<i>7-UP:</i> Generating in silico CODEX from a small set of immunofluorescence markers
Eric Q. Wu, Alexandro E. Trevino, Zhenqin Wu, Kyle Swanson, Honesty J. Kim, H. Blaize D’Angio, Ryan Preska, Aaron E. Chiou, Gregory W. Charville, Piero Dalerba, Umamaheswar Duvvuri, A. Dimitrios Colevas, Jelena Levi, Nikita Bedi, Serena Chang, John B. Sunwoo, Ann Marie Egloff, Ravindra Uppaluri, Aaron T. Mayer, James Zou
Abstract
Abstract Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. 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. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. 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. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP's imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.