Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation

Abstract

AbstractRecent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins or RNAs enabling deep spatial characterization of both healthy and tumor tissues. Parameters for design of optimal sequencing-based experiments have been established, but such parameters are lacking for multiplex imaging studies. Here, using a spatial transcriptomic atlas of healthy and tumor human tissues, we developed a new statistical framework that determines the number of fields of view necessary to accurately identify all cell types that are part of the tissue. Using this strategy on imaging mass cytometry data, we identified a measurement of tissue spatial segregation that enables optimal experimental design and that is technology invariant. This strategy will enable significantly improved design of multiplexed imaging studies.