It’s an interesting question and there are many of our users out there with various answers. We decided to ask our users some questions about why they bought Progenesis QI and what difference it has made to their research. Here’s what Research Professor Jace W. Jones had to say on the matter:
Our research involves development of mass spectrometry-based platforms that couple biomarker discovery to quantitative validation, from circulating and tissue lipids. In particular, the use of high resolution tandem mass spectrometry to structurally elucidate, identify, and quantify biologically active lipids to further understand disease/injury mechanisms of action and provide insight for drug development targets. To this end, we first design untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments to identify differentially expressed plasma and tissue-bound lipids using in vivo models. Our discovery–based instrument platform of choice is the Waters UPLC coupled to a Synapt G2-S operated in HDMSE acquisition mode. Our typical LC conditions elute lipids over a 20-minute gradient using a UPLC C18 column. The HDMSE data is acquired in both positive and negative ion modes. Experimental parameters vary depending on the particular in vivo model under study but involve multiple biological replicates per condition, per time point. In addition, quality control samples and addition of internal standards are standard operational procedure. The resulting output from this type of workflow is a tremendous amount of analytical data per sample that ideally generates a list of identified lipids that are differentially expressed between the conditions under study.
What problems did you experience prior to using Progenesis?
The data generated from the UPLC-HDMSE workflow is highly complex and results in 1000s of m/z values being identified by a number of analytical parameters, such as retention time, drift time, accurate mass precursor ions, and diagnostic product ions. In order to expedite biomarker discovery and fully utilise the multidimensional data generated on the UPLC HDMSE platform, we realised there was an immediate need for a bioinformatics solution that could efficiently process multidimensional datasets.
What made you convert to Progenesis QI?
We decided to go with Progenesis QI for its ability to handle multidimensional datasets, especially HDMSE workflows. In addition, a primary goal with our discovery/un-targeted mass spectrometry experiments is to generate lipid markers that can then be pipelined for targeted, high-throughput assays. Progenesis QI is an efficient bioinformatics solution that allows us to make the transition from discovery to validation. The ability to process multi-vendor data was also a major selling point.
What difference has Progenesis QI made to your research?
Progenesis QI enables us to efficiently process multidimensional lipidomic datasets in a systematic and straightforward manner. We can also now process HDMSE data on a single software platform.
One of the biggest differences we have seen is our ability to incorporate more biological replicates at the same time including temporal time points and multiple conditions. This gives us the ability to bolster our statistical significance and conduct experiments where we can evaluate potential biomarkers across time over varied conditions.
Please can you give a specific example of the success that Progenesis QI has helped you to achieve?
Progenesis QI has enabled us to increase our lipidomic workflow while increasing the amount of analytical data per sample. Because our data processing has been streamlined with Progenesis QI, we now spend more time on optimizing chromatography (e.g. orthogonal column chemistries) and mass spectrometry acquisition (e.g. ion mobility with tandem mass spectrometry) for more confident lipid identification.
How will it help you in your future research?
The demand for lipidomic experiments from not only our existing collaborators but also from outside researchers has grown steadily over the past couple years. Progenesis QI has enabled us to keep pace with that demand by allowing us to efficiently and confidently process multidimensional lipidomic datasets. This, in turn, expedites the experimental process of generating potential lipid biomarker candidates.
What advice would you give to a metabolomics/lipidomics scientist struggling with similar problems?
The amount of data generated by metabolomic/lipidomic workflows means a tremendous reliance on data processing. Often, the data processing aspect of ‘omics data is time-consuming and beyond the expertise of the scientist performing the experiments. Consequently, having a bioinformatics solution that is efficient, versatile, and reliable is a valuable investment and allows researchers to focus on optimization of their experimental approach and validation studies for potential targets. I highly recommend the use of Progenesis QI as your bioinformatics solution.
If you are a Progenesis QI user and would like to tell us about your research, please contact us – we’d love to hear from you.