Spatial analysis, whether its genomics, transcriptomics or proteomics, provides the spatial context for assessing diversity of cell types and for understanding cell function, cell states, and cellular interactions. Spatial resolution is particularly useful for understanding diseases like cancer and neurodegeneration where there is a lot of cellular heterogeneity and structural organization. It has been shown that some cellular changes are evident only when the sample is looked at by region, and not as a whole.

This Bench Tips webinar brings together a group of early-career scientists who are actively involved in using spatial multiomics technologies and are keen to share their expertise in designing experiments, sample preparation, and data analysis. Their short presentations will be followed by a live Q&A session for open discussion and the sharing of best practices and technical know-how.

This webinar will address some key questions:

• How do you know if spatial biology will lead to a better understanding of gene and protein expression profiling and cellular pathways involved in your studies?
• How do you decide which spatial biology technique is best suited for your sample and the biological question you are trying to address?
• What are some of the pros and cons of the various methodologies and what should you consider before making those trade-offs?
• How do you design and optimize the experimental workflows and prepare samples correctly?
• How do you ensure data quality and accuracy, while optimizing throughput and costs?
• What are some of the commonly encountered challenges with data analysis for spatial biology experiments?
• How do you minimize biases and batch effects?
• How do you normalize and integrate data from different spatial assays or with single-cell sequencing to get meaningful results?

• Spatial biology
• Genomics, proteomics, transcriptomics
• Single-cell sequencing
• Data integration and visualization
• Imaging mass spectrometry