IonStar has shown up on this blog a couple of times before after it was revealed at ASMS 2016. Full disclaimer -- my friend Jun Qu and his team at the University of Buffalo developed this methodology and I have helped with some of the data processing and algorithm comparisons, so this post is probably even more biased than my typical ones.
I'm writing about it now because the paper fully describing the method was just accepted at JPR, you can find the ASAP copy here.
What is it? It is a universal clinical proteomics workflow that is focused on these 2 things in this specific order:
1) Absolute reproducibility
2) Greatest possible sample depth available without compromising #1
I worked in a clinical chem lab for almost 6 years. In those environments there is one central rule -- you can not be wrong. Patient lives depend on you being correct. Our instruments were thoroughly and completely QC'ed every 4 hours, 24 hours a day. If a QC metric was out by 2 standard deviations, you could run a blank, rerun the QC and if it failed again -- an alert was placed into the system for every sample ran since the last QC. The doctors were warned to not trust the results or the results were pulled entirely. We were then allowed to recalibrate the systems while someone pulled all the samples for the last 4 hours from the refrigerator. Then we randomly pulled 1/10 of the samples and tested them. If they were not spot on, we stopped everything, and reran every sample of the last 4 hours and started checking the ones from before the previous QC (in case the instrument passing was a fluke). All this time a backlog piles up and you get to start planning for your overtime. I still sometimes have nightmares about these late nights trying to get caught up.
We all know proteomics is moving toward the clinic, but it is never going to get there until it has quality control and assurance metrics and is highly reproducible. IonStar is a great shot at this and it is composed of the following components:
1) A universal protein extraction/digestion methodology
2) A high capacity peptide trapping (4ug of peptides are trapped on a relatively large column) and elution nanoLC method (they employ a custom designed commercially available 100cm(!) nanoLC column that is maintained at a relatively high temperature for reducing back pressure and keeping chromatography perfect
3) A universal mass spectrometry method on (in this case) and Orbitrap Fusion (high resolution MS1 and MS/MS). This paper describes the optimization of this methodology
4) A data processing method that requires robust retention time alignment and extremely strict requirements for peptide/protein result reporting. Peptide retention time is even considered in terms of FDR calculations.
Q: Could they possibly get a few more peptides if they tweaked this parameter for this study or changed this cutoff for this different sample?
But the goal here is to have a set of (huge) files where they can compare a sample ran 2 years ago to a sample ran today -- and the peptide RTs line up. And...now that they don't have anything to tweak or optimize in the sample prep or instrument parameters -- they can just run and get to the biology! With the exception of the fragmentation energy optimization paper below, this is what they've knocked out so far...though and they haven't updated this in a bit, I can think of at least 2 that I've seen that aren't on this list.
Worth noting -- there is robust QC employed in this lab with heavy peptide standards spiked into complex matrices. Also, due to the extremely high peptide RT reproducibility, peptide inference is in play here. As the cohort of samples increases -- so do the number of confident peptide IDs!