Report research with confidence – Who else wants to publish small molecule LC-MS analysis confidently?

Meaningful results

Over 800 groups worldwide are using Progenesis QI routinely to generate small molecules and proteomics results that really reflect the effect of the conditions in the experimental design, so they can have confidence in presenting these results to their peers, with minimal fear of false positives. How can we claim that only Progenesis gives this sort of confidence? The answer is in the unique and powerful co-detection approach Progenesis takes to peak picking of LC-MS data. Read on to find out about some of the interesting applications Progenesis technology is being applied to…

Research publications news from Asia-Pacific

We’ve seen a huge growth of Progenesis QI sales in Asia-Pacific over the past 3-4 years and it’s good to see that this is now being followed by a similarly impressive boost in publications citing Progenesis QI from the region, particularly from China where there were 16 publications citing Progenesis QI last year and 3 already this year.

Each year, more and more publications are citing the use of Progenesis

Figure 1: The number of publications per year (worldwide).

There have also been publications from Institutes in Japan, South Korea, Taiwan, Singapore and India in the same period. In view of this I thought it would be a good time to review the applications covered by these publications and to highlight a couple of particularly interesting ones in a little more detail.

Broad applications

The broad application fields covered by the publications include clinical and health science, food and nutrition, plant science, natural products and environmental research. Natural products research in China is dominated by research into Traditional Chinese Medicine (TCM) for which there are large departments in many universities and even entire universities specialising in this field. The research involves investigating the mechanism of action, active ingredients and safety of traditional remedies, definitely an interesting case of “old meeting new”. Traditional remedies which may have been used for hundreds of years are now being analysed with cutting edge technology including high resolution LC-MS systems and Progenesis QI software in the hope that isolating the active ingredients and understanding how they work could lead to development of new drugs. Food and nutrition is another field where modern omics research is being applied to a traditional industry for purposes such as improving food quality and taste as well as food safety and quality control. It’s the exceptional ability of Progenesis QI to “find the needle in the haystack” – detect subtle differences in the profiles of complex metabolite mixtures that enables success in these different fields of research and analysis.

Some markets which already use Progenesis QI software

Figure 2: Some markets which already use Progenesis QI software

I’d now like to highlight two very recent publications which illustrate the high level and quality of research being performed with the help of Progenesis QI in Asia. Firstly, a publication from China which shows that in addition to the large amount of plant and natural products research, there is also high level clinical research taking place.

Lipid Profiles in Maternal Plasma

A group at Chongqing Medical University have performed a lipidomics study on the mechanism of Gestational Diabetes Mellitus (GDM) by monitoring changes in lipid profiles in maternal plasma throughout pregnancy. The ultimate goal is to understand the mechanism and cause of a condition which can lead to serious long term consequences for both mother and foetus.

The study involved 61 participants, 34 controls and 27 diagnosed with GDM. Plasma lipid levels were monitored at different stages of pregnancy (at each of the trimesters) through separation and detection on a Waters Acquity UPLC I-class system coupled to a Waters Xevo G2 QTof MS. The data was processed in Progenesis QI including relative quantification via the unique and powerful co-detection method which generates no missing values leading to more reliable statistics and therefore better results, plus identification using Progenesis MetaScope and ChemSpider. As Progenesis QI can export data using a flexible *.csv format, sophisticated multivariate statistical analysis can easily be performed in external software to extract some meaningful results from the experiment despite the large biological variance always found in clinical studies. In this way trends in relative levels of a large number of lipid species throughout pregnancy were monitored in both control and GDM subjects and a number of polyunsaturated or chemically modified phospholipids were found to be present at significantly lower relative abundances in GDM compared to control subjects throughout pregnancy. These results will contribute to better understanding of the mechanism and causes of GDM.

Progenesis Alignment and co-detection Workflow infographic

Figure 3: How the Progenesis workflow enables 100% matching and no missing values, meaning reliable statistics which will lead you to confident biological discoveries.

Singapore Phenome Centre Publishes Research to Benefit the Environment

The second publication I’d like to highlight is significant as I believe it is the first to be produced by the Singapore Phenome Centre (SPC) based at the Lee Kong School of Medicine, Nanyang Technological University (NTU). The SPC is a member of the International Phenome Centre Network (IPCN) and was set up in association with the National Phenome Centre at Imperial College, London and the Waters Corporation to conduct research in two main areas, clinical and environmental. This initial publication is in the environmental field and is concerned with profiling the metabolites and lipids that form the Soluble Microbial Product (SMP) contained in the effluent from Bioreactors used in wastewater treatment. Since SMPs can lead to issues such as fouling of membranes in the bioreactors, it’s important to understand their composition, origin and the system parameters that can influence their production.

The experiment consisted of analysing filtered samples taken from a continuously stirred tank anaerobic bioreactor at intervals of 0, 4 and 48 hours after batch feeding it with a synthetic feed mixture. To ensure maximum metabolite coverage, liquid-liquid extraction was used to separate samples into polar metabolite and lipid fractions which were both analysed on positive and negative mode (three replicate injections per condition). Analysis was performed using Waters Acquity UPLC system with HSS T3 column for polar and CSH C18 for lipid separations, coupled to a Xevo G2-XS Q-Tof MS. Again, the power of Progenesis QI co-detection was used to process the data and find the compounds that were significantly changing in relative abundance between the different time points, as well as for identification of compounds of interest. For further multivariate analysis including OPLS-DA, data was exported from Progenesis QI directly into EZinfo. Identification used both Progenesis MetaScope and ChemSpider in both of which it is possible to search many databases for maximum coverage while filtering by isotope distribution and theoretical fragmentation analysis to improve discrimination of results.

Image of a Waters Xevo G2 XS QTOF machine

Image of a Waters Xevo G2 XS QTOF machine

Due to good experimental technique and the alignment and co-detection capabilities of Progenesis QI, technical replication was excellent enabling very clear distinctions between the conditions as shown with PCA. Using multivariate statistics such as OPLS-DA and the S-plot (in EZinfo) it was possible to extract the compounds that contributed most significantly to the pair-wise differences between the conditions. Of these compounds, those that increased in relative abundance from 0-4 hours (fermentation stage) included both polar metabolite and lipid species while those that increased from 4-48 hours (methanogenesis stage) were all lipids, mainly phospoholipids and cardiolipins (diphospholipids). As this study was the first to include both polar metabolite and lipid SMPs, a more complete picture than previously of the metabolic processes occurring at different stages of wastewater treatment could be obtained.

I’m sure we will see many more great publications citing Progenesis QI in the coming months so perhaps I’ll get the opportunity to give some further updates on them quite soon. In the meantime I invite you to download Progenesis QI and see how it can help you to generate high quality data for publication.

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