ArticleNature communications2024
Optimizing the design of spatial genomic studies.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Spatial multi-omics technologies in gastric cancer: applications and advances.Frontiers in immunology · 2026Review
- SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.Frontiers in bioinformatics · 2026Article
- Smart spatial omics (S2-omics) optimizes region of interest selection to capture molecular heterogeneity in diverse tissues.Nature cell biology · 2025Article
- Integrative Omics and AI-Driven Systems Biology: Multilayer Networks DecodingJournal of proteome research · 2025Review
- Designing smart spatial omics experiments with S2Omics.bioRxiv : the preprint server for biology · 2025Article
- Optimizing the design of spatial genomic studies.Nature communications · 2024Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.