Although single-cell techniques have helped researchers understand diseases by evaluating cells on an individual level, they lack spatial context within the tissue surrounding these cells. With the introduction of spatial transcriptomics, researchers can use everything from relational data to imaging data to better understand gene expression and cell dynamics.
Relational data provides context into where cells are located in relation to one another and makes it possible to overlay imaging data with molecular data. The localization of cells and how they interact within their environment is critical for research, particularly when looking at rare cell types. However, spatial omics provide more context than local cell interactions, showcasing how a disease progresses within a tissue’s architecture. As a result, scientists are able to gain previously unknown spatial context for rare cell types and disease progression.
With NVIDIA’s accelerated computing and AI platform for spatial transcriptomics, researchers and developers can:
- Enable novel methods of analysis by accelerating bottlenecks and increasing accuracy.
- Use NVIDIA GPUs in spatial analysis to reduce analysis time for processing large amounts of spatial data.
- Use generative AI for high-accuracy cell segmentation with VISTA-2D, an NVIDIA AI foundation model.