UMITIC: An unsupervised framework for the joint characterization of cellular phenotypes and spatial neighborhoods in multiplex and hyperplex immunofluorescence imaging data

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Abstract

Multiplexed imaging technologies enable the simultaneous measurement of dozens of protein markers while preserving context, providing a high-resolution view of tissue organization schemes. However, extracting meaningful insights from these high-dimensional datasets--particularly in hyperplex settings (>20 markers)--remains a major computational challenge, especially in the absence of annotated data. Here, we present UMITIC (Unsupervised Analysis of Multiplex Images via TIssue Characterization), a modular and unsupervised computational framework for the joint characterization of cell phenotypes and tissue neighborhoods from multiplex imaging data. UMITIC integrates three components: (i) CellCut, a strategy that combines nuclear and cytoplasmic predictions to improve the delineation capabilities of the framework; (ii) CellMap, a contrastive learning approach that generates low-dimensional representations of single-cell image crops that are enriched with morphological features; and (iii) TissueNet, a graph neural network that models spatial cell-cell interactions to identify tissue neighborhoods. We evaluated UMITIC across four datasets of increasing complexity to assess its robustness, scalability and biological relevance. With respect to a 7-plex human tonsil dataset, the framework identified canonical immune cell populations and reconstructed well-established anatomical regions. When applied to a 43-plex tonsil image, UMITIC preserved these tissue-level structures while enabling a finer cell subtype stratification process driven by increased marker dimensionality. We further validated our method on a 58-plex colorectal cancer cohort, where UMITIC was able to recover previously reported immune composition differences and spatial organization variations between patient groups with different prognoses. Finally, when an expert-annotated mass cytometry imaging dataset concerning human lung tissue was used, UMITIC achieved higher agreement with the reference tissue annotations than the existing approaches did, demonstrating improved lung microanatomy reconstruction accuracy. Together, these results show that UMITIC enables consistent and interpretable analyses of both cellular phenotypes and tissue architectures across diverse multiplex and hyperplex imaging datasets without the need for manual annotations. Author summaryUnderstanding how cells are organized within tissues is fundamental to deciphering diseases, yet analyzing tissue imaging data remains a major challenge. The recently developed imaging technologies enable the visualization of dozens of proteins in a single tissue section, revealing unprecedented cell identity and spatial organization details. However, extracting meaningful biological insights requires extensive manual annotation work performed by expert pathologists, limiting the scalability. Here, we present a fully automated computational framework that characterizes tissue architectures in an unsupervised manner at two complementary levels: it identifies cell types based on their protein expressions and morphologies and maps how those cells are organized into spatially coherent tissue structures, and it does so without requiring any manual annotations. Our approach is modular and interpretable at the cell level. We validated our framework across four independent datasets with panels consisting of 7 to 58 simultaneous protein markers, including healthy human tissue and a colorectal cancer cohort in which patients with distinct immune profiles were analyzed. Remarkably, UMITIC improved upon the performance of existing methods across both qualitative and quantitative assessments. These results suggest that our framework provides objective, interpretable and reproducible image processing tools for conducting tissue analyses in both research and clinical settings.

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