Library biases still remain in proteomics hardware particularly for low input TIMSTOF data!

 

I was first going to start with something like this - 

When I read this title 

But I realized that 

1) That's sorta mean.

2) I bet a lot of people thought that all the work that has been done to adjust spectral libraries and deep learning algorithms has been successful

3) Not everyone is doing loads of weird cell types by single cell proteomics on TIMSTOFs and probably doesn't run into this every single day that their TIMSTOF happens to be working.

4) The giant red light on the whole front of my instrument is bumming me out. 

Here is the thing. The Orbitraps had a HUGE head start on data on public repositories. And in the libraries we used to train deep learning algorithms. And every other data type is just different. Especially when you're going down to low load. Even there, we know the Orbitraps struggle against high load libraries. I should put a link in but I can't find it. 

We absolutely find that having reference libraries in single cells helps a lot. On an Ultra2 we like a 25, 50 or even a 100 (for very small cells) cell pool that we run a couple of times and include that in our data analysis workflows. For big studies I've had luck making the library with those 100 cell pools and then just searching the single cells against those new libraries. Now...you'll probably miss that rare cell type and what makes it special, but you might not care about that in every experiment. 

Anyway - this group has some really smart tips for how to build these libraries and the observations in different software. Ultimately they report a 90%(!!!!!) improvement in low load peptide ID rates, so...that's absolutely worth looking at!