Machine Learning-Assisted Nanoplasmonic Barcode Array-Coupled Microfluidic Platforms for Exosome Profiling and Organotropic Metastasis Analysis

Abstract

Elucidating the mechanisms of organotropic metastasis requires the in situ profiling of exosomes within the tumor microenvironment. However, conventional assays are normally based on the isolation of exosomes from biological fluids, which disrupts their native spatial context. Moreover, these assays frequently exhibit limited analytical sensitivity, and the types of exosome phenotypes detected simultaneously are inherently restricted by fluorescent spectral overlap. To address the issue, we present an integrated microfluidic platform coupling a biomimetic tumor-stroma coculture system with a nanoplasmonic antibody barcode array, enabling the in situ profiling of exosomes within the cellular microenvironment on-chip. By utilizing gold nanoislands (GNIs) as substrates, the device leverages localized surface plasmon resonance (LSPR) to achieve fluorescence enhancement and improved sensitivity. By employing spatial barcoding, this system overcomes the constraints imposed by spectral overlap and circumvents the limitations associated with the number of conventional multicolor fluorescence detection channels. This platform possesses the capability of conducting multiplexed qualitative analyses of exosome biomarkers. Downstream machine learning decoded these high-dimensional profiles, resolving distinct exosomal subpopulations on the basis of their unique phenotypic profiles. Overall, this platform establishes a robust, universal strategy for investigating exosome-mediated intercellular communication and metastatic niche formation.