Hi-N Quantitation For Clinical Discovery Proteomics

Progenesis QI for proteomics provides untargeted absolute quantitation of all identified proteins via the Hi-N method. This post explains the method and how it can be a useful tool for discovery proteomics in a clinical setting.

What is Hi-N?

Graph from Silva et al. (2006)Hi-N is a label-free quantitation method allowing absolute quantitation of all identified proteins in a sample, using just a single un-labelled internal standard. Other literature variously describes the method as Top3 or Hi3.

The method relies on a discovery made by Silva et al. (2006) that the average integrated signal intensity of the top 3 most intense tryptic peptides is proportional to the absolute amount of a protein present in a sample. Their graph (right) shows a linear relationship over 2 orders of magnitude for 6 proteins on 6 samples (R2 = 0.9939).

The Hi-N method in Progenesis QI for proteomics chooses, for each protein, the N peptides with the highest abundance and averages their abundances to produce a Hi-N measurement. The number of peptides to consider (N) is configurable, but defaults to 3 as per the majority of literature. By incorporating a known amount of a single internal standard to your samples, the absolute amount of all other proteins can then be calibrated:

Absolute amount of protein A = (Hi-N value for protein A)/(Hi-N value for internal standard) * (Absolute amount of internal standard)

How well does it work?

The principle that the method relies upon (i.e. that the average abundance of the top 3 peptides is proportional to the absolute amount of protein) has been verified by a number of studies, using different instruments and data collection techniques:

The relationship between the average MS signal response of the three most intense tryptic peptides and the absolute quantity of protein can be immediately inferred from the relative ratio of the average MS signal responses. The relative ratios of the average MS signal responses are proportional to the absolute quantities of each protein present in the sample.

Silva et al. (2006) [Waters Q-TOF with LCMSE]

We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements.

Ahrné et al. (2013) [Thermo Orbitrap]

Fig. 2 shows a linearity between the average of the three most intense MS signals of tryptic peptides of one protein and the protein abundance.

Grossman et al. (2010) [Thermo LTQ-FT-ICR]

Further studies have shown the method to have a good dynamic range, high reproducibility and excellent correlation with typical clinical quantitation methods such as routine immunoassays:

The dynamic range of protein abundances spanned four orders of magnitude. The correlation between technical replicates of the ten biological samples was R2 = 0.9961 ± 0.0036 (95% CI = 0.9940 – 0.9992) and the technical CV averaged 7.3 ± 6.7% (95% CI = 6.87 – 7.79%). This represents the most sophisticated label-free profiling of skeletal muscle to date.

Burniston et al. (2014)

One of the key factors required for accurate quantification is high reproducibility of abundance (intensity) measurements. The abundance coefficient of variation (CV) was calculated for all detected peptides in the three data sets (Fig. 6). The average CVs were 0.08 ± 0.1, 0.26 ± 0.09, and 0.18 ± 0.09 for the 4-protein mixture, serum, and tissue data sets, respectively (mean±standard deviation).

Levin et al. (2011)

Our study demonstrates that LCMSE allows reproducible untargeted quantitation of abundant plasma proteins. It gives fair to excellent correlation with immunoassays, and is achieved at low setup costs, without costly isotope-labelled standards used in targeted proteomics approaches. Reasonable variability compared to these targeted-approaches also gives confidence with regard to using this method.

Kramer et al. (2015)

This high correlation with the “gold-standard” of immunoassays suggests that discoveries made using Hi-N will transfer well to further validation studies using targeted methods such as MRM or immunoassays. This makes it a good candidate for quantitation of large numbers of proteins in clinical discovery proteomics.

How do I use it?

By default, Progenesis QI for proteomics provides relative Hi-N values calculated without an internal standard. This provides you with an abundance measure that is proportional to the absolute amount of protein in your samples, without any additional processing or sample preparation steps.

To obtain absolute measurements for all proteins in your experiment, you simply need to add a known amount of internal standard to each sample. Then it’s a simple case of telling Progenesis the accession and amount of your internal standard added. Progenesis will automatically re-calibrate your abundance values to provide absolute measurements (in fmol). So with just the addition of a single internal standard, you get absolute quantitation of all proteins in your sample effectively “for free” – with no additional analysis steps.

Protein quant options in the automatic processing tool Protein quantitation options in Progenesis QI for proteomics

You can configure your internal standard (refered to as “calibrant” in Progenesis QI for proteomics) either at the automatic processing set up wizard, or later on in the workflow when reviewing your identified proteins.

Why should I use it?

The label-free Hi-N method provides quantitative precision similar to labelled methods, without the greater expense, preparation time and variability the labelling process brings. The label-free approach is applicable to any kind of sample, in comparison to some labelled approaches – not all labelling methods are applicable in all scenarios, and in some methods only a subset of proteins are actually labelled.

Quantitative measurements in label-free proteomics have typically only allowed for relative “cross-run” comparison. Such measurements can only be validly compared for a single protein across runs. The linearity of the Hi-N method allows, in addition, comparison between proteins in the same run, providing much more information about the relative amounts of different proteins in your samples.


In conclusion, the Hi-N method provides a useful tool for quantitation in clinical discovery proteomics. The measurements obtained correlate well with routine immunoassays and labelled approaches, so make it likely that discoveries will transfer well to MRM/immunoassay validation studies. The only extra sample preparation required is the addition of a known amount of a single (non-labelled) internal standard to each sample.

Progenesis QI for proteomics performs Hi-N quantitation (without an internal standard) by default. Absolute quantitation using an internal standard is simply a case of entering the standard’s accession and spiked amount. If you’d like to find out more, get in touch, or download Progenesis QI for proteomics and try it for yourself.


Silva, J. C., Gorenstein, M. V., Li, G. Z., Vissers, J. P. C., & Geromanos, S. J. (2006). Absolute quantification of proteins by LCMSE – a virtue of parallel MS acquisition. Molecular & Cellular Proteomics, 5(1), 144-156.

Ahrné, E., Molzahn, L., Glatter, T., & Schmidt, A. (2013). Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics, 13(17), 2567-2578.

Grossmann, J., Roschitzki, B., Panse, C., Fortes, C., Barkow-Oesterreicher, S., Rutishauser, D., & Schlapbach, R. (2010). Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. Journal of proteomics, 73(9), 1740-1746.

Burniston, J. G., Connolly, J., Kainulainen, H., Britton, S. L., & Koch, L. G. (2014). Label-free profiling of skeletal muscle using high-definition mass spectrometry. Proteomics, 14(20), 2339-2344.

Levin, Y., Hradetzky, E., & Bahn, S. (2011). Quantification of proteins using data-independent analysis (MSE) in simple and complex samples: A systematic evaluation. Proteomics, 11(16), 3273-3287.

Kramer, G., Woolerton, Y., van Straalen, J. P., Vissers, J. P. C., Dekker, N., Langridge, J. I., Benyon, R. J., Speijer, D., Sturk, A. & Aerts, J. M. F. G. (2015). Accuracy and Reproducibility in Quantification of Plasma Protein Concentrations by Mass Spectrometry without the Use of Isotopic Standards. PloS one, 10(10), e0140097.

Food for thought at RAFA 2015

Last week, I attended the 7th symposium on Recent Advances in Food Analysis with some of my colleagues from NLD and Waters.

The castle at nightThe congress was held in the beautiful city of Prague in the Czech Republic, famous for its old architecture and buildings like the astronomical clock, Charles Bridge and the castle. It was my first time in the Czech Republic and Prague so I took this opportunity to do some sightseeing of Prague by night, with my colleague Martin Wells.

It was a busy 4 day meeting with a lot of interesting presentations on food safety, food contaminants, food fraud and authenticity, and natural toxins.

On Tuesday, there was a great presentation from Zoltan Takats about REIMS (Rapid Evaporative Ionization Mass Spectrometry) and the iKnife, which was originally developed for surgical cancer tissue detection. Interestingly, it can also be used for instantaneous characterisation of different food types, as discussed in one of our previous posts.

There was also the brilliant talk from Chris Elliott on food fraud and food authenticity. After  the horsemeat scandal in 2013, that first hit the UK then spread right across Europe, he was asked by the UK government to conduct an independent review of the UK food supply system. The objective was to determine what had gone wrong and to suggest measures to make the system much more robust from fraud. This resulted in the Elliott Review report.

A good turn out for a talk on CCS values

There is currently great interest in how advances in analytical science can help to combat food fraud, and we’re pleased to say that Progenesis QI is one of these emerging tools being put to the test for suitability for food authenticity studies.

If you haven’t tried Progenesis QI yet, please get in touch for a FREE trial.

Big society for small molecules

Last week I was invited to attend Waters’ Metabolomics user meeting of 2015, held at their UK headquarters in Wilmslow, Cheshire. There was a great turnout, with a good variation in applications, from institutes all over the world.

Waters UK headquarters

The meeting kicked off after lunch with an introductory talk from Ian Edwards (Waters UK), who gave an overview of the latest solutions offered by Waters for metabolomics. This included a mention of Progenesis QI which was a cue for me and my colleague, development manager Ian Morns, to introduce ourselves. It’s been a while since I was last in a lab, so it was nice to see the latest advancements, particularly with regards to UPLC and the advantages it offers over HPLC.

The rest of the afternoon was filled with talks from metabolomics researchers from across the globe. Dr Panagiotis Vorkas, Imperial College London, UK, presented “Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage” with particular focus on the applications to cardiovascular disease. Panagiotis highlighted that there are particular challenges for clinical metabolomics due to the vast physiochemical diversity of the human metabolome, and how discovery metabolomic profiling is now possible thanks to advancements in instrument technology.

This was aptly followed by a talk from Dr Alexander Fauland, Karolinska Institutet, Sweden on “Overcoming challenges and developing methods for targeted metabolomics of lipid mediators” with particular reference to the application to better diagnostics and treatments to prevent death by anaphylaxis.

Sharing is caring

The rest of the talks were equally engaging, with a few making references to their use of Progenesis QI and the extended statistics module, EZinfo. The main message of the day was definitely centred around the need for more sharing within the community, particularly for libraries, since this is a main limitation for the field currently.

At Nonlinear, we’ve been providing tools to help researchers curate their own neutral mass databases by combining MOL files in Progenesis SDF Studio, and also to create their own fragment databases within Progenesis QI. Whilst it was great to see so many references to people using these tools, it’s still got us thinking more about the need for a community driven project for curating libraries. With that in mind, I have a few questions for you:

In fact, Waters are already working on a community CCS library which they intend to distribute to users of their latest instrument, Vion. CCS is more consistent and reproducible than other measurements such as retention time and fragments, which makes it an ideal community library subject, but there’s still a need to collect libraries for anyone who isn’t fortunate enough to have access to a Waters ion mobility system. A community project needs to be driven by the whole community, so please do get involved by voting on the above poll.

Missed us?

If you weren’t able to attend this meeting, we’ll be at RAFA, Prague, Czech Republic on November 3rd – 6th. We’d love to talk to you, particularly about your thoughts on community libraries. Hopefully we’ll see you soon. Smile

HUPO 2015: Proteomics, Progenesis and poutine

Just over 2 weeks ago I arrived in Vancouver, British Columbia, Canada to attend HUPO 2015 – my first conference with Nonlinear. I was accompanied by Dave from our UK office, Mark and Jonathan who are based in the US, and also Rob who works for Waters in their UK headquarters.

We had some free time on the Sunday morning to get acquainted with the city, and what better way to do so than by going on a seaplane tour to take in all the sights from above? As well as taking to the skies (and the sea), we also explored the area by foot – the Gastown Steam Clock and the Digital Orca statue were definite highlights.

Digital OrcaGastown Steam ClockBeautiful view of Vancouver from the seaplane

The plenary talks were held on the Sunday evening, with the Eagle Talking Stick being passed from speaker to speaker as a nod to Vancouver’s Native American heritage. Aled Edwards gave a very engaging talk about the current state and possible future of the drug discovery field, including a tribute to the late Frances Oldham Kelsey, who was born on nearby Vancouver Island and was responsible for preventing the use of thalidomide in the US.

Monday was our first full day on the booth. Walking from the hotel to the conference centre, in beautiful weather, coffee in hand with a stunning sunrise emanating golden rays across the wakening city sure made a positive change from my usual daily commute!

There were some great talks throughout the week, especially those on standardisation and QC. The Proteome Standards Initiative and ProteomeXchange Consortium session on Tuesday morning was particularly excellent, showing that data exchange, searching and reprocessing are very much becoming a reality. Albert Sickmann also gave a great talk during Wednesday’s “Standardisation in proteomics” session, providing an excellent perspective on the need for QC – something we also focussed on with our release of v2 of Progenesis QI for proteomics.

TobyAside from the talks, it was great for me to meet customers face to face, as well as some of my Waters colleagues who are based further afield. I also enjoyed meeting HUPO’s celebrity exhibitor, Toby the therapy dog.

I’m pleased to say I thoroughly enjoyed HUPO and my first visit to Vancouver – it’s a city with a lot to offer (not just poutine which may be my new favourite thing!). Next year’s host, Taipei, has a lot to live up to!

Missed us at HUPO?

Don’t worry, check out our events page to see where we are next. We hope to see you soon!

"Welcome back!" to Gavin Hope, Principal Software Engineer

We’ve been busy as always at Nonlinear HQ, having been all over the world over the course of the summer attending conferences. We’ve released v2.1 Progenesis QI which sees our first steps into working with Direct Sample Analysis techniques with the iKnife. We’ve also released a free SDF management tool, Progenesis SDF Studio. So, to help us to keep up the pace, we’ve expanded our development team by welcoming back Gavin Hope.


Gavin last worked for Nonlinear back in 2006 before seeking new challenges elsewhere, first as a developer and then as a project manager. He’s now returned to software development and specifically to Nonlinear as he missed the passion of our development team, making high quality software, with a close relationship with our users.

Like a lot of our developers, Gavin is a big fan of strategy board games and a regular attendee of our weekly games night. When not gaming, he enjoys snowboarding, although this hobby has been put somewhat on hold with the recent arrival of his son, whom he’s hoping to have snow-mobile as soon as possible!

Interested in a career at Nonlinear Dynamics?

If Nonlinear Dynamics sounds like somewhere you’d like to work and you think you can strengthen our team, you might want to check out our recruitment page.

From bees or not from bees: that is the question!

Dr Chris Buck and the next generationToday’s post is a guest post, kindly provided by Dr Chris Buck, an LC-MS Field Support Specialist for Waters Australia. He’s recently had an article published in Chromatography Today, looking at food adulteration – specifically, at adulteration of honey products. Here, he presents an overview of some of the issues that article covered and how Progenesis QI was key in the monitoring of both honey and adulterant markers. Over to you, Chris!

After recent revelations of fake honey products being sold in Australia labelled as real honey, the Australian Competition & Consumer Commission (ACCC) Commissioner, Sarah Court, declared in a statement that products sold under the label of ‘honey’ must be produced entirely by honey bees. It had been shown, however, that an imported product made from corn and sugar cane (and no honey) was being labelled as a domestic pure honey product. This sort of dishonesty and mislabelling of products can take place to substitute alternative products of lesser value, cheating consumers and potentially can be disastrous for allergy sufferers. It’s also reminiscent of scares such as the 2008 Chinese milk scandal where diluted milk was spiked with melamine to fake higher protein content. The question arises: can a food safety lab tell the difference between a good product and a bad product, be it fake or adulterated in some way?

Applying chemometrics to the problem

In my recent article in Chromatography Today, I describe an experiment to assess the application of chemometrics to a test case of honey adulteration. Food safety is of great concern at the Australian National Measurement Institute, and my goal was to demonstrate a clear and unambiguous comparison of honey, likely substitutes, and mixtures of honey with those substitute products in a very rapid manner.

Quite often, the process of identifying molecules to use as markers can require a great deal of research, but literature may have few answers as to what should be present in a product and how much should be there. Finding such molecules through a literature search, developing a multiple reaction monitoring (MRM) mass spectrometer method and testing the method with standards and different products could take months; not only to source the high purity standards, but also to optimize the methods of extraction and analysis. And that’s not to mention that a devious product counterfeiter may spike in exactly those same high purity standards, if they are available, to fool product testing.

Rapid turnaround with Progenesis QI

My approach was to apply high resolution mass spectrometric fingerprinting of honey and the adulterants, using a Waters quadrupole time of flight instrument (Xevo G2 QTof) and the Progenesis QI software to process the data. A major goal was to show the groundwork for testing a food product could be set very rapidly.

The honey, wheat glucose syrup based product, and “golden” cane sugar syrup were purchased in the morning of day one. Later that day, the samples were prepared for analysis, with the preparation of mixtures of adulterants with honey in addition to the individual food products. Before injection, sample preparation was a simple dilution in water for each sample and filtration via 0.22 micron microcentrifuge filters. The sample set was then run overnight using triplicate injections of each sample onto C18 reverse phase chromatography with positive ion mode acquisition to target basic and hydrophobic compounds. This was followed by repeat injections of all samples onto an amide HILIC column coupled to negative ion mode acquisition to target polar compounds such as simple sugars and acidic compounds.

Data was collected from the instrument on Day 2 and, by lunchtime, data had been processed using Progenesis QI. Further data analysis was done over the next few days but effectively it could be seen the experiment had worked well, and compound markers of interest were already tabulated, in less than two days.

Identifying markers

Using a UPLC for chromatography coupled to the QTof, I was able to generate an Exact Mass Retention Time (EMRT) data set that could clearly differentiate the samples of honey, its potential adulterants, and honey adulterated with the other foods. This process is also sometimes called mass fingerprinting, as a very large number of components are observed in every injection, creating a very extensive and detailed picture of what makes up each sample. The size and complexity of the data set is where Progenesis QI is invaluable in allowing comparison of groups of injections and the application of multivariate statistics to find components that are unique or clearly elevated in the different foods.

PCA plot in EZ info

Potential markers – either highly specific to the honey or found at higher levels in the adulterants – were identified.  These markers could be further used to develop targeted analysis via other instruments more plentiful in food labs, such as MRM on tandem quad mass spectrometers like the Waters TQS.  Progenesis QI can, in combination with carefully designed experimental methods, make any adulterated product stand out from the crowd of good products based upon unusual chemical components, without any preconceived idea what the tell-tale components will be.

3D montage in Progenesis QI


Christopher Buck. Streamlining the Use of High Resolution Mass Spectrometry Data to Fingerprint Adulterated Honey using Multivariate Data Analysis to Facilitate Food Product Quality Control.  (2015)  Chromatography Today 8 (3), pages 48-52.

5 key steps to peak picking that make Progenesis QI better

Nonlinear Dynamics were at MetSoc2015 this year, talking to attendees about our small molecule software Progenesis QI. One common confusion we discovered was our use of the term peak picking. In the minds of many, this simply refers to detecting MS peaks in profiled LC-MS data. In Progenesis QI, we use this term a lot more broadly, to refer to the entire process of reducing your data from MS spectra to a set of detected compounds and their quantitative measurements.

In fact, what is commonly termed peak picking is performed in Progenesis QI at the Import Data phase. Here, your raw data is reduced to a set of peak models, which reduces storage requirements significantly, whilst retaining all necessary quantitative information. Importantly, our peak modelling process retains information about the shape of your peaks, which is lost in typical centroiding implementations.

After data import and peak modelling, the next step is to align your runs in the retention time direction. This enables the first step in the peak picking process:

Step 1 – Run aggregation

Progenesis QI employs co-detection, which means that compound ion detection is performed once on a single aggregate run. The advantage of this approach is that you obtain no missing values.

In this first step, Progenesis overlays the runs selected for peak picking to produce a single aggregate run. You may notice that early on in the peak picking process, the ion map at the Peak picking screen updates. This means the aggregation process has finished, and the ion map has been updated to show the aggregate run.

Aggregate run has been created in Progenesis Figure 1. An example section of the ion map of an aggregate run. Compound ion detection is performed once on this single aggregate, and detected outlines are then measured on all runs to ensure a measurement for all compound ions on all runs.

Step 2 – Chromatogram detection

The next step is to transform the MS spectra into a list of chromatograms. In basic terms, peak models at similar m/z’s in neighbouring mass spectra are joined together to form a chromatogram. Progenesis also analyses the shape of the chromatogram to distinguish overlapping chromatograms for partially co-eluting compounds. If your data contains ion mobility information, Progenesis will also use this information to distinguish co-eluting chromatograms.

The chromatograms have been detected on the aggregate run Figure 2. The same section of the aggregate run, with the detected chromatograms highlighted. Chromatograms are formed by joining together MS peaks from neighbouring spectra, where the m/z of the peaks coincide.

Step 3 – Isotope deconvolution

Very often, multiple isotopic forms of the same compound ion will be detected in your data. Progenesis analyses the detected chromatograms and groups together chromatograms which appear to be isotopes of the same compound ion. If more than one isotope of an ion is detected, Progenesis can also determine the charge of the ion, by analysing the m/z shift between isotopes.

Image showing isotope deconvolution on the ion map Figure 3. The same section of the aggregate run, showing the result of isotope deconvolution. In this case, the three chromatograms have been joined into a charge 1 compound ion, since the isotopes are all ~1 Da apart.

Step 4 – Adduct deconvolution

One often detects multiple adducted forms of the same compound or peptide. Progenesis QI will analyse the retention time profiles and m/z separations of your detected ions to group compound ions together into compounds. If multiple adducts of a compound are detected, Progenesis will also calculate and show the neutral mass of the compound. You can manually review the results of this process at the Review Deconvolution screen.

In QI for proteomics, a slightly different but analogous process is performed when peptide identifications are imported. For each identified peptide ion, its identifications are propagated to all peptide ions that deconvolve with it. This means that if your database search only identifies one charge state of a peptide, all other charge states will be given the same identification by Progenesis.

Adduct deconvolution in Progenesis QI Figure 4. The result of adduct deconvolution for the compound ion shown in previous figures. Here it has been determined to be an M+H ion, and two other adducted forms of the same compound have been grouped with it. The compound has been assigned a neutral mass of 472.2542 Da, based on the masses of the adducted forms.

Step 5 – Quantitation and normalisation

Accurate quantitation information can then be determined for each of the detected compounds. Note that because we perform isotope and adduct deconvolution, the quantitative measurements more accurately reflect the total amount of a given compound present in your samples. Since detection was performed on the aggregate run, every compound and compound ion can be measured on every run (this is the no missing values approach referred to earlier).

Finally, Progenesis normalises the quantitative data across all runs in your experiment. This corrects for experimental or technical variation when running samples, and allows comparison of abundances between conditions in your experiment.

Abundance profile graph Figure 5. The normalised abundances for the compound shown in Figure 4. The compound abundance on a given run is the sum of its constituent ions’ abundances on that run. Co-detection means that every compound can be measured on every run. The abundances are normalised across runs so that comparisons can be made.


As you can see, the Peak Picking screen in Progenesis QI does much more than you might assume from its name. Progenesis QI employs a comprehensive set of algorithms to reduce your raw data from a list of mass spectra into accurately quantifiable, isotope and adduct deconvoluted compounds.

Strictly speaking, then, the Peak picking screen in Progenesis should be renamed Run aggregation, chromatogram detection, isotope deconvolution, adduct deconvolution, quantitation and normalisation. But we thought that didn’t quite fit into the workflow UI :)

If you have any questions or want to find out more about Progenesis QI, please check our FAQs, or get in touch.

Progenesis QI and the iKnife: the cutting edge of food monitoring

Dr Sara Stead, a Senior Strategic Collaborations Manager in the Food and Environment division at WatersEarlier this year at ASMS 2015, Waters previewed their Rapid Evaporative Ionisation Mass Spectrometry (“REIMS”) research system, incorporating the iKnife – an exciting new technology that’s already hitting the mainstream news headlines in the monitoring of food adulteration. Here at Nonlinear, we were incredibly proud to announce that Progenesis QI would provide the data analysis support, starting with Progenesis QI v2.1, which was released just last week.

In this article, we’ll talk with Dr Sara Stead, a Senior Strategic Collaborations Manager in the Food and Environment division at Waters, to learn more about the technology, its potential applications and how Progenesis QI helps.

Mal Ross: Hi Sara. Thanks for talking to us today. Can you start by telling us a little about your role at Waters, please?

Sara Stead: Thanks Mal. I’m based at the Waters mass spectrometry headquarters in Wilmslow, UK, and work within the Food and Environment market development group. I’ve been with Waters for 4 years and am responsible for strategy business development. Prior to joining, I worked at the Food and Environmental Research Agency (FERA) in York for 13 years as a senior scientist focusing on food safety, quality and integrity, gaining a lot of experience of the challenges associated with food testing, the industry needs and requirements.

At present, I’m working on the development of innovative solutions for the detection of food fraud & authenticity using MS-based techniques, including REIMS with iKnife technology.

Mal: OK, so I imagine you’ll have a strong appreciation of the benefits that a technology like REIMS can bring. First though, for those who’ve not heard, can you tell us a bit more about REIMS and the iKnife technology in particular?

Sara: REIMS with iKnife is certainly an interesting and innovative technology. REIMS is a new ionisation technique and the iKnife (Intelligent Knife) technology was originally developed for application in surgery, allowing surgeons to make real-time decisions during operations. A conventional electrosurgical generator and knife are used to generate gaseous molecular ions of the major tissue components – for example, phospholipids – via a diathermic process. The combination of surgical and MS techniques also offers a possibility for in-situ chemical analysis of tissue during surgery.

REIMS research system: iKnife and Waters Xevo G2-XS QTofREIMS offers a number of unique benefits that address the requirements of the food testing industry. One example is the direct sample analysis, which means results can be generated in near real-time, with minimal intervention. At the ASMS conference in June 2015, Waters made the technology available for research use and it’s currently available as a direct ionisation technique on the Xevo G2-XS QTof and Synapt G2-Si instruments. In combination with Progenesis QI, it’s possible to develop databases of chemical markers and generate MVA models allowing the real-time classification of unknown samples to be performed at the point of control.

Mal: Sounds great. I guess the really revolutionary thing here is the speed with which you can get results telling you exactly what you’re sampling? There’s no sample prep or chromatography step here?

Sara: That’s absolutely correct, Mal. Using the iKnife for sample introduction, there is no requirement for sample preparation, extraction or chromatographic separation, which are some of the major bottlenecks in the routine analysis of complex and diverse foodstuffs. The workflow’s greatly simplified, involving a few seconds’ “burning” of the analytical sample to generate an aerosol that’s then transferred to the MS using a pump mounted on the instrument. The use of Progenesis QI means you have a powerful multivariate statistics package to convert the MS raw data into meaningful results. And from here, you can develop databases of unique chemical markers, representative of different sample types.

Fish at market (photo courtesy of Lucas Jans)Mal: Can you give us an example of the type of analysis you’ve done using REIMS data in Progenesis QI? I imagine many people, in Europe at least, are quite familiar with the horsemeat adulteration that made the headlines last year, but I hear you’ve been doing something with fish too – is that right?

Sara: Yes, that’s right. I’ve been working on the development of a database using REIMS with iKnife and Progenesis QI for white fish speciation. Fish fraud’s thought to be one of the most widely perpetrated food frauds in the world. Estimates of the amount of fraudulent practice range from 25-75% depending on the geographic region and the species of fish involved.

Mal: Wow, that’s a lot!

Sara: Indeed! Species substitution is the most common type of fraud. This is where a higher value species e.g. cod is substituted by a lower value species e.g. whiting. It’s often used in situations where organoleptic identification may be difficult, e.g. processed foods.

So far, I’ve analysed samples of the most commonly sold white fish in UK shops using REIMS and the iKnife on the Xevo QTof. The data’s then been processed using Progenesis QI and EZinfo to develop unsupervised PCA and OPLS-DA models to separate the fish species based on their unique chemical profiles. The model can then be used to determine whether an unknown sample belongs to one of these species included in the model.

PCA plot of fish species samples in Progenesis QI v2.1

Mal: And is that as far as you’d go with this kind of application in Progenesis QI? Or would you also want to identify the molecular markers for the different species?

Sara: Oh, identifications are important too. It’s important to understand which chemical compounds are responsible for causing the differences observed in the MVA models so that we can assess both the quality and validity of the statistical models. We use the tools within Progenesis QI – such as database searching and tagging – to propose tentative identifications and generate marker libraries representative for the different fish species under investigation.

Mal: Excellent. So, I guess the applications we’ve seen so far are really just the tip of the iceberg. This kind of analysis sounds widely applicable and it seems REIMS in particular can revolutionise monitoring of the food supply chain.

Sara: Yes, REIMS technology is directly applicable to many areas of testing along the food supply chain – food authenticity, brand protection, food safety and quality, and nutritional analysis, for example. We also see applicability for the REIMS technology in areas such as environmental testing, sports doping and chemical materials analysis.

Mal: Exciting times, then! OK, thanks Sara – it’s been great talking to you. Hopefully, we’ll catch up with you again later to hear about more of the latest developments and success stories using REIMS and Progenesis QI.

Sara: Thanks for the opportunity to discuss the REIMS with iKnife work; definitely exciting times! From my prior experience in food analysis, I really believe we have a revolutionary technology platform here. It has the potential to evolve into a point-of-control analysis tool, operated in a field testing laboratory such as a border inspection post – it really could transform the way testing is performed! :)

If you want to see how Progenesis QI supports the analysis of REIMS data and other direct sample analysis techniques, you can download Progenesis QI v2.1 along with a quick-start guide that walks you through a small-scale version of the fish species study mentioned above. And for more information on the whole REIMS research system, visit the Waters website.

Progenesis QI v2.1 – available to download now

Back in March, we released Progenesis QI v2.0 and we’re pleased to bring you the news that v2.1 is now available to download. This point release brings further improvements to the identification process as well as a couple of other exciting new features.

What’s New?

  • Improvements to ChemSpider functionality with the option to perform theoretical fragmentation and filter your search results based on elemental composition
  • Integration with the NIST LC-MS MSMS libraries*
  • Integration with the IPA pathways tool from QIAGEN*
  • A new dedicated workflow for Direct Sample Analysis techniques such as REIMS, DESI (non-imaging), DART and LD-TD using a Waters mass spectrometer

*Integration comes as standard, but you must already have a licence for IPA and the NIST libraries are an extra cost option.

Where can I download it?

If you’re an existing customer with an up to date coverwise plan, you will receive an email with a direct download link. If you’re already using v2.0, there’s no need to upgrade your dongle to use this version. In addition, if your Progenesis PC is connected to the internet, there should be a message in the Experiments list sidebar notifying you of this new version – if you click this, and your dongle is plugged in, you’ll be sent to the download page:

Update panel in Progenesis QI

Please note this update will automatically overwrite any previous version currently installed.

If you’re thinking of trying Progenesis QI for the first time, you can download the software from here.

If you want to make use of the NIST libraries, please contact us and a member of our sales team will be happy to help.

How will I know how to get the most out of the new features?

We’ve expanded our FAQs to cover the new features, as well as updating any previously available FAQs to correctly reflect new behaviour.

MetSoc2015 – Bridging the gap between community and industry

At the end of June, I, along with some of my colleagues, headed across to San Francisco, CA, for Metabolomics 2015. This was my third time attending this conference so I was looking forward to seeing some old faces, as well as new ones. I was also curious to see whether the same hot topics from last year were still a focus.

We arrived before the conference was officially underway, so we took the opportunity to explore the busy city around us. Cycling across the Golden Gate Bridge wasn’t on my bucket list, but in hindsight I think it should have been; it was awesome.

The impressive Golden Gate BridgeAli cycling across the Golden Gate Bridge

As with other years, compound identification was a hot topic, and one of the first sessions of the conference was focussed on this area. Due to problems with the projector, the session began with an impromptu open discussion where people shared details on what libraries they were using, and what criteria they require before trusting an identification in a publication. We’ve recently added the option to search the ChemSpider libraries in Progenesis QI so it was good to hear ChemSpider is a popular choice. While ChemSpider proved popular, there’s still the issue of the huge number of possible identifications it can retrieve so it was great to be able to demonstrate the soon-to-be-released Progenesis QI v2.1 which allows filtering of ChemSpider searches using theoretical fragmentation.

Speaking of theoretical fragmentation, Dr. Steffan Neumann from the Leibbniz Institute of Plant Biochemistry, stated that “in-silico fragmentation is the next best thing if there is no reference spectra library around”. Steffan is head of the group that developed MetFrag, the basis for the theoretical fragmentation done in Progenesis QI – he was pleased with the work we’d done using this, and that the source code, with unit tests, is available on GitHub for the rest of the community.

MetFrag isn’t the only area where we’ve been working with the metabolomics community – we recently implemented the option to search LipidBlast, a computer-generated MS/MS database produced by the Metabolomics Fiehn Lab. Progenesis QI v2.1 will also include support for the NIST MS/MS libraries. Of course, as well as using tools developed by community projects, we also recently released Progenesis SDF Studio v1.0, a free compound database management tool – just one of our ways of saying “thank you”. In other industries it’s quite common for commercial organisations and community projects to work together for software development, so it’s great that the metabolomics world is starting to see the benefits of this, and I’m very pleased to be a part of it.

There was a great turn out for the talksAli, Jon and Mark busy on the Nonlinear booth

If you’re working on a project you would like to integrate with Progenesis QI, get in touch – we’d love to hear from you.