Direct and Label-Free Detection of MicroRNA Cancer Biomarkers using SERS-Based Plasmonic Coupling Interference (PCI) Nanoprobes

SERS-based methods for the detection of genomic biomarkers of cancer

Genomic biomarkers of cancer are based on changes in nucleic acids, which include abnormal expression levels of some miRNAs, point mutations in DNA sequences, and altered levels of DNA methylation. The presence of tumor-related nucleic acids in body fluids (blood, saliva, or urine) makes it possible to achieve a non-invasive early-stage cancer diagnosis. Currently existing techniques for the discovery of nucleic acids require complex, time-consuming, costly assays and have limited multiplexing abilities. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique that is able to provide molecular specificity combined with trace sensitivity. SERS has gained research attention as a tool for the detection of nucleic acids because of its promising potential: label-free SERS can decrease the complexity of assays currently used with fluorescence-based detection due to the absence of the label, while labeled SERS may outperform the gold standard in terms of the multiplexing ability. The first papers about SERS-based methods for the measurement of genomic biomarkers were written in 2008, and since then, more than 150 papers have been published. The aim of this paper is to review and evaluate the proposed SERS-based methods in terms of their level of development and their potential for liquid biopsy application, as well as to contribute to their further evolution by attracting research attention to the field. This goal will be reached by grouping, on the basis of their experimental protocol, all the published manuscripts on the topic and evaluating each group in terms of its limit of detection and applicability to real body fluids. Thus, the methods are classified according to their working principles into five main groups, including capture-based, displacement-based, sandwich-based, enzyme-assisted, and specialized protocols.

Plasmonic Nanoparticle-Enhanced Optical Techniques for Cancer Biomarker Sensing

This review summarizes recent advances in leveraging localized surface plasmon resonance (LSPR) nanotechnology for sensitive cancer biomarker detection. LSPR arising from noble metal nanoparticles under light excitation enables the enhancement of various optical techniques, including surface-enhanced Raman spectroscopy (SERS), dark-field microscopy (DFM), photothermal imaging, and photoacoustic imaging. Nanoparticle engineering strategies are discussed to optimize LSPR for maximum signal amplification. SERS utilizes electromagnetic enhancement from plasmonic nanostructures to boost inherently weak Raman signals, enabling single-molecule sensitivity for detecting proteins, nucleic acids, and exosomes. DFM visualizes LSPR nanoparticles based on scattered light color, allowing for the ultrasensitive detection of cancer cells, microRNAs, and proteins. Photothermal imaging employs LSPR nanoparticles as contrast agents that convert light to heat, producing thermal images that highlight cancerous tissues. Photoacoustic imaging detects ultrasonic waves generated by LSPR nanoparticle photothermal expansion for deep-tissue imaging. The multiplexing capabilities of LSPR techniques and integration with microfluidics and point-of-care devices are reviewed. Remaining challenges, such as toxicity, standardization, and clinical sample analysis, are examined. Overall, LSPR nanotechnology shows tremendous potential for advancing cancer screening, diagnosis, and treatment monitoring through the integration of nanoparticle engineering, optical techniques, and microscale device platforms.

Alkyne-tethered oligodeoxynucleotides that allow simultaneous detection of multiple DNA/RNA targets using Raman spectroscopy

Detection of multiple DNA/RNA targets is essential for understanding cellular function. Herein, we propose a general method for the simultaneous detection of plural nucleic acids based on surface-enhanced Raman scattering (SERS) using gold nanoparticles bearing functional oligodeoxynucleotides (ODNs) on their surface. Modified ODNs bearing an acetylene tag hybridized with their complementary ODNs on the surface of the gold nanoparticles, inducing a strong SERS signal of the acetylene tag. The addition of the target nucleic acid to the system resulted in a spontaneous displacement of the strand on the particle and dissociation of the alkyne-tagged ODN from the particle, resulting in a dramatic decrease in signal intensity. By using an alkyne tag for each of the multiple target nucleic acids, each target could be detected simultaneously. In addition, we successfully detected cellular microRNA. Different targets showed changes with different wavenumbers in the Raman spectra, allowing for the detection of multiple nucleic acids.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

A Review of Biosensors for Detecting Tumor Markers in Breast Cancer

Breast cancer has the highest cancer incidence rate in women. Early screening of breast cancer can effectively improve the treatment effect of patients. However, the main diagnostic techniques available for the detection of breast cancer require the corresponding equipment, professional practitioners, and expert analysis, and the detection cost is high. Tumor markers are a kind of active substance that can indicate the existence and growth of the tumor. The detection of tumor markers can effectively assist the diagnosis and treatment of breast cancer. The conventional detection methods of tumor markers have some shortcomings, such as insufficient sensitivity, expensive equipment, and complicated operations. Compared with these methods, biosensors have the advantages of high sensitivity, simple operation, low equipment cost, and can quantitatively detect all kinds of tumor markers. This review summarizes the biosensors (2013-2021) for the detection of breast cancer biomarkers. Firstly, the various reported tumor markers of breast cancer are introduced. Then, the development of biosensors designed for the sensitive, stable, and selective recognition of breast cancer biomarkers was systematically discussed, with special attention to the main clinical biomarkers, such as human epidermal growth factor receptor-2 (HER2) and estrogen receptor (ER). Finally, the opportunities and challenges of developing efficient biosensors in breast cancer diagnosis and treatment are discussed.

Smartphone-Based Device for Colorimetric Detection of MicroRNA Biomarkers Using Nanoparticle-Based Assay

The detection of microRNAs (miRNAs) is emerging as a clinically important tool for the non-invasive detection of a wide variety of diseases ranging from cancers and cardiovascular illnesses to infectious diseases. Over the years, miRNA detection schemes have become accessible to clinicians, but they still require sophisticated and bulky laboratory equipment and trained personnel to operate. The exceptional computing ability and ease of use of modern smartphones coupled with fieldable optical detection technologies can provide a useful and portable alternative to these laboratory systems. Herein, we present the development of a smartphone-based device called Krometriks, which is capable of simple and rapid colorimetric detection of microRNA (miRNAs) using a nanoparticle-based assay. The device consists of a smartphone, a 3D printed accessory, and a custom-built dedicated mobile app. We illustrate the utility of Krometriks for the detection of an important miRNA disease biomarker, miR-21, using a nanoplasmonics-based assay developed by our group. We show that Krometriks can detect miRNA down to nanomolar concentrations with detection results comparable to a laboratory-based benchtop spectrophotometer. With slight changes to the accessory design, Krometriks can be made compatible with different types of smartphone models and specifications. Thus, the Krometriks device offers a practical colorimetric platform that has the potential to provide accessible and affordable miRNA diagnostics for point-of-care and field applications in low-resource settings.

Rapid and label-free identification of different cancer types based on surface-enhanced Raman scattering profiles and multivariate statistical analysis

Rapid detection and classification of cancer cells with label-free and non-destructive methods are helpful for rapid screening of cancer patients in clinical settings. Here, surface-enhanced Raman scattering (SERS) was used for rapid, unlabeled, and non-destructive detection of seven different cell types, including human cancer cells and non-tumorous cells. Au nanoparticles were used as enhanced substrates and directly added to cell surfaces. The single cellular SERS signals could be easily and stably collected in several minutes, and the cells maintained structural integrity over one hour. Different types of cells had unique Raman phenotypes. By applying multivariate statistical analysis to the Raman phenotypes, the cancer cells and non-tumorous cells were accurately identified. The high sensitivity enabled this method to discriminate subtle molecular changes in different cell types, and the accuracy reached 81.2% with principal components analysis and linear discriminant analysis. The technique provided a rapid, unlabeled, and non-destructive method for the detection and identification of various cancer types.