Label-Free Oligonucleotide-Based SPR Biosensor for the Detection of the Gene Mutation Causing Prothrombin-Related Thrombophilia

Surface Plasmon Resonance (SPR) Spectroscopy and Photonic Integrated Circuit (PIC) Biosensors: A Comparative Review

Label-free direct-optical biosensors such as surface-plasmon resonance (SPR) spectroscopy has become a gold standard in biochemical analytics in centralized laboratories. Biosensors based on photonic integrated circuits (PIC) are based on the same physical sensing mechanism: evanescent field sensing. PIC-based biosensors can play an important role in healthcare, especially for point-of-care diagnostics, if challenges for a transfer from research laboratory to industrial applications can be overcome. Research is at this threshold, which presents a great opportunity for innovative on-site analyses in the health and environmental sectors. A deeper understanding of the innovative PIC technology is possible by comparing it with the well-established SPR spectroscopy. In this work, we shortly introduce both technologies and reveal similarities and differences. Further, we review some latest advances and compare both technologies in terms of surface functionalization and sensor performance.

Acoustic Array Biochip Combined with Allele-Specific PCR for Multiple Cancer Mutation Analysis in Tissue and Liquid Biopsy

Regular screening of point mutations is of importance to cancer management and treatment selection. Although techniques like next-generation sequencing and digital polymerase chain reaction (PCR) are available, these are lacking in speed, simplicity, and cost-effectiveness. The development of alternative methods that can detect the extremely low concentrations of the target mutation in a fast and cost-effective way presents an analytical and technological challenge. Here, an approach is presented where for the first time an allele-specific PCR (AS-PCR) is combined with a newly developed high fundamental frequency quartz crystal microbalance array as biosensor for the amplification and detection, respectively, of cancer point mutations. Increased sensitivity, compared to fluorescence detection of the AS-PCR amplicons, is achieved through energy dissipation measurement of acoustically "lossy" liposomes binding to surface-anchored dsDNA targets. The method, applied to the screening of BRAF V600E and KRAS G12D mutations in spiked-in samples, was shown to be able to detect 1 mutant copy of genomic DNA in an excess of 10⁴ wild-type molecules, that is, with a mutant allele frequency (MAF) of 0.01%. Moreover, validation of tissue and plasma samples obtained from melanoma, colorectal, and lung cancer patients showed excellent agreement with Sanger sequencing and ddPCR; remarkably, the efficiency of this AS-PCR/acoustic methodology to detect mutations in real samples was demonstrated to be below 1% MAF. The combined high sensitivity and technology-readiness level of the methodology, together with the ability for multiple sample analysis (24 array biochip), cost-effectiveness, and compatibility with routine workflow, make this approach a promising tool for implementation in clinical oncology labs for tissue and liquid biopsy.

Simultaneous and sensitive detection of two pathogenic genes of thrombotic diseases using SPRi sensor with one-step fixation probe by a poly-adenine oligonucleotide approach

Single-base mutations of Factor V Leiden G1691A and Prothrombin gene G20210A are the main genetic risk factors for inherited thrombotic tendency. The establishment for rapid and efficient detection method is of great significance to the prevention of venous thrombosis. In this work, a multiplexed, highly sensitive and regenerable surface plasmon resonance imaging (SPRi) sensor is described to identify and detect the two pathogenic genes by fixing probes in one-step. The probes are fixed by ployA, which is a simpler, faster and lower cost modification method compared with traditional thiol (-SH). PolyA-DNA-AuNPs is used to amplify the signal to improve sensitivity. The detection limit of the sensor is 8 pM, and it has a wide dynamic range between 8 pM and 100 nM and a good linear relationship between 8 pM to 50 pM. The equilibrium dissociation constant (KD) of 3.0 (± 0.3) pM indicates a high binding capacity. Based on the advantages of high-throughput detection, the SPRi chip can simultaneously identify and detect two genes related to thrombotic Diseases. In addition, more than 90% signal intensity can still be obtained on the surface of the chip after being regenerated of 25 times, indicating that this SPRi sensor has good stability and reproducibility. The established SPRi sensor has the advantages of high-throughput, high-sensitivity, label-free and no need for amplification, which is expected to become an effective technical means for real-time online detection of gene point mutations, and can be extended to detect and quantify a wider range of DNA mutation diseases.

A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling

Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as 'black boxes' and it is unclear how decisions are derived. Recently, techniques have been applied to help us understand how specific ML models work and explain the rational for outputs. This study aims to determine why a given type of cancer has a certain phenotypic characteristic. Cancer results in cellular dysregulation and a thorough consideration of cancer regulators is required. This would increase our understanding of the nature of the disease and help discover more effective diagnostic, prognostic, and treatment methods for a variety of cancer types and stages. Our study proposes a novel explainable analysis of potential biomarkers denoting tumorigenesis in non-small cell lung cancer. A number of these biomarkers are known to appear following various treatment pathways. An enhanced analysis is enabled through a novel mathematical formulation for the regulators of mRNA, the regulators of ncRNA, and the coupled mRNA-ncRNA regulators. Temporal gene expression profiles are approximated in a two-dimensional spatial domain for the transition states before converging to the stationary state, using a system comprised of coupled-reaction partial differential equations. Simulation experiments demonstrate that the proposed mathematical gene-expression profile represents a best fit for the population abundance of these oncogenes. In future, our proposed solution can lead to the development of alternative interpretable approaches, through the application of ML models to discover unknown dynamics in gene regulatory systems.