Workshops

Sunday, July 31st - 3:00pm-5:00pm

Workshop Organizer:

With the advent of high-throughput technologies, biological experiments have shifted from generating small datasets that focus on single genes or proteins to generating large data sets that document expression profiles on a whole-genome wide basis. Similarly, metabolomicsvtechnologies allow quantification of lipids, glycans and other molecules on a similar scale,leading to the generation of large metabolomics expression profiles. Each of the transcriptomic,proteomic and metabolomics assays offers a different view on the investigated biology that can support findings by the other assays and give additional complementary insights. Integration strategies that map the findings across different assays can help to maximize the insight obtained by the different assays. Integration between metabolomic and transcriptomic or proteomic technologies can be achieved on multiple levels. On the lowest level a researcher can investigate if the transcript omic or proteomic datasets identify enzymes that either directly catalyze reactions of the identified metabolites or of closely related precursors and derivatives.On the second level a researcher can analyze if a set of metabolites maps to a pathway that was identified by the other omic datasets as well. On the highest level a researcher can finally investigate if pathways that were identified for the metabolites are functionally related to pathways identified based on other omic assays. Here, we will discuss the integration of metabolomics with single cell and bulk RNA seq and proteomic datasets. We will investigate theintegration strategies on each of the three different levels. We will also discuss an example where the integration of metabolomics with other omic datasets and prior biological knowledge allows hypothesis generation about function and relevance of a major metabolomics finding.

Workshop 3: An Introduction to SIMS, DESI, and LASEI for Mass Spectrometry Imaging (Part A)