Morphology-aware profiling of highly multiplexed tissue images using variational autoencoders

Spatial proteomics (highly multiplexed tissue imaging) provides unprecedented insight into the types, states, and spatial organization of cells within preserved tissue environments. To enable single-cell analysis, high-plex images are typically segmented using algorithms that assign marker signals to individual cells. However, conventional segmentation is often imprecise and susceptible to signal spillover between adjacent cells, interfering with accurate cell type identification. Segmentation-based methods also fail to capture the morphological detail that histopathologists rely on for disease diagnosis and staging. Here, we present a method that combines unsupervised, pixel-level machine learning using autoencoders with traditional segmentation to generate single-cell data that captures information on protein abundance, morphology, and local neighborhood in a manner analogous to human experts while overcoming the problem of signal spillover. The result is a more accurate and nuanced characterization of cell types and states than segmentation-based analysis alone. We demonstrate the generality of this technique by applying it to a range of whole-slide, highly multiplexed human tissues acquired using platforms such as cyclic immunofluorescence (CyCIF), Lunaphore COMET, and Akoya PhenoCycler, and show that it can learn histological features across multiple spatial scales.