Helping us to help you

Some time ago, we posted about the Progenesis Improvement Program (PIP), specifically about the reason why you should opt in:

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

That’s a pretty ambitious claim – one lots of software companies make when trying to persuade you to participate in their feedback programs – so we thought you might like to know exactly how we’ve been using the data from ours to improve the Progenesis experience.

But first we’ll run over how the program works…

How it works

Without collecting details on the data or results of the analysis, the PIP starts collecting information as soon as the software is launched and stops once it is closed. Information collected includes how long is spent on each screen, what actions were carried out on the screens and, for some actions, how long those actions took to complete.

Since each event is time stamped, it generates an accurate audit trail of how people are interacting with our software and how the software is responding. This information is then sent to us periodically, and securely, in a bundle to analyse at a later date.

So, what do we do with this data?

How we use the data

1. Support cases

Data from the Progenesis Improvement Program can – and often is – used to assist with support cases. We can use it in conjunction with error reports to pinpoint where issues were encountered and the steps that led up to them. Since this can help us to determine the exact area of the software that’s affected, and quickly, it can help us to resolve issues more quickly.

2. Promote awareness of infrequently used features

Sometimes the data shows that only a small percentage of our opted-in users are using features we’d anticipated being more popular – features that are often the solution to a support query. This information can help us to reconsider our UI design, but sometimes a bit of promotion is necessary. Did you know about the following features?

  • The Go To Location tool: Want to manually align using a known standard / spike? Want to quickly validate your peak picking by zooming in on a known spike? This tool is the answer!
  • The Clip Gallery: Want to export figures or tables from Progenesis without generating an HTML report? We implemented the Clip Gallery which allows export of figures and tables throughout the software with the option to caption each “clip” which is perfect when it comes to writing up your study.
  • Creating custom compound fragment databases: Can’t find a suitable fragment database to identify the compounds in your sample? Want to start creating your own fragment databases using known standards? Progenesis QI to the rescue!

3. Software development

This is arguably the most important use of the data collected from the program. Here are some examples of information we’ve been using to assist the development of Progenesis:

  • The range of screen resolutions in use, so we can make sure our software looks great on whatever hardware you’re using.
  • How long it takes to move from one stage of the workflow to another, which is great for determining how well Progenesis is performing and whether we need to improve this (we’ve still got work to do here).
  • How many runs per experiment people are analysing, so we know what size of studies people are doing with our software to ensure we’re able to keep up with demand (as expected, this is on an upward trend which is guiding us to look at how we can improve the handling of larger data sets).
  • What features are and aren’t being used – sometimes we find that features we’d predicted to be less helpful are the ones that prove most popular, and without this “inside” knowledge, it’s possible we could have (incorrectly) changed or removed a feature. We actually used some PIP data recently to confirm that the changes we made to the Review Proteins screen in v2.0 of Progenesis QI for proteomics were helping people to be more efficient. :)
  • What order stages of the workflow are being accessed as well as which screens are used in tandem and the screens that aren’t being interacted with; this helps us to determine whether our assumed workflow is correct and where we can move features to be available in other screens.

Want to take part?

If, after reading the above, you want to participate in the program, but initially opted out, have no fear: you can change your preference at any time by selecting the Progenesis Improvement Program… option from the File menu above the list of recent experiments. Thanks for helping us to continue improving your analysis experience.

Structural biology with Progenesis: Hybrid Vigour

We recently published a blog in which Progenesis QI was being turned to new uses (in food standards); I’m happy to say that we can now say likewise for Progenesis QI for proteomics, this time in structural biology!

A 2014 publication in Nature Methods (Argyris Politis and Florian Stengel et al., [1]) described the development of a hybrid methodology for determining protein complex structures using MS-based approaches, with Progenesis providing label-free quantitative data that were essential to the structural modelling. We’re naturally thrilled for our software to have contributed to such a cutting-edge project, but first, I’ll go through a little bit of background and the work itself.

The accurate determination of the structure of protein assemblies can be very complex; established high-resolution methods include X-ray crystallography and nuclear magnetic resonance (NMR), but these both face particular challenges. Complexes may not crystallise effectively, intact, or in a biologically appropriate state for X-ray studies, for example; NMR analysis avoids the need for a crystal structure, but tends to require a relatively large amount and concentration of protein sample, and may require various isotopic labelling strategies and/or specialised methodology for large complexes. As such, there are many complexes for which these methods cannot be effectively applied. Lower-resolution methods such as cryo-electron microscopy (EM) and interactomics methods such as co-immunoprecipitation (co-IP) have their part to play, but there is a real need to improve the repertoire of methods available for structural elucidation of multi-unit complexes.

This is where hybrid MS-based analyses come in [2], allowing improvement in structural modelling of protein complexes, even transiently formed ones, with modest amounts of protein sample and tolerance of different sample conditions. The Nature Methods authors’ hybrid MS approaches comprise both top-down and bottom-up proteomics analyses; the bottom-up analyses firstly include label-free quantitation using Progenesis to determine the protein subunits present and their relative abundance. This provides a critical set of constraints, fed into all subsequent structural modelling. The label-free results are also coupled with cross-linking studies, to identify points of interaction between protein subunits at the ‘peptide-resolution’ level. Again, Progenesis is of use here by generating a peptide database library for use in identifying the linked peptides generated.

On the top-down side, native MS provides complex and sub-complex masses and stoichiometry, building up an interaction network by identifying hierarchies of subunit associations. Furthermore, ion-mobility MS is used to gain topological information on the complexes and sub-complexes; in a nice nod to our colleagues, the determination of CCS values using Waters’ ion mobility technology is also a critical piece of the puzzle.

The constraint data from these approaches are then coupled with high-resolution structural data for individual subunits (or homology models thereof) to build up a picture of the complex as a whole. In doing this, the particular challenges that high-resolution methods can face with large protein complexes can be mitigated, requiring only existing subunit-level information.

Initially, this hybrid approach was carried out on three varying ‘learning structures’. By optimising the relative weighting of the information provided by each method, and assessing the fit of the resulting model structures with the known data, the authors were able to refine their methodology and then bring it to bear on new complexes. In a particularly exciting demonstration, the structure of the proteasome lid was modelled, which previously was only available at EM level. The model was sufficiently accurate to make predictions about the location of a lid subunit missing from the EM structure that fit with published experimental data. Furthermore, through affinity pull-down work coupled with their hybrid MS approach, the authors were also able to propose realistic structures for proteasomal assembly intermediates, demonstrating the ability of the method to help elucidate the dynamic interactome that complexes are part of in reality.

I’d really recommend reading the paper, as we cannot do it justice here; the combination of approaches is both elegant and effective. The synergy between the methods provides enough structural information, and restraints to fit with it, that complex modelling becomes a realistic prospect.

From our point of view, it’s worth returning to the use of Progenesis in the bottom-up part of the method. We were lucky enough to talk to and get the opinion of Florian Stengel himself on our software; he told us that:

“Progenesis was an easy-to-use and indispensable tool to define the content and quantity of subunits within samples and helped to define the search boundaries for other MS based approaches used in this study.”

You can see examples of the data generated by Progenesis in this study in the online supplementary material for the paper. Specifically, Figure 13 shows the use of Progenesis to confirm successful co-enrichment of proteasomal lid subunits, while Figures 21 and 22 show the use of Progenesis quantitative data in proteasome lid pull-down structural modelling, identifying and confirming interactions of the proteasome base subunits with partners in assembly.

Of course it is always great to see another example of Progenesis producing robust data contributing to biological studies; it’s also particularly nice to see our label-free quantitation software effectively applied to structural questions! If you’ve got a recent publication that features the use of Progenesis that you’d like to see discussed on our blog, get in touch.

About Florian Stengel

Florian Stengel studied biochemistry at the FU Berlin and Harvard University. After completing his diploma thesis as a DAAD foreign exchange scholar with Pamela Silver in functional genomics at Harvard Medical School, he went to the University of Cambridge to earn his PhD with Carol Robinson working on the architecture and dynamics of protein complexes using ion mobility and mass spectrometry of intact assemblies.

Since 2011 he is a Sir Henry Wellcome Fellow with the Wellcome Trust and Postdoctoral Research Associate in the laboratory of Ruedi Aebersold at ETH Zurich, where he uses cross-linking mass spectrometry and develops novel hybrid methods for structural biology.

Florian Stengel will start his own laboratory as an Assistant Professor at the University of Konstanz in 2015 and his group will focus on developing and applying novel mass spectrometric and proteomic approaches to quantitatively study the content, assembly and dynamics of intact protein assemblies.

References

[1] Argyris Politis, Florian Stengel, Zoe Hall, Helena Hernández, Alexander Leitner, Thomas Walzthoeni, Carol V Robinson & Ruedi Aebersold (2014). A mass spectrometry–based hybrid method for structural modeling of protein complexes. Nat Methods 11 (4): 430-6. (Supplementary material and PMC version of main text freely available).

[2] Florian Stengel, Ruedi Aebersold and Carol V. Robinson (2012). Joining Forces: Integrating Proteomics and Cross-linking with the Mass Spectrometry of Intact Complexes. Mol Cell Proteomics 11 (3): R111.014027.

Happy Holidays from everyone at Nonlinear!

We’re just about wrapping things up here at Nonlinear HQ ready for our Christmas closedown so we’d like to take this opportunity to wish everyone a Merry Christmas and best wishes for 2015.

So, what are our highlights from the year in which we marked 25 years in life sciences data analysis?

Last week we got to let our hair down at our office Christmas party which followed a relaxing cruise down the River Tyne; here we all are after a glass of mulled wine:

team building

Here’s hoping 2015 is just as exciting!

Progenesis goes nuts in Brazil

coconuts

Last week, I, along with my colleague Mark Bennett, had the pleasure of attending the 2nd Brazilian Proteomics Society and Pan-American HUPO joint meeting which was hosted in Búzios, Rio de Janiero. Falling coconuts at the opening function did little to dispel the Brazilian enthusiasm for celebrating proteomic research in their beautiful country.

We were there to listen to the latest proteomic developments and to hear from users starting to explore the analysis of their data with Progenesis QI for proteomics. One such user we met up with was Angelo Heringer, from the Universidade Estadual do Norte Fluminense who has been studying the effects of different wavelengths of LED treatment on the maturation process of sugarcane. Taking a label-free proteomics approach with LC-MS separation performed on a Waters Synapt G2-Si, the data was then analysed in Progenesis QI for proteomics to look at the differential expression of proteins across the various LED treatments. Sugarcane has long been one of the most important contributors to Brazil’s economy, and with the requirement for the development of biofuels, this work is certainly of interest.

Angelo

Angelo is hoping to further his proteomic experiences with Progenesis by spending some time in United States.

When we weren’t busy at the conference, we took the opportunity to check out the view from Sugarloaf Mountain, and of course made the most of the Brazilian cuisine – and it wasn’t all just lots of red meat!

sugarloaf

asparagussteak

If you want to see where we’ll be next, keep an eye on our events page.

Progenesis QI turns food standards detective

Basmati rice being rinsed before boilingAt Nonlinear, we always like to see our software being applied to new scientific areas – it feels very much like we’re on the right track when that happens! With that in mind, I’d like to highlight a new involvement in food forensics: adulteration testing of Basmati rice.

Basmati is an aromatic, high quality rice grown only in certain regions of India and Pakistan but is often mixed with lower quality rice. In the UK, for example, the maximum non-Basmati content before rice must be labelled a mixture is 7%, but 16% of samples labelled as Basmati rice, assessed as recently as 2009/10, were in violation of this standard [1].

Currently, adulteration testing methods focus on DNA microsatellite analysis [2], but our colleagues at Waters have been bringing mass spectrometry and the multivariate statistical visualisations in Progenesis QI to bear on the problem, with a view to developing novel and complementary approaches.

As a proof-of-principle study, Cleland et al. [3] studied off-the-shelf rice samples using Atmospheric Pressure Gas Chromatography (AP-GC) followed by HD-MSE analysis with a SYNAPT G2-Si mass spectrometer. The samples included Basmati rice from four manufacturers, and one long grain and two Jasmine rice samples as comparators. The data generated were analysed in Progenesis QI with supplementary visualisations in EZinfo (Umetrics).

One of the strengths of metabolomics and subsequent multivariate statistical analysis is that a huge range of data points can be simultaneously quantitated, and samples classified sensitively based on correlated differences arising across the data set. Naturally, our no-missing-values approach helps to ensure the conclusions reached are statistically robust! In this study, 3885 compound ions were detected using Progenesis QI; when the data were plotted using PCA, two conclusions were easily reached (Figure 1).

Figure 1 Figure 1. Separation of rice by type and manufacturer. Note that of the four Basmati samples, one clusters with Jasmine rice (behaving similarly to the Jasmine rice from the same manufacturer) and one with Long Grain rice. Reproduced from [3].

Firstly, the method appears reproducible in terms of inter- versus intra-sample variation. Secondly – and very interestingly given the purpose of the study – some of the Basmati rice samples cluster very distinctively with Jasmine and Long Grain samples, potentially with implications about the provenance or purity of those samples. It should be noted that this is a proof-of-principle study only, so no definitive conclusions on causation can be drawn, but it seems that the methodology can distinguish different grains effectively.

Naturally, to function as a test for adulteration on an ongoing basis, it would be important to identify the species driving this separation such that appropriate targeted assays can then be designed. Towards this, Progenesis QI and EZinfo also allowed the application of OPLS-DA (Orthogonal Projection to Latent Structures Discriminant Analysis) to determine the compounds most associated with the sample clustering discrimination, and the visualisation of their abundance profiles across the groups (Figure 2). Potential markers could then be provisionally identified through database searching.

Figure2 Figure 2. A cluster of potential markers elevated in Basmati rice relative to other grains derived from Progenesis QI analysis. (A) and (C) show the cluster in hierarchical dendrogram and abundance profile views. Reproduced from [3].

While it is early days as yet, this proof-of-principle study raises the prospect of novel tests for rice adulteration, which might increase the accuracy and confidence of its detection. Further work would initially focus on validation of the results using a larger, well-characterised sample set.

Want to know more?

If you are interested in learning more about this study, there are two ways you can do this. The application note itself is available for downloading; but there is also an opportunity to hear from the author directly when Gareth Cleland delivers a webinar on the 9th of December on this work.

References

  1. Food Standards Agency, UK. “UK Local Authorities Imported Food and Feed Sampling Report 2009/10.
  2. Nader W.F., Brendel T., Schubbert R. (2013) “DNA-analysis: enhancing the control of food authenticity through emerging technologies.” Agro FOOD Industry Hi Tech 24(1): 42-46.
  3. Cleland G., Ladak A., Lai S. and Burgess J. (2014) “The Use of HRMS and Statistical Analysis in the Investigation of Basmati Rice Authenticity and Potential Food Fraud.” Waters application note, part number 720005218en.

QC Metrics: Helping you make the most of your time

Proteomics as a field is rapidly maturing; there is a real sense that established techniques are being improved, and excitement at emerging techniques for quantitation. Central to this revolution is the application of effective quality control (QC) – understanding what adversely affects proteomics data, monitoring for problems, and being able to pin down and address them when they arise.

We’ve been at the forefront of QC implementation over the years, from our early involvement in the Fixing Proteomics campaign to our staff (in a previous guise!) publishing on proteomics QC[1], and it’s an area that’s very important to us – we want you to have confidence in your data and your results, as well as our software.

For that reason we’re making the application of QC easier, by introducing automatic QC metrics into Progenesis QI for proteomics. These constitute a range of charts that present your LC-MS data in an easy to visualise way, summarising key experimental statistics and allowing you to check for any batch-to-batch and run-to-run variation in your processing. We’re really digging in to the data; you can examine overview charts for the whole experiment and also detailed ones focussing in at the run level.

overview metrics blog

If you’d like to get a flavour of the comprehensive range of metrics we’re offering, have a look at the FAQ page. They range from overview metrics including abundance dynamic range, summaries of identifications obtained and precursor mass errors, and missed cleavages, to detailed run monitoring including MS1 scan rates. We’ve also included a chart detailing the level of overlap between your conditions in Venn diagram format.

Proteins per condition

You’ll see these charts at the QC Metrics page after you initially run an analysis using the auto-processing wizard  so that you’ll get an immediate flavour of your data, and you can also visit the QC Metrics page in the workflow at any time – the charts update with changes you make, so that you can both evaluate your data quality, and also the choices you’ve made as you have processed it. They’ll also re-plot the data as you change your experimental design so you can investigate any variable you like, and you can flag up particular charts with comments or export them as a QC report. You can also add new runs to an existing experiment to measure the metrics over an extended period.

The intention is that QC Metrics will provide you with a versatile and simple aid for process optimisation, troubleshooting and quality assurance. We think this should be a very useful addition to the software, and we’re always interested in feedback on developing this further – please have a look and let us know what you think!

[1] Jackson, D. and Bramwell, D., 2013: “Application of clinical assay quality control (QC) to multivariate proteomics data: a workflow exemplified by 2-DE QC.” J Proteomics 95: 22-37.

Nonlinear scales up support and development teams

We’ve been very busy over here at Nonlinear HQ with the recent release of v2.0 of Progenesis QI for proteomics, a number of conferences to prepare for, and training up our 2 most recent additions to the team: Angus and Janusz.

Angus Black

Angus Black, the latest addition to our customer support team, has joined us from a medical devices company that specialises in diagnostics using electrophoretic techniques – in fact, it’s the same company that Vicki joined us from last year.

Angus is an avid music enthusiast (think heavy metal!) and plays  bass in his spare time as part of a band. When he’s not deafening his neighbours, he can be found either at the gym or enjoying a bit of PC gaming.

 

 

Janusz NykielJanusz Nykiel is our newest software engineer and has relocated all the way from Wroclaw (Poland) to join us. Developing scientific software is new for Janusz but is the challenge that attracted him to the job initially, having previously worked on shrink-wrap, line-of-business software.

Janusz is another fan of heavy music and also enjoys console gaming.

 

 

I’m pleased to say they’ve both settled in extremely well, with plenty to keep them busy now that development work for v2.0 of Progenesis QI is well underway. You’ll be making use of Janusz’s work in all future releases of Progenesis, and if you’ve got a query on the use of the software, if you email our support team, you may find Angus is the one to help you out.

Nonlinear in the Netherlands

Last Friday, I visited a team of proteomics users who adopted Progenesis as early as 2009.  I feel connected with this group because I made the first phone call to Prof Theo Luider, Head of Laboratory, Neuro-Oncology, Erasmus Medical Centre in the Netherlands back in 2008.  I remember it vividly, as I was calling to see if he’d be interested in trying out the new Progenesis technology, having got his name via a chain of referrals.  Theo managed to reply to my introduction with 2 words (polite ones!) that made me burst out laughing and from there a great collaboration was born :).  Theo’s group evaluated the software and bought a license early in 2009; they have gone on to be our most prolific publishers with over 16 papers citing the use of Progenesis QI for proteomics.

This visit was a good one; I was accompanied by 2 of my Waters colleagues, Dr Perry Derwig (Product Specialist) and Dr Ian Edwards (Omics Business Development Manager), both very knowledgeable about instrumentation.  We spoke with Theo and his colleagues, Christoph Stingl and Dr Lennard Dekker, and over coffee and cakes we learned about the work that the team at Erasmus is doing and where they perceive the current limitations of the technology to be.  As a company, we are keen to form collaborations with our users and, over the years, the team at Erasmus have helped us with their suggestions and feedback.  Our development team here at Nonlinear Dynamics insists upon fully understanding the problem so that they can find the best solution, which is why good collaborations are so important to us; it takes time and effort to fully explain a detailed problem in the research workflow.

During a very nice lunch at The Harbour Club, I explained how Nonlinear Dynamics is run and roughly what our schedule for releases is.  We ended with a tour of the labs on various floors of the building, including a stunning view over Rotterdam from floor 23.  The group has 10 instruments from various vendors and, of course, they can analyse their data from all these different instruments using Progenesis QI for proteomics, as it is multi-vendor.  This is one of the strengths most cited by our users when asked what they like about Progenesis.

WP_20141017_001

I managed to get a quick photo of Theo, Ian and Perry by their SYNAPT with an M-class UHPLC system just before we departed for Schiphol.  Theo said that the life of the group changed dramatically after they started using Progenesis, which is very encouraging to hear!  Here is a new quote Theo kindly gave us after the visit:

“We use Progenesis for label free analysis of nanoLC-high resolution MS data, the software gives good opportunities for alignment, quantitative comparison and assigning identification to quantitative data. We were able to use Progenesis software for relative large numbers of samples (hundreds).”

If you’d like to read more about the work being done by Theo and his team, take a look here.

Progenesis QI draws the crowds at HUPO 2014

This year, the 13th session of HUPO was held in the beautiful city of Madrid. We had such lovely warm weather compared to Paris or Newcastle Upon Tyne, with temperatures 10°C higher in Madrid. Sadly, with all the activity going on at the conference, there wasn’t much time to enjoy the sun. I could however enjoy the long evenings, eating at the very (very!) late Spanish time and having a walk around the old city, visiting the famous Placa de Majorque. I even had the chance to see some students singing serenades as a traditional tuna.

20141008_002207_Pano

The week kicked off for Nonlinear with a pre-conference course on Progenesis, organised by the congress. It was a great success, with a lot of interest and, since it was fully booked, we had to run an extra session!

This year, we had a joint booth for Waters and Nonlinear which was perfect to show off the latest MS instruments from Waters with ion mobility, and the newest version 2.0 of Progenesis QI for proteomics which is fully compatible with this additional dimension in resolution.

20141007_102543

Waters gave a lunch seminar on Monday where we showed the latest developments in Progenesis QI for proteomics in more detail; the QC Metrics along with the automation of the workflow were highly appreciated.

There are now many QC metrics tools in Progenesis, aimed at both process optimisation and validation of the input data. If you wish to know more, just contact us and we will arrange a demonstration for you! Or you can simply download it and have a try at your convenience!

Next year, the 14th HUPO congress will be held in Vancouver – we hope to see you there to show off even more new features.

Out now – Progenesis QI for proteomics v2.0

We’re delighted to announce that Progenesis QI for proteomics v2.0 has been released and is now available to download. We’ve enhanced the software in a number of important directions for this release, from increasing your productivity to helping put your observations in a wider biological context – and all of the improvements are a direct response to your feedback.

What’s New?

Highlights of this release include:

  • Pathway analysis: export your identified proteins to external pathway tools.
  • QC metrics: diagnostic charts to help you identify any problems with your analysis.
  • Automated data processing: run from Import Data to a completed peptide search without intervention.
  • “Hi-N” quantification: for both relative and absolute protein quantification.

You can read about these new features in more detail here.

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.

where-to-download

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 correctly reflect new behaviour.

Alternatively, if you’d like a private demonstration, we’ll be at HUPO from 5th-8th October at Waters booth numbers 20 and 22, so get in touch to book an appointment.

Special thanks

One final thing: we’d like to say a massive THANK YOU to the participants of our Early Access Program for their feedback in evaluating beta versions of this release; the comments received are vital in helping us to build a high quality product.