Smartphone-Assisted Lab-In-A-Tube Device Using Gold Nanocluster-Based Aptasensor for Detection of Muc1-Overexpressed Tumor Cells Publisher Pubmed



Sanati A¹ ; Esmaeili Y¹ ; Khavani M² ; Bidram E^{1, 4} ; Rahimi A³ ; Dabiri A³ ; Rafienia M¹ ; Arbab Jolfaie N¹ ; Mofrad MRK² ; Haghjooy Javanmard S³ ; Shariati L^{3, 4} ; Zarrabi A⁵ Show Affiliations
Source: Analytica Chimica Acta Published:2023

Abstract

Developing smartphone technology for point-of-care diagnosis is one of the current favorable trends in the field of biosensors. In fact, using smartphones can provide better accessibility and facility for rapid diagnosis of diseases. On the other hand, the detection of circulating tumor cells (CTCs) is one of the recent methods for the early diagnosis of cancer. Here, a new smartphone-assisted lab-in-a-tube device is introduced for the detection of Mucin 1 (MUC1) overexpressed tumor-derived cell lines using gold nanoclusters (GNCs)-based aptasensor. Accordingly, commercial polyurethane (PU) foam was first coated with graphene oxide (GO) to increase its surface area (8.45-fold), and improve its wettability. The surface of the resulting three-dimensional PU-GO (3DPU-GO) platform was then modified by MUC1 aptamer-GNCs to provide the required sensitivity and specificity through a turn “on/off” detection system. The proposed biosensor was first optimized with a spectrophotometer method. Afterward, findings were evaluated based on the red color intensity of the lab-in-a-tube system; and indicated the high ability of the biosensor for detection of MUC1-overexpressed tumor cell lines in the range of 250–20,000 cells mL−1 with a limit of detection of 221 cells mL−1. In addition, the developed biosensor showed a decent selectivity against positive-control cell lines (MCF-7, and HT-29) in comparison to negative-control cell lines (HEK293, and L929). Notably, the results represented good accordance with reference methods including spectroscopy devices. Ultimately, the results of this work bring a new perspective to the field of point-of-care detection and can be considered in future biosensors. © 2023 Elsevier B.V.