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.

Stay in the fast lane with Progenesis at ASMS 500, Indianapolis, 2017

The ASMS 2017 meeting follows the Indianapolis 500 – if you’re there, maybe you can still smell the burning rubber in the air. Comparisons can be drawn between the intense Indy 500 race and the jam-packed annual ASMS meeting… instead of IndyCars battling through the pack to secure their winnings, scientists race between the convention center and hospitality suites to ensure they’re knowledgeable about the best tech investments to enable their teams to keep on winning.

The Waters suite, ready to roll, at ASMS 2017
The Waters suite, ready to roll, at ASMS 2017, Indianapolis.

We had some of our own exciting tech presentations at the Waters breakfast seminar on Monday:

  1. Dr. Martha Stapels, Principal Scientist of the Bioanalytics Characterization Group of Sanofi Corporation, presented some host cell proteins (HCP) data analysis using new spectral library function which resulted in more efficient use of time and improved confidence.
  2. Dr. Andrew Collins, lead developer at Omic Analytics presented an interesting SILAC study of Phospho-Proteomes analyzed using Proteolabels software, which extends Progenesis QI for proteomics (QI·P) capabilities beyond just label-free proteomics.

In this post, I’d like to convince you that Progenesis is a winning brand worth investing in and will do that by firstly recapping the research value enabled by the traditional Progenesis Quantify then Identify approach. Secondly, I’d like to highlight how Progenesis QI has been evolving with user feedback.

Progenesis trusted to keep you in the fast lane

Let me strengthen your confidence in Progenesis with some key stats. Waters Corporation saw high value in the Nonlinear Dynamics team after working closely on projects, ultimately, resulting in the decision to invest in Progenesis. Nonlinear Dynamics has 28 years of experience on the discovery software circuit and although now a part of Waters, the Progenesis team continues to serve the wider community with data analysis capability for all major MS vendors. To date, there are over 850 groups invested in Progenesis QI technology – for their small molecules or proteomics research.

The Progenesis team year on year draws in crowds of curious scientists to the Waters suite at ASMS (and other meetings), wowing them with the easy to use software and visual workflow.

It’s easy to lose sight of the finish line with all of the interesting ‘new tech’ when you’re racing around the Indiana Convention Center. Make sure you take a pit stop at the Waters booth [#501] where the Progenesis team will provide you the tech updates to get your data analysis in the fast lane. Meaning you can secure the winnings from good research.

Progenesis QI: the difference engine you can rely on

Built on a solid foundation, the Progenesis QI alignment and co-detection engine ensures no missing values in your data matrix so you can draw conclusions based on a complete data matrix and resultant solid statistics. This means your research is going to tell the truth about your experiment, allowing you to create better-controlled experiments. You will see the important change with greatly increased sensitivity, ensuring you’re a winner in the race to progress your research, your teams’ objectives and your career.

At acquisition Waters Corp. identified Progenesis as a well-recognised and strong brand, so they kept this branding and added a core aspect of the Progenesis workflow, Quantity then Identify, hence Progenesis QI. This quantify then identify approach enables a comparative analysis free from the bias of identification challenges. Meaning you are able to focus your identification efforts after you know what is worth further investigation. The achieved clarity and confidence prior to the identification stage means you can spend your time positively on the features that matter.

Build your own spectral libraries in Progenesis QI·P v4.0

Screenshot from Progenesis QI for proteomics software showing search result from a spectral library search.
A screenshot from the Progenesis QI for proteomics software, showing search results and their fragment matching from a spectral library search.

Progenesis QI for proteomics has numerous options for database searching, interfacing with 14 different search engines. Version 4.0 brings further flexibility with the functions of spectral library creation and searching. You now have the facility to use and build spectral libraries such as NIST.msp, Mascot.msp and SWATH Atlas sptxt files. This feature enables improved specificity and time saving when searching proteomics and HCP data.

At Monday’s breakfast seminar Dr. Stapels presented a study in which the new spectral library functionality in Progenesis QI·P was used to analyze HCPs resulting in reduced false positive IDs, greatly reduced time taken for data analysis, and improved confidence in selection of peptides used for HCP quantification.

Proteolabels: a solution for label-based proteomics quantification

The peak co-detection feature in Progenesis QI for proteomics gives a 75% gain in the number of peptides quantified and a 39% gain in the number of proteins in that sample, via a Proteolabels analysis of public data set PXD003284.The peak co-detection feature in Progenesis QI for proteomics gives a 75% gain in the number of peptides quantified and a 39% gain in the number of proteins in that sample, via a Proteolabels analysis of public data set PXD003284. (htttp://

Dr. Andrew Collins of Omic Analytics presented his talk entitled ‘Application of Novel SILAC Analysis Software Proteolabels for Large-Scale Meta-Analysis of Phospho-Proteomes’ at Monday’s breakfast seminar. The team from the Liverpool University spin out [Omic Analytics] wanted to leverage the benefits gained in sensitivity from Progenesis co-detection and the various QC steps to analyze labeled data and so developed Proteolabels as a plugin to Progenesis.

Proteolabels is for quantitative proteomics, supporting studies involving stable isotope labels. Workflows include SILAC (two and three channels) and dimethyl (two and three channels).

Progenesis QI·P v4.0

With the imminent release of a new proteomics version of Progenesis, this post has focussed more on the large molecules workflow. So to conclude this thread it is worth mentioning Progenesis support for SONAR data. SONAR is a new data independent acquisition mode which is also being promoted at ASMS. SONAR provides additional specificity and clarity to DIA experiments.

Not all of the new capabilities of Progenesis QI·P v4.0 have been covered in this post as I’m racing to get it to you before the 500 miles have been covered. To find out more simply get in touch with us either at the Waters booth or via email.

Waters adds METLIN MS/MS Library to Progenesis QI Software

Progenesis demo station in Waters booth
Offline searching of the METLIN MS/MS database brings convenience and efficiency

For those of you that have been to the Progenesis station in the Waters booth or you looked carefully at the image on the left, you can see that METLIN MSMS is mentioned above one of the visible screens. Click here to read technology networks news release on this from March this year.

We are excited to be able to have METLIN integrated with the Progenesis QI software.

To summarize, Progenesis QI is enabling both small molecules and proteomics researchers to stay in the fast lane with a workflow built on the solid foundations of ‘Analyse ALL of your data’ followed by ‘Quantify then Identify’. Specific to proteomic research workflows Progenesis QI·P v4.0 is now geared up to give you an efficient and accurate HCP analysis with the addition of spectral library building and searching. You can now even analyze your MS1 labeled data with Proteolabels!

Again, if you’d like more information, simply skid into the ASMS Waters booth or send us an email.

Stop looking; start seeing

We are heading into the busy conference season and it’s an especially exciting time for the Waters and Nonlinear teams as we get ready to showcase complete biomarker discovery workflows based around our new data independent acquisition mode SONAR™. After a launch at HUPO last year we have completed work on data processing compatibility with Progenesis QI and Progenesis QI for Proteomics. Our customers will now be able to dig a little deeper into the Metabolome and Proteome with the Xevo G2-XS. SONAR delivers new levels of selectivity and speed to DIA approaches on a bench top high-resolution mass spectrometer, allowing for more accurate quantitation and more confident biomarker identification.

sonarStop looking. Start seeing

Since the introduction of MSE almost 12 years ago, Waters has been addressing the challenge of getting more from complex untargeted analysis with true Quan/Qual solutions. The more we push the size of our studies, the more we tax our instrument platforms. Compressing gradients to run faster reduces the system peak capacity of our analysis and creates the need for higher selectivity in our mass spectrometer data acquisition systems. Incorporating ion mobility greatly improved MSE on the Synapt and later Vion mass spectrometers by adding a separation prior to TOF analysis. Now SONAR adds selectivity to DIA on the Xevo G2-XS QTof with a rapid scanning quadrupole during mass analysis.

Xevo Capture

Data analysis and workflow flexibility.

The prior work done by Waters electronics engineers to collect LC – ion mobility – MS data on the Synapt gave the Xevo G2-XS a head start with SONAR data collection. With the SONAR data being collected in a similar manner to HDMSE, Progenesis QI was perfectly positioned to complete the SONAR workflow. All of the benefits of Progenesis QI, including alignment, co-detection and comprehensive biomarker identification options mean that we are hitting the ground running. The true beauty of the SONAR data file is it can fall not just into a Progenesis workflow but can be processed by other quantitative workflows such as Skyline and more to follow. Implementing tools such as Symphony allows even greater flexibility and productivity by automating the data processing pipeline.

SONAR, stop looking and start seeing….. with us at ASMS

So if you happen to be at ASMS in June please stop by our booth or hospitality suite, register for our users meeting or one of our breakfast seminars. All the information can be found here at We would be happy to discuss how you can apply SONAR to metabolomics, lipidomics and proteomic research.

Do you only check data quality when something has gone wrong?

Generating proteomics data from an LC-MS platform is by no-means inexpensive, a great deal of time is invested into preparing samples, preparing the columns and optimizing the mass spec conditions to generate this complex and rich data. With so many parameters that can and do go wrong, can you really afford to throw your data into a “black box” and trust the results that come out of it?

I began writing this as I flew back from Berlin having had some great conversations about the importance of data quality with scientists congregated for the Potsdam Proteomics Forum. Conversing with Progenesis customers demonstrated to me the great value that the variety of visualizations are providing. These enable results that Progenesis users are confident about. Dr. Dominik Megger from Ruhr University Bochum told me about an experiment where everything seemed fine (good TICs and good alignment scores) until protein identification was carried out and some of the runs were showing very few identifications. This flagged a potential issue and using Progenesis, Dominik was able to look back at the QC metrics page (fig.1) to find that for some of the samples there were high numbers of missed cleavages in Trypsin digestion, indicating that it had stopped working well. Although this was a painful realization, there was a quick resolution to what could otherwise have been a very drawn out procedure of looking back step by step through all of the things which could have gone wrong. Dominik was therefore very pleased about the time he was able to save here.

QC metrics from Progenesis QI

Figure 1 – QC metrics in Progenesis QI for Proteomics

Speaking to a customer from the Otto von Geuricke University of Magdeburg, highlighted an issue that we all recognize. “I do a search and get two different accession numbers for the same protein!” I can’t say that we are able to solve that issue, as it pertains to the quality of the libraries and database redundancies, however Progenesis does offer you more confidence in the assignments of peptides to proteins and therefore in the quantitative accuracy. Peptide correlation scores (see figure 2) can help you remove peptides that have been incorrectly assigned to a protein. Once you have refined your dataset to the proteins of interest (those that are significantly changing between conditions), you should expect in most cases that the peptides of a particular protein should show the same direction of change, i.e. up or down regulation, so if you see a peptide that is behaving differently, you can remove it from the protein to give you better, more confident quantitation.

(NOTE: watch out for the upcoming application note based on the analysis of an ABRF dataset that clearly highlights the benefit of peptide correlation scores.)

Peptide correlation's to qualify correct assignment of peptides to proteins

Figure 2 – Protein review in Progenesis QI for Proteomics

LC-MS data analysis inevitably comes with a variety of assumptions and those assumptions don’t always stand up to the test: – if your data analysis happens in a “black box”, it’s quite possible that the results are misleading you. This can result in spending valuable time researching false positives or neglecting the real interesting results due to false negatives, which are very costly.


Do you only check data quality when something has gone wrong?

Progenesis QI software presents you with 4 crucial ways to QC your data. Before, during and after analysis.

1) 2D ion intensity maps (see fig. 3) can flag sample running problems, this quick view gives you the ability to:

a) pick up on any samples that may need re-running

b) adjust your chromatography to improve separation

Import Screen 2D ion intensity maps to QC your runs

Figure 3 – Ion intensity map shown at the Import Data step

If you do need to re-run problematic samples then Progenesis is flexible enough to enable you to add those samples into your experiment at a later point, maximizing your time and resources.

If you want to hear more from a real life example, Prof. Paul Langlais gives a very informative and entertaining account entitled ‘From the Dark to the Light: How Progenesis Added Years to my Life’, offering some great insights about how he was able to use visual QC in Progenesis to optimize the LC-MS set-up in his lab.

2) The review alignment screen (see fig.4), allows quick visual assessments (and improvement if needed), of alignment quality. Progenesis provides percentage alignment scores and color coded views so you can easily assess the quality of alignment before you start drawing conclusions from the peak picking and co-detection

Review alignment step - a quick way to see if you have good alignment

Figure 4 – Review alignment screen

3) The PCA plot in the statistics screen (see figure 5) will allow you to quickly gauge whether your conditions are the primary reason for the separation in your experiment or if there is a systematic reason (/error) for separation between samples, such as the running order. The PCA plot below shows an experiment in which the samples are not clustering according to the experimental design and there are other factors that need to be controlled in order to get good results.

Principle Component Analysis- quickly find outlier samples or qualify your samples separate based on your experimental design

Figure 5 – PCA Plot showing poor separation of groups

4) QC Metrics screen (figure 1), this screen offers many useful metrics to help you make sure your system is running optimally and, in case you spot something strange happening, this screen can also offer insights to help you find the cause of problems such as the trypsin degradation that our friend from Bochum picked up on.

Quality data analysis now extended to MS1 labelled data

You can now confidently analyze your SILAC or di/tri- methyl labelled proteomics data with an export from Progenesis QI for proteomics into Proteolabels. You will benefit from the “no-missing-values” approach of Progenesis co-detection and gain a great advantage from Proteolabels’ ability to auto-detect and find pairs or triplets, even when only one of the doubles or triples has been identified. This, together with the many visual QC displays means that you can be confident of getting maximum information from your samples.

Figure 6 shows the benefit in sensitivity that you gain through Progenesis co-detection and Proteolabels.

Proteolabels slide showing benefits in terms of sensitivity gained by Progenesis co-detection

Figure 6 – Diagram to show benefits of peak co-detection

A couple of other Proteolabels features that will further increase confidence in your labelled data analysis are peptide scoring (figure 7) and the use of these scores in weighted averaging at the protein quantitation step (figure 8).

images showing QC graphics from Proteolabels to help you qualify the acuracy of your quantitation with peptide scoring

Figure 7 – Peptide scoring

Proteolabels protein inference and peptide scoring

Figure 8 – Protein inference and weighting factors in peptide ratio

Proteolabels gives many visualizations which will help you to QC your data analysis before you draw conclusions. We have only shown a few here. For more information on Proteolabels please get in touch with us via email at the address

Finally, while on the topic of data integrity, you can automate even more of your data handling using Symphony Data Pipeline, thus removing some of the manual steps where ‘things’ could go wrong.

To summarize, Progenesis QI for proteomics offers data quality and assurance along with data transparency (QC metrics, alignment scores, etc.), as does Proteolabels (peptide scoring and weighting). This also means the benefits of co-detection are extended to your labelled analysis. Symphony reduces human error of repetitive tasks, allowing you to support data quality and thereby giving you confidence and reliability in your results.

If you’re using a “black-box” solution and would also like to have more transparency and confidence in your data analysis, get in touch with us by email at the address

You can now analyse your labelled data with Progenesis!

During an upcoming webinar on 28th March, you can hear about a new offering for quantitative proteomics. Progenesis QI for proteomics (QIP) now has the capability to analyse samples where stable isotope labels have been added, including SILAC and dimethyl labelling. These capabilities are added through a new module called Proteolabels, from Omic Analytics Ltd. Proteolabels supports data sets from any vendor and search engines that are supported by Progenesis QI for proteomics.

How does it work?

In a regular label-free experiment in Progenesis QI for proteomics, different LC-MS runs are aligned and then, using the co-detection method, the same (peptide) ions are quantified in every LC-MS map. In your workflows, if you introduce an in vivo label on an amino acid (e.g. in SILAC) or following digestion (e.g. in dimethyl labelling), different samples can be multiplexed within the same run, with a mass shift introduced per peptide. Proteolabels is able to detect pairs (duplex mode) or triples (triplex mode) and produce peptide and protein ratios as well as statistics for differential expression analysis. Labs that employ label-free and label-based methods can now use the same software for both types of analysis!


Slide from: Discovery and Analysis of Peanut Allergens using Proteomic approaches with Ion Mobility and High Resolution Mass Spectrometry, by Waters Corporation Food Research.

How does it interface with Progenesis QI for proteomics?

In Progenesis QIP version 3 onwards, there is an option to launch Proteolabels for those customers that have purchased the add-on module. Keeping with the usual workflow, Progenesis QI for proteomics performs alignment, peak co-detection and database searching; thereafter, clicking “Export to Proteolabels” will open the data in the Proteolabels module.

Screenshots showing the connection between Progenesis QI for proteomics and Proteolabels

Figure 1: The connection between Progenesis QI for proteomics and Proteolabels. If you have the Proteolabels module installed, clicking “Export to Proteolabels” launches the new software and performs downstream quantitative processing.

Detecting high-quality peptide pairs

Proteolabels first reads the identified peptides, works out the labelling strategy used, and the best thresholds for detecting pairs (or triples in triplex mode) using its “Auto-Detect” function. You can ask Proteolabels to quantify only peptides where both the light (unlabelled) and heavy (labelled) ions have both been identified in the search. However, Proteolabels is able to increase sensitivity by profiling these pairs, and then looking for a quantified peptide in the expected location of the LC-MS map to form pairs (without needing both to have been identified). Due to the way in which Proteolabels finds and scores the quality of peptide pairs detected, if you click the setting requiring only that a confident ID has been made for one of the light or heavy peptides (Blind Pairing), there can be major increases in the number of proteins quantified, with no loss of precision, as shown in Figure 2:

Chart showing a 30% gain in the number of proteins quantified with no loss of precision

Figure 2: Analysis of one experimental data set deposited in the ProteomeXchange repository (PXD003284 – (1)) exploring co-efficient of variance (CV) across replicates for protein-level ratios. Enabling the feature in Proteolabels to quantify peptides without requiring both to be identified, gives a 30% gain in the number of proteins quantified, with similarly high levels of precision.

Since Proteolabels follows on from Progenesis QI for proteomics, there are also sensitivity gains from the co-detection method. This means there are no missing values when analysing multiple replicates or multiple sample conditions, as peptides can be quantified with identification evidence propagated across multiple runs. For example, we ran a simple test on data from one fraction of a public data set – PXD003284 ( to see the difference between running only a single replicate, versus analysis of that same file alongside two further replicates. As shown in Figure 3, in this analysis, co-detection gains around 75% in the number of peptides quantified, and 39% in the number of proteins.

Chart showing the benefits of co-detection to the number of peptides and proteins quantified

Figure 3: Analysis of one fraction, one sample from PXD0003284 versus the same sample, co-detected with two additional replicates. The co-detection feature in Progenesis QI for proteomics gives a 75% gain in the number of peptides quantified and a 39% gain in the number of proteins in that sample via a Proteolabels analysis.

Improving data quality with the Peptide Score

Proteolabels applies a “Peptide Score” to all pairs (or triples) based on profiling the chromatogram match and drift time (where ion mobility separation has been applied). Peptides with a low pair score get down-weighted when it comes to protein-level quantification. As an example in Figure 4, both peptide pairs have been confidently identified, and there is a good elution time and mass/charge match. Most software packages would accept this as a reliable quantification value. Proteolabels is able to detect that the elution profile of the light and heavy peptide on the right panel do not match well and this quantification is likely to be less reliable.

Screenshots showing improved data quality via Proteolabels' peptide score

Figure 4: Peptide scoring in Proteolabels detects poorly matched pairs or triples, up-weighting the most reliable quantitative values at the protein-level.

How is protein quantification performed?

For most proteins, there are multiple peptides reported that could contribute to the final protein quantification value. Proteolabels first performs grouping of proteins based on shared peptides, and then applies a novel weighted averaging of signals based on the Peptide Scores and the signal intensity of peptides to arrive at the protein-level ratio. In other label-based approaches, it is common for the protein ratio to be inferred from the median peptide ratio to remove outliers. In Proteolabels, weighted averaging is superior to the median peptide ratio, especially for proteins quantified by a small number of peptides, as it allows all peptides to have some contribution towards the final protein-level quantification value. The combination of co-detection, peptide profiling/scoring and intelligent protein quantification affords both high precision and high accuracy quantification (Figure 5).

A volcano plot showing how the high precision of co-detection and pair finding gives the ability to detect differential expression with confidence

Figure 5: A volcano plot of data from one experiment of PXD003284 data set, processed with Proteolabels. The high-precision from co-detection and pair finding enables reliable detection of differential expression (FDR corrected p-values<0.05), down to modest fold change values.

How to check the quality of your data?

Proteolabels has a variety of intuitive QC metrics and plots for examining your data (Figure 6), and is interactive at each stage, enabling you to be confident that the data is high-quality, ready for downstream interpretation. Different plots can show you how well the instrument was calibrated, the distribution of identification and peptide scores, and any relationships between the abundance of peptides and the reliability of the quantification.

A selection of the plotting and data exploration features in Proteolabels

Figure 6: Proteolabels provides a range of plotting and data exploration features.

Do you want to hear more about Proteolabels?

If you would like to learn more, please register for the webinar and/or read the press release.  If you would like to try Proteolabels, please contact us.

Prof. Andy Jones


  1. Patella, F., Neilson, L. J., Athineos, D., Erami, Z., Anderson, K. I., Blyth, K., Ryan, K. M., and Zanivan, S. (2016) In-Depth Proteomics Identifies a Role for Autophagy in Controlling Reactive Oxygen Species Mediated Endothelial Permeability. J Proteome Res 15, 2187-2197