Fluorescence analysis of circulating exosomes for breast cancer diagnosis using a sensor array and deep learning

Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer detection. However, the complexity and heterogeneity of exosomes require improved methods to achieve the desired sensitivity and accuracy. Tsinghua University researchers have developed a breast cancer liquid biopsy system, including a fluorescence sensor array and deep learning (DL) tool AggMapNet. The researchers used a 12-unit sensor array composed of conjugated polyelectrolytes, fluorophore-labeled peptides, and monosaccharides or glycans to collect fluorescence signals from cells and exosomes. Linear discriminant analysis (LDA) processed the fluorescence spectral data of cells and cell-derived exosomes, demonstrating successful discrimination between normal and different cancerous cells and 100% accurate classification of different BC cells. For heterogeneous plasma-derived exosome analysis, CNN-based DL tool AggMapNet was applied to transform the unordered fluorescence spectra into feature maps (Fmaps), which gave a straightforward visual demonstration of the difference between healthy donors and BC patients with 100% prediction accuracy. This work indicates that the fluorescent sensor array and DL model can be used as a promising noninvasive method for BC diagnosis.

Jin Y, Du N, Huang Y, Shen W, Tan Y, Chen YZ, Dou WT, He XP, Yang Z, Xu N, Tan C. (2022) Fluorescence Analysis of Circulating Exosomes for Breast Cancer Diagnosis Using a Sensor Array and Deep Learning. ACS Sens [Epub ahead of print]. [abstract]

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