Incorporating spatial biology into preclinical workflows provides a significant advantage for understanding therapeutic effects. By analyzing gene expression within its native tissue architecture, researchers can gain insights that traditional bulk sequencing methods cannot offer. A central practice involves utilizing spatial transcriptomics to map the precise location of cell types and their functional states. The STOmics platform supports this approach, enabling a more informed and efficient path in drug development.
Implementing Spatial Context in Disease Models
A core best practice is applying spatial transcriptomics to characterize complex disease models like organoids or animal tissues. This technique allows scientists to visualize how different cell populations are organized and interact in a diseased state. For instance, research on Alzheimer’s disease has employed spatial transcriptomics to identify spatial transcriptomics trait-associated cells, revealing specific neural and glial cell types located in high-density plaque regions. Mapping these cells provides a clearer understanding of disease mechanisms and potential therapeutic targets.
Identifying and Validating Cellular Targets
The identification of spatial transcriptomics trait-associated cells is a critical step. These are cells whose gene expression signatures, found in a specific spatial zone, correlate with a key disease trait. In preclinical development, confirming that a drug candidate engages its intended target within these specific cells is vital. Spatial transcriptomics enables this validation by showing whether a treatment alters the molecular signature or prevalence of these key cells in their native microenvironment, offering a direct measure of pharmacological activity.
Integrating Multi-Omics for a Complete View
Advanced practice involves integrating spatial gene expression data with other molecular layers. Platforms like STOmics provide end-to-end solutions that combine high-resolution spatial transcriptomics with protein detection. This multi-omics approach allows teams to not only locate spatial transcriptomics trait-associated cells but also to understand their functional protein outputs. This combined data strengthens the link between a drug’s mechanism, cellular response, and overall tissue-level outcome.
Adhering to these spatial biology practices refines the preclinical drug development process. The ability to identify, locate, and monitor key spatial transcriptomics trait-associated cells within a tissue context reduces uncertainty in therapeutic targeting. The integrated tools from STOmics furnish researchers with the data necessary to make more confident decisions, potentially accelerating the journey of effective treatments from the laboratory to the clinic.
