Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation.

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.