Label-Free Plasmon-Enhanced Spectroscopic HER2 Detection for Dynamic Therapeutic Surveillance of Breast Cancer

Designing SERS nanotags for profiling overexpressed surface markers on single cancer cells: A review

This review focuses on the chemical design and the use of Surface-Enhanced Raman Scattering (SERS)-active nanotags for measuring surface markers that can be overexpressed at the surface of single cancer cells. Indeed, providing analytical tools with true single-cell measurements capabilities is capital, especially since cancer research is increasingly leaning toward single-cell analysis, either to guide treatment decisions or to understand complex tumor behaviour including the single-cell heterogeneity and the appearance of treatment resistance. Over the past two decades, SERS nanotags have triggered significant interest in the scientific community owing their advantages over fluorescent tags, mainly because SERS nanotags resist photobleaching and exhibit sharper signal bands, which reduces possible spectral overlap and enables the discrimination between the SERS signals and the autofluorescence background from the sample itself. The extensive efforts invested in harnessing SERS nanotags for biomedical purposes, particularly in cancer research, highlight their potential as the next generation of optical labels for single-cell studies. The review unfolds in two main parts. The first part focuses on the structure of SERS nanotags, detailing their chemical composition and the role of each building block of the tags. The second part explores applications in measuring overexpressed surface markers on single-cells. The latter encompasses studies using single nanotags, multiplexed measurements, quantitative information extraction, monitoring treatment responses, and integrating phenotype measurements with SERS nanotags on single cells isolated from complex biological matrices. This comprehensive review anticipates SERS nanotags to persist as a pivotal technology in advancing single-cell analytical methods, particularly in the context of cancer research and personalized medicine.

Dual amplification for PEC ultrasensitive aptasensing of biomarker HER-2 based on Z-scheme UiO-66/CdIn2S4 heterojunction and flower-like PtPdCu nanozyme

As an important prognostic indicator in breast cancer, human epithelial growth factor receptor-2 (HER-2) is of importance for assessing prognosis of breast cancer patients, whose accurate and facile analysis are imperative in clinical diagnosis and treatment. Herein, photoactive Z-scheme UiO-66/CdIn2S4 heterojunction was constructed by a hydrothermal method, whose optical property and photoactivity were critically investigated by a range of techniques, combined by elucidating the interfacial charge transfer mechanism. Meanwhile, PtPdCu nanoflowers (NFs) were fabricated by a simple aqueous wet-chemical method, whose peroxidase (POD)-mimicking catalytic activity was scrutinized by representative tetramethylbenzidine (TMB) oxidation in H2O2 system. Taken together, the UiO-66/CdIn2S4 based photoelectrochemical (PEC) aptasensor was established for quantitative analysis of HER-2, where the detection signals were further magnified through catalytic precipitation reaction towards 4-chloro-1-naphthol (4-CN) oxidation (assisted by the PtPdCu NFs nanozyme). The PEC aptasensor presented a broader linear range within 0.1 pg mL-1-0.1 μg mL-1 and a lower limit of detection of 0.07 pg mL-1. This work developed a new PEC aptasensor for ultrasensitive determination of HER-2, holding substantial promise for clinical diagnostics.

SERS biosensors for liquid biopsy towards cancer diagnosis by detection of various circulating biomarkers: current progress and perspectives

Liquid biopsy has emerged as a promising non-invasive strategy for cancer diagnosis, enabling the detection of various circulating biomarkers, including circulating tumor cells (CTCs), circulating tumor nucleic acids (ctNAs), circulating tumor-derived small extracellular vesicles (sEVs), and circulating proteins. Surface-enhanced Raman scattering (SERS) biosensors have revolutionized liquid biopsy by offering sensitive and specific detection methodologies for these biomarkers. This review comprehensively examines the application of SERS-based biosensors for identification and analysis of various circulating biomarkers including CTCs, ctNAs, sEVs and proteins in liquid biopsy for cancer diagnosis. The discussion encompasses a diverse range of SERS biosensor platforms, including label-free SERS assay, magnetic bead-based SERS assay, microfluidic device-based SERS system, and paper-based SERS assay, each demonstrating unique capabilities in enhancing the sensitivity and specificity for detection of liquid biopsy cancer biomarkers. This review critically assesses the strengths, limitations, and future directions of SERS biosensors in liquid biopsy for cancer diagnosis.

Encapsulation and assessment of therapeutic cargo in engineered exosomes: a systematic review

Exosomes are nanoscale extracellular vesicles secreted by cells and enclosed by a lipid bilayer membrane containing various biologically active cargoes such as proteins, lipids, and nucleic acids. Engineered exosomes generated through genetic modification of parent cells show promise as drug delivery vehicles, and they have been demonstrated to have great therapeutic potential for treating cancer, cardiovascular, neurological, and immune diseases, but systematic knowledge is lacking regarding optimization of drug loading and assessment of delivery efficacy. This review summarizes current approaches for engineering exosomes and evaluating their drug delivery effects, and current techniques for assessing exosome drug loading and release kinetics, cell targeting, biodistribution, pharmacokinetics, and therapeutic outcomes are critically examined. Additionally, this review synthesizes the latest applications of exosome engineering and drug delivery in clinical translation. The knowledge compiled in this review provides a framework for the rational design and rigorous assessment of exosomes as therapeutics. Continued advancement of robust characterization methods and reporting standards will accelerate the development of exosome engineering technologies and pave the way for clinical studies.

An ultrasensitive ratiometric electrochemical aptasensor based on metal-organic frameworks and nanoflower-like Bi2CuO4 for human epidermal growth factor receptor 2 detection

An ultra-sensitive ratiometric electrochemical aptasensor was constructed based on metal-organic frameworks (MOFs) and bimetallic oxides for the detection of the human epidermal growth factor receptor 2 (HER2), a breast cancer marker. The aluminum metal-organic framework (Al-MOF) and cerium-metal-organic framework (Ce-MOF) have higher specific surface area, which is conducive to load more aptamers or complementary DNA (cDNA), and realize the amplification of internal reference signal Fc. Furthermore, nanoflower-like bismuth copper oxide (Bi2CuO4) with abundant active sites was introduced to modify more aptamers on its surface, which were then fixed to the glassy carbon electrode (GCE) to amplify the detection signal. The quantitative detection of HER2 was achieved by differential pulse voltammetry (DPV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The materials were characterized by scanning electron microscope, transmission electron microscope, Zeta potential analyzer, X-ray diffraction and X-ray photoelectron spectroscopy. The ratiometric electrochemical aptasensor based on nanomaterial and chain displacement signal amplification technology could discern HER2 in a very wide range (0.001-20.0 ng/mL) with an extremely low detection limit (0.049 pg/mL) and has demonstrated good performance in clinical serum analysis. This strategy also provides a feasible idea for sensitive analysis of other clinical tumor markers.

Absolute Quantification of Serum Exosomes in Patients with an SERS-Lateral Flow Strip Biosensor for Noninvasive Clinical Cancer Diagnosis

Exosomes (exos) widely existing in body fluids show great potential for noninvasive cancer diagnosis. Quantitative analysis of exos is traditionally performed by targeting specific exosomal surface proteins, but it is often imprecise due to the common expression of exosomal proteins and subtle expression differences between different cancer subtypes. Herein, we report quantitative surface-enhanced Raman spectroscopy (SERS) of serum exos through a combination of a paper-based lateral flow strip (LFS) biosensor with multivariate spectral unmixing analysis rather than simply quantifying exosomal proteins. Our SERS-LFS biosensor enables absolute quantification of two different serum exos with a limit of detection down to ∼106 particles/mL for both exos. We further exemplify the application of this strategy in quantitative dual-plex detection of serum exos from breast cancer patients. We find that human epidermal growth factor receptor 2+ (HER2+) and luminal A breast cancer patients undergoing no surgery are enriched in serum exos derived from SKBR-3 cells and MCF-7 cells (denoted as SKBR and MCF exos), respectively. The surgical treatment of these breast cancer patients accompanies an obvious decrease of either SKBR or MCF exos in the serum. These results suggest the great potential of the combination of the SERS-LFS biosensor and multivariate spectral unmixing for breast cancer subtyping and therapeutic surveillance with the powerful quantitative capability of exos in clinical samples.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering

Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.