Nonlinear Dynamics Blog

1st Belgium-Netherlands Metabolomics Symposium

May 16, 2013 – 3:06 pm

Earlier this week I attended the First Belgium-Netherlands Joint Symposium on Metabolomics: Translational Approaches in Metabolomics, in the picturesque city of Spa in Belgium.

There were some interesting talks with invited speakers like Dr Johan Trygg from Umea Sweden, Prof. Dr. Thomas Hankemeier, Scientific director of the Netherlands Metabolomics Centre in Leiden, and Dr Augustin Scalbert, head of Biomarkers Group – from International Agency for Research on Cancer (IARC), Lyon.

Dr Scalbert did an interesting presentation about the role of the diet in the aetiology of chronic diseases. Using the Phenol Explorer database, a large area of research opens to develop new tools for epidemiological research and understand the role of polyphenols in the prevention of diseases and more particularly cancers.

Dr Scalbert co-developed Phenol Explorer, which contains values for 500 different polyphenols in over 400 foods, at INRA in collaboration with in collaboration with AFSSA, the University of Alberta, the University of Barcelona and In Siliflo. The project had the financial support of the French government, Unilever, Danone, Nestlé and the Institut National du Cancer (Paris).

The database will contain additional information about polyphenols in foods cooked in different manners, so that you can check which amount of Polyphenols you lose when boiling your vegetables. This new Phenol-Explorer release will be made public by this summer. Dr Scalbert gave me a demonstration and I must admit I was almost tempted to get back to the bench and start studying polyphenols! Smile

I was discussing Progenesis CoMet with Dr Scalbert and the challenges of metabolite Identification. Something that is really specific to Phenol Explorer is its traceability: all information used to generate the database, is accessible (publications, number of samples analysed and more), they are more than useful to help the users to validate the identification.

That validation approach is similar to the approach we have within Progenesis CoMet, using our integrated search tool Progenesis MetaScope. In a single-click you can search your own data and return compound identifications directly back into the workflow. The results, with a MetaScope compound identification score applied, are automatically associated with the quantified compound ions. You have additional information i.e. mass similarity, isotope similarity and Retention Time similarity, which help you to validate the metabolites identifications.

You can test it yourself and query various database, i.e. Lipid Maps, HMDB, PubChem and others.

Why not download Progenesis CoMet and trial it with your own data? My next conference is the Metabolomics Society 2013 meeting which is being held in Glasgow, Scotland, July 1st – 4th. If you are also attending, please stop by booth #14 to see the very latest developments in Progenesis CoMet as we will be showing the exciting new features which will be in the next release. Smile

3 ways you can review deconvolution in Progenesis CoMet

May 1, 2013 – 2:49 pm

In my previous post, I introduced the adduct deconvolution we’ve added to the workflow in Progenesis CoMet and explained how it provides greater confidence in your measurements and identifications and a little of how it works. This time around, I’ll focus on reviewing the deconvolution, with the aim of validating your compounds of interest.

Visualising the results of deconvolution

While the deconvolution process is completely automatic, thereby maximising objectivity, we anticipated that you’d want to review its results too. And Progenesis wouldn’t be Progenesis without high-quality visualisations.

With that in mind, we created the Review Deconvolution step:

The Review Deconvolution screen, showing an unidentified compound with multiple adduct forms

The Review Deconvolution step helps you to:

check that all ions that were grouped as the same compound were grouped correctly
find ions that should have been grouped as the same compound, but weren’t

It includes 3 important visualisations:

ion map locations: these help you find ions that, perhaps, represent additional adduct forms of the selected compound
mass spectra: these help you confirm that all ions grouped in the selected compound have the same neutral mass
chromatograms: these help you check that the elution profiles of the selected compound’s ions are consistent (as they should be)

As we’ll see, the mass spectra and chromatograms are especially helpful in understanding why ions weren’t grouped in the selected compound.

1. The ion map locations

In the window shown earlier in this post, the selected compound has ionised with many adduct forms; these are seen most clearly in the series of ion map sections:

The ion map locations of all adduct forms for our example compound; the same colour-coding of adduct forms is used throughout the software

Each of the boxes here shows a small section of the overall ion map, focussing on the same retention time range, but different m/z ranges. Each m/z range is chosen to show the position of a single adduct form of the selected compound. Within each box, a number of pieces of information are shown:

A small section of the ion map highlighting a single adduct form of the selected compound
The annotated ion map for a single adduct form of a deconvoluted compound

The ion maps are the primary reviewing tool for false negatives in deconvolution: that is, for finding ions that should have been grouped as the same compound, but weren’t.

In such a case, moving the mouse over a missing adduct’s box shows a small blue circle at the point where the ion’s monoisotopic peak was expected. If, after seeing this, you feel there is a detected ion in the correct location and with the correct charge, you can add it to the compound. To do so, simply right-click on the ion’s outline and select the Add To Compound command.

2. The mass spectrum graphs

The mass spectra of each of the ions in the selected compound are shown in the lower-left panel, with the peak positions for each compound ion transformed into neutral mass values by removing the adduct mass and charge:

The neutral-mass-transformed mass spectrum of a deconvoluted compound
The graph of mass spectra, showing the perfectly overlaid peaks of correctly deconvoluted ions

The reason for the transformation is that the compound ions’ corresponding isotope peaks should overlay perfectly. If any of the peaks are in positions that aren’t common with the other ions’ peaks, or if the monoisotopic peaks don’t all have the same mass, then it’s clear that the deconvolution is not valid.

Of course, that situation should never arise with automatic deconvolution. However, it’s a very useful indicator of an ion’s validity when you move the mouse over an ion outline in one of the ion map boxes, as seen here:

Clear evidence that the ion in the M+CH3OH+H is not part of the selected compound
Hovering the mouse over an ion outline shows that its monoisotopic mass is quite different to that of the other adduct forms in the compound

3. The chromatograms

The chromatograms of each of the ions in the selected compound are shown in the lower-right panel of the Review Deconvolution screen. As mentioned in the previous blog post, ions’ retention times and chromatographic profiles must be very similar for them to be grouped as part of the same compound (given that ionisation occurs after the LC stage), resulting in graphs similar to this one:

The scaled chromatographic profiles of a single compound's detected adduct forms
All adduct forms of this compound have very similar chromatographic profiles, but quite different peak intensities

Notice that each profile’s intensities have been scaled into the same 0-100 range. This allows the shape of the chromatograms to be compared more easily; without this scaling, many low-abundance ions would appear to have a very flat profile and their inclusion in a compound would be difficult to validate. The relative abundances of the different adduct forms are plotted separately in the Peak chart at the right.

As with the mass spectra, this visualisation is especially useful for highlighting ions that don’t belong as part of the selected compound, as it’s easy to distinguish an outlier profile from either the peak’s shape, position, or both:

Clear evidence that the sodiated adduct form of the selected compound is not present in our samples
Hovering the mouse over the ion in the M+Na ion map shows that its elution profile is quite different from that of the selected compound’s ions.

The start of a busy conference season

April 24, 2013 – 9:27 am

We attend many conferences throughout the year, but we are about to enter our busiest time over the next few months.

There are some key meetings that we attend each year, and some new meetings that we are attending for the first time. It all makes for an exciting few months, so I wanted to share with you some of meetings we’re going to; if you’re going too, we’d love the chance to catch up with you, and hear about your latest research.

2013 AOCS Annual Meeting & Expo – April 28th – May 1st, 2013

The American Oil Chemists’ Society’s annual meeting is taking place in Montreal this year. The meeting is a global business forum on fats, oils, surfactants, detergents, and related materials, and attracts more than 1,600 delegates. Progenesis CoMet, our analysis software for lipids and other small molecules, will be the main focus of our presentations; and we are excited to show some of the people we met at this meeting last year the latest developments in version 2.0. We will be represented by Mark Bennett and Jonathan McSayles on booth #903.

1st Belgium-Netherlands Joint Symposium on Metabolomics: Translational Approaches in Metabolomics – May 13th – 14th, 2013

We are constantly on the lookout for new conferences with a focus on metabolomics, and the Belgium-Netherlands Symposium is the first meeting of this group of researchers, so we are looking forward to showing Progenesis CoMet and finding out more about the metabolomics research in this region. The meeting aims introduce the metabolomics approach to researchers who are new to the field, and around 100 delegates are expected. Agnès Corbin will be attending the meeting, which is being held in Spa, Belgium.

61st ASMS Conference on Mass Spectrometry and Allied Topics

We have been attending ASMS for many years; it is probably the largest conference we attend in terms of delegate numbers, exhibitors and posters on display. We are working hard on some very exciting new CoMet-related developments which will be shown for the first time at ASMS – watch this space for more news!

We are sending a large team to the conference, which is being held in Minneapolis this year. Mark Bennett and Jonathan McSayles will be managing our booth (#54) so please stop by. Dr Andy Borthwick (Applications Scientist), Dr Ian Morns (Development Manager) and Dr Jackson Pope (Software Developer) will also be in attendance. Please contact us if you would like to arrange a meeting, as we know what a busy week it will be!

VIII ItPA National Congress – June 18th – 21st, 2013

This is the Italian Proteomics Society’s annual meeting, and it being held in Padua, Italy this year. There are a number of high profile speakers at this year’s meeting which has sessions on Human Proteomics, Technological Innovation, Non-Human Proteomics and Systems Biology & Informatics. Juliet Evans is attending the conference, so please stop by our exhibition stand and find out the latest Progenesis product news.

9th Annual Conference of the Metabolomics Society – July 1st – 4th, 2013

This international meeting of the Metabolomics Society is held in a different location each year, and this year Glasgow, Scotland is the location of choice. We’re very happy about this, as it is only a short journey from our base in Newcastle-upon-Tyne in the North East of England.

We’re delighted to be Silver Sponsors of the meeting, where we will show the exciting new advances in Progenesis CoMet that will be released later this year. There are 700 delegates expected to attend this meeting which brings together a diverse mixture of scientists from many disciplines.

Our booth at the meeting will be looked after by Martin Wells, Juliet Evans, Agnès Corbin and Dr Ian Morns, our development manager. It promises to be a great 4 days.

That’s not all….

We are still confirming our attendance at some meetings in Europe, so please check our events page regularly to see if we are attending a meeting close to you. After the Glasgow Metabolomics meeting, we have a short conference break in August before the 12th HUPO world Congress which is being held in Yokohama, Japan, Sept 14th – 18th.

By Beth McArdle | Posted in April 2013 | Tagged ASMS, Conferences, The Nonlinear Team | Comments (0)

An introduction to adduct deconvolution in Progenesis CoMet v2.0

April 12, 2013 – 3:52 pm

When it was released at the start of 2013, Progenesis CoMet v2.0 introduced adduct deconvolution to its metabolomics analysis workflow, prior to compound identification. But what is deconvolution and why is it so important?

What is deconvolution and why is it important?

Deconvolution in Progenesis CoMet is the process of grouping different adduct forms of the same compound, for the purposes of compound quantification and identification.

For quantification, we’re typically only interested in the overall abundance of each compound in our samples, not in the separate abundances of the different adduct forms. By deconvoluting and looking at the compound as a whole, we also avoid misleading fold differences that can be produced by variation in ionisation between runs e.g. the ratio of protonated to doubly-protonated adduct forms may vary while the overall compound abundance remains the same.
For identification, if we find more than one adduct form, we can calculate the compound’s neutral mass, immediately taking us a step closer to identification before we even perform database searches. Indeed, by identifying compounds based on their neutral mass (where possible), there’s none of the ambiguity that could come from different adduct forms having different putative IDs.

The overall result is greater confidence in both your measurements and identifications.

Deconvolution, by example

The deconvolution process itself is actually very simple. Before analysis, we specify the adduct forms (e.g. protonated) that we expect to find in our samples. Then, after peak picking, Progenesis compares each detected ion with each of its co-eluting ions; if their mass difference matches the difference between two adduct masses, there’s a good chance they’re adducted forms of the same compound.

As an example, let’s consider an experiment with only 2 adduct forms: protonated (M+H) and sodiated (M+Na). For simplicity in this example, I’ve chosen adducts that are singly charged, as it makes it easier to relate to the ion map (an ion’s m/z value will be equal to its molecular mass).

After peak picking, we can see a detected ion on our ion map at a retention time of 5.20 minutes and an m/z value of 437.1932. We know it could be either the M+H or the M+Na form of a given metabolite. For the moment, let’s call it M+X.

If our ion is the M+H form, then the M+Na form, if present, should be at an m/z of 459.1751. This is calculated by taking the mass of M+X (437.1932) then subtracting the mass of H⁺ (1.0073) and adding the mass of Na⁺ (22.9892).
If our ion is the M+Na form, the calculation is simply reversed; we take the mass of M+X then subtract the mass of Na⁺ and add the mass of H⁺, giving 415.2113.

So, we now know 2 places on the ion map where we might find other ions of the same compound. Looking at the ion map in the area around M+X and the potential adduct locations, we see:

Co-eluting ions detected in a range of m/z values near to the M+X ion. Our example ion, M+X, seen among a number of co-eluting ions on an ion map. Its isotopic peak has an m/z of 437.1932; the positions of other potential ions of the same compound are marked at 415.2113 and 459.1751.

Looking at the above, we can see that there is a co-eluting ion with its monoisotopic peak at an m/z value of 415.2113. Great! This means that M+X must be M+Na, while the ion with the lower mass must be M+H:

The compound ions and their deduced adduct forms The deconvoluted compound ions highlighted on the ion map, showing the adduct form of each ion

Using this information, Progenesis then groups these two ions as the same compound, assigning the compound ions their respective adduct forms. Not only that, but we’re able to calculate the compound’s neutral mass:

Neutral mass = (437.1932 – 22.9892)
Neutral mass = 414.2040

And that, in a nutshell, is the deconvolution process that Progenesis CoMet performs automatically, immediately after peak picking. Every ion and all possible adduct differences are considered, leaving us with a list of compounds that we can then identify.

Of course, for adducts with different charges, the calculations are slightly more complicated, and there are always tolerances to take into account for both m/z and RT values, but it’s still a relatively simple process. And it’s made all the more simple for being fully automatic.

Next time

In my next post, I’ll look at how you can review the deconvolution. If you can’t wait that long, however, you can download Progenesis CoMet today to see it in action yourself.

By Mal Ross | Posted in April 2013 | Tagged Progenesis CoMet | Comments (0)

A round up from the Proteomic Forum Berlin meeting

March 27, 2013 – 3:45 am

Last week I attended the Proteomic Forum Berlin, held every 2 years at the Freie Universität in the Dahlem district of Berlin. Despite the snowy weather around 400 participants attended the many talks and social events over five days.

A snowy Henry-Ford building at the Freie Universität, Berlin

The meeting is full of presentations and posters that reflect the high concentration of PhD students, post-doctoral students and group leaders involved in proteomics within Germany. It also has a strong international flavour with talks by high-profile researchers and visitors from USA, China, Japan and Korea as well as countries across Europe.

A large amount of the program was dedicated to help anyone starting out in proteomics projects. Educational lectures, “doctor’s office” sessions and a news corner session provided direct access to experts in a particular technology as well as giving researchers and companies the chance to share results and developments. Our main educational focus was sharing the new features added to Progenesis LC-MS with many users, in particular those from Europe and the USA. The new automated data processing steps with quality assurance measures were of strong interest to core facility users, with the time saving and ease-of-use it gives them or their end-users.

Breaks in sessions gave our customers the chance to visit our booth and see the latest developments with Progenesis CoMet, for metabolomics, and Progenesis LC-MS, for proteomics.

The program and poster sessions

The meeting started on Sunday, the 17th March, with a full-day workshop on the activities and current challenges facing the HUPO Chromosome-Centric Human Proteome Project. This included updates from each of the teams who are focussed on identifying and quantifying the proteins from 25 human chromosomes, which is really starting to build momentum now. The rest of the meeting covered different topics each day, including; moving from protein inventories to assigning functions, proteomics applied to human health, proteomics in biology and biotechnology ending with a half day on understanding protein function and systems biology to put results in a broader context. Our customers, many of them from Germany, were represented at least once in each session and in the accompanying poster sessions each day.

The poster sessions were busy with only 2 hours each day to see around 100 posters before they were replaced by another 100 the following day. This meant over 300 posters were shown over 3 days, so it was a challenge to see them all. I was able to take a little more time to review the posters whilst it was quiet during some of the main sessions. A noticeable number of posters were dedicated to projects that used MALDI imaging, showing how this technology is being used in many early stage research projects.

Progenesis posters

However, the biggest share of posters related to labelled or label-free LC-MS for quantitative proteomics or identification of proteins of interest. There was a significant increase in the number of posters citing label-free LC-MS, almost twice as many as those that cited labelled approaches, compared to previous meetings. Progenesis LC-MS and the TransOmics™ Informatics software, supplied by Waters and powered by Nonlinear Dynamics, were visible on 15 posters. Methods using 2D gels were also surprisingly well represented. Those that named the software used included 5 references to Progenesis SameSpots. We had our own poster, which highlighted using both Progenesis SameSpots and Progenesis LC-MS to maximise proteome coverage of C. japonicas, a soil bacteria with applications in biofuel production.

Where you can you meet us next?

The current list of exhibitions and meetings is here. We hope to see you there, but if you’re not planning on attending any of these meetings and wish to see the Progenesis software in action, please contact us.

By Paddy Lavery | Posted in March 2013 | Tagged Conferences, Progenesis LC-MS, Progenesis SameSpots | Comments (0)

A new release of Progenesis LC-MS. Version 4.1 now available

March 13, 2013 – 3:34 pm

Progenesis LC-MS has been developed to help you find and quantify the proteins showing interesting behaviour in your label-free samples. The benefits of using this software are wide-ranging, including accurate protein quantification, minimal data loss in your experiments, increased protein coverage and much more.

We have been working hard on a new release of Progenesis LC-MS, and I’m happy to announce it is now available! In this post, we are highlighting the very latest developments in the software, so you can see what our development team has been working on.

The focus of this release is automation and quality assurance, features that we believe will be important to most researchers, so we hope that you will be as excited about this new release as we are. These changes can radically change the way you work with the software, freeing up more of your valuable research time and giving you more confidence in your results.

What’s New in Progenesis LC-MS v4.1?

Reference run selection and alignment can be started, and the runs will be processed automatically as they load – you can leave it to process without any further interaction
The alignment reference can be selected automatically by the software, increasing objectivity and reproducibility of your analysis results

There are new alignment quality measures so you can be confident that your downstream analysis steps will deliver reliable results
Your experiment design can be set up from an imported file which contains your group set-up
Fast review of any step in a completed analysis is now possible – you no longer have to move stepwise through all of the analysis steps
A clip gallery has been introduced so you can capture print quality images from some of the most important data displays which can be used in posters and presentations
Some user interface changes and bug fixes (more details available on the update flyer)

Automated data processing summarised in Progenesis LC-MS v4.1

There is more information on the improvements to data processing in our new FAQ or you can run through an analysis yourself by downloading the software, and analysing the tutorial data set. If you would like to analyse some of your own data — as we know you’ll want to see how the software performs on some of your own runs — please contact us.

By Beth McArdle | Posted in March 2013 | Tagged Progenesis LC-MS | Comments (0)

Progenesis CoMet Application Note – rapid validation of LC-MS approach for non-targeted metabolomics

March 4, 2013 – 3:15 am

If you need to set-up and validate the potential of LC-MS for non-targeted metabolomics, Progenesis CoMet makes it much simpler and quicker. Our application note shows data analysis could be performed in hours, not weeks, to demonstrate LC-MS would differentiate pig adipose samples with and without “taint”, a characteristic that impacts boar meat production.

The challenges

In many countries male piglets are castrated shortly after birth to avoid the production of meat with an unpleasant smell and flavour known as boar taint. This has financial and ethical issues in terms of raising animals and providing good quality food. Several compounds have been reported to be associated with this condition, however, the level of these compounds does not always correlate with results from classical sensory panels and other factors are thought to be involved¹.

The application note was written with help from researchers in Zurich, Switzerland at the Institute of Veterinary Pharmacology and Toxicology and the Functional Genomics Center. Specifically, using data provided by Malin Olson from her PhD project titled “Multiplex Profiling of Boar Taint by Non-Targeted Metabolomics”¹.

The result of this original research demonstrated the feasibility of LC-MS to discriminate tainted and non-tainted carcasses based on a sub-set of biomarker compounds. However, some of the challenges that had to be resolved over months of work included:

limited access to LC-MS system
applying many separate applications
specialist biostatistical support for interpreting results

Our data analysis solution

Over a hundred LC-MS runs generated from a nanoAcquity UHPLC^® connected to a Synapt G2™ HDMS™ mass spectrometer were reanalysed by Progenesis CoMet. Our aim was to see if the same proof-of-concept results could be produced using a much simpler approach. Our approach also included several restrictions compared to the original analysis, including:

we wanted to complete analysis and review results within a working day
we were not able to run the same compound database searches as the original study
we used the univariate and multivariate statistics built into our software rather than use complex off-line applications or rely on input from biostatisticians

You can download the application note for details of the method and results. But in summary we automatically generated compound quantification and identification results from Progenesis CoMet using default parameters. The first check on the results was a PCA plot based on a list of compounds that showed a “significant” (p<0.05) abundance increase in tainted samples relative to non-tainted samples.

Compound identifications, or RT and m/z measures for unidentified compounds, were compared between our list of eighteen significant compounds and a final list of sixteen compound biomarkers chosen in the original research¹. Ten of the eighteen compounds were found to be common, three with identifications confirmed as testosterone, androstenadione and 3-oxohexadeanoic acid. In addition, nine new compounds of interest were found compared to the existing list of sixteen compound biomarkers.

Conclusion

Progenesis CoMet showed advantages in simplifying and speeding up data analysis, providing comparable proof-of-concept results in less than 3 hours. This can be a great benefit in providing speed, objectivity and accessibility of running discovery-focussed experiments prior to committing further resource into research.

You can see for yourself how quickly Progenesis CoMet can generate results for non-targeted metabolomics. Download the software and follow our user-guide to analyse the tutorial data included.

“Untargeted metabolomics, where there is no known answer, is a challenge that can involve applying many different applications and significant time to generate results. Progenesis CoMet provides a single workflow combining all the major steps needed for semi-quantitation and annotation of putative compounds for further validation that can quickly validate LC-MS approaches.”
Malin Olson, Institute of Veterinary Pharmacology and Toxicology, Zurich, Switzerland

1. Olson M, Laczko E, Lewis F, Ampuero S, Bee G, Naegeli H. Multiplex Profiling of Boar Taint by Non-targeted Metabolomics. 2012.

By Paddy Lavery | Posted in March 2013 | Tagged Progenesis CoMet | Comments (0)

The one reason you should join the Progenesis Improvement Program

February 13, 2013 – 2:10 pm

Because it’s in your interest; you’ll get better, faster software as a result.

But there are other reasons too:

It’s completely automated, so you won’t ever have to do anything after opting in
If you ever change your mind, you’re in control; you can opt-out instantly, at any time
We only collect information that helps us understand how people use our software, not anything that would allow us to look at your analysis
It won’t affect the speed of your analysis
It’s genuinely in your interest (keep reading for more information…)

“But wait a second… what is the Progenesis Improvement Program?”

Good question. When you opt-in to the program, the software will collect information about how it’s being used and about any problems you encounter. Back at Nonlinear HQ, we can then use this information to identify which features are used most heavily, which are rarely used, and which parts of the analysis are fast or slow. This helps us to see where we need to improve performance, where we could add a small but helpful feature, where we need better instructions, and much more.

All of this should result in better software for you; software that helps you more in your research.

“Okay, you’ve convinced me. How do I opt in?”

We’ll be rolling out the Progenesis Improvement Program in the next releases of all Progenesis products. The first time you launch Progenesis software that supports it, you’ll see the following prompt (shown here for Progenesis LC-MS):

improvement-program-opt-in2

To join the program, simply select the first option and click OK. And that will be the last time you’re interrupted; you’ll never be asked for any more information. If you’ve already opted out, but now want to take part, just launch the software and select the Progenesis Improvement Program option from the File menu to change your settings. Of course, if you ever want to opt out again, you can do that from the initial screen’s File menu too.

One last plea from Mal Ross

Mal Ross, software developer Speaking on behalf of the Progenesis development team, I have to say that we’ve put a lot of thought into this program. We know that many people tend to opt out of programs like this, and we understand that. However, we really do feel this is worthwhile. This is about making analysis easier and about your productivity; it’s data that can help me and my fellow developers to help you, not data about your personal life.

If you’ve already opted out, I fully respect that – you’re very much in control. But if you’re undecided, please do give it a try – we would really appreciate your contribution.

Thanks for listening,

By Mal Ross | Posted in February 2013 | Tagged Progenesis CoMet, Progenesis LC-MS, Progenesis SameSpots, Usability | Comments (0)

Number of publications citing Progenesis for quantitative proteomics continues to increase

January 22, 2013 – 3:45 am

We’ve been keeping track of the number of papers that we can find citing Progenesis SameSpots or Progenesis LC-MS for over 4 years now. January is often a time to reflect back on the previous year, so how did the number of publications in 2012 compare to previous years?

You can see the number of publications citing either Progenesis LC-MS or Progenesis SameSpots continues to rise and shows no sign of slowing down.

(Left) Number of publications found citing Progenesis LC-MS from 2009-2012 (Right) Number of publications found citing Progenesis SameSpots from 2007-2012

Both products have been developed to solve the specific challenges of analysing quantitative proteomics data from 2D-gel based analysis and label-free LC-MS/MS based analysis. The continuing rise in publications citing the Progenesis software suggests quantitative proteomics, as well as the use of 2D gels analysis and label-free LC-MS, continue to be actively used in supporting research around the world.

Benefits of using Progenesis software

The increasing use of Progenesis SameSpots and Progenesis LC-MS suggests greater recognition of applying effective, well-designed data analysis software and not just being restricted to the software that comes with the instruments used. Applying Progenesis can help you to increase throughput, get more reliable data and support multiple workflows.

We’ll continue to monitor these publication trends throughout 2013 and hope to continue seeing them rise as our customers realise the range of applications supported by Progenesis SameSpots (e.g. analysing secondary-stained gels) or Progenesis LC-MS (e.g. analysis of fractionated samples). And you can now use Progenesis LC-MS for analysing SILAC labelled data as well as “Top X” absolute quantification with the Progenesis Post-Processor.

If you would like to see how Progenesis SameSpots or Progenesis LC-MS can support your quantitative proteomics methods, then get in touch, and we can arrange a trial with your data.

By Paddy Lavery | Posted in January 2013 | Tagged Progenesis LC-MS, Progenesis SameSpots | Comments (0)

At last! Pain-free reviewing in the Progenesis workflow

January 15, 2013 – 8:32 am

One of the great benefits of all of the software in the Progenesis range is the visualization of your data throughout the analysis process. Being able to skip back and review any stage of the analysis gives you confidence in that analysis and the freedom to explore your data.

The fully-enabled workflow of Progenesis CoMet v2.0

For a long time, however, there was also a small cost associated with this: if you skipped back to the first screen, you ended up having to step through all subsequent screens, one at a time, to get back to the analysis results. Speaking as a software developer (and therefore also a software tester), this has been a constant irritation – even to me – so it’s with great delight that I can say it will soon be a thing of the past.

In the next major release of all Progenesis products, you’ll be able to move around in the workflow to any screen for which there’s analysed data, directly. That is, in a fully-analysed experiment, you’ll no longer have to step forwards one screen at a time. In fact, Progenesis CoMet v2.0, released at the end of 2012, has this feature already.

So… what’s your pain point?

I realise this may not sound like a major new feature, but it’s usability issues like this that can make the difference between a straightforward analysis and a day of tearing your hair out. With every release of our software, we aim to remove pain points like this, helping to make your analysis smoother and, dare I say it, a more enjoyable experience.

If there’s any part of your typical analysis that you feel is frustrating, tedious, or simply too slow, we want to hear about it. The more reports we receive from users like yourself, the better we can prioritise our usability improvements. Send us your top 3 wishes for a simpler analysis today. Thanks!

By Mal Ross | Posted in January 2013 | Tagged Progenesis CoMet, Progenesis LC-MS, Usability | Comments (0)