For a large production lab, nonideal samples are a no no but we don't have the luxury to refuse. Academic core spend a lot of time and intellectual investment to troubleshoot and suggest improvements. This is especially important in the age of open data science.

Well said, Cathy. Only when we tinker with hard samples with many limitations do methods get developed so that you can even conceive going to production mode.