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Optimizing the design of spatial genomic studies

Author

Listed:
  • Andrew Jones

    (Princeton University)

  • Diana Cai

    (Flatiron Institute)

  • Didong Li

    (University of North Carolina at Chapel Hill)

  • Barbara E. Engelhardt

    (Gladstone Institutes
    Stanford University)

Abstract

Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and labor-intensive. Traditionally, tissue slicing for spatial sequencing involves parallel axis-aligned sections, often yielding redundant or correlated information. We propose structured batch experimental design, a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studies—one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor—our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and cost-efficient design strategies, allowing spatial genomics studies to be deployed by smaller, resource-constrained labs.

Suggested Citation

  • Andrew Jones & Diana Cai & Didong Li & Barbara E. Engelhardt, 2024. "Optimizing the design of spatial genomic studies," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49174-4
    DOI: 10.1038/s41467-024-49174-4
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    References listed on IDEAS

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