Spatial multi omics integration metabolites meet RNA

Recently emerged spatial omics technologies provide unprecedented insights into spatial aspects of gene expression, protein localisation, and metabolite concentrations in tissues. Now, the increasingly recognised challenge lies in integrating layers of individual spatial omics into a multi-omics molecular inventory describing cellular machinery and its spatial co-organisation across transcription, translation, and metabolism.
This webinar will focus on spatial metabolomics and cover various computational approaches for analysing Spatial-multi-omics datasets, emphasising the integration of transcriptomics and metabolomics at both spatial and single-cell resolution. It will elucidate the core principles of each method and their application to key biological questions in the spatial omics domain. Additionally, a case study will be presented to demonstrate the effectiveness of these methods on a combined dataset of spatial metabolomics and highly-multiplex RNA FISH.
Who is this course for?
This webinar is suitable for any researcher in life sciences who is interested in exploring spatial omics and learning about relevant methods and resources . No prior knowledge of bioinformatics is required, but an undergraduate level knowledge of biology would be useful.
This event is part of a webinar series focusing on concepts, resources, and recent advancements in the field of spatial omics. For details on all topics covered in this series and registration information, please visit the following link: Advances in spatial omics: exploring concepts, innovations, and resources.
Outcomes
By the end of the webinar you will be able to:
Explain how spatial metabolomics data is generated, processed and prepared for downstream analysis
Explore key computational methods for both preprocessing and functional analysis of spatial multi-omics datasets
Identify how to match methods to common biological questions in spatial omics, demonstrated through a case study combining metabolomics and transcriptomics

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