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Agilent Science Futures – An Interview With Max Lennart Feuerstein
Industry Insight

Agilent Science Futures – An Interview With Max Lennart Feuerstein

Agilent Science Futures – An Interview With Max Lennart Feuerstein
Industry Insight

Agilent Science Futures – An Interview With Max Lennart Feuerstein


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In this instalment of Science Futures, we hear from Max Lennart Feuerstein.

Max is a PhD student at the Institute of Analytical Chemistry at the University of Natural Resources and Life Sciences (BOKU) in Vienna. He is working with ion mobility-mass spectrometry (IM-MS) and is mainly involved in the development of new acquisition strategies and suitable applications using partly prototype hardware and software for IM-MS in the field of metabolomics.

In this interview, Max tells us more about his research and how it could drive technical enhancements of data acquisition workflows.

Can you tell us more about your research?  

Max
Lennart Feuerstein (MF): Coupling ion mobility to mass spectrometry (IM‑MS) is not a new concept, but there have been some substantial technical developments, including the introduction of several commercial instruments, in recent years. Besides addition of a separation dimension, IM allows determination of the collision cross section of a molecule, which can be simplified as the rotational cross section (the “size”) of a molecule in the gas phase. This property can be used to increase confidence with characterizing or confirming the identity of an unknown analyte molecule.

However, accurate mass and so-called fragment spectra, are the more relevant “markers” for correct identification of analyte molecules in mass spectrometry. Analyte molecules can be fragmented in MS instruments (e.g., the quadrupole-time-of-flight (QTOF) MS, which we are working with), and these fragment ions can then be detected. The fragment pattern is highly selective for a single molecule under controlled conditions, contains information about the structure/substructures of a molecule and can be matched against databases containing such spectra for supporting analyte identification.

When coping with complex samples, complete separation of all analytes using a single technique might not be possible. This can make it challenging to generate clean fragment spectra for all analytes using MS. One way to overcome these limitations is to make use of multistage MS instruments (composed of more than one MS device, e.g., quadrupole, collision cell and TOF are the parts of a QTOF instrument), allowing us to select and isolate a “precursor ion” (our analyte) using the quadrupole as a mass filter, fragment this molecule in a collision cell and use the TOF MS to detect the fragment ions. We might end up in a trade-off situation between selectivity (“clean spectra”) and coverage of our method. IM-MS is another possibility to enhance analyte separation/to reduce the amount of “interferences” (contributions from other molecules) in the generated fragment spectra, especially because IM separation is quite fast and reproducible (at least using drift tube instrumentation).

In this project we combine quadrupole isolation and IM separation on an Agilent drift tube IM-QTOF instrument to enhance selectivity. For this purpose, we use prototype hardware and software to control the quadrupole of the instrument.

What are the main or most important outcomes of your research?

MF:
In analytical chemistry, we often suffer from a discrepancy between the selectivity and coverage of developed methods – this means we obtain high quality information for only a few metabolites of interest, or we stretch our methods and try to provide data for as many metabolites as possible. Technical developments are helping us to build generic methods for a wide range of analyte molecules while still maintaining a high level of selectivity. Especially combining high resolution MS with IM is regarded as a suitable next-generation toolbox for these kinds of workflows. By nesting IM between chromatographic separation and MS detectors, we were able to establish a method for our instrumentation that allows us to analyze a broad range of metabolites in a non-targeted fashion with a high level of selectivity. We are optimistic that this can increase the level of confidence we can put on our results.

What global or societal challenges does your research address?

MF:
Our understanding of the world is changing tremendously fast, partly, because humanity is producing more data than ever before. However, high quality datasets are necessary to allow robust conclusions to complex scientific questions. The time spent on measuring and evaluating such data is still a limiting factor for answering many research questions. Especially in the context of life-science, scientists are heavily relying on in-depth analysis of large sample cohorts and MS is one of the major “workhorse” technologies that can help to depict reality in a measurable fashion.

Using MS, we are generating huge datasets, and data processing, curation and interpretation is not a trivial task. Especially when analyzing complex samples; differentiation between data containing relevant information and data that is either non-relevant, redundant or simply an artefact of the method used, can be challenging. Our research could be seen as a piece of the puzzle that tries to speed up measurement times while maximizing data quality by means of technical enhancements of data acquisition workflows.

How easy has it been to access the technology required for your research work?

MF:
I was in the fortunate position that the instrumentation was not overloaded with measurement hours during the last year, but this can change fast in our institute where many diverse projects are undertaken simultaneously. In general, we are very reliant on good laboratory management because several researchers are using the same instrumentation.

Have you been given opportunities to interact with industry and companies to progress your research? 

MF:
The research project I am working on is financially supported by one of Agilent’s University Relations Grants. I applied for a position that was supported by this project starting in late 2018. Additionally, the hardware and software are developed by Agilent, and we receive technical support by the developers and have discussions and meetings on a regular basis.

I would definitely consider collaborating with industry in the future. Aside from the possibility to test prototype technology and develop applications and acquisition strategies, I have enjoyed the scientific discussions with the instrument developers and have learned a lot from this collaboration.

What challenges do you face as a PhD student in understanding your options at the end of a PhD?

MF:
I am still in the middle of my PhD studies and I am confident of building some promising collaborations during my time at BOKU that will help me to find interesting follow up projects, e.g., as part of a post-doc position. From discussions with former colleagues and other scientists, I have the feeling that the combination of short or mid-term project-based positions in academia and the required level of flexibility might be challenging at some point. 

As a result of your studies and research work, what do you envisage your career destination as being?

MF:
To me, analytical chemistry, especially when it comes to IM-MS and related techniques, is a really exciting research field. It ranges from fascinating technical developments, detailed studies helping to generate fundamental understanding of structural properties of single molecules, up to the analysis of large cohorts of complex samples, e.g., in the context of environmental, medical or biotechnology-related research. I hope that I can further contribute to academic research in this field in the future.

How prepared do you consider yourself to be for real-world achievement?

MF:
Maybe I am a little naïve, but I am not worried at all when I think about the time after my PhD. Besides a really detailed training in analytical chemistry, I will have a broad basis in different fields of natural science and related methods after finishing my studies. For example, working with large datasets requires some skills regarding statistics, computational methods or programming and for successful data interpretation biologic knowledge might be necessary. We will see how things will turn out in the end, but I am an optimist!

You can catch up on the previous instalment of Agilent Science Futures, an interview with Alexandra Richardson, here

Meet The Authors
Anna MacDonald
Anna MacDonald
Science Writer
Karen Steward PhD
Karen Steward PhD
Senior Science Writer
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