Expert Insight: On-Demand Webinar: Combating Challenges in Small Molecular Compound Identification with Mass Spectrometry

Technology experts from Thermo Fisher Scientific answer your questions about their latest mass spec intelligent data acquisition and analysis solutions

25 Oct 2018

Determining the chemical structure for the majority of compounds "identified" in a typical untargeted analysis remains a stubborn challenge and a persistent logjam, holding up progress in many fields including metabolomics, metabolism studies, forensics, contaminant analysis, and E&L identification.

In a recent SelectScience® webinar — now available on demand — Seema Sharma and Tim Stratton, from Thermo Fisher Scientific, describe a fundamental new approach to untargeted small molecule analysis involving the new Thermo Scientific™ Orbitrap™ ID-X Tribrid™ Mass Spectrometer, intelligent data acquisition solutions and streamlined data analysis software to translate high-quality mass spectra into more, confidently-assigned small molecule structures.

During the live event, we received questions from our audience — you can catch up with some of the highlights from the Q&A session below.



Q: What is the primary difference for using an inclusion list to trigger tandem mass spec data during large-scale sample analysis, as opposed to the AcquireX workflow?

SS: For a large-scale workflow, the major benefit of AcquireX is that you can automatically generate your inclusion and exclusion lists. You don’t need to do a previous run with offline analysis. You can just set up your methods and the instrument will generate the inclusion list based on your sample of material.


Q: How much resolution is really needed to help determine chemical formula when the product ion spectra is not in the database?

TS: There are a couple of different things that we can use the increased resolution for. If we think of just the MS1 data, the higher the resolution goes, the better we are at doing feature detection, but we can also get access to fine isotopic information. We can see the presence of a sulfur atom by looking at the Sulfur-34 isotope or the presence of nitrogen in our unknown, based on the nitrogen isotopes.

This is also true in the fragmentation data. So, if we're looking at a case where we have fragmentation data MS2 or MSN on a completely unknown compound, having high-resolution MS2 data can give us information on the elemental composition of fragment ions as well. That information can be fed back into making a better elemental composition/structure determination.


Q: Is the ion tree data acquisition significantly different from a product ion-rich tandem mass spectrum? That is, increasing the collision energy for tandem mass spec fragmentation may generate the same fragments. Will this work?

SS: If you're doing a higher-energy collision dissociation, you can get multiple generations of product ions. However, when you do an ion tree, you are isolating your precursor and then you are generating the fragments, which is followed by another round where you're isolating a particular fragment, which is then followed by fragmentation. This way the ion-tree generation allows you to tie in the product ions with the precursor, which gives you more information for structural characterization.


Q: Your examples tend to be compounds that charge in positive mode. Could you comment on how Mass Frontier is being updated to work with compounds that charge in negative mode and/or APCI?

TS: The prediction of fragmentation in negative mode, and construction of libraries in negative mode, are both things that we've been focusing on. The release version of Mass Frontier 8.0, which is coming out very soon, has a large expansion of the fragmentation prediction library.

For those of you who are not familiar with Mass Frontier, it actually uses a built-in database, a library of reference fragmentation mechanisms from published literature. That's been expanded in this current version and the expansion was specifically to improve the negative-mode fragmentation. That will help with prediction and annotation in negative mode. A lot of that also leverages what we learned from building the mzCloud library, where positive and negative-mode data are acquired on all compounds, depending on whether or not they will ionize.

Find out more on this topic by watching the full webinar on demand >>

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