Recently, multiplexed molecular imaging assays have enabled the quantification of cell types and molecules in their native tissue context. Common techniques for understanding the TME like mass spectrometry 1 and flow cytometry 2 allow for bulk measurements of many cell biomarkers but discard valuable spatial information in the process. The tissue microenvironment (TME) is a complex milieu comprising many cell types and heterogeneous cell states. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available. Furthermore, 7-UP’s imputations generalize well across samples from different clinical sites and cancer types. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. However, high-plex imaging can be slower and more costly to collect, limiting its applications, especially in clinical settings. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section.
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