Accepted paper in Biosensors and Bioelectronics

Peptide-based biosensing approaches for targeting breast cancer-derived exosomes

Exosomes are nanovesicles present in all the biological fluids, making them attractive as non-invasive biomarkers for diseases like cancer, among many others. However, exosomes are complex to separate and detect, requiring comprehensive molecular characterization for their routine use in diagnostics. This study explores the use of peptides as cost-effective and stable alternatives to antibodies for exosome binding. To achieve that, phage display technology was employed to select peptides with high specificity for target molecules in exosomes. Specifically, a selected peptide was evaluated for its ability to selectively bind breast cancer-derived exosomes. Proteomic analysis identified 38 protein candidates targeted by the peptide on exosome membranes. The binding of the peptide to breast cancer-derived exosomes was successfully demonstrated by flow cytometry and magneto-actuated immunoassays. Furthermore, an electrochemical biosensor was also tested for breast cancer-derived exosome detection and quantification. The peptide demonstrated effective binding to exosomes from aggressive cancer cell lines, offering promising results in terms of specificity and recovery. This research shows potential for developing rapid, accessible diagnostic tools for breast cancer, especially in low-resource healthcare settings.

Rafael da Fonseca Alves1,2,3, Arnau Pallarès-Rusiñol 1,2, Rosanna Rossi 1,2, Merce Martí 1, Emilia Rezende Vaz4, Thaise Gonçalves de Araújo4, Maria Del Pilar Taboada Sotomayor3 and Maria Isabel Pividori 1,2* 

1 Biosensing and Bioanalysis Group, Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona.

2 Grup de Sensors i Biosensors, Departament de Química, Universitat Autònoma de Barcelona, Bellaterra, Spain

3 Institute of Chemistry, State University of São Paulo (UNESP), Brazil.

4 Institute of Biotechnology (IBTEC), Federal University of Uberlandia (UFU), Uberlandia, MG, Brazil.

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