30 years of Nonlinear Dynamics

30 years logo

Nonlinear Dynamics celebrates 30 years of being in business this year end.  We thought it would be interesting to take a trip down Memory Lane and see what was happening around the world while founder, Will Dracup, was starting his embryonic company.

1989 was an interesting time to be around, especially in Eastern European countries.  There was huge hope and optimism in the air as these countries opted for democracy.  Here are just some of the Global events that took place in December 1989:

  • USSR President Mikhail Gorbachev meets Pope John Paul II at the Vatican
  • V. P. Singh sworn in as the 8th Prime Minister of India
  • Soviet President Mikhail Gorbachev and US President George H. W. Bush, declare the Cold War over
  • France TGV train reaches world record speed of 482.4 kph
  • President Gustav Husak of Czechoslovakia, resigns
  • “The Simpsons” created by Matt Groening, premieres on Fox TV as a full animated series with the episode, “Simpsons Roasting on an Open Fire”
  • After 23 years in power, Romania ousts Nicolae Ceausescu
  • Japanese scientist achieves -271.8°C, coldest temperature ever recorded
  • Alexander Dubček elected Chairman of the Federal Assembly (Parliament) of Czechslovakia
  • Václav Havel is selected to be president of Czechoslovakia by the Federal Assembly shortly after the Velvet Revolution
  • Wayne Gretzky and Martina Navratilova, named athletes of decade by the Associated Press

After setting the scene of what was going on in 1989, we then decided to talk to Will about his motivations and ambitions when starting his company.  Here’s what he told us:

“I’d been working as an employee for a scientific company when two scientists inspired me that the time was right to set up a 2D gel analysis company.  Back then my main aim was to make enough money so I could take summers off work and spend time by the river.”  It didn’t quite work out that way, Will and his colleague, Dr David Bramwell have established a pioneering research company, Biosignatures, whose ultimate aim is to be able to analyse a single sample of blood and provide an early diagnosis for as many as 20 or 30 life changing diseases.

At our ASMS breakfast seminar this year, Nonlinear’s Jonathan McSayles, gave a brief history of Nonlinear Dynamics that we’d like to share with you. It shows the growth of the products and how we adapted to the changing market to continue to serve our customers.

Landmarks of the early years of Nonlinear

Nonlinear Dynamics was established in 1989 and launched the first product in 1991 which was Phoretix 2D gel analysis software. Two years later in 1993, a second product for 1D Gel analysis was released. Not long after, came the Phoretix Array and TotalLab software products.

In 2000 there came a major leap for Nonlinear – opening an office in the US and the following year launching the first Progenesis product for 2D gel analysis.  After the success of Progenesis Discovery we continued development with the launch of Progenesis SameSpots in 2005.

At that time, we also signed a co-marketing agreement with Waters Corporation, and this set the stage for Nonlinear to diversify.

Landmarks of the later years of Nonlinear, now a Waters Company

In the mid 2000’s the 2D gel market changed, but we were already on the path to move our development into the emerging label-free mass spec market. 

Progenesis MALDI was launched in 2008 followed by Progenesis LC-MS, and many of our customers had moved from 2D based techniques to mass spec-based analysis. We moved into the metabolomics field in 2011 with the launch of Progenesis CoMet.

In 2012 another significant milestone was made with the Co-development agreement with Waters Corporation. The Waters Corporation branded LC-MS product TransOmics Informatics was born. The success of that partnership and product development led to the acquisition of Nonlinear by Waters in 2013, followed by the fully integrated products we have today – Progenesis QI and Progenesis QI for proteomics.

We hope you have enjoyed the brief history of Nonlinear Dynamics.  Before we close out the year, we’d like to wish you Season’s Greetings and a very successful New Year.  Stay tuned for our first blog post in 2020 to see how Progenesis can help you achieve success in your research. As always, if you would like to download or learn more about the software then don’t hesitate to get in touch. We will be more than happy to help you.

New release of the Progenesis QI for proteomics software

Nonlinear Dynamics, a Waters Company, is proud to announce a new release of the Progenesis QI for proteomics software. Version 4.2 has now been released and is available to download. If you are working in the area of Host Cell Proteins (HCP), this release is the one for you.

While many of our customers are using the software for HCP analysis already, this latest release has a quantitation method that has been specifically designed for HCP analysis. If you choose to use the HCP quantitation method, then a new column has been added for greater understanding of your HCP data.

Screenshot of the HCP quantitation workflow option
Screenshot of the HCP quantitation workflow option

Not to be overshadowed by the HCP content, we are excited to let you know that we now integrate with Metacore, a Cortellis solution for pathway analysis. MetaCore is one solution in the Cortellis™ suite of intelligence solutions that enables precise, actionable answers to specific questions across the R&D lifecycle. MetaCore delivers high-quality systems biology content in the context of comprehensive pathway-based analytics, molecular network building and insightful visualizations. This plug-in now comes as standard with the software, but you must be on this new version in order to take advantage of it.

For existing users, you can update through the software or download the update here. To help you get the most out of the new features, we’ve updated our list of FAQs.

This update will also require you to update your plugins, which can be downloaded from the relevant FAQ pages:

As always if you aren’t currently using it and are looking to make a difference in your lab, then Progenesis QI for proteomics could be the change you are looking for. We can set you up with an evaluation so you can see what difference it can make to your workflow. We offer online and in-person walkthroughs of the software so get in touch now, you could be writing the next review of Progenesis QI for proteomics, like the one below.

Progenesis QI for proteomics 5-star review

How was Progenesis QI used in an untargeted lipidomics workflow?

Following on from last month’s blog post, I’d like to continue with the other exciting video presentation from ASMS 2019. Dr. Jace Jones from the University of Maryland, School of Pharmacy, Department of Pharmaceutical Sciences in Baltimore, Maryland gave a very interesting talk on how the Progenesis QI software was used effectively and robustly to process a variety of lipidomic datasets from a traumatic brain injury model. His talk was entitled: The Use of Progenesis QI to Efficiently Process Lipidomic Data: Application to Traumatic Brain Injury

At a first glance, people only see what they want to see as we are preprogrammed to view things from our own perspective. The same can happen in science where we look at what we know over and above what we don’t. Dr. Jones highlighted the fact that the Progenesis QI software facilitates a process for them to be able to see what is not always visible at first so they could focus on groups that may not have been obvious.

After using the Progenesis QI software in his discovery workflow he then went on to do his quantitation workflow which corresponded well with the discovery results. This gave him great confidence that his workflows were robust in that he could go back and forth between discovery and quantitation.

The abstract for the talk Dr. Jones gave is below, but I again highly recommend you watch the video as the work they are doing can have a big impact on how we treat traumatic brain injuries moving forward.   

Presentation of the lipidomics talk presented by Dr. Jace Jones


The Use of Progenesis QI to Efficiently Process Lipidomic Data: Application to Traumatic Brain Injury

Jace W. Jones, Ph.D.

University of Maryland, School of Pharmacy, Department of Pharmaceutical Sciences, Baltimore, MD

Lipids have significant potential to inform on disease and injury due to the pivotal role they play in many biological processes including cellular integrity and permeability, energy storage and metabolism, and signaling pathways. Heightened interest in the mechanism by which disruption of lipid metabolism and homeostasis contributes to a variety of human diseases and injuries (e.g., cancer, diabetes, neurodegenerative disorders, infectious diseases, and pulmonary conditions) has led to a substantial increase in lipidomic research. The field of lipidomics, broadly described as the comprehensive biochemical characterization of “all” lipids (referred to as the lipidome) within a cell, tissue, or organism presents a variety of analytical and data processing challenges. The analytical challenges result primarily from the dynamic range, vast structural diversity, and sheer number of biological lipids. These challenges have been commonly addressed using liquid chromatography coupled to high resolution tandem mass spectrometry. This workflow results in an information-rich data matrix that must be processed for retention time alignment, peak picking, adduct deconvolution, abundance, identification, and statistical analysis. Successful implementation of the aforementioned lipidomic workflow in a model system where two appropriate cohorts (e.g., disease vs control) are comparatively analyzed yields differential expression data delineating lipid profiles between the two groups and abundance of individual lipids.

The data processing challenges associated with efficiently and properly handling lipidomic data is not trivial and has been meet with a variety of software solutions. One such software solution is Progenesis QI from Nonlinear Dynamics (Waters). Progenesis QI offers a comprehensive workflow for efficiently processing LC-MS/MS generated lipidomic datasets. Progenesis QI is streamlined to handle multidimensional data in the form of chromatographic retention time, ion mobility, accurate mass, and data-independent acquisition. Data will be presented detailing the use of Progenesis QI to effectively and robustly process a variety of lipidomic datasets from a traumatic brain injury model.  

Try it for yourself

Now you can see how our Progenesis QI users are having success with the software in their research, why don’t you see how the software can help you in your research? The Progenesis QI team are more than willing to work with you to see how you can transform your results. We would like to help you see results in your workflows that you may not already be seeing. Please get in touch or comment below.

Are you missing out on how Progenesis QI can help your research?

ASMS 2019 has come and gone but the research continues. Nonlinear and Waters were lucky to have speakers at ASMS 2019 who shared their research with us and explained how the Progenesis QI software was an integral part of their work.

If you didn’t get to attend the talk by David W. Gaul, PhD., from the Georgia Institute of Technology or would like to hear the presentation again then we have the video for you. The title of his talk was “Preoperative Metabolomic Signature of Prostate Cancer Recurrence” To get an idea of what the presentation entails, you can read the abstract below, but I highly recommend you watch the video so you can see how important the work David and his group are doing with regards to giving people more information on how they can change their treatment decisions moving forward from a simple blood test.


Up to 50% of prostate cancer surgical patients will suffer from biochemical recurrence manifested by detectable serum prostate specific antigen levels even after prostate removal. Currently available pre-operative information has not yielded adequate prognosis to guide a patient’s treatment decision.  More clinical data is needed to improve prognosis. New biomarkers can arise from metabolome analysis of patient serum to aid physicians and patients in developing a treatment plan. We applied a multiplatform (NMR + LC−MS) metabolomics approach to the study of preoperative metabolic alterations associated with prostate cancer recurrence.

If you would like to trial the Progenesis QI software in your lab to see how it can impact your research, then please get in touch.

Why are reviews important?

The internet is a huge resource at our fingertips that enables us to find lots of information about any product that’s of interest to us, but how can we be confident the information is accurate, unbiased and a true representation of the product? 

That’s where reviews come in. If a real live person has used a product and given a review, then you can be more confident about that product and its capabilities and the choice you are making.

Here at the Progenesis QI team, we want you to be happy with your mass spec software so we’ve been working with SelectScience for a while now to generate informative reviews of Progenesis QI and Progenesis QI for proteomics software that will be a valuable resource for our potential users.

Who are SelectScience?

Image of the SelectScience logo celebrating their 20 year anniversary
SelectScience an independent product review website

I’d be surprised if you haven’t heard of SelectScience already but if you haven’t, they are a trusted, independent website for laboratory scientists to find impartial reviews written by their peers, as well as application notes and videos. They work with scientists to inform them about the best products and applications. Google recognizes them as a trusted review site for independent product reviews and they have won the Queen’s Award for International Excellence.

two images. One showing the logo for the Queens Award for Enterprise and the other shows the five star trusted review image.

As a Progenesis QI or Progenesis QI for proteomics user, you’ll be receiving an email shortly with a link to enter a prize drawing for the chance to win an *iPad® or a $400 *Amazon® gift card in return for simply writing a review and sharing information about Progenesis QI or Progenesis QI for proteomics software with your peers.  If you don’t receive the email, then let me know and I can send you the link. You will have to act quickly as the option to enter the draw will only be available for a limited time.

If you have used the software in the past then you also have the option to write a review here or you can access the review form direct from our website.

You can see below some of the reviews we currently have for both the Progenesis QI and the Progenesis QI for proteomics software. As more and more people use it, we hope many of them will share their experiences.

Image of the review from SelectScience for the Progenesis QI for proteomics software.

Review for the Progenesis QI for proteomics software
Image of a review from SelectScience for the Progenesis QI software

Review for the Progenesis QI for proteomics software

If you haven’t tried the software yet and would like to talk further, don’t hesitate to get in touch.

*iPad® and Amazon® are not affiliated with this drawing and are independently owned and operated.

Identification scoring in Progenesis QI

With the amount of information available today, important and helpful information can easily get lost and overlooked. I’d like to take this opportunity to repost this blog post about identification scoring in Progenesis QI of as many of our customers find this very useful in their research and still refer to it today.

One of the advantages of using Progenesis QI is its ability to combine results from multiple search methods and databases. Progenesis QI uses a common scale to score results from all the databases and search methods it supports, so you can compare search results obtained from different search methods. This post explains the scoring method we use in Progenesis QI, and how you can improve your search scores by searching additional dimensions of your data.

Progenesis QI search methods

At the time of writing, Progenesis QI supports these search methods and databases:

Progenesis MetaScope

Searches SDF and MSP files from any source. Supports retention time, CCS, theoretical fragmentation and spectral libraries.

METLIN™  MS/MS Library (requires purchase)

The Waters® METLIN™ MS/MS Library for Progenesis QI contains a local copy of the METLIN database and allows you to search this copy rapidly.


Searches the LipidBlast MS/MS database provided by Metabolomics Fiehn Lab.

Elemental composition

Produces putative formulae for compounds based on mass, isotope profile, and the Seven Golden Rules.


Searches the ChemSpider structure database. Supports theoretical fragmentation, isotope similarity filtering, and elemental composition filtering.

NIST MS/MS Library (requires purchase)

Searches the NIST MS/MS library for spectral matches.

You can find out more about each of these search methods in the search methods and databases FAQ. This blog post, however, will focus on how we calculate scores so that identifications from different search methods can be compared.

The Progenesis scoring method

For any given search, there are a possible five properties that can contribute to the overall score:

  1. Mass error
  2. Isotope distribution similarity
  3. Retention time error
  4. CCS error
  5. Fragmentation score

Each of these individual scores is on a scale from 0-100. If your search criteria do not include a given piece of data, the score for that piece of data is 0. The overall score is the mean of these 5 scores.

Note that the more search criteria you use, the higher the maximum possible score becomes, as described in the following example.


Suppose we have searched ChemSpider using theoretical fragmentation. For a given compound we find Identification A, with these scores:

Note that the scores for retention time and CCS errors are 0, because ChemSpider does not support searching those properties.

If we then perform a MetaScope search, this time including a CCS constraint, we might obtain the following scores for Identification B:

We have identical scores for the mass error, isotope distribution, and fragmentation. However, we also have an extra piece of information in the CCS score. This provides additional evidence for Identification B, so it is given a higher score than Identification A.

Note that in the ChemSpider case, if an identification scores 100 on all 3 items, it obtains a score of 60. In the MetaScope case, if an identification scores 100 on all items, it obtains a score of 80. So, for each additional piece of data we include in our search, the maximum score increases by 20.

The component scores

Here we’ll briefly describe how the five component scores that make the final score are calculated.

Mass error, retention time error, and CCS error

These are all functions of the magnitude of the relative error, Δ:

The score profile for mass error, retention time error and CCS error.
Figure 1: The score profile for mass error, retention time error and CCS error.

For the mass error, Δ is the ppm mass error and N = 4000. For the retention time and CCS errors, Δ is the percentage error, and N = 20.

Isotope distribution similarity score

This compares the intensities of each isotope between observed and theoretical distributions. A total intensity difference of 0 gives a score of 100, which falls linearly to 0 when the total intensity difference is equal to the maximum isotope intensity.

Fragmentation score

The fragmentation score is more complicated and depends on the fragmentation method used. The FAQs describe how scoring works for theoretical fragmentation and database fragmentation.

Improving identification scores

The best way to improve the scores of your identifications and your confidence in them is to use more search constraints.


In general, most searches will be able to produce a mass error score and an isotope similarity score. With just these two pieces of information, the maximum score for any identification is only 40/100. In this example we’ve identified Warfarin using only mass error and isotope similarity.


By including fragmentation data in your search criteria (either theoretical fragmentation or a fragmentation database), this increases the possible score for identifications to 60/100. Here we’ve added theoretical fragmentation to our search parameters.


Finally, if you use an appropriate data source (e.g. an SDF and additional properties file) you can add search constraints for retention time and CCS, giving a maximum score of 100/100. Here we don’t have CCS information but have added retention time to our search parameters for a maximum of 80/100.

Future improvements

Currently Progenesis gives equal weight to the five component scores – mass error, isotope similarity, fragmentation score, retention time error, and CCS error. In some cases, this might not be ideal, so if you have any suggestions for different weightings we’d love to hear from you in the comments section below.

As always, if you have any further questions, check our FAQ or get in touch.

How do you know your raw materials are as they should be?

Agnès Corbin of Nonlinear Dynamics gives us an overview of what’s needed to maintain high manufacturing standards.

Agnes Corbin

We all know it, reproducibility is one of the key parameters to master for maintaining a product’s quality. As a customer, we all like our favorite products of a consistent high quality; as a manufacturer, we want to preserve our quality and customers’ satisfaction

That starts with the supply chain of the raw materials and ingredients used to manufacture a finished product.

A non-conformity, be it a cross-contamination, adulteration or degradation, can have huge economic, clinical and sanitary consequences, especially with high cost raw materials.

With this in mind, you might be interested in the below application note, produced in collaboration with Robertet Group, the world leader in sustainable natural raw materials for fragrance and flavor.

Click on the image to download the Vetiver essential oils application note
Vetiver essential oils application note

It describes how Progenesis QI was used, to spot an ‘out-of-the-blue’ potential non-conformity in Vetiver essential oil, using an Untargeted Metabolomics Profiling approach with LC-HRMS and a variety of Ionization techniques.

Progenesis QI helped to detect and identify adulteration with Castor Oil, a non-volatile compound, in a new batch of Vetiver Essential Oil. It would have been missed with the use of classical and common GC-MS techniques applied on volatile compounds.

The combination of LC separation equipment UPC² and UPLC (with different ionization sources ESI, APCI, ASAP) were used to get a better understanding of the product’s composition. Suppliers of natural raw materials must increase their phytochemistry knowledge of their products, as per the recent change in the REACH regulation based on the Natural Complexes Substances (NCS).

The easy-to-set-up LC-MS techniques for non-volatile compounds, can be considered as complementary to GC-MS for volatile compounds QC.

Robertet could have missed out if they hadn’t used the Progenesis QI software.

Are you missing out by not using it?

Please contact us for an evaluation today and we can help you with your research.

The importance of the surfaceome and its interactors

We love to hear how our customers are using Progenesis QI and Progenesis QI for proteomics. It’s great to learn what they are researching and how Progenesis can help them. It’s also nice to find out more about the people behind the research. Our latest blog post features Dr Maria Pavlou and her interesting work with Dualsystems Biotech AG. First, here’s some background about Maria:

Dr Maria Pavlou

Maria Pavlou received her PhD in translational proteomics from the Department of Laboratory Medicine and Pathobiology at University of Toronto, Canada. Upon PhD completion, Maria moved to Switzerland to pursue a post-doctoral fellowship in the Institute of Molecular Systems Biology at the Swiss Federal Institute of Technology (ETH) in Zurich focusing on host-pathogen interactions. In 2017, she joined Dualsystems Biotech AG as a senior scientist and a year later, she was promoted to Chief Scientific Officer leading the research team to develop further the Ligand-based Receptor Capture (LRC) methodology and establish new services.

Now, onto the research:

The importance of the surfaceome and its interactors

If the plasma membrane is considered the gateway through which cells communicate and interact with their environment, then proteins associated with the surface – referred as the surfaceome – can be seen as the gatekeepers. The surfaceome largely dictates the shape, polarity, differentiation and motility of cells. It also mediates cellular behaviors such as cell-cell communication, self and non-self recognition, and cell signalling. Given the crucial role of surface-associated proteins in every aspect of cellular life, it is not surprising that they are the molecular targets for roughly 70% of FDA approved drugs [1].

The original concept, depicting the plasma membrane as a homogeneous fluid bilayer with freely diffusing proteins, has been evolved to another depicting a highly organized and crowded mosaic of interacting lipids and glycoproteins. This higher organization modulates the biological processes occurring on the cell surface, exemplified by receptors being active only when they form dimers, hetero-dimers or higher order oligomers [2]. Interactions of proteins in the cell membrane of the same cell (cis), and interactions of proteins of neighboring cells, the extracellular matrix and circulating ligands (trans) are collectively referred to as extracellular protein-protein interactions (ePPIs).

Elucidating ePPIs in a systemic fashion is pivotal to gain a better understanding of the surfaceome function. More specifically, identifying the targets of key ligands on the cell surface provides valuable mechanistic information about signal transduction, drug action or off-target effects. For instance, pathogen or growth factor interactions are important for developing novel therapies. Additionally, numerous ligands exist – both biologics and small molecules – involved in biological functions mediated at the cell surface through still unknown protein targets.

Towards target identification, an advanced cell-based chemo-proteomic approach has been developed namely ligand-based receptor capture (LRC) [3-4]. In this approach, the endogenous receptor repertoire of a given cell serves as an existing bait library that can be probed for ligand interaction. The key component of the LRC methodology is a trifunctional compound (TriCEPS or its latest development named HATRIC [3-4]) that utilizes the extensive glycosylation displayed by the majority of cell surface proteins to capture receptor interactions on living cells. Experimentally, the first arm of TriCEPS is conjugated with the primary amines of a ligand and the conjugates are added on living cells (mildly oxidized). There the ligand binds to its target(s) and the second arm of TriCEPS is covalently crosslinked to the glycans of the binding partner. The third arm facilitates target purification for mass spectrometric analysis.

In a typical LRC-TriCEPS experiment, at least two treatment arms are performed in parallel: one with the ligand of interest and a second with a control ligand (that is, a ligand with a known target). Upon identification, the relative abundance of cell surface proteins in the ligand samples is compared to those in the control samples using MS1-based label-free quantification. Randomly identified cell surface proteins are expected to have equal abundance in both samples, whereas the corresponding receptors are found enriched in the ligand sample.

Progenesis QIP gives the user full control but does not require advanced computational skills

Progenesis QI for proteomics (QIP) has been the workhorse when it comes to data analysis. Performing MS1-based label-free quantitation in Progenesis is extremely straightforward through an intuitive user-friendly interface.

The alignment of features is performed by sophisticated algorithms but at the same time the software provides the user with visual inspection of the whole procedure. This is extremely useful as the user has full control of the data and a better understanding on how the samples are processed. It can also reveal technical issues related to the liquid chromatography separation prior to mass spectrometric analysis or sample quality. Notably, proper feature alignment is pivotal for robust quantitation. Moreover, the feature-picking algorithm (peak picking) has been developed to minimize missing values therefore the requirement for imputation; another asset for robust quantitation.

Through the various filtering options, the user can eliminate features that are not of interest (such as polymers or contaminants) focusing on what really matters. The QC metrics tab provides a qualitative overview of the experiment giving the opportunity (once more) to assess whether the LC and MS parameters used were optimal.

Upon protein inference and calculation of relative abundances, the user can easily review protein characteristics (such as number of peptides, peptide sequence and modifications, expression profiles, see figure 1) and confirm results or flag outliers. Once more, this option gives the user full control over the data and eliminates, to a great extent, experimental artefacts.

LRC-TriCEPS analysis to identify the receptors of Insulin and Transferrin on HEK293 cells; a screenshot of Progenesis QIP.

Two LRC experiments were performed using insulin and transferrin as ligands of interest on HEK293 cells with receptor capture at two different pH (6.5 and 7.4).

(A) Following the intuitive and straightforward progenesis pipeline, the identification and quantitation (MS-1 based) was completed within 6 hours.

(B) A total of approximately 300 surface proteins were identified and quantified across all samples. Transferrin receptor (TFR1), the known target of Transferrin, was also identified with roughly 40 unique peptides.

(C) Using the protein filter the relative abundance of TFR1 across the four conditions was visualized; TFR1 is clearly enriched in the Transferrin samples.

(D) For more detailed information the user can check the quantitation of every peptide identified and spot any irregularities.

(E) The user can use the statistics run by Progenesis or export the data for post-hoc analysis.

As statistical testing is incorporated in the software, the final outcome of an analysis provides immediate information regarding proteins being significantly regulated. However, there is still the option to export all necessary information in order to perform post-hoc statistical analysis using different tools. This increases greatly the flexibility of the user.

Finally, Progenesis QIP is readily scalable when it comes to number of samples and performs analysis in a time-efficient manner, allowing for complete label-free quantitation in the course of a working day. This is extremely important given that data analysis is usually the beginning of a series of experiments aiming to verify and interpret the identified quantitative differences. It provides a variety of different plots and graphs that can be readily used for publications or reports.

In summary, the analysis of LRC-TriCEPS data with Progenesis QIP offers unique advantages. The software is user-friendly and intuitive therefore can be used by researchers with experience in data analysis but also by users that are just starting or do not perform data analysis daily. Progenesis QIP provides the user with full control over data analysis which is very important to spot and resolve experimental artefacts and to understand how the final outcome is reached. At the same time, the sophisticated algorithms provide high quality label-free quantitation and robust results. Finally, the nice visualization aspects can generate high quality graphs that can be used to communicate the results of each study.

1. Uhlén M, et al. Tissue-based map of the human proteome. Science (80- ). 2015.

2. Milligan G, G protein-coupled receptor dimerisation: molecular basis and relevance to function, Biochim Biophys Acta. 1768(4):825-35, 2007

3. Frei AP, Moest H, Novy K, Wollscheid B. Ligand-based receptor identification on living cells and tissues using TRICEPS. Nat Protoc. 2013;8:1321–36.

4. Sobotzki N, et al. HATRIC-based identification of receptors for orphan ligands. Nat Commun. 2018;9:1–16.

Thank you to Maria for a very interesting blogpost. Finally, please get in touch

• If you are a user with an interesting research project using Progenesis. We are keen to share user stories via our blog.

• If you would like to try Progenesis on your own data

Thank you

Acknowledgement: Maria Pavlou, PhD, Paul Helbling, PhD

A Progenesis QI workflow in Exposomics

Following on from the previous post about our 3 Progenesis QI lunchtime presentations at IMSC 2018, we are proud to present to you the talk given by Emilien Jamin from the Toxalim, Research Centre in Food Toxicology, Toulouse University, available to view here.

Emilien’s work in contaminant discovery and analysis shows how Progenesis QI can be used very effectively for untargeted analysis in the Exposomics field.   This workflow goes beyond suspect screening which requires prior knowledge.  Emilien uses several examples of how Progenesis QI was used to discriminate between different populations and finally touches on a proof of concept on lipids peroxidation.

You can view the talk for yourself and you can read an overview of his presentation below.

Metabolomic profiling of reactive metabolites in toxicology by MSE and Progenesis QI

Metabolomic profiling of reactive metabolites in toxicology by MSE and Progenesis

Emilien Jamin, Robin Costantino, Jean-François Martin, Françoise Guéraud, Laurent Debrauwer

Toxalim (Research Centre in Food Toxicology) Toulouse university, INRA, ENVT, INP-Purpan, UPS, F-31027 Toulouse, France.

Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, F-31027 Toulouse, France

In food safety, current exposure assessment approaches are based on food consumption data crossed with food contamination data or biomonitoring data. This allows evaluating exposure only in a targeted way on a few families of compounds. Based on our previous results in exposomics [1], food or environmental toxicology should focus on the exposure to a mixture of compounds (contaminant cocktails), mostly at low doses, and in an untargeted way to detect/identify unknown compounds. And among these numerous known and unknown metabolites, it seems a priority to focus on potentially toxic compounds.

In this context; we developed an untargeted method using high resolution mass spectrometry coupled to liquid chromatography to specifically profile electrophilic metabolites, in parallel with a classic untargeted metabolomic study. This allows the study of the exposure of potentially toxic compounds on one hand, and the study of the effects of this exposure on the endogenous metabolites on the other hand. More precisely, we used the MSE mode of a Synapt G2-Si mass spectrometer to detect all the metabolites displaying a neutral loss specific of metabolites conjugated with mercapturic acid. Data from MSE and from untargeted HRMS analyses were processed with Progenesis QI, to highlight discriminant reactive metabolites, as well as endogenous metabolites.

As a proof of concept, this approach has been applied to the study of different groups of rats fed diets containing various oils. According to our previous results on lipid peroxidation [2] these diets led to the production of different aldehydes conjugated to mercapturic acid. The most well known is DHN-MA which corresponds to the mercapturate conjugate of 4-hydroxynonenal (4-HNE), which is commonly used as a biomarker of lipid peroxidation [2]. Using our methodology, we were able to detect without a priori, dozens of mercapturate conjugates, including DHN-MA and other known conjugated aldehydes. Furthermore, our approach also allowed the detection of conjugates of unexpected aldehydes, and of other chemical classes, for which putative identifications have been proposed based on complementary structural analyses. Interestingly, multivariate statistical analyses of the HRMS signals carried out on the mercapturate conjugates yield a better characterization of the studied animal groups compared to results obtained from a classic untargeted metabolomic approach.

[1] Jamin E.L. et al. Anal Bioanal Chem (2014) 406:1149–1161

[2] Guéraud F. Free Radic Biol Med (2017) 111:196-208

Progenesis QI is a powerful tool for contaminant analysis and has been used in the food, cosmetics, natural products, chemical materials, sports doping, biopharma, metabolomics and proteomics fields.

Why not download the software and see how it can help you in your research? Progenesis QI for Progenesis QIP for proteomics

Can Progenesis QI impact your research project?

At IMSC 2018, we were lucky to have not one, not two, but three researchers give their presentations at our Progenesis QI lunchtime seminar.

Progenesis–Three personal accounts showing the power of Progenesis QI

  • Untargeted metabolomics using Progenesis QI for small molecules: Developing ion-chromatography-mass spectrometry for the investigation of cancer metabolism – James S.O. McCullagh, University of Oxford, UK
  • Metabolomic profiling of reactive metabolites in toxicology by MSE and Progenesis – Emilien Jamin, Toxalim (Research Centre in Food Toxicology) Toulouse university, INRA, France
  • Novel strategies for discovery of cardiovascular biomarkers in human plasma – Donald JL Jones, Leicester Cancer Research Centre, RKCSB, University of Leicester, UK

These were recorded so we’d like to draw your attention to the interesting and varied presentations over the next few blog posts.

As one of the presenters is awaiting publication, we will present these in reverse order, starting with a lively 23-minute presentation by Prof Don Jones of the University of Leicester.

Below is a short written summary of Don’s talk.  Even better, watch it for yourself and learn which features of Progenesis QI for proteomics Don found so helpful in this ambitious project.  It really is 23 minutes well spent!

Screenshot of the title page for the talk

Novel Strategies for Discovery of Cardiovascular Biomarkers in Human Plasma


Donald JL Jones1,2, Sanjay Bhandari2, Paulene Quinn2, Jatinderpal Sandhu2 and Leong L Ng2

1Leicester Cancer Research Centre, RKCSB, University of Leicester, Leicester, LE2 7LX, United Kingdom

2Department of Cardiovascular Sciences and NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, LE3 9QP, United Kingdom

Background: The search for blood-based biomarkers is particularly compelling in the cardiovascular clinical arena. Whilst understanding the genetic basis of cardiovascular disease will provide a clear indication of risk, phenotypic markers represent the pathological changes that occur during disease processes. Methods for investigating the plasma proteome have ostensibly relied on complex pre-analytical protocols that are expensive and limit throughput.

Methods: 100 Coronary heart disease Patients with 20 healthy control were analyzed on the SYNAPT G2-Si, using label-free data-independent acquisition LC-MS with ion mobility optimized (HDMSE). Samples were treated with Calcium Silicate matrix (CSM). Raw data was then analyzed using Progenesis QI for Proteomics. Models of panels of markers were developed using SPSS and RapidMiner.

Results: From 50 µL of plasma, in excess of 1800 proteins are realized that can be reliably observed between samples. Of these, >1100 are quantified. The data shows high reproducibility with known differences predictably demonstrated. New markers are revealed which can be strongly aligned with potential novel mechanisms of coronary artery disease (CAD). The method is shown to be highly reproducible.

Conclusion: We demonstrate that CSM provides sufficient coverage to enable single shot analysis of plasma, historically, a very challenging proteomic sample to analyze, and can provide potential markers for CAD which could feasibly be extended to several classes of disease. This provides a method that can run alongside other omic technologies to profile large-scale numbers of patients individually and thus usher in a new era of precision medicine. Importantly, there are advantageous savings to be made in terms of cost and throughput, which mean that for the first time large scale cardiovascular cohorts, conducted in a realistic timeframe, can be analyzed using proteomics

If you would like to try the Progenesis QI software on your own data then please don’t hesitate to get in touch.


Professor Donald JL Jones