A Novel Protein Corona-Based Secretome Profiling Assisting Cell Multiomics Analysis

Abstract

Multiomics analysis integrates complementary molecular layers to decode biological complexity; however, the secretome, representing the extracellular and dynamic response of cellular function, remains largely absent from existing multiomics frameworks. Secretome profiling has been constrained by high background interference, poor sensitivity for low-abundance proteins, and perturbations caused by conventional enrichment strategies. Here, we develop a protein corona-based secretome profiling strategy and incorporate it into a multiomics analytical framework. By exploiting the selective adsorption of secreted proteins onto multiple functionalized magnetic nanoparticles under serum-containing and label-free conditions, this method enables the enrichment of secreted proteins under near-physiological conditions. Combining this method with data-independent acquisition (DIA) mass spectrometry enhances identification depth, quantitative dynamic range, and reproducibility. In total, 4,081 protein groups were identified from Michigan Cancer Foundation-7 (MCF-7) cells, including 858 annotated secreted proteins, spanning nearly 5 orders of magnitude in abundance, with >94% consistency across replicates. By integrating secretome profiling with intracellular proteomics and metabolomics, we conducted a multiomics analysis of Tohoku Hospital Pediatrics-1 (THP-1) macrophages across distinct polarization states, revealing differences between M1 and M2 phenotypes in immune activity and metabolic regulation. Notably, polarization-associated variations were more prominent at the secretome level than in intracellular proteomes, highlighting the unique contribution of extracellular protein dynamics to functional state definition. We further applied this strategy to patient-derived organoids (PDOs), enabling secretome profiling in a model that better preserves tumor heterogeneity and microenvironmental context. Collectively, this study establishes a robust and scalable strategy for incorporating the secretome into multiomics analyses.