ArticleNature methods2023
Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- Mechanisms of ferroptosis in primary hepatocellular carcinoma and progress of artificial intelligence-based predictive modeling in hepatocellular carcinoma.World journal of gastroenterology · 2025Review
- Kontextual reframes analysis of spatial omics data and reveals consistent cell relationships across images.Cell reports methods · 2025Article
- Imaging Mass Cytometry-Based Immune Profiling of Human Peyer's Patches in Patients with Crohn's Disease.European journal of immunology · 2025Article
- Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.Nature methods · 2025Article
- MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.Genome medicine · 2025Article
- Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.Cell reports methods · 2025Article
- PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.PLoS computational biology · 2025Article
- From surfing to diving into the tumor microenvironment through multiparametric imaging mass cytometry.Frontiers in immunology · 2025Review
- Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research.Cancer discovery · 2024Review
- Review
- Redefining clinical practice through spatial profiling: a revolution in tissue analysis.Annals of the Royal College of Surgeons of England · 2024Article
- Multiplex protein imaging in tumour biology.Nature reviews. Cancer · 2024Review
- Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases.Genome medicine · 2024Review
- Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem.bioRxiv : the preprint server for biology · 2023Article
- An end-to-end workflow for multiplexed image processing and analysis.Nature protocols · 2023Review
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Multielement Z-tag imaging by X-ray fluorescence microscopy for next-generation multiplex imaging.Nature methods · 2023Article
- Designing spatial transcriptomic experiments.Nature methods · 2023Article
- Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.Biomolecules · 2023Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins and hundreds of RNAs, enabling deep spatial characterization of both healthy and diseased tissues. Parameters for the design of optimal multiplex imaging studies, especially those estimating how much area has to be imaged to capture all cell phenotype clusters, are lacking. Here, using a spatial transcriptomic atlas of healthy and tumor human tissues, we developed a statistical framework that determines the number and area of fields of view necessary to accurately identify all cell phenotypes that are part of a tissue. Using this strategy on imaging mass cytometry data, we identified a measurement of tissue spatial segregation that enables optimal experimental design. This strategy will enable an improved design of multiplexed imaging studies.
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