and so on... this is very easy for me to grasp intuitively. I will also be able to know how large the colocalized patches are, how many there are, where they are located, and so on... In my opinion, this is much more informative that a Pearson's coefficient of, let's say, + 0.43. My main issue with the standard correlation approach is that it is pixel-based, as opposed to object-based, and that the results are highly dependent on how the images were collected (sampling) and processed, how much noise and background they have, how well registered the different channels are (always true, but even more so in this case, especially if looking at colocalization of very small objects) and which regions and thresholds are used for analysis. Used properly, these methods are quite powerful though, and will give very useful information such as colocalization coefficients, % of A colocalized, % of B colocalized, etc.... but I have seen so many results that didn't make sense just because someone was using a poorly sampled image, or a noisy image (such as a confocal image under limiting signal conditions), that I am very careful and tend to prefer a more visual approach where objects are identified based on thresholds, and then analyzed. Anyhow, the paper above discusses some of the issues to take into consideration when performing the pixel-based type of colocalization analysis. hope this helps, with kind regards, Julio. -- Julio Vazquez Fred Hutchinson Cancer Research Center Seattle, WA 98109-1024 http://www.fhcrc.org/ - On Aug 27, 2008, at 6:58 AM, Judy Trogadis wrote: > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal > > Hi, Michael, > > You're right, 2 runs would do it - but one of the users of our > imaging facility has triple labelled preps ready to analyze. He is > looking for the presence of 3 proteins in a cell but I am not sure > about their proximity to each other. Visual observation at high > magnification could give a clue. I think the user wants some > numerical value for a grant. > > Thanks. > Judy > > >>>> Michael Weber <[hidden email]> 08/27/08 9:45 AM >>> > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal > > Judy, > > colocalization analysis is quite straight forward with ImageJ and the > "Colocalisation Threshold" plus "Colocalization Test" plugin > according to > Costes. > > Regarding triple labeling, which type of questions do you plan to > answer? > I can think about a scenario with two marked structures and how > they are > colocalizing with the nuclei - then you do two runs: nuclei vs. > staining > 1, nuclei vs. staining 2. I am not aware of an established three-color > colocalization equation. Or do I miss something here? > > Michael > > >> Search the CONFOCAL archive at >> http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal >> >> I have a triple labelled sample and would like to do colocalization >> analysis. What approaches are most people using? The plugins I >> have seen >> or used only handle double labelled specimen. A 3-D fluorogram >> perhaps? >> >> Thank you. >> >> Judy Trogadis >> Bio-Imaging Coordinator >> St. Michael's Hospital, 7Queen >> 30 Bond St. >> Toronto, ON M5B 1W8, Canada >> ph: 416-864-6060 x6337 >> pager: 416-685-9219 >> fax: 416-864-6043 >> [hidden email] |
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In reply to this post by Julio Vazquez
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Julio suggests that 'In my opinion, this is much more informative than a Pearson's coefficient of, let's say, + 0.43.' the problem here is that the term colocalisation has a very broad meaning, covering (a) fluorophores in the same place and (b) a relationship between intensities/concentrations (Pearson correlation coefficient). Being is the same place may be trivial - the same physico-chemical properties. A major problem with measurements that depend solely upon intensity thresholding is that they are very sensitive to the threshold chosen and in noisy fluorescent images this can be hard to set - inevitably some pixels with high noise are included while excluding pixels with a combination of low nosie and low levels of fluorescence - we can't tell then appart. A relationship between the intensities (correlation) suggests some form of interaction, this could be direct or involving an additional molecule or structure. A much potentially more interesting observation. Equally important a correlation is a sensitive measurement and likely to change with a cell's physiological state. Object based methods merely pose the 'in the same place question' but can be made more interesting if a relationship between the intensities across the population of objects is tested for, or even across all pixels deemed to be in all the objects. Essentially measure the correlation in a ROI (Region of Interest). While I have not seen the latest Zinchuk paper, an earlier paper (Acta Histochem Cytochem 2007, 4, 101-111) on the same topic has some has some very odd features. They very decently include scattergrams of their paired images but the scattergrams suggest that many of the pixels are saturated and therefore unsuitable for the Pearson correlation analysis that they used. They reported Pearson correlation coefficients very close to 1, meaning a perfect match between the 2 fluorescent images, however this is not at all apparent in the accompanying scattergrams which should therefore have a single noise free straight line. We assume that the measurements come from a subset of the pixels in scattergram, but on what basis was the subset chosen ?. We twice approached the authors for clarification but none was forthcoming. We have touched on some of these topics in an article in Microscopy and Analysis (September, 2008, 7-11) which should out soon. Jeremy Adler Cell Biology The Wenner-Gren Inst. Arrhenius Laboratories F5 Stockholm University Stockholm 106 91 Sweden ________________________________ From: Confocal Microscopy List on behalf of Julio Vazquez Sent: Wed 03-Sep-08 19:59 To: [hidden email] Subject: Re: colocalization analysis Search the CONFOCAL archive at http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Judy, Just a few more thoughts: There have been a number of papers on colocalization. I just found a recent one in Current Protocols in Cell Biology that gives a working protocol so to say for doing colocalization analysis with the ImageJ colocalization plugins: Zinchuck and Zinchuk, Quantitative Colocalization Analysis of Confocal Fluorescence Microscopy Images. CPCB 4.19.1-4.19.16, June 2008. This can be obtained from the Journal's web site. The various colocalization coefficients are derived from the Correlation coefficients used in statistics. Wikipedia defines correlation as a measure of the relationship between two variables. Maybe the various correlation coefficients could be modified for use with three variables, but I am not even sure how the numbers could be interpreted, since different combinations of intensities could probably give similar results, and a lot of potentially useful information would be lost. So a workaround when using this type of approach would be to do pairwise colocalization comparisons (A vs B, B vs C and C vs A). If my concern were to evaluate the presence of three proteins (or other markers) inside a cell, my approach would be to select suitable thresholds for each signal, generate a mask for each channel with the selected thresholds, and then combine the three masks through Boolean or Image Arithmetic operations. This would generate a channel where all three markers are present (above set threshold). You could also create masks by combining two markers, and use that mask to analyze the distribution of the third marker. I would then quantitate the different markers through those masks (integrated intensity, surface area/volume, or whatever variable is the most relevant biologically), and either look at the absolute numbers, or the relative numbers as a function of the total cell area, or total intensity in any given channel, etc... To be even more accurate, I could make a mask for the cells (or nuclei, etc...) and restrict my analysis to those regions. As I mentioned in the previous post, it should be up to the investigator to let you know which of these numbers or pairwise comparisons are the most relevant biologically for their experiment. With the mask approach you can determine, for instance, that 20% of protein A is present in regions where proteins B and C are also present, and so on... this is very easy for me to grasp intuitively. I will also be able to know how large the colocalized patches are, how many there are, where they are located, and so on... In my opinion, this is much more informative that a Pearson's coefficient of, let's say, + 0.43. My main issue with the standard correlation approach is that it is pixel-based, as opposed to object-based, and that the results are highly dependent on how the images were collected (sampling) and processed, how much noise and background they have, how well registered the different channels are (always true, but even more so in this case, especially if looking at colocalization of very small objects) and which regions and thresholds are used for analysis. Used properly, these methods are quite powerful though, and will give very useful information such as colocalization coefficients, % of A colocalized, % of B colocalized, etc.... but I have seen so many results that didn't make sense just because someone was using a poorly sampled image, or a noisy image (such as a confocal image under limiting signal conditions), that I am very careful and tend to prefer a more visual approach where objects are identified based on thresholds, and then analyzed. Anyhow, the paper above discusses some of the issues to take into consideration when performing the pixel-based type of colocalization analysis. hope this helps, with kind regards, Julio. -- Julio Vazquez Fred Hutchinson Cancer Research Center Seattle, WA 98109-1024 http://www.fhcrc.org <http://www.fhcrc.org/> / - On Aug 27, 2008, at 6:58 AM, Judy Trogadis wrote: Search the CONFOCAL archive at http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Hi, Michael, You're right, 2 runs would do it - but one of the users of our imaging facility has triple labelled preps ready to analyze. He is looking for the presence of 3 proteins in a cell but I am not sure about their proximity to each other. Visual observation at high magnification could give a clue. I think the user wants some numerical value for a grant. Thanks. Judy Michael Weber <[hidden email]> 08/27/08 9:45 AM >>> Search the CONFOCAL archive at http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Judy, colocalization analysis is quite straight forward with ImageJ and the "Colocalisation Threshold" plus "Colocalization Test" plugin according to Costes. Regarding triple labeling, which type of questions do you plan to answer? I can think about a scenario with two marked structures and how they are colocalizing with the nuclei - then you do two runs: nuclei vs. staining 1, nuclei vs. staining 2. I am not aware of an established three-color colocalization equation. Or do I miss something here? Michael Search the CONFOCAL archive at http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal I have a triple labelled sample and would like to do colocalization analysis. What approaches are most people using? The plugins I have seen or used only handle double labelled specimen. A 3-D fluorogram perhaps? Thank you. Judy Trogadis Bio-Imaging Coordinator St. Michael's Hospital, 7Queen 30 Bond St. Toronto, ON M5B 1W8, Canada ph: 416-864-6060 x6337 pager: 416-685-9219 fax: 416-864-6043 [hidden email] |
 
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Daniel James White |
Re: colocalization analysis
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In reply to this post by Judy Trogadis
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Dear Judy and Julio, Some more thoughts to add to that... > > > > There have been a number of papers on colocalization. I just found a > recent one in Current Protocols in Cell Biology that gives a working > protocol so to say for doing colocalization analysis with the ImageJ > colocalization plugins: > > Zinchuck and Zinchuk, Quantitative Colocalization Analysis of > Confocal Fluorescence Microscopy Images. CPCB 4.19.1-4.19.16, June > 2008. I would be a little weary here. The methods they show in this paper have no objective way of setting the thresholds? this means you can easily fiddle the results until you get what you want. the method of Costes provides a good (but not totally fool proof) method for objectively and reproducibly setting the thresholds of the 2 channels using pearsons correlation coefficient to find where there is and is not correlation. Alos, if you look at the 2D histograms / scatter plots in the figures of this paper, most of the data is very clearly intensity saturated, meaning most of the higher intensities are crashed onto the highest value of the detector (usually 225 for 8 bit scale in confocal). The gain / laser power was too high, and the highest intensity information (usually the most interesting!) has bee LOST! This makes the all the calculation turn into nonsense as the saturated numbers arent as high as tyhey should be compared to the in range data. This is not good. > > > If my concern were to evaluate the presence of three proteins (or > other markers) inside a cell, my approach would be to select suitable > thresholds for each signal, generate a mask for each channel with the > selected thresholds, and then combine the three masks through Boolean > or Image Arithmetic operations. This would generate a channel where > all three markers are present (above set threshold). This is a good approach, but again there must be a repeatable, non subjective, math/stats sensible method to set these thresholds, else you can maniplulate then until you get what you want, instead of what actually might be there. > > My main issue with the standard correlation approach is that it is > pixel-based, as opposed to object-based, and that the results are > highly dependent on how the images were collected (sampling) and > processed, how much noise and background they have, how well > registered the different channels are (always true, but even more so > in this case, especially if looking at colocalization of very small > objects) and which regions and thresholds are used for analysis. These are very important points. Noisy confocal images can still be measured for colocalisation, but they will give lower Manders coefficients and Peasrons than is really true, as these are both very sensitive to noise. THere is a way around that, be measuring the Pearsons of 2 consecutive images of the same channel of the same object, then using that to back correct the between channel Pearsons coefficient. ... but Pearsons coefficient is sensitive to different relative intensities of the 2 channels, so its a poor measure of colo in many situations. Manders are not sensitive to this. > Used > properly, these methods are quite powerful though, and will give very > useful information such as colocalization coefficients, % of A > colocalized, % of B colocalized, etc.... but I have seen so many > results that didn't make sense just because someone was using a > poorly sampled image, or a noisy image (such as a confocal image > under limiting signal conditions), that I am very careful and tend to > prefer a more visual approach where objects are identified based on > thresholds, and then analyzed. Anyhow, the paper above discusses > some of the issues to take into consideration when performing the > pixel-based type of colocalization analysis. Here is where I disagree fundamentally. The way one should approach a problem is to define a hypothesis, then measure something, and test the hypothesis using appropriate stats. This data analysis must be blind and objective. As soon as a researcher, who has a strong idea of what the result needs to be so they can get their next grant, is allowed to subjectively manipulate parameters of the analysis (moving sliders until you see what you wanted to see) the whole scientific process fall apart. The brain is a visually biased computer, and your eyes seldom tell you the truth, especially when looking at a 2 colour merge image. We are evolved to find bright coloured fruit with high contrast against the green of leaves. We are very bad at asessing quantitatively a whole grey scale range, as our brains only really pick out very contrasty objects, and we miss much information. I think colour merge images are a very bad idea, as they are misleading for the reader. Much better to get the reader to understand what they see in a 2D hisotgram (its just like a FACS data presentation, which everyone has no trouble understanding... at 2D dot plot if you like) Here you get a much better visualisation of the corellation between 2 images which is not intensity dependent (in the actual visualisation) For single channel images, I much preffer to use at the very least grey scalem but much better a rainbow colour look up table, where each intensity iod a different bright colour. Our eyes and brain are much better at interpreting that than greyscale where the lower intensitites just look like black, and the higher intensities all look nearly white, and you cant tell the difference. With different colours it is much clearer. No to colour merge images!!! cheers Dan Dr. Daniel James White BSc. (Hons.) PhD Senior Microscopist / Image Processing and Analysis Light Microscopy Facility Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauerstrasse 108 01307 DRESDEN Germany New Mobile Number!!! +49 (0)15114966933 (German Mobile) +49 (0)351 210 2627 (Work phone at MPI-CBG) +49 (0)351 210 1078 (Fax MPI-CBG LMF) http://www.bioimagexd.net http://www.chalkie.org.uk [hidden email] ( [hidden email] ) |
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John Oreopoulos |
fluorophore spectral overlap database for FRET
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Search the CONFOCAL archive at
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Hi listserver,
I would like to know if there's an online database out there that catalogs the spectral overlap (the J integral) between different fluorophores for FRET imaging. I'm thinking of something similar to the Molecular Probes Java Spectral Viewer that allows you to call up the absorption and emission profile of most of their probes and then you can also superimpose on the graph various laser lines, color filters, etc. It would be great to have something similar that would calculate the J integral for any two FRET pairs. Thanks. John Oreopoulos, BSc, PhD Candidate University of Toronto Institute For Biomaterials and Biomedical Engineering Centre For Studies in Molecular Imaging Tel: W:416-946-5022 |
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In reply to this post by Daniel James White
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The brain is a visually biased computer, I'd like to reiterate the point that the human visual system is terrible at accurately estimating absolute values in data displayed with a color map. Psychophysical experiments have shown that simultaneous contrast effects in the gray scale map, in particular, can cause value estimation errors of up to 20% of the data value range displayed by the scale (Colin Ware, Color sequences for univariate maps: Theory, experiments, and principles, IEEE Computer Graphics and Applications, 8(5), pp. 41-49, 1988). See http://web.mit.edu/persci/people/adelson/checkershadow_illusion.html for a good demonstration of simultaneous contrast. What the human visual system IS good at is detecting areas of high contrast, which lets us do things like detect outlines of objects so we can avoid them while walking. Unfortunately, for the purpose of data estimation from a color map, the same mechanisms that enhance edge detection prevent accurate absolute estimation of data values from the gray scale map.
Absolutely. Moreover, showing merged color images where one of the chosen colors is blue is a particularly bad choice. Our sensitivity to contrast changes in blue is far less than to that of green or red, so using the blue channel will almost guarantee that any visual analysis, even qualitative, will be confounded.
For univariate data, your best bet is to use a color map that varies in hue while linearly varying in illumination as well. The blackbody radiation color map (black-red-orange-yellow-white) is a good example. Inclusion of hue variation will reduce the simultaneous contrast effects that cause severe value estimation errors. Regardless of your color map choice, never rely on the human visual system for absolute value estimation! -- Cory Quammen Department of Computer Science University of North Carolina at Chapel Hill http://www.cs.unc.edu/~cquammen |
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In reply to this post by Judy Trogadis
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Hello, What is the relation between FCS and relaxation spectroscopy? Debasis On Wed, Aug 27, 2008 at 7:30 AM, Judy Trogadis <[hidden email]> wrote: > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal > > I have a triple labelled sample and would like to do colocalization analysis. What approaches are most people using? The plugins I have seen or used only handle double labelled specimen. A 3-D fluorogram perhaps? > > Thank you. > > Judy Trogadis > Bio-Imaging Coordinator > St. Michael's Hospital, 7Queen > 30 Bond St. > Toronto, ON M5B 1W8, Canada > ph: 416-864-6060 x6337 > pager: 416-685-9219 > fax: 416-864-6043 > [hidden email] > |
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In reply to this post by Michael Weber-4
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Hello, What is the relation between FCS and relaxation spectroscopy? Debasis On Wed, Aug 27, 2008 at 8:45 AM, Michael Weber <[hidden email]> wrote: > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal > > Judy, > > colocalization analysis is quite straight forward with ImageJ and the > "Colocalisation Threshold" plus "Colocalization Test" plugin according to > Costes. > > Regarding triple labeling, which type of questions do you plan to answer? > I can think about a scenario with two marked structures and how they are > colocalizing with the nuclei - then you do two runs: nuclei vs. staining > 1, nuclei vs. staining 2. I am not aware of an established three-color > colocalization equation. Or do I miss something here? > > Michael > > >> Search the CONFOCAL archive at >> http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal >> >> I have a triple labelled sample and would like to do colocalization >> analysis. What approaches are most people using? The plugins I have seen >> or used only handle double labelled specimen. A 3-D fluorogram perhaps? >> >> Thank you. >> >> Judy Trogadis >> Bio-Imaging Coordinator >> St. Michael's Hospital, 7Queen >> 30 Bond St. >> Toronto, ON M5B 1W8, Canada >> ph: 416-864-6060 x6337 >> pager: 416-685-9219 >> fax: 416-864-6043 >> [hidden email] > |
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Jean-Pierre CLAMME |
Re: fluorophore spectral overlap database for FRET
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In reply to this post by John Oreopoulos
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Hi, I'm using http://omlc.ogi.edu/spectra/PhotochemCAD/html/du98.html It is not a database but It's a good software to calculate spectral overlap etc. It takes text files so you can add the dyes you are in interested in. Hope it will help, JP John Oreopoulos wrote: > Search the CONFOCAL archive at > http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Hi listserver, > > I would like to know if there's an online database out there that > catalogs the spectral overlap (the J integral) between different > fluorophores for FRET imaging. I'm thinking of something similar to > the Molecular Probes Java Spectral Viewer that allows you to call up > the absorption and emission profile of most of their probes and then > you can also superimpose on the graph various laser lines, color > filters, etc. It would be great to have something similar that would > calculate the J integral for any two FRET pairs. > > Thanks. > > John Oreopoulos, BSc, > > PhD Candidate > > University of Toronto > > Institute For Biomaterials and Biomedical Engineering > > Centre For Studies in Molecular Imaging > > > Tel: W:416-946-5022 > > > |
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John Oreopoulos |
"Red-edge" excitation of fluorescent proteins
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal
Recently I've been trying to detect homo-FRET between fluorescent proteins using various FRET techniques and then I came across this paper that talks about "red-edge" excitation of fluorescent probes which I had never heard of:
Red-edge anisotropy microscopy enables dynamic imaging of homo-FRET between green fluorescent proteins in cells Author(s): Squire A, Verveer PJ, Rocks O, Bastiaens PIH Source: JOURNAL OF STRUCTURAL BIOLOGY Volume: 147 Issue: 1 Pages: 62-69 Published: JUL 2004 As it turns out, I'm using 532 nm in TIRF to image YFP, and 532 nm does sit very far on the "red-edge" of the YFP absorption spectrum. As a consequence, I may be unintentionally missing the homo-FRET I'm trying to detect because of the effect described in this paper. Can anyone out there explain to me the "red-edge failure of energy transfer" effect originally reported by Weber and Shinitzky? I has always been under the impression that exciting a dye at any wavelength along the absorption spectrum didn't matter (except for the intensity of fluorescence that comes back of course), but I suppose when FRET is involved, things are more complicated. What's happening at the molecular level to prevent homo-FRET when you excite with longer wavelengths? Thank you. John Oreopoulos, BSc, PhD Candidate University of Toronto Institute For Biomaterials and Biomedical Engineering Centre For Studies in Molecular Imaging Tel: W:416-946-5022 |
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In reply to this post by Debasis Manna
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http://listserv.acsu.buffalo.edu/cgi-bin/wa?S1=confocal Adler et al. (Replicate-based noise corrected correlation for accurate measurements of colocalization. Jeremy Adler, Stamatis N Pagakis, and Ingela Parmryd. Journal of Microscopy 230(1):121-133, 2008) suggests using replicate images to estimate errors due to Poisson and background noise to derive a correction factor to the Pearson correlation coefficient. They also note the distinction between selection of a ROI in a scattergram (which can produce some very high correlations), for example Zinchuck, Zinchuck and Okada, Acta Histochem Cytochem _40_ 101 (2007), and selection of a ROI on the image (which may be more meaningful). I too, have concerns about the arbitrary thresholding and intensity saturation in the Zinchuck et al. publications as expressed by Dan White earlier in the thread. I am hoping that the error correction described by Adler et al. can be applied to the Manders coefficients in a similar way. Is anyone statistically confident enough to venture an opinion? If so, replicate-based noise corrected correlation, combined with the Costes method ( Costes, S.V., Daelemans, D., Cho, E.H., Dobbin, Z., Pavlakis, G. & Lockett,S. (2004) Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys. J. 86, 39934003. ) of objective thresholding and choosing image ROIs may well the current best practice in quantitation of colocalization. Kingsley Micklem -- Medical Informatics Unit Nuffield Department of Clinical Laboratory Sciences University of Oxford Room 4A12A, Level 4, Academic Block John Radcliffe Hospital Oxford OX3 9DU (44) 1865 220555 |
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George McNamara |
Re: fluorophore spectral overlap database for FRET
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In reply to this post by Jean-Pierre CLAMME
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Hi John and Jean-Pierre,
Janos Szollosi and Horvath Gabor sent me an Excel FRET calculator, that I have modified slightly: http://home.earthlink.net/~fluorescentdyes/McNamara 20050709 FRET Janos Szollosi Horvath Gabor FRET calculator .xls Source spectra data can be found in the Excel files in the PubSpectra dyes/fluorescent proteins zip file McNamara_Boswell_000_2006_Index _Dyes_FPs_Filters_Lamps_Other_Spectra.xls (someone from a company volunteered their staff to consolidate to one file. I haven't heard back from them in a while, so on some rainy - but not too rainy, this being Miami - Sunday, I may merge all of PubSpectra into a single Excel 2007 "big grid" file. I'm also a bit behind in adding spectra from the literature. If anyone has new spectra, please send me the data). PhotoChemCAD also has a FRET calculator, http://www.photochemcad.com/ All these links are available at http://home.earthlink.net/~pubspectra/ Enjoy, George At 05:48 PM 9/5/2008, you wrote: Search the CONFOCAL archive at |
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