Detecting cancer cells with a highly sensitive LbL-based biosensor

Talanta. 2021 Oct 1:233:122506. doi: 10.1016/j.talanta.2021.122506. Epub 2021 May 11.

Abstract

Early diagnosis of cancer is crucial for therapeutic methods to be more effective and to decrease the mortality rate due to this disease. Current diagnostic methods include imaging techniques that require expensive equipment and specialized personnel, making it difficult to apply them to many patients. To overcome these limitations, many biosensors have been developed to monitor cancer biomarkers. Here, we report on the electrochemical biosensor for selective detection of tumor cells using a simple and low-cost methodology. Layer-by-layer (LbL) self-assembly was used to modify indium tin oxide (ITO) electrodes with alternating layers of polyallylamine hydrochloride (PAH) and folic acid (FA), which binds to overexpressed folate receptors alpha (FRα) in tumor cells. The LbL-based biosensor showed high sensitivity in detecting cervical cancer cells (HeLa cells) using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). A linear dependence with the logarithm cell concentration was observed and excellent detection limits were found, 4 cells mL-1 and 19 cells mL-1 for EIS and CV measurements, respectively. The developed biosensor also presented great reproducibility (RSD = 1.7%) and repeatability (RSD = 1.8%). The selectivity was confirmed after the biosensor interaction with healthy cells (HMEC cells), which did not produce significant changes in the electrochemical signals. Furthermore, it was demonstrated that selective detection of tumor cells occurs via an interaction with FA. The LbL-based biosensor provides a simple, accurate, and cost-effective platform to be applied in the early diagnosis of cancer.

Keywords: Electrochemical biosensor; Folic acid; Layer-by-layer self-assembly; Tumor cells detection.

MeSH terms

  • Biosensing Techniques*
  • Electrochemical Techniques
  • Electrodes
  • HeLa Cells
  • Humans
  • Neoplasms* / diagnosis
  • Reproducibility of Results