Single-cell technologies increased bulk omics data resolution by enabling molecular profiling of individual cells when they first emerged. Spatial genomics built on that foundation by adding critical information about tissue architecture and cellular context to provide a more complete picture of cells, their environments, and interactions. Each technological advance has introduced new sample prep, workflow, and computational considerations that increase the risks of variability and bias.
In this GEN Learning Lab, our expert panelists Linda Orzolek, MS, MB, Xuhuai Ji, MD, PhD, and Christina Chang, PhD, will discuss the current landscape of imaging- and next-generation sequencing-based approaches for spatial genomics. By comparing the two modalities, they will explore how NGS-driven tools address analytical and operative challenges faced by imaging methods, including cell segmentation versus deconvolution strategies, tissue preparation trade-offs, and large-scale data interpretation. The discussion will also highlight spatial workflows that combine transcriptomics with chromatin accessibility, genomic variation, immune profiling, and protein analysis. Lastly, they will share case studies that demonstrate how spatial genomics spans biological discovery through translational research. Key takeaways include:
- How imaging- and NGS-based spatial technologies compare in terms of their respective strengths, limitations, and workflow considerations.
- How spatial multiomics enhances discovery in oncology, neuroscience, developmental biology, and immunology
- Future directions for the field including the integration of artificial intelligence, the development of spatial reference datasets, and workflows for translational and clinical applications
A live Q&A session will follow the presentations, offering you a chance to pose questions to our expert panelists.
Produced with support from:
