LipidSpace is a stand-alone tool to analyze and compare lipidomes by assessing their structural differences.
A graph-based comparison of lipid structures that allows to calculate distances between lipids and to determine similarities across lipidomes. It allows for a rapid (re)analysis of experiments, identifies lipids responsible for shaping the respective lipidome, and provides methods for quality control.
Lipidomics encompasses analytical approaches that aim to identify and quantify the complete set of lipids, defined as lipidome in a given cell, tissue, or organism, and their interactions with other molecules. Most lipidomics workflows are based on mass spectrometry and have been proven a powerful tool in system biology in concert with other Omics disciplines. Unfortunately, computational approaches for this relatively young discipline are limited and only accessible to some specialists. Search engines, quantification algorithms, visualization tools, and databases developed by the ‘Lipidomics Informatics for Life-Science’ (LIFS) initiative will provide a structured and standardized format for broad access to these specialized bioinformatics pipelines. Many medical challenges related to lipid metabolic alterations will be highly supported by such capacity-building. Within LIFS, we already provide access to several tools, workflows, tutorials, and training via a unified web portal (https://lifs-tools.org/).
Platelet integrity and function critically depend on lipid composition. However, the lipid inventory in platelets was hitherto not quantified (Peng et al., Blood, 2018). Today our lab examines the lipidome of murine and human platelets using lipid-category tailored protocols on quantitative lipidomics platforms. We commonly can cover the platelet lipidome, which is comprised out of 500 lipids (99.9% of the total lipid mass) over a concentration range of seven orders of magnitude. We conduct systematic comparison of lipidomics network in resting and activated murine platelets, validated in human platelets, where we inter alia revealed that less than 20% of the platelet lipidome is changed upon activation, involving mainly lipids containing arachidonic acid. However, the most interesting work that we currently conducting in close collaboration with our partners is the analysis of different diseases models (Scheller et al.,Haematologica, 2019) which display and thrombotic phenotype. E.g., Sphingomyelin phosphodiesterase-1 (Smpd1) deficiency results in a very specific modulation of the platelet lipidome (Peng et al., Blood, 2018) with an order of magnitude up-regulation of lyso-sphingomyelin (SPC), and subsequent modification of platelet activation and thrombus formation, which sheds light on novel mechanisms important for platelet function, and has therefore the potential to open novel diagnostic and therapeutic opportunities.
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