7-UP: generating in silico CODEX from a small set of immunofluorescence markers

Eric Wu; Alexandro E. Trevino; Zhenqin Wu; Kyle Swanson; Honesty Kim; H. Blaize D’Angio; Ryan Preska; Aaron E. Chiou; Gregory W. Charville; Piero D. Dalerba; Umamaheswar Duvvuri; Alexander Dimitrios Colevas; Jelena Levi; Nikita Bedi; Serena Chang; John B. Sunwoo; Ann Marie Egloff; Ravindra Uppaluri; Aaron T. Mayer; James Zou

doi:10.1093/pnasnexus/pgad171

BioinformaticsPNAS NexusMay 19, 2023

Peer-reviewed journal article

7-UP: generating in silico CODEX from a small set of immunofluorescence markers

Eric Wu iD, Alexandro E. Trevino iD, Zhenqin Wu iD, Kyle Swanson iD, Honesty Kim iD, H. Blaize D’Angio iD, Ryan Preska, Aaron E. Chiou iD, Gregory W. Charville iD, Piero D. Dalerba iD, Umamaheswar Duvvuri iD, Alexander Dimitrios Colevas iD, Jelena Levi iD, Nikita Bedi iD, Serena Chang iD, John B. Sunwoo iD, Ann Marie Egloff iD, Ravindra Uppaluri iD, Aaron T. Mayer iD, James Zou iD

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 imaging 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-UPs 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.

PDF DOI