Development of plasmon-enhanced biosensing for multiplexed profiling of extracellular vesicles

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The parent R01 project aims to advance nanoplasmonic sensing technology for robust multiplexed extracellular vesicle (EV) analysis and good reproducibility. The developed technology is validated using well- established preclinical and clinical samples to demonstrate the feasibility and potential of the new technology for clinical applications. In the course of the project, we have generated a large amount of imaging data from cells and EVs that are originating tumor and non-tumor models, as well as human clinical samples. This provides a new opportunity to develop a deep learning model to analyze high-dimensional and high-variate imaging data for machine-derived classification and uncover new insights. For robust deep-learning models, however, the quality of training data, besides the quantity of the data, is critical. Unbalanced data or embedded technical confounding factors often lead to the deep- or machine-learning models' decisions based on non- related or arbitrary parameters. Another problem is making false classifications when new input data is different from the data used for training, which is called out-of-distribution samples. These issues significantly hampered the deep-learning models' robustness with variable results and accuracies, resulting in disappointment and reduced enthusiasm for using AI models. The goal of this Administrative Supplement to the Parent R01 project is to develop a deep-learning-based data management software that digests massive cellular and molecular imaging data and produces balanced, confounder-free data sets ready for new deep- or machine-learning tasks. Specifically, we will design the algorithm for general users who do not necessarily have knowledge of the deep-learning framework. We will apply and test the software for multi-channel EV imaging data generated from the Parent R01 project and other cellular and EV imaging data from the past NIH project and other laboratories. The final software package and AI-ready data will be publicly shared with other researchers, and we will continuously provide feedback and updates through our IT core team. We envision that the software will offer a unique opportunity for researchers to create quality training data ready, reduce the technical barrier for researchers, and promote the use of their data for deep- or machine-learning models.