I think the question is very hard to answer because so many things are interrelated, including
- experimental design
- tissue preparation / thickness / size / sampling
- imaging technology / image type (including fluorescence, brightfield, something else…?)
- image quality / compression / resolution
- method of analysis, e.g. using software or not
- output metrics (e.g. a cell count, H-score, Allred score, density, some intensity-based value)
- how the output metrics are interpreted / overinterpreted
Then, if QuPath or any other image analysis software is used, there remains no one method for ‘protein quantification’. There will inevitably be variation and limitations in
- the algorithms and functionality within the chosen software
- the way in which the user applies the software
Unpicking all of these would be hard work for anyone, even if they had an in-depth knowledge of what kind of data you are thinking of quantifying – and really impossible without this information, or even example images.
If you would like anything more specific than the excellent answers already given by others above, I think the question needs to be more precise and specific. My initial feeling as to whether there are any limitations/drawbacks when using QuPath is ‘yes, probably hundreds!’, but the overwhelming majority aren’t limited to QuPath and not all would apply in all cases.
For that reason, I think it makes most sense to consider limitations in comparison to some other method of assessment.
With that in mind, can you explain for context what is the purpose of the question?
And could you list the relevant limitations/drawbacks of any other method or evaluating the samples (e.g. visual assessment)?
Since it’s free and open source, with lots of docs and tutorials online, I’d recommend just downloading it and making your own judgement
You can also see how it has been used by others in the list of publications.