Proteomics: a Peptide’s journey to emergence

We are pleased to present a blog post from one of our users, Dr. Maarten Dhaenens.  Read on…

Dr Maartens DhaenensDr. Maartens Dhaenens

Head of Proteomics Department
Lab of Pharmaceutical Biotechnology
Ghent University


In philosophy, systems theory, science, and art, emergence is a phenomenon whereby larger entities arise through interactions among smaller or simpler entities such that the larger entities exhibit properties the smaller/simpler entities do not exhibit. The most obvious example of emergence is life itself. Think about it: while anyone, or any algorithm, would still recognize you on a picture of 10 years ago (ok, not that one picture maybe), only a few molecules in your body are still the same as on that picture. It is as if you are the shape and matter is merely circulating through you, through time. Thus, explaining life itself by only focusing on the molecules we are built from and the laws of chemistry alone will be a dashing exploit. Yet, in proteomics, we currently have no other choice than to weigh molecular masses to fathom life. We need to be aware of this limitation and leverage knowledge with our mind, which actually is an emergent phenomenon in itself!

If I have learned anything from the dozens of collaborations at our lab, it is that the term “Proteomics” is actually very confusing to the outside world. We measure peptides. What we actually report on, the proteins is merely inferred. In a time where productivity is key, people tend to focus only on trying to automate this inference. Yet, to date, only human intervention, i.e. the human mind, can assure that the most correct or least ambiguous outcome is reported. I would argue that proteomics is in itself an emergent – not “emerging” – field. Once you start to look at it like that, facilitating human inspection of the visualized data should be the primary focus in order to fill the gap between what is measured and what can be concluded in terms of potential biomarkers or biology.

To illustrate this point, we look at histones in this webinar. These proteins are often used to normalize entire proteomes because they are rightfully considered as one of the most robust household genes in Eukaryotes. However, while these five low molecular weight proteins (10-25kD) have a very predictable expression profile, people tend to forget that they can get modified in ways that little other proteins can. Histones can theoretically generate roughly 7.1017 different proteoforms when you consider all the histone posttranslational modifications (hPTM) that have been previously reported. This translates into 50.106 different peptide forms with ArgC-like specificity. Indeed, we do not measure the proteoforms in bottom-up proteomics and we do not consider each of these hPTM in the searches we do. This, in turn, implies that it is practically impossible to quantify these proteins accurately when you apply bottom-up proteomics.

The fact that studying histone modifications is an intrinsically peptide-centric approach, however, made us realize that inferring protein abundance is extremely hard and in some cases even impossible. In this webinar, we will follow one such peptide on its journey through the Progenesis QI for proteomics workflow, to emergence. Using peak reviewing, QC metrics, conflict resolving and spectral library matching, we will detect artifacts, verify experimental reproducibility, detect outlier samples,… For histones specifically, it is invaluable that we can do several sequential searches (each with another combination of hPTM and using different search engines) and combine them all into this single analysis. Equally essential, we curate ambiguity that arises through these sequential searches by applying in-house scripting to the result files and then generate lists of tags to re-import into Progenesis QIP for manual validation and resolving conflicts. Because isobaric peptides carrying the same hPTM combinations elute very closely, we also manually verify and adjust peak picking of histone peptides. Finally, we are fascinated by the power of ion mobility separation in HDMSE acquisition and webinar concludes with a daring effort to match DDA libraries to HDMSE data.

In conclusion, while this webinar follows very specific peptides, i.e. those derived from complexly modified histones, it mainly illustrates that no automation process to date is able to anticipate the complexity of protein abundance. I thus argue that the final list of e.g. potential biomarkers should always be manually inspected and visualized in order to save time and money in the downstream validation process.

Indeed, Progenesis QIP allows us to peek behind the curtain and catch a glimpse of emergence.

YPIC Challenge

Have you ever wondered whether you can express an English sentence in Escherichia coli? No? Well, you are probably not the only one… Yet, intrigued as they are by the complexity and limits of life, the Young Proteomics Investigators Club (YPIC) went ahead and tried this exciting idea, with the help of PolyQuant. PolyQuant transfected E.Coli bacteria to see if “English can be expressed as a protein”. And guess what?! E. coli does speak English and has blessed this world with the first-ever three-dimensional grammar. Why did YPIC do this? Because they like challenges and they believe, you do too. Therefore, they dare you to join them in studying this unique protein, assembled by this fascinating creature with the sole purpose of challenging you.

Welcome to the second YPIC Challenge. Everybody is welcome, no matter where you come from!

Why would you participate? Well, because

  1. You are one of the few on this planet who can actually crack this code, since advanced proteomics skills are of the utmost importance.
  2. Because you want to become the pride of your country in this worldwide challenge. Not to mention how proud your mom will be when you win this game!
  3. Because there is a separate competition for every discipline and expertise in proteomics:
    1. Three-dimensional Grammar (find out how this sentence folds)
    2. Bioinformazing (develop the coolest bioinformatics approach to decipher the sentence)
    3. Protein Punctuation (Look for the biological equivalent of punctuation: PTMs left behind by E. coli)
    4. #Bioreactivity (can you generate and describe bioreactivity in this Twitter-sized message?)
    5. Best manuscript, the main prize
  4. A manuscript you say? Indeed, following your experimental wizardry, you should consolidate your beautiful work by writing a manuscript. The deeper all contenders can drill into the mysteries of the biology of language, the higher the impact factor of the journal that dedicates an entire issue to our joint effort. So, an extra, official publication for just playing a game? That doesn’t sound too bad now, does it?

And why is Nonlinear Dynamics posting this? Well, as a vendor-independent software platform, any team can use the Progenesis QIP functionalities to tackle any of the challenges. Just a few suggestions that could get you going:

  1. Perform several digests with different enzymes to increase the coverage. Run them separately and run a mixture of the samples, as you would do when you run a QC sample. This sample would then serve as a template to align everything and merge the different searches you do into a single analysis.
  2. There are plugins for 13 different search engines. If you used a fasta file of the oxford dictionary, you could start merging searches from different engines! And why not use spectral libraries to link words together?
  3. All this flexibility allows you to make Progenesis QIP a part of a bioinformazing pipeline.
  4. Progenesis QIP is very well suited for PTM research, did you know?
  5. Is it the #bioreactivity challenge you are most interested in? Well, digest the sentence with different enzymes and spike the peptides into your cell culture. Progenesis QIP is built to find out what these peptides do to the cellular proteome!
  6. As a part of Waters, we know that ion mobility separation could help to do a structural analysis.

At the EuBIC Winter School 2019, in January, YPIC will award the prizes. They will try to broadcast this session live, so that every competitor can follow!

We can actually feel your index finger hoovering over the mouse to register. Don’t hesitate; YPIC didn’t either, did they? And neither did PolyQuant, who took the risk of finding out that life not only speaks Latin or Greek (as one would expect?), but also likes English!

The only thing you need to do is become a YPIC member, for free (as is the challenge obviously) and gather three chosen ones in a well-considered research team. Trust us; you will need them! In last year’s challenge, 19 teams enlisted and only 7 cracked the code. And that one seemed easy: just 19 synthetic peptides together forming a sentence from a book. Just ask last year’s winners Alexander Hogrebe and Rosa R. Jersie-Christensen (Jesper V. Olsen’s group) how easy that was*. And this year, they cranked it up a notch. That means that you will not find the sentence anywhere and there probably will be some digestion involved (unless you do top down, Edmann Degradation or – hey, why not – nano pore sequencing?).

Getting suspicious about all this costless awesomeness? Don’t! Look at it from their perspective: they get to extend their membership and, in doing so, your network. That is only one of the reasons that EuPA founded YPIC at EuPA2016 in Istanbul. Actually, they want to represent all the extra-scientific aspects of being a young scientist. Just check out their survey and you will start to get the picture. A digital Proteomics job fair made by EuBIC is already in place (, but there are countless other things they could do for you in the future.

Be sure to check-out their Facebook page if you want regular updates on our activities as well.

See you at EuPA2018, with all the contenders.

The new generation of science has arrived! Power to the Proteomics people,


* Read all last year’s manuscripts here. The winning manuscript was entitled: “Sweet Google O’ Mine – The Importance of Online Search Engines for MS-facilitated, Database-independent Identification of Peptide-encoded Book Prefaces”.

What value can Progenesis QI provide in the world of co-polymer characterization?

Polymers are critical to meeting key societal needs

The use of polymeric materials in our everyday lives is increasing rapidly driven by innovations in materials development and design. Examples of the scope of polymer uses include: structural materials for cars and airplanes, fabrics for clothing, packaging materials for food and medicines, medical devices like heart valves and joint replacements and as substrates for revolutionary 3D-printing applications. The latest innovations have delivered smart materials which can change their shape or properties based upon changes in their environment.  However, this wealth of new materials must be properly characterized in order to manufacture these polymers reproducibly and to achieve the required property characteristics, thus appropriate analytical technologies and comprehensive data are needed.

There are many advanced technologies available for polymer analysis. Today we will consider Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) and how multivariate analysis of the data it produces provides novel insights into polymer structure.

Why use Py-GC/MS and what are the limitations?

Py-GC/MS is one of major analytical techniques for chemical structural elucidation of polymers. It involves identification of the gaseous products generated from degradation of a polymer heated to 600°C under inert gas providing data from which the detailed chemical structure of polymer can be estimated.

Typically, the GC/MS in these analyses uses a hard ionization technique; electron impact (EI). However, the data obtained by such ionization becomes increasingly complex, especially when there are increasing monomer numbers in the co-polymer. Many pyrolysis products are formed and each of them generates many fragment ions upon ionization. This can prove a limitation of the approach.

A new approach to Py-GC/MS

The experimental data can be simplified using a soft ionization technique like Atmospheric Pressure GC (APGC) ionization in place of EI as the high sensitivity and soft ionization allows observation of the molecular ion without fragmentation. (See this link for a  White Paper about APGC). Reduction in fragmentation enables the determination of larger fragments from the polymer backbone, enabling the connectivity of the monomer units to be inferred. Why does this matter? Well, different arrangements of the units in a polymer like a block copolymer vs. random copolymer would result in final material having different physical properties which can affect its end use. Therefore an understanding of what type of substructure exists in the polymer is very important.

Combining a high resolution mass spectrometry instrument such as quadrupole-time-of-flight (QToF) mass spectrometer with an APGC source (see Figure 1) enables the MS and MS/MS spectrum of each peak to be simultaneously collected. This data provides the elemental composition and fragment ion information needed for elucidation of chemical structures (see Figure 2).

Py-GC/MS setup Figure 1: Py-GC/MS setup in one of Waters laboratories using an EGA/PY-3030D pyrolysis unit attached to a GC equipped with an atmospheric pressure source for GC/MS (APGC source) and a Waters Xevo G2-XS QTof mass spectrometer.

Block co-polymer low and high energy spectra from MSE data acquisition Figure 2: Block co-polymer low and high energy spectra from MSE data acquisition. This is a data independent acquisition mode enabling simultaneous acquisition of low energy and high energy spectra. The low energy spectrum provides molecular ion related information from which elemental composition can be derived. High energy spectrum contains fragments from the molecular ion which help to confirm structure.

How is Progenesis QI applied to Py-APGC/MS data ?

Applying multivariate analysis to the Py-APGC-MS data enabled the characteristic pyrolysis products from the different co-polymer types to be automatically detected and identified as structural markers. The application of PG QI software removes the need to manually sift through the vast array of spectral data generated from each sample trying to detect and identify structurally significant pyrolysis products.

The data for two acrylic acid – styrene copolymers, one block and one random, were processed using Progenesis QI and following data alignment and peak picking the samples were analysed using an OPLS-DA model to compare the two groups. We can see the two polymer types are easily distinguished in the scores plot (Figure 3).

OPLS-DA model to compare the samples Figure 3: Following replicate analysis of the two co-polymer samples the data was aligned and peak picked using the workflow presented by Progenesis QI. The resulting data was analysed using an OPLS-DA model to compare the samples. The scores plot resulting from that analysis is shown here where it can be seen that the two co-polymer types are clearly discriminated.

From this model the block co-polymer marker components were extracted from an S-plot and confirmed on a trend plot (Figure 4).  The chemical structures of the marker components were determined from the MSe spectrum as described previously. Random co-polymer marker components were extracted and chemical structures elucidated using same procedure. Some of the structures determined are shown in Figure 5 where we can see how they are representative of block and random structures.

Plotting all the identified markers on an S-Plot Figure 4: Plotting all the identified markers on an S-Plot allows extraction of those which we are most confident provide significant discrimination between the samples. The intensity of these individual markers can then be plotted against the sample identities in a Trend Plot which, in this figure, shows the abundance of markers of the block co-polymer components extracted from data.

Examples of markers Figure 5: Here we show examples of markers that were identified for the block and random samples of styrene – acrylic acid co-polymers using the elucidation workflow described in the main text. Below the structures are some of the monomer sequences that they correspond to demonstrating how this approach can provide information about co-polymer backbone substructure.

Concluding thoughts

The analysis of Py-APGC-MS data by Progenesis QI enabled the discovery of markers which contributed towards the difference between co-polymers. These structural differences can be due to different polymerization methods used to produce the materials, or different monomer ratios used during production.

This study shows the utility of a pyrolyzer connected to a gas chromatograph and a mass spectrometer using soft, atmospheric pressure ionization for the characterisation of co-polymer structure. Analysing the information rich datasets using Progenesis QI software enabled markers to be identified that provide insight into the differences in monomer connectivity in block and random copolymers. Further details on this work can be found in the poster publication at the following link – Py_GCMS_Poster.

In addition to the use with pyrolysis GC/MS, multivariate analysis with Progenesis QI is also very useful in troubleshooting product failures like discoloration in a batch of polymeric material or mechanical or chemical failure of components. In some of the latest applications, polymer chemists have utilized this approach for marker analysis to understand different product performance of functional polymers such as photoresists and color-resists related to semiconductor and display manufacturing.

So, next time you look at your phone or tv, step into your car or take your seat on an airplane; remember the critical dependence you have on polymeric materials and that a lot of analytical testing has gone into the development process to provide you with such attractive, robust, safe and functional products!


Tim Jenkins, Waters, Wilmslow
Baiba Babovska, Waters, Milford
And our colleague in Japan, Tatsuya Ezaki of Nihon Waters K.K.

Recent Advances in Food Analysis (RAFA) 2017

Recently I had the pleasure of returning to Prague for the 8th International Symposium on Recent Advances in Food Analysis (RAFA) conference. The Clarion Conference hotel again provided a great venue to host the event and accommodate many of the delegates on site making logistics very convenient for all.

The conference brings together a wide range of scientists from across Europe working in the field of food analysis covering both regulatory and research applications. The conference has grown in reputation and size over the years and, with over 550 delegates and 3 parallel sessions this year, there was a wealth of information being presented. From my perspective, it was great to see the Progenesis QI software being successfully applied and cited in many talks and posters to help in the comparison and distinction of some very complex sample matrices.

Waters booth at RAFA 2017 Lots of interest and activity at the Waters booth following Sara Stead’s lunchtime seminar.

I spent most of my time at the Waters booth discussing the Progenesis QI software with delegates and researchers who were interested in seeing how the Progenesis QI software could be implemented into their workflow. I did manage to visit one nice restaurant and see some of the old town, on one of the evenings.

Panorama of Prague Panorama of Prague.

Here’s what Dr. Sara Stead, Strategic Collaborations Manager at Waters had to say:

“The key themes at RAFA this week have been addressing the major challenges facing the food industry such as fraud, authenticity, quality and safety to secure the food supply chain and protect all consumers in an era of globally traded commodities.

The use of HRMS coupled with intelligent informatics systems is emerging as a key player in this space capable of holistic profiling, keeping pace with the industry demands.

Techniques such as QTof MS, ion mobility enabled MS and direct analysis in combination with comprehensive chemometric software packages such as Progenesis QI and LiveID are being established as the ‘go to’ solutions for pioneering researchers tackling these complex challenges.”

A recent application note details how the Progenesis QI software and the Waters Ion Mobility workflow was used in the food allergen research area. Food allergens are a hot topic for many scientists. The application note Identification and Quantitative Analysis of Egg Allergen Peptides Using Data Independent Ion Mobility Mass Spectrometry can be found here. It is most certainly an interesting read for those looking at LC-MS/MS quantitative analysis.

The Progenesis QI software is a versatile piece of software that can be used, not just in this food fraud area, but also in the fields of metabolomics, environmental research and chemical analysis to name but a few.

If you’re interested in looking further at the Progenesis QI software to see how it can help you in your research then you can download the software here or get in touch with us. We will be more than happy to answer any of your questions.

If you get the chance to attend this conference in the future I would definitely recommend it to anyone working in this field.

Don’t get stung by your Manuka Honey!

One of our Waters colleagues, Dr. Joanne Connolly, gave an interesting presentation at the 38th BMSS Annual Meeting 2017 in Manchester last month. The research involved the use of Progenesis QI in a non-targeted metabolomics approach to honey analysis and floral marker elucidation. Previous blog posts have discussed how Progenesis QI was used to detect food fraud across a wide diversity of foods in an untargeted approach.

The problem

There have been a number of food scandals in recent years, the more serious resulting in fatalities.

Why do people adulterate food?

The main reason is for financial gain, to make the key ingredient ‘go a bit further’, for substitutions, or to cut manufacturing costs.  Sometimes, it is worse with deliberate maliciousness such as reputation damage – trying to destroy a competitor’s reputation or even terrorism.

Either way, laboratories need to develop ways to test food products in an untargeted way, to explain the differences between genuine and fraudulent products.

An example: Manuka and commercial honey

Every honey is a unique, complex matrix made up of plant and bee secondary metabolites including flavonoids, phenolics and sugars.  Each honey has its own “fingerprint” which will differ depending on region, forage targets and biological properties.

A lot of honey found commercially has honey from several plant species and is known as polyfloral or multifloral honey. The term unifloral describes a honey that is derived from one plant species, and unifloral honey is becoming of more commercial interest as consumers appreciate the possibility to choose between different honey types.

Leptospermum scoparium Leptospermum scoparium

In addition to this, there have been studies discussing therapeutic or technological uses of certain honey varieties which also contributes to the demand of a reliable determination of their botanical origin. Some of the unifloral honeys are sold at premium prices, and are therefore a target for food fraud to occur (e.g. adulteration, mislabelling).   Manuka honey falls into this group.  Manuka honey is made from the nectar of Leptospermum scoparium or Manuka bush, a shrub native to New Zealand and Southern Australia.  It is reported to have biocidal activity and a unique antibacterial non-peroxide activity (NPA).  Suppliers have to demonstrate this activity for labelling and to attract premium price.

Current approaches

Understanding, deconvoluting and identifying the biochemical profile of a food sample of interest can help give manufacturers and regulators key information in the fight against fraud. Many different analytical techniques were used to determine the floral origin of honey, including MS, NIR, FT-IR, and Raman spectroscopic fingerprinting, and NMR.

Another possible approach is untargeted metabolomics, as hinted at earlier.

Identification of a MS-derived biochemical “fingerprint” is an important tool for understanding the question of “What is normal?”

Where does Progenesis QI fit in?

In this recent application note, an LC-MS metabolomics approach was taken to chemically profile four different types of unifloral honey.  Progenesis QI was used in untargeted analysis of LC-MS data (HDMSe in ESI+ and ESI- modes) to find candidate biomarkers for Manuka honey when compared to the other mono-floral honey types (Buckwheat, Heather and Rape). Each sample type was run in triplicate, plus pooled QCs.  After importing the data into Progenesis QI and processing it through the unique Progenesis alignment and co-detection workflow, PCA analysis showed Manuka was clearly separated.

Principal component analysis Principal component analysis (PCA) scores plot from EZinfo (ESI negative ion HDMSE data).

By automatically exporting data to EZinfo for discriminate analysis, it was then possible to extract the best candidate biomarkers from an S-plot of Manuka honey versus all other honey types. Tentative identifications were also generated for several of the extracted potential biomarkers. MRM was used to validate that the peak identified as Leptosperin really did differ in abundance between Manuaka and non-Manuka honey.

Manuka markers Review of standardized abundance profiles and assignment of identity for three markers of Manuka honey as displayed in Progenesis QI software.

In summary

Food authenticity, adulteration and safety is a major concern across the globe.  A non-targeted high resolution MS OMICS approach combined with multivariate data analysis can identify ‘normal’ profiles of foodstuff allowing detection of fraud during the investigative stages.  It is possible to get biologically meaningful information by comparing multiple samples using an all-in-one high-throughput guided workflow in Progenesis QI.  Confident structural assignment in Progenesis QI means markers can be annotated and identified by databases of user choice.  Combining ion mobility with MS gives ‘cleaner’ fragmentation data allowing easier identification of markers.  Validation of markers is important using complimentary technology for confirmation.  New innovative rapid evaporative techniques will open new doors into authentication techniques at “point of entry”.

Are you doing untargeted LC-MS analysis? Would you like to see how the Progenesis QI software can help you get the results you are looking for?

You can download the software or contact us. A member of our friendly team can discuss the software further with you.

What are our customers saying?

Every so often one of our enthusiastic users emails us with some praise. It’s really appreciated and gives us an insight into how they are using Progenesis QI and Progenesis QI for proteomics. We thought we’d put some of these together in a way that answers the questions that scientists ask themselves when considering software options and share their experiences with you. We go through getting started to technical support. Please feel free to email us your own Progenesis feedback if you’d like to give us an endorsement for the website.

Progenesis QI logo Progenesis QI for proteomics logo

We are just starting label free, where do we begin?

“We are impressed by the user interfaces of all Nonlinear products, and Progenesis QI for proteomics is no exception. We have never done label-free quantitation before until we used Progenesis QI for proteomics for the first time. Some time ago, we did some shotgun proteomics on zebrafish plasma and thought we saw some interesting differences. We thought of doing label-free quantitation but were worried that we might need a long time to learn how to do it properly. To our surprise, with Progenesis QI for proteomics, we practically started getting publishable data within the first few hours. We started writing the paper while doing more experiments to confirm the quantitation. In 4 weeks, our paper was written! As usual, Nonlinear provided wonderful technical support for us. Label-free quantitation is now a routine technique in my lab.”
Dr. Lam Yun Wah
Department of Biology and Chemistry, City University of Hong Kong, Hong Kong

How do I analyze my large datasets?

“We have been running samples for 2 months non-stop, resulting in a data set of 1115 samples, each sample consisting of a 30 minutes gradient separation and an average of about at least 3000 compound ions per sample. A gradually increasing retention time shift up to more than one minute was observed towards the end of the gradient for the last 400 samples, but with Progenesis QI we could align all of them without any exception! This was a challenging task which to my opinion no other software would be able to handle!”
Geert Goeminne
Mass Spectrometry Expert, Department of Plant Systems Biology, VIB, Ghent University, Belgium

100% matching with no missing values Figure – Screenshot from the Progenesis Qi for proteomics software showing 100% matching with no missing values.

Am I detecting expression changes accurately?

“Progenesis QI for proteomics provided me an excellent tool to profile hundreds of proteins with incredible precision to map proteins in cellular compartments of the photoreceptor cells of retina.”
Nikolai Skiba, Assistant Professor
Albert Eye Research Institute, Duke University, USA

How do I avoid spending weeks analyzing my data?

“I am fascinated by the capabilities and efficiency of Progenesis QI for proteomics. One of the most important aspects of this software is the reliability of alignment algorithm. I had 36 LC-MS runs to align: the alignment score was > to 70% for all runs! It is very easy to use due to video tutorials and technical support. I analyzed all of my data in two days while it had taken up to one week on other software. Moreover, it is possible to tag interesting features and perform multivariate statistics based on ANOVA test which can be used in cases where there are more than two groups.
I tried many other software, but Progenesis QI for proteomics is the best I have used!”
Marianne Ibrahim, Ph.D
Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), Strasbourg, France

How can I reduce training time?

“The Progenesis QI for proteomics package has enabled our service lab customers to be taught in a very short time how to extract reliable results with which they are happy. The interface is truly intuitive and the alignment accurate and the results analysis package enables them to perform the work without the need for a statistician.”
Peter James
Prof. of Protein Technology, Lund University, Sweden

How can I analyze the data from all my different instruments?

“In my research of inflammatory diseases, I have successfully applied Progenesis QI for proteomics for the subsequent label-free analysis of our proteomics data from various samples ranging from highly complex tissue biopsies to semi-complex body fluids. The program supports an impressive range of vendors and search engines, and it is easy to import even complex experimental designs. Furthermore, the program is highly user friendly, allowing our students to use the program and analyze their proteomics data after only a short introduction.”
Tue Bennike
Laboratory for Medical Mass Spectrometry, Aalborg University, Denmark

How well is my software supported?

“Technical support for Progenesis QI for proteomics has been marvelous. Support personnel are knowledgeable, helpful and prompt. Given the complexity of some of the data that Progenesis handles, this level of support is vital.”
Kizhake V. Soman, Ph.D.
Assist. Professor of Biochemistry & Molecular Biology, UTMB NHLBI Proteomics Center, University of Texas Medical Branch at Galveston, USA

These are just a few of our favourites that we feel help in answering questions that new users and interested parties may ask when using the software. For more information and feedback on the software, you can also read other scientists’ reviews on our SelectScience pages for both the Progenesis QI and Progenesis QI for proteomics software packages.

If you would like to download the software and try it out on your own data, don’t hesitate to get in touch. We can get you started with the software. Isn’t it time you ANALYZED ALL of your data?

Helping you to help yourself

Sometimes you want the answer to a question straight away and you don’t want to ask someone and have to wait for a reply. In this instance when you don’t know the answer what do you do? For the Progenesis QI range of software, I’d like to share with you the resources we have to help you help yourself. You’ll be able to find answers to your questions quickly, easily and confidently.

What are these resources?

The Progenesis QI team have long been known for their great support and customer orientated philosophy. We have designed our website to help you make use of the software confidently and be comfortable knowing you can find help for any questions that you may have. Don’t forget too that you can use the search tool, top right on all our web pages, to quickly reach relevant help.

Image showing search box on web page site search

User Guides

When you are just starting you need a little help to get you on your way with the software. With this in mind, we have created a user guide for you that you can download from the website to help you run through the workflow of the software. The user guide takes you through the complete analysis of the software using tutorial data that you can download from the website. Once you are confident with this workflow you can move on to your own data. We have a user guide for both the Progenesis QI software and the Progenesis QI for proteomics software.

Progenesis QI Starter Pack

Following hot on the heels of the user guide we have developed a unique starter pack for our customers. On our blog, we have written key informative articles about the software but sometimes it is difficult to know what to read first. The starter pack highlights some of those key articles, like understanding your analysis, what things should you be doing to confirm your analysis? And taking your analysis further etc. This isn’t specifically for new users of the software. Many existing customers use these targeted articles. Again we have designed specific starter packs for each of the products, Progenesis QI for proteomics and Progenesis QI.


When you need a specific answer to a specific question then head over to the FAQ section on our website. Both FAQ pages have a multitude of questions. Questions that you may never even have thought about. Questions like “How do my experiment designs affect my analysis? And “Which runs should I use for peak peaking?”

The FAQ section on the Progenesis QI page has over 100 questions while the Progenesis QI for proteomics page has close to 100 questions. We keep the answers up-to-date when new versions come out and add popular questions that we feel are of benefit to our customers. Is there a question you have that we haven’t covered? If so let us know and we will be more than happy to get back to you.


We at Nonlinear love our blog. It houses resources that you can’t find elsewhere on the website. We have technical in-depth articles from our very own software developers. We have in-depth articles from our customers explaining how the software has helped them in their labs and how it too can help you in yours. We also have some lighter articles where we thank all of our customers for supporting Nonlinear though the years to help us make the software what it is today.

As I noted above the starter pack will highlight some of the posts we feel you should start with but if you want further reading about the software and areas like data quality or the new integration of Proteolabels for quantitative proteomics, supporting studies involving stable isotope labels then we have those blog posts for you.

Screenshot of the Nonlinear Blog


In addition to our website, we do also have a YouTube channel with videos that have been designed to give you a walk-through of the software. Broken down into bite size chunks so you can view the area you want to look at and not an entire workflow. You can view experimental design setup, exporting results or creating and using tags to name but a few.

We have also grouped together playlists for your ease of use. Go ahead and check us out.

Screenshot of the Nonlinear Dynamics YouTube channel Nonlinear YouTube channel


We have tried to cover all the areas that you may need help with when using the Progenesis QI range. We take you from the beginning of the software analysis process through to advanced questions we receive from proficient power users. This combined with our YouTube channel gives you the tips you need to make sure you are confident in getting the results that you need from your analysis.

If you have used these resources but still need help with the software, then don’t hesitate to get in touch with our dedicated support specialists. They will be more than happy to help you,  If you are interested in trying the software for yourself then you can download them here:

Thanks for your continued interest in the Progenesis QI range of analysis software.

Out now – Progenesis QI for proteomics v4.0

Our developers have been busy working on a new version of Progenesis QI for proteomics, and I’m very pleased to announce that it is now available to download.

What’s New?

  • Spectral libraries:  streamline the identification process with the use of the new spectral library search. You can now create and search spectral libraries in Progenesis QI for proteomics.
  • Support for SILAC: Progenesis QI for proteomics now seamlessly integrates with Proteolabels software, with workflows for SILAC and dimethyl labelling.
  • Integration with Symphony: you can now create a Progenesis QI for proteomics experiment from the Symphony data pipeline.
  • Support for SONAR data: SONAR is the latest DIA mode from Waters, providing additional specificity and clarity.
  • Export to PRIDE: mzIdentML exports can now be produced for upload to the PRIDE repository.

Other improvements

  • Improved workflow for Waters MSe data, with automatic peak detection thresholding to maximise number and quality of identifications, whilst improving software performance.

For more details, please download our “What’s New” document.

Where can I download it?

If you’re an existing customer with an up to date Coverwise plan, this upgrade is totally free of charge and very simple – you will receive an email with a direct download link as well as specific instructions on how to upgrade your dongle. 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.

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

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 reflect new behaviour. We’ve also updated our user guide if you’re looking for a step-by-step guide from start to finish.

A review of Metabolomics, Brisbane 2017

After 25 hours of travel… a short rest with some adorable baby kangaroos and koalas, there was a series of pre-conference workshops on Sunday and Monday morning.

Image of Brisbane Bridge at night time

The Waters Monday workshop was well attended, and we had two very interesting presentations from Progenesis users in the FoodOmics and Health Sciences applications: Dr. Martin Snel from South Australia Medical Health Research Institute (SAMHRI) presented “Are identical mice the same? The gut microbiome and fecal metabolome in mouse models” in which he discussed the influence of the microbiome on the phenotype of “identical” mice. Dr. Fe Calingacion (University of Queensland) presented “A multi-omics approach to understand grain quality using a diverse set of rice” in which she discussed how omics profiling may be used to distinguish different types of rice and assess the quality.

At the conference itself, discussions were held on various applications and topics during the sessions.  These included Natural Products, Lipids, Marine Microbiomes, Diet, Health and Disease, Wine and Quantitative Metabolomics. Across this broad diversity of Metabolomics applications, it was clear that the main challenge people are seeing is compound annotation.

Image of poster entitled “Automatic CCS and MS/MS Library Creation and Application for Large Scale Metabolic and Lipidomic Profiling”

Although it is clear that metabolites annotation is still perceived to be the main challenge for the community, missing values (in statistical data for relative quantitative analysis) is also a hot topic. Indeed, from various discussions I had with scientists, missing values are a well-known problem but no one has managed to resolve them in a satisfactory manner so far.

But did you know missing values need not be a critical issue? Progenesis QI software has a solution for the problem in the Co-detection approach (see also How Progenesis QI resolves the problem of missing values). This is vitally important since, in addition to the issues of compound identification mentioned above, it’s also vital that you attempt to identify the correct compounds, i.e. ones that are showing some interesting expression changes in your experiment. Only with confident quantitative results can you be sure that the compounds you found are the potential biomarkers you were looking for.

Another main topic highlighted during the conference was the constant need for better data quality.

I spoke to a bio-informatican whose approach was to use only data with a SD <3 as valid compound ions for analysis and further data exploration – a form of “data cleansing” approach. With the great hardware technologies available nowadays, High-Resolution Mass spectrometers coupled with Liquid Chromatography separation, you can generate large amounts of complex data fairly rapidly. So you need reliable tools to “separate the wheat from the chaff”, enabling valid results and a relevant biological interpretation. Progenesis QI provides a range of QC tools such as the ability to quickly and easily filter the data by CV%. Using “tags”, you can then easily choose to hide or display data based on parameters such as p-value, fold change, compound abundance and highest mean condition, enabling very flexible data exploration and reliable validation of results.

If you’d like to see how Progenesis QI can improve your discovery metabolomics and lipidomics analysis results, you can download the full version from our website along with some tutorial data and a user guide.

Finally, just to encourage you, here is a quote from one of our users:

“We have been running samples for 2 months non-stop, resulting in a data set of 1115 samples, each sample consisting of a 30 minutes gradient separation and an average of about at least 3000 compound ions per sample. A gradually increasing retention time shift up to more than one minute was observed towards the end of the gradient for the last 400 samples, but with Progenesis QI we could align all of them without any exception! This was a challenging task which to my opinion no other software would be able to handle!”

Geert Goeminne
Mass Spectrometry Expert, Department of Plant Systems Biology, VIB, Ghent University, Belgium

Just because it’s natural doesn’t mean it’s safe

A major analytical challenge in natural products is the complexity of the samples.  Why does this matter?

Waters recently hosted a webinar, available on-demand, entitled “Authenticate Herbal Supplements with a Metabolomics Approach – Reporting and Analytics” featuring our own Dr Giorgis Isaac, whose current research is focused on novel analytical and informatics method development to solve the analytical challenges in natural product analyses. The webinar was based upon some collaborative work between Waters and the University of Mississippi, you can read the full paper here.

During the webinar Giorgis talked about the market in Natural Products, currently standing at $61.84 billion. There are many examples of reported adulteration. The take-home message was “Just because something is natural doesn’t mean that it is safe”. Herbal supplements are complex and so is their analysis. Giorgis talked about the problems of using a targeted approach. What compounds do you identify for your analysis? People are managing to meet required thresholds of active ingredients but what else is there? How can you be sure of the purity of your specimen?

A holistic analysis

Giorgis and his collaborators therefore wanted to explore an untargeted approach. Untargeted analysis is holistic so you can analyse ALL of the data in ALL of your samples and compare them, finding the differences without any prior knowledge. In short, you don’t need to know what you are looking for. Using Progenesis QI, you can produce a ‘metabolomic fingerprint’ for your samples which can then be compared against similar species which may be being used for adulteration. These metabolomics fingerprints can also be compared against a QC sample. Giorgis emphasized the importance of good experimental design in untargeted analysis. He emphasized the usefulness of using QC samples and how essential a standard workflow is. This is so you can minimise and measure technical variance in your analysis and focus on your biological variation.

QI: Quantify then Identify

Untargeted analysis has been made possible by the use of software that allows unbiased peak picking and retention time alignment of signal. Progenesis QI is renowned for its unique approach to alignment, co-detection and peak picking, best explained in this diagram:

Giorgis walked us through how Progenesis QI was used in the exploration of three botanicals: Hoodia, Terminalia, and chamomile. He pointed out the flexibility of Progenesis QI, being multi-vendor, able to handle profile and centroided data formats, as well as high and low energy formats, including MSE.

Useful statistics

He explained how he uses the PCA analysis to check the quality of the data and to spot any suspicious finding quickly. We have seen this done successfully time and time again, for example, if samples have got mixed up or there is an anomaly in the samples. Using the dendogram, you can also look at groups of compounds behaving in a similar way across the experiment. It is possible to tag such features of interest and drill further down into the data. Giorgis also described how Progenesis QI can export into EZinfo so you can run further multivariate statistical tests. You can then import the results back into Progenesis QI for further analysis.


So far so good: we have minimised our technical variation, we know what is changing significantly but… how do we identify these compounds of interest? One of the advantages of using Progenesis QI is its ability to combine results from multiple search methods and databases. Progenesis QI is able to score results from all the databases and search methods it supports, so you can compare search results obtained from different search methods. Progenesis QI currently supports these databases:

I don’t want to give the game away here, as the webinar and the paper are both worth viewing, but the results from the analyses of Hoodia, Terminalia, and chamomile were very clean and very encouraging, showing what can be done to identify marker compounds for each species in order to detect any adulteration. Progenesis QI had a large role to play in this neat piece of detective work. During the questions at the end of the webinar, Giorgis discussed how this untargeted approach compares to DNA methods and U-V NMR IR methods. He emphasised that LC-MS is more stable and easy-to-use these days, is very sensitive and, alongside Progenesis QI, can handle the analysis of complex mixtures. Once again questions led to the standardised workflow as it is so important to have maximum control of the variables. Why a QC group? Is that really necessary? Giorgis emphasized that a QC group gives you confidence: if your QC group lies in the centre of your PCA analysis, your variation is in the sample, not the methodology.

I came away from this webinar excited at the chance for Progenesis QI to play a pivotal role in the natural products market. As this market grows and the temptation to adulterate grows with it, it is reassuring to know that untargeted LC-MS analysis makes it virtually impossible to cheat the system.