In this interview, Lucy Woods, Business Unit Manager for Phenomics and Metabolomics at Bruker Daltonics talks to New-Medical Life Sciences about the new MetaboScape®, all-in-one software for compound identification for non-targeted workflows.
Please can you introduce yourself and tell us about your role at Bruker?
My name is Lucy Woods and I’ve been working for Bruker for just over five years, and I’m based in London in the UK. My role is as the Business Unit Manager for metabolomics and lipidomics. And one of my main aims is to show people the benefit of using TIMS separation for metabolomics and lipidomics. One of the reasons why is that in my former role at Bruker, I was the timsTOF product manager and was responsible for the launch of both the timsTOF Pro and timsTOF fleX platforms.
Why should someone consider the MetaboScape for compound identification?
MetaboScape is the software solution from Bruker, which is useful for the identification of all small molecules and lipids. And one of the main benefits of using MetaboScape is that you can use the same annotation workflow across different Bruker platforms. So you can acquire data using PASEF on the timsTOF Pro, or you can acquire MALDI imaging data where you need to identify drug metabolites directly from tissue.
The same workflow can be used for each of these datasets within MetaboScape. Now MetaboScape is one of the few software packages that can do this, and it’s also one of the few software packages that can process CCS values, which is an additional property of an ion that you obtain when you use TIMS separation. MetaboScape can also process very large datasets both quickly and efficiently.
Tell us about CCS and how the CCS-aware features of MetaboScape enable customers to carry out better compound identification?
CCS values are obtained for all ions measured using TIMS separation, which is a type of ion mobility separation. It stands for trapped mobility spectrometry, and this technology is proprietary to Bruker. We’ve already shown that when using TIMS you can measure CCS values extremely accurately, and you can also measure these values reproducibly across a number of different systems. We’ve also tested the accuracy of CCS values compared to different databases and we’ve compared the reproducibility of the CCS values when they’ve been acquired in different samples, from different matrices.
One of the main benefits of CCS is the property is completely independent of how the ion is introduced. So whether you introduce the ion using an LC system, whether it’s analytical flow or nano flow, it doesn’t matter. The CCS of the compound is measured in the gas phase and it doesn’t change. And with this it’s possible to use CCS as an additional identifier of a compound. You can use this to replace things like retention time, which have been shown to vary to quite a large degree across different labs when they’re employed globally. CCS could eventually replace the need to rely on retention times.
How has MetaboScape been made practically advantageous in terms of features like high throughput?
I’ve already mentioned that MetaboScape can process large sample cohorts. For example, if PASEF data has been acquired or standard LCMS data, then MetaboScape can process hundreds of different samples within a single sample cohort. And this increases even further for our MRMS aXelerate workflow which is performed on our highest resolution instruments like the scimaX system. For this, you can compare statistical differences across thousands of different samples. And MetaboScape has an integrated algorithm that can perform retention time alignment and statistically compare differences across these hundreds of samples. What’s more, within MetaboScape, you can also filter or select certain features from an experiment. So you can rapidly see how that compound of interest has varied across your different data sets.
What innovative technologies does MetaboScape use?
One I’ve mentioned previously is the peak picking algorithm, and this is called T-Rex. This is what enables you to process samples across these different types of experiments; across PASEF, MRMS, even MALDI imaging experiments. This algorithm works by detecting ions within the sample. So regardless of if you use chromatography-free acquisition, LCMS or an imaging dataset then the ions that are detected will then combine adducts and losses into a single feature, which can then be annotated as a compound. Because this algorithm works with PASEF, you can also get information about the CCS value. This innovative peak picker, which has been developed in-house at Bruker, means that MetaboScape is one of the very few softwares out there that can process different sample types as well as report the CCS value.
What features of MetaboScape enable optimized data processing and communication?
One of the main features of MetaboScape and one of the main requests from our users is that data needs to be accessed by multiple users. This is of particular importance for facilities such as core lab facilities. MetaboScape is comprised of both a client and of a server. The data is kept on the server PC and MetaboScape is provided with several licenses so different users can log on using different clients and they can access the shared data. They can access process data from a single project and further explore this data to mine or to for look for different compounds that have become significant.
Tell us about MetaboScape’s support of complementary MS/MS spectral libraries and how this benefits customers.
We work very closely with our collaborators and one of our collaborators is professor Gary Siuzdak who is based in Scripps. Gary Siuzdak founded the METLIN library some years ago, and this has continuously grown. It’s now the largest commercial MS/MS library available and it contains hundreds of thousands of different MS/MS spectra from different compounds.
MetaboScape can search compounds from this database so that it can identify observed MS/MS spectra and match them to the known MS/MS spectra from the reference standards. By using such a comprehensive database, compounds can be annotated more confidently using the MS/MS information. This is a huge benefit to some customers who may only be used to searching MS only databases or libraries that contain fewer numbers of MS/MS spectra. By having that additional confidence, you can really reduce the number of false positives that are annotated within the sample.
What is non-targeted profiling and how does MetaboScape enable this successfully?
Non-targeted profiling is a workflow that is typically used for the discovery of as many features as possible within a sample. With discovery workflows, you’re aiming to explore novel or unusual compounds, that have not yet been discovered within that sample before. You can combine this profiling approach with targeted approaches in order to give you both the highest level of quantitation, in addition to annotating the largest number of compounds.
With non targeted profiling, you are relying on confidently annotatation of these unknown compounds. And this again goes back to matching against these comprehensive MS/MS spectral libraries such as METLIN, so that you can achieve a high level of confidence.
You can increase this confidence further by adding CCS values. With CCS values, you can gain a higher level of confidence that the feature that you’re annotating is the compound that you believe that it is. MetaboScape also has a novel scoring system so the user can easily see the confidence of the match by matching the accurate mass, the retention time information, the isotopic pattern fit in addition to the MS/MS and the CCS value.
MetaboScape is well-suited to the identification of drug metabolites in the pharmaceutical industry. Tell us more about the features that enable this and the industries and practices it will impact.
MetaboScape has a number of features that are beneficial for pharma. One of these is the introduction of the BioTransformer module that enables the prediction of small molecule metabolism. This is metabolism within mammals and from the gut microbiome and the BioTransformer module enables you to annotate associated metabolites from a drug of interest. Also integrated within MetaboScape is MetFrag, which is an in silico prediction for MS/MS spectra.
With both of these workflows combined, you can predict the structure of these associated metabolites, and you can then fragment the metabolite and match the observed MS/MS to that of the expected metabolite. Additionally, within MetaboScape, there is a function that you can plot how these metabolites change over time. This means you can observe the increase of your drug metabolites whilst observing a decrease of the drug compound itself. This is a really nice tool that can be used for metabolism, and also for analysing changes in spent media analysis.
How does compound identification influence our knowledge and the treatment of diseases?
Compound identification is often considered as the bottleneck in any kind of non-targeted analysis. Small molecules can populate a wide range of structurally diverse metabolites. These are present in biological samples and for non-targeted metabolites, you rely on being able to identify these and relate them to the pathogenesis of disease and the downstream functional products of ongoing biological processes. When you detect dysregulated metabolic processes, then you can use these to gain an insight into the system-wide mechanisms of an organism by overlaying these compounds with previously identified pathways so that you can get an oversight of not just of the metabolism, but also from the input from the genome on the proteome as well.
Tell us more about better compound identification and how it applies to foodomics, ensuring standards are achieved in this industry.
Foodomics is a very rapidly expanding area and it’s a very broad research area that covers a range of different areas. It is crucial that you can identify new compounds that can give an indication of the food’s quality or origin. This can be done using non-targeted approaches by looking at the composition of food, as well as looking at the nutritional value of food and the effect that eating different types of foods has on your body. Standards are needed within the food industry to determine the quality of the food that we buy, as well as their authenticity, in that what we’re eating is actually as it is labeled as well as guaranteeing the safety of the food.
How can a chromatography free workflow improve sample throughput and make this software better at phenomics research? What will more efficient phenomics research mean for the life science industry?
One of the main challenges in phenomics is high sample throughput for large sample cohorts. Additionally, you need to detect a wide range of different compounds. By removing chromatography you can detect both polar and non-polar analytes and you can significantly increase the throughput. This chromatography free workflow is implemented on extreme resolution systems such as the scimaX, where the mass resolution will typically can exceed more than two million.
This increase in mass resolution enables you to separate compounds without the need for this additional chromatographic separation. With this workflow, you can screen more than 200 samples per day and the data can be processed in MetaboScape to look for statistical differences between different sample groups. This is proving to be a real benefit for phenomic studies.
The identification of lipids presents a major challenge for researchers. How is this achieved and what recent developments have simplified this task?
Lipids in particular consist of a wide number of isobaric and isomeric species. It’s a major task for the researcher to separate out coeluting lipid classes and to be able to correctly annotate a large number of lipids. One of the ways this can be achieved is using PASEF on the timsTOF Pro. Here we’re able to separate and fragment coeluting isobaric lipids using both mobility and mass selection. This means is that with each fragment spectra, you get a clean MS/MS spectra which relates only to that particular lipid. enabling a confident ID.
And within MetaboScape, there is an inbuilt annotation workflow. This uses a series of rules for different lipid classes where each lipid is annotated according to characteristic fragments, such as the loss of the head group or characteristic fragments from the backbone, as well the detection of defined adducts.
With this, you can annotate a large number of different lipid classes. There is the added benefit that we have CCS values. And this is particularly useful for lipids because algorithms exist to predict CCS values of different lipids. The LipidBlast library now contains more than half a million CCS values, and these can also be used to match to the CCS of the observed lipid to increase the confidence in annotation.
Better compound identification can improve targeted and non-targeted approaches to metabolomics research. How does it do this and what developments in metabolomics will this achieve?
TASQ stands for target analysis for screening and quantitation, and combined with MetaboScape, this enables the user to use both non-targeted and targeted workflows. Non-targeted workflows can be used to identify novel compounds and new biomarkers, and then these can then be quantified using a targeted approach. One benefit of using our QTOF platforms is that you can use both of these approaches on the same platform. This means that you can use the same system to find these markers and then validate them using a targeted approach. This really simplifies the workflow and also speeds up the validation process.
Why is compound identification such an integral part of environmental analysis and why is this so crucial in today’s climate?
Contaminants in the environment are continuously emerging and there is an increasing need to be able to retrospectively mine the data to look for any new contaminants of concern. And there’s a requirement by the authorities that you need to be able to detect these contaminants that may be harmful to environmental and human health for more than a decade. So in acquiring data using a non-targeted approach, samples can then be screened for hundreds or even thousands of suspected environmental pollutions months or even years after the sample was acquired. It’s thought that using this type of non-targeted approach with the retrospective data mining could become a gold standard in Europe or even globally in the future.
How does MetaboScape compare to other software on the market today?
There’s a lot of different non-targeted software packages that are out there, and there are very few that can do what MetaboScape can. Especially being able to process different data sets ranging from direct infusion, so chromatography free, but also to imaging workflows, and being able to detect the features from these different sample types and then annotate them, is something that’s quite unique to MetaboScape.
And in addition, once you have an annotation for these compounds of interest, to be able to statistically compare different sample groups within the same software package is again, something that’s quite unique to MetaboScape. Quite often you’ll just have to switch between multiple different software programs. Within MetaboScape, we have both the annotation or identification, as well as the ability to statistically look at these different sample groups, all within a single software package. And additionally, there’s only a handful of software that are within the community that are really able to report back these CCS values. So MetaboScape really is a single software package that suits almost every workflow.
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