Since the initial version of Lipostar, there have been significant strides to add features in collaboration with users to enable further data analysis in ways that other software platforms seldom provide. Here we present how the trend analysis, a new tool for global lipid profiling data analysis setup by Sanofi, was implemented and incorporated in Lipostar. The trend analysis is a versatile tool, which finds applications not only in biomarker’s discovery, but also in isotope clustering, in detecting in-source fragmentation or dimerization. In addition, clustering using K-Means or Bisecting K-Means are also available to discover new trends in data beyond what is anticipated at the outset. During this presentation, the various application of trend analysis tool will be described.
Lipid biomarker research represents one of the most widespread applications in lipidomics. In this context, stable isotope labelling has become a staple technique in the study of lipid metabolism and dynamics, as it allows to directly measuring biosynthesis, remodeling and degradation of biomolecules. The application of stable isotope labelling to lipidomics is still considered a challenging task, especially for the complexity of the necessary data analysis. Lipostar offers data analysis solutions for flux analysis experiments based on stable isotope labelling. During this presentation, we will describe both the Lipostar data analysis workflow for flux analysis and how flux analysis data can be easily integrated in untargeted studies.
Over the last two decades, lipids have come to be understood as far more than merely components of cellular membranes and forms of energy storage. Indeed, their pivotal role in a variety of diseases including diabetes, obesity, heart diseases, cancer, or neurodegenerative diseases, is now commonly accepted. Consequently, lipidomics represents an emerging field with the aim of unravelling diagnostic biomarkers, new drug targets, and of rationalizing toxicity effects. Mass spectrometry, due to its sensitivity and selectivity, is the elected method for qualitative and quantitative lipidomics analysis, and the recent improvements in MS technologies have moved interest from targeted to untargeted approaches. However, untargeted lipidomics requires tailored software solutions for data analysis. To this aim, we recently developed Lipostar, a vendor-neutral high-throughput software to support targeted and untargeted LC-MS lipidomics. Lipostar supports the overall data analysis steps including raw files import, data mining, statistical analysis (including prediction), lipid identification, data interpretation of bio-pathways. During this presentation, a general overview of the software will be provided, together to hints and tips to adapt the software to your experimental protocols.