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Mass spectrometry versus western blotting for validation
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Mr. Testtubehead
Mass spectrometry versus western blotting for validation
It is a common practice to prove a result using an orthogonal technique. Like RNAseq followed by qRT-PCR etc.
Western blotting is not a robust technique and cross comparisons are difficult because of difference in the avidities/affinities of different antibodies. So comparisons can be made only with one protein-control pair in different conditions.
LC-MS is more sensitive and less biased(IMO). So the general trick is- don't do westerns for all proteins just report the ones that behave well (say that you "chose" these because they are important). I know this is a wrong practice and I should not be advocating it. For your own scientific validation try it with another MS technique; if you used ESI-Quadrupole/ion-Trap etc then try with MALDI-TOF or iTRAQ. Some luddites will continue to cling to westerns.
I think it is better to not test the proteins that have low peptide counts. See what is known as MA plot. This is frequently used for microarrays. Here M denotes fold change and A denotes total expression in both samples. Don't pick proteins that are low in expression in both samples; they might not be meaningful. For example if you have 2 molecules of X and 100 molecules of Y in control condition and 5 molecules of X and 200 molecules of Y in test; then the fold change in X would seem important; however this change may not be relevant and can be a result of stochastic fluctuation/measurement error. If you have many samples you can see if something is stochastic or not but the limitation is the number of samples.
Note: I am not saying that 2→5 molecule increment should be meaningless. But to know if they have a meaning or not you would require more complex models; better avoid them at this moment.
It is a common practice to prove a result using an orthogonal technique. Like RNAseq followed by qRT-PCR etc.
Western blotting is not a robust technique and cross comparisons are difficult because of difference in the avidities/affinities of different antibodies. So comparisons can be made only with one protein-control pair in different conditions.
LC-MS is more sensitive and less biased(IMO). So the general trick is- don't do westerns for all proteins just report the ones that behave well (say that you "chose" these because they are important). I know this is a wrong practice and I should not be advocating it. For your own scientific validation try it with another MS technique; if you used ESI-Quadrupole/ion-Trap etc then try with MALDI-TOF or iTRAQ. Some luddites will continue to cling to westerns.
I think it is better to not test the proteins that have low peptide counts. See what is known as MA plot. This is frequently used for microarrays. Here M denotes fold change and A denotes total expression in both samples. Don't pick proteins that are low in expression in both samples; they might not be meaningful. For example if you have 2 molecules of X and 100 molecules of Y in control condition and 5 molecules of X and 200 molecules of Y in test; then the fold change in X would seem important; however this change may not be relevant and can be a result of stochastic fluctuation/measurement error. If you have many samples you can see if something is stochastic or not but the limitation is the number of samples.
Note: I am not saying that 2→5 molecule increment should be meaningless. But to know if they have a meaning or not you would require more complex models; better avoid them at this moment.
I mentioned in my original post that we sent our samples to another lab for processing and mass spec. They returned "normalized spectral count" data back to us. From what I have been able to gather, the MA plot would be utilized in obtaining these normalized spectral counts (though I am not sure what method(s) they used for normalization). If I understand correctly, I would need the raw data - not the normalized data - in order to generate the MA plot, is this correct? Lastly, I have read about volcano plots; would it be more appropriate than an MA plot?More
@syntonicC Perhaps you can ask them what normalization they have done. It is certainly possible that the overall expression was low in a certain sample and this can be normalized so that the two samples are comparable (as in quantile normalization). Even then the low expressing ones will remain so. And you can remove them. You don't actually need to plot. You can use a general low expression protein (from knowledge) for comparison. I hope you have the entire normalized data including the non-differentially expressed ones.More
@ WYSIWYG I have emailed the lab for the information on the normalization method. And yes, I have the full data set. Thank you for your information on the comparison, this will be helpful for me moving forward. Additionally, I found an article discussing some of the things mentioned in this thread and I've added it to my original post.More
It is a common practice to prove a result using an orthogonal technique. Like RNAseq followed by qRT-PCR etc.
Western blotting is not a robust technique and cross comparisons are difficult because of difference in the avidities/affinities of different antibodies. So comparisons can be made only with one protein-control pair in different conditions.
LC-MS is more sensitive and less biased(IMO). So the general trick is- don't do westerns for all proteins just report the ones that behave well (say that you "chose" these because they are important). I know this is a wrong practice and I should not be advocating it. For your own scientific validation try it with another MS technique; if you used ESI-Quadrupole/ion-Trap etc then try with MALDI-TOF or iTRAQ. Some luddites will continue to cling to westerns.
I think it is better to not test the proteins that have low peptide counts. See what is known as MA plot. This is frequently used for microarrays. Here
Mdenotes fold change andAdenotes total expression in both samples. Don't pick proteins that are low in expression in both samples; they might not be meaningful. For example if you have2molecules ofXand100molecules ofYin control condition and5molecules ofXand200molecules ofYin test; then the fold change inXwould seem important; however this change may not be relevant and can be a result of stochastic fluctuation/measurement error. If you have many samples you can see if something is stochastic or not but the limitation is the number of samples.Note: I am not saying that 2→5 molecule increment should be meaningless. But to know if they have a meaning or not you would require more complex models; better avoid them at this moment.
It is a common practice to prove a result using an orthogonal technique. Like RNAseq followed by qRT-PCR etc.
Western blotting is not a robust technique and cross comparisons are difficult because of difference in the avidities/affinities of different antibodies. So comparisons can be made only with one protein-control pair in different conditions.
LC-MS is more sensitive and less biased(IMO). So the general trick is- don't do westerns for all proteins just report the ones that behave well (say that you "chose" these because they are important). I know this is a wrong practice and I should not be advocating it. For your own scientific validation try it with another MS technique; if you used ESI-Quadrupole/ion-Trap etc then try with MALDI-TOF or iTRAQ. Some luddites will continue to cling to westerns.
I think it is better to not test the proteins that have low peptide counts. See what is known as MA plot. This is frequently used for microarrays. Here
Mdenotes fold change andAdenotes total expression in both samples. Don't pick proteins that are low in expression in both samples; they might not be meaningful. For example if you have2molecules ofXand100molecules ofYin control condition and5molecules ofXand200molecules ofYin test; then the fold change inXwould seem important; however this change may not be relevant and can be a result of stochastic fluctuation/measurement error. If you have many samples you can see if something is stochastic or not but the limitation is the number of samples.Note: I am not saying that 2→5 molecule increment should be meaningless. But to know if they have a meaning or not you would require more complex models; better avoid them at this moment.
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