Biosensor for the Characterization of Gene Expression in Cells

Dielectrophoresis: Measurement technologies and auxiliary sensing applications

Dielectrophoresis (DEP), which arises from the interaction between dielectric particles and an aqueous solution in a nonuniform electric field, contributes to the manipulation of nano and microparticles in many fields, including colloid physics, analytical chemistry, molecular biology, clinical medicine, and pharmaceutics. The measurement of the DEP force could provide a more complete solution for verifying current classical DEP theories. This review reports various imaging, fluidic, optical, and mechanical approaches for measuring the DEP forces at different amplitudes and frequencies. The integration of DEP technology into sensors enables fast response, high sensitivity, precise discrimination, and label-free detection of proteins, bacteria, colloidal particles, and cells. Therefore, this review provides an in-depth overview of DEP-based fabrication and measurements. Depending on the measurement requirements, DEP manipulation can be classified into assistance and integration approaches to improve sensor performance. To this end, an overview is dedicated to developing the concept of trapping-on-sensing, improving its structure and performance, and realizing fully DEP-assisted lab-on-a-chip systems.

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

Sensing of gene expression in live cells using electrical impedance spectroscopy and DNA-functionalized gold nanoparticles

A novel electrical impedance spectroscopy-based method for non-destructive sensing of gene expression in living cells is presented. The approach used takes advantage of the robustness and responsiveness of electrical impedance spectroscopy and the highly specific and selective nature of DNA hybridization. The technique uses electrical impedance spectroscopy and gold nanoparticles functionalized with single-stranded DNA complementary to an mRNA of interest to provide reliable, real-time, and quantifiable data on gene expression in live cells. The system was validated by demonstrating specific detection of the uidA mRNA, which codes for the β-glucuronidase (GUS) enzyme, in Solanum lycopersicum MsK8 cells. Gold nanoparticles were functionalized with single-stranded DNA oligonucleotides consisting of either a sequence complementary to uidA mRNA or an arbitrary sequence. The DNA-functionalized gold nanoparticles were mixed with cell suspensions, allowing the gold nanoparticles to penetrate into the cells. The impedance spectra of suspensions of cells with gold nanoparticles inserted within them were then studied. In suspensions of uidA-expressing cells and gold nanoparticles functionalized with the complementary single-stranded DNA oligonucleotide, the impedance magnitude in the frequency range of interest was significantly higher (146 %) in comparison to all other controls. Due to its highly selective nature, the methodology has the potential to be used as a precision agricultural sensing system for accurate and real-time detection of markers of stress, viral infection, disease, and normal physiological activities.

The application of an applied electrical potential to generate electrical fields and forces to enhance affinity biosensors

Affinity biosensors play a crucial role in clinical diagnosis, pharmaceuticals, immunology, and other areas of human health. Affinity biosensors rely on the specific binding between target analytes and biological ligands such as antibodies, nucleic acids, aptamers, or other receptors to primarily generate electrochemical or optical signals. Considerable effort has been put into improving the performance of the affinity technologies to make them more sensitive, efficient and reproducible, of the many approaches electrokinetic phenomena are a viable option. In this perspective, studies that combine electrokinetic phenomena with affinity biosensor are discussed about their promise for achieving higher sensitivity and lower detection limit.

Dielectrophoresis-Based Biosensor for Detection of the Cancer Biomarkers CEA and CA 242 in Serum

We show that dielectrophoresis (DEP) spectroscopy is an effective transduction mechanism for detection of the concentration levels of the pancreatic cancer biomarkers cancer antigen (CA) 242 and carcinoembryonic antigen (CEA) in serum. We noticed a frequency dependence of the negative DEP force applied by interdigitated electrodes on functionalized polystyrene microspheres (PM) with respect to changes in the number of these cancer antigens bound to the PM. An electrode array with a well-defined gradient of the electric field was designed and used, which enabled the automation of the signal processing and reproducibility of the signal acquired by the biosensor.