Recent Progress on Liquid Biopsy Analysis using Surface-Enhanced Raman Spectroscopy

SERS and Machine Learning-Enabled Liquid Biopsy: A Promising Tool for Early Detection and Recurrence Prediction in Acute Leukemia

Acute leukemia (AL), classified as acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL), is a hematologic malignancy caused by the uncontrolled proliferation of leucocytes in the bone marrow. Early detection of AL is crucial for clinical treatment. Detection methods of AL are currently blood tests, bone marrow tests, imaging, and spinal fluid tests. However, these tests have drawbacks, such as high cost and time consumption. Liquid biopsy using biological fluids such as blood or serum is an emerging technique for noninvasive cancer detection and monitoring. Surface-enhanced Raman spectroscopy (SERS), which enhanced Raman signals by the interaction of plasmonic nanostructures with the analyte, is a highly sensitive and specific detection method with simple sample preparation that has been used in combination with machine learning techniques to analyze liquid biopsy. In this study, we developed a SERS-based liquid biopsy approach that enables accurate classification of AML and ALL subtypes and the prediction of disease recurrence. SERS spectra of serum samples from 24 healthy individuals, 43 AML patients, and 18 ALL patients were obtained using an Ag-based SERS substrate and clustered using hierarchical cluster analysis (HCA). The spectra were then classified using three commonly used classifiers, namely, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN). Our findings demonstrate that the RF classifier has the highest accuracy values, with 96.1, 95.5, and 98.5% for classifying three groups and predicting the recurrence of AML and ALL, respectively. The combination of SERS-based serum analysis with machine learning algorithms represents a remarkable advancement in the realm of hematological disease diagnostics, particularly for AML and ALL. This approach not only facilitates the precise differentiation of disease subtypes but also introduces the novel capability of prognosticating disease recurrence.

Liquid Biopsy in HPV-Associated Head and Neck Cancer: A Comprehensive Review

Objectives: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer globally, with HPV-positive cases emerging as a distinct subtype with unique clinical and molecular characteristics. Current diagnostic methods, including tissue biopsy and imaging, face limitations in terms of invasiveness, static disease assessment, and difficulty in distinguishing recurrence from treatment-related changes. This review aimed to assess the potential of liquid biopsy as a minimally invasive tool for the diagnosis, treatment monitoring, and surveillance of HPV-associated HNSCC. Methods: This systematic review analyzed literature from PubMed/MEDLINE, Embase, and Web of Science, focusing on original research and reviews related to liquid biopsy applications in HPV-positive HNSCC. Included studies were evaluated based on the robustness of the study design, clinical relevance, and analytical performance of liquid biopsy technologies. Biomarker types, detection methods, and implementation strategies were assessed to identify advancements and challenges in this field. Results: Liquid biopsy technologies, including circulating HPV DNA, ctDNA, and extracellular vesicles, demonstrated high sensitivity (90-95%) and specificity (>98%) in detecting HPV-positive HNSCC. These methods enabled real-time monitoring of tumor dynamics, early detection of recurrence, and insights into treatment resistance. Longitudinal analysis revealed that biomarker clearance during treatment correlates strongly with patient outcomes. Conclusions: Liquid biopsy is a transformative diagnostic and monitoring tool for HPV-associated HNSCC, offering minimally invasive, real-time insights into tumor biology. While challenges remain in standardization and clinical implementation, ongoing research and technological innovations hold promise for integrating liquid biopsy into personalized cancer care, ultimately improving patient outcomes.

Rational side-by-side self-assembly of gold nanorods with short and medium aspect ratios via the self-evaporation method to boost their potential as a surface-enhanced Raman scattering (SERS) substrate

Surface-enhanced Raman scattering (SERS) represents a compelling detection methodology centered on the electromagnetic fields, commonly termed "hot spots", generated around noble nanoparticles. Nonetheless, the efficacy of electromagnetic field (EMF) amplification is constrained when utilizing individual nanoparticles. There has been a notable lack of experimental and theoretically simulated studies regarding the increase of the electromagnetic field when gold nanorods with different aspect ratios undergo self-assembly in either perpendicular or parallel orientations to substrates. This research presents a novel and facile methodology for fabricating SERS nanosubstrates. This method entails self-assembling gold nanorods (AuNRs) with short and medium aspect ratios (ARs) through natural evaporation. By manipulating the water-to-ethanol ratios, we ascertain the appropriate conditions for the rational alignment of the nanorods in both perpendicular and parallel orientations relative to the silicon substrate. These nanosubstrates have been experimentally evaluated for their ability to improve the Surface-Enhanced Raman Scattering (SERS) performance, presenting a novel perspective in this field. In addition, a computational analysis employing the finite-difference time-domain (FDTD) method was conducted to elucidate the electromagnetic field generated by nanoarrays when subjected to incident light of varying wavelengths, including 532 nm, 638 nm, and 785 nm. Notably, the FDTD simulation outcomes indicated that gold nanorods (AuNRs) possessing an aspect ratio of 3.0 and nanogaps of 2.0 nm exhibited exceptional electromagnetic field characteristics when aligned parallel to the substrate under 532 nm laser illumination. Conversely, when the AuNRs were oriented perpendicular to the substrates, they produced lower EMFs upon interaction with excitation laser light. These findings can potentially contribute to the advancement of SERS nanosubstrate design.

Surface-Enhanced Raman Scattering (SERS)-based biosensors for advanced extracellular vesicle detection: A review

BACKGROUND

Extracellular Vesicles (EVs), as nano-scale vesicles rich in biological information, hold an indispensable status in the biomedical field. However, due to the intrinsic small size and low abundance of EVs, their effective detection presents significant challenges. Although various EV detection techniques exist, their sensitivity and ease of operation still need enhancement.

RESULTS

Surface-Enhanced Raman Scattering (SERS) is known for its high sensitivity and specificity. It stands out in tackling the challenges that traditional EV detection methods face. In this review, we focus on the application of SERS-based biosensors in EV detection. It provides a detailed introduction to the recognition and capture of EVs, strategies for mediating signal amplification, and detection of EV biomarkers. Finally, the challenges and prospects of SERS-based biosensors are discussed.

SIGNIFICANCE

SERS-based biosensor enhances the Raman signal, allowing for the detection of biomarkers at low concentrations. This capability reveals its substantial potential in identifying EVs and analyzing molecular data. It paves the path for advanced EV detection.

Raman Spectroscopy and Exosome-Based Machine Learning Predicts the Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer

Early prediction of the neoadjuvant therapy efficacy for HER2-positive breast cancer is crucial for personalizing treatment and enhancing patient outcomes. Exosomes, which play a role in tumor development and treatment response, are emerging as potential biomarkers for cancer diagnosis and efficacy prediction. Despite their promise, current exosome detection and isolation methods are cumbersome and time-consuming and often yield limited purity and quantity. In this study, we employed Raman spectroscopy to analyze the molecular changes in exosomes from the sera of HER2-positive breast cancer patients before and after two cycles of neoadjuvant therapy. Utilizing machine learning techniques (PCA, LDA, and SVM), we developed a predictive model with an AUC value exceeding 0.89. Additionally, we introduced an innovative HER2-positive exosome capture and detection system, termed Magnetic beads@HER2-Exos@HER2-SERS detection nanoprobes (HER2-MEDN). This system enabled us to efficiently extract and analyze HER2-positive exosomes, refining our predictive model to achieve an accuracy greater than 0.94. Our study has demonstrated the potential of the HER2-MEDN system in accurately predicting early treatment response, offering novel insights and methodologies for assessing the efficacy of neoadjuvant therapy in HER2-positive breast cancer.

Machine learning assisted dual-modal SERS detection for circulating tumor cells

Detecting circulating tumor cells (CTCs) from blood has become a promising approach for cancer diagnosis. Surface-enhanced Raman Spectroscopy (SERS) has rapidly developed as a significant detection technology for CTCs, offering high sensitivity and selectivity. Encoded SERS bioprobes have gained attention due to their excellent specificity and ability to identify tumor cells using Raman signals. Machine learning has also made significant contributions to biomedical applications, especially in medical diagnosis. In this study, we developed a detection strategy combining encoded SERS bioprobes and machine learning models to identify CTCs. Dual-modal SERS bioprobes were designed and co-incubated with tumor cells by the "cocktail" method. An identification model for CTCs was constructed using principal component analysis (PCA) and the Random Forest classification algorithm. This innovative strategy endows SERS bioprobes with both effective magnetic separation and highly sensitive identification of CTCs, even at low concentrations of 2 cells/mL. It achieved a high detection rate of 98% for CTCs and effectively eliminated interference from peripheral WBCs. This simple and efficient strategy provides a new approach for CTCs detection and holds important significance for cancer diagnosis.

Circulating Extracellular Vesicles as Promising Biomarkers for Precession Diagnostics: A Perspective on Lung Cancer

Extracellular vesicles (EVs) have emerged as promising biomarkers in liquid biopsy, owing to their ubiquitous presence in bodily fluids and their ability to carry disease-related cargo. Recognizing their significance in disease diagnosis and treatment, substantial efforts have been dedicated to developing efficient methods for EV isolation, detection, and analysis. EVs, heterogeneous membrane-encapsulated vesicles secreted by all cells, contain bioactive substances capable of modulating recipient cell biology upon internalization, including proteins, lipids, DNA, and various RNAs. Their prevalence across bodily fluids has positioned them as pivotal mediators in physiological and pathological processes, notably in cancer, where they hold potential as straightforward tumor biomarkers. This review offers a comprehensive examination of advanced nanotechnology-based techniques for detecting lung cancer through EV analysis. It begins by providing a brief overview of exosomes and their role in lung cancer progression. Furthermore, this review explores the evolving landscape of EV isolation and cargo analysis, highlighting the importance of characterizing specific biomolecular signatures within EVs for improved diagnostic accuracy in lung cancer patients. Innovative strategies for enhancing the sensitivity and specificity of EV isolation and detection, including the integration of microfluidic platforms and multiplexed biosensing technologies are summarized. The discussion then extends to key challenges associated with EV-based liquid biopsies, such as the standardization of isolation and detection protocols and the establishment of robust analytical platforms for clinical translation. This review highlights the transformative impact of EV-based liquid biopsy in lung cancer diagnosis, heralding a new era of personalized medicine and improved patient care.

Surface-enhanced Raman scattering-based strategies for tumor markers detection: A review

The presence of malignant tumors poses a significant threat to people's life and well-being. As biochemical parameters indicate the occurrence and development of tumors, tumor markers play a pivotal role in early cancer detection, treatment, prognosis, efficient monitoring, and other aspects. Surface-enhanced Raman scattering (SERS) is considered a potent tool for the detection of tumor markers owing to its exceptional advantages encompassing high sensitivity, superior selectivity, rapid analysis speed, and photobleaching resistance nature. This review aims to provide a comprehensive understanding of SERS applications in the detection of tumor markers. Firstly, we introduce the SERS enhancement mechanism, classification of active substrates, and SERS detection techniques. Secondly, the latest research progress of in vitro SERS detection of different types of tumor markers in body fluids and the application of SERS imaging in biomedical imaging are highlighted in sections of the review. Finally, according to the current status of SERS detection of tumor markers, the challenges and problems of SERS in biomedical detection are discussed, and insights into future developments in SERS are offered.

Label-Free Exosome Analysis by Surface-Enhanced Raman Scattering Spectroscopy with Laser-Ablated Silver Nanoparticle Substrate

Early diagnostics of breast cancer is crucial to reduce the risk of cancer metastasis and late relapse. Exosome, which contains distinct information of its origin, can be the target object as a liquid biopsy. However, its low sensitivity and inadequate diagnostic tools interfere with the point-of-care testing (POCT) of the exosome. Recently, Surface-enhanced Raman Scattering (SERS) spectroscopy, which amplifies the Raman scattering, has been proved as a promising tool for exosome detection. However, the fabrication process of SERS probe or substrate is still inefficient and far from large-scale production. This study proposes rapid and label-free detection of breast cancer-derived exosomes by statistical analysis of SERS spectra using silver-nanoparticle-based SERS substrate fabricated by selective laser ablation and melting (SLAM). Employing silver nanowires and optimizing laser process parameters enable rapid and low-energy fabrication of SERS substrate. The functionalities including sensitivity, reproducibility, stability, and renewability are evaluated using rhodamine 6G as a probe molecule. Then, the feasibility of POCT is examined by the statistical analysis of SERS spectra of exosomes from malignant breast cancer cells and non-tumorigenic breast epithelial cells. The presented framework is anticipated to be utilized in other biomedical applications, facilitating cost-effective and large-scale production performance.

Real-time monitoring of single dendritic cell maturation using deep learning-assisted surface-enhanced Raman spectroscopy

Background: Dynamic real-time detection of dendritic cell (DC) maturation is pivotal for accurately predicting immune system activation, assessing vaccine efficacy, and determining the effectiveness of immunotherapy. The heterogeneity of cells underscores the significance of assessing the maturation status of each individual cell, while achieving real-time monitoring of DC maturation at the single-cell level poses significant challenges. Surface-enhanced Raman spectroscopy (SERS) holds great potential for providing specific fingerprinting information of DCs to detect biochemical alterations and evaluate their maturation status. Methods: We developed Au@CpG@PEG nanoparticle as a self-reporting nanovaccine for DC activation and maturation state assessment, utilizing a label-free SERS strategy. Fingerprint vibrational spectra of the biological components in different states of DCs were collected and analyzed using deep learning Convolutional Neural Networks (CNN) algorithms, aiding in the rapid and efficient identification of DC maturation. Results: This approach enables dynamic real-time detection of DC maturation, maintaining accuracy levels above 98.92%. Conclusion: By employing molecular profiling, we revealed that the signal ratio of tryptophan-to-carbohydrate holds potential as a prospective marker for distinguishing the maturation status of DCs.

Innovative optical imaging strategies for monitoring immunotherapy in the tumor microenvironments

BACKGROUND

The tumor microenvironment (TME) plays a critical role in cancer progression and response to immunotherapy. Immunotherapy targeting the immune system has emerged as a promising treatment modality, but challenges in understanding the TME limit its efficacy. Optical imaging strategies offer noninvasive, real-time insights into the interactions between immune cells and the TME.

OBJECTIVE

This review assesses the progress of optical imaging technologies in monitoring immunotherapy within the TME and explores their potential applications in clinical trials and personalized cancer treatment.

METHODS

This is a comprehensive literature review based on the advances in optical imaging modalities including fluorescence imaging (FLI), bioluminescence imaging (BLI), and photoacoustic imaging (PAI). These modalities were analyzed for their capacity to provide high-resolution, real-time imaging of immune cell dynamics, tumor vasculature, and other critical components of the TME.

RESULTS

Optical imaging techniques have shown significant potential in tracking immune cell infiltration, assessing immune checkpoint inhibitors, and visualizing drug delivery within the TME. Technologies like FLI and BLI are pivotal in tracking immune responses in preclinical models, while PAI provides functional imaging with deeper tissue penetration. The integration of these modalities with immunotherapy holds promise for improving treatment monitoring and outcomes.

CONCLUSION

Optical imaging is a powerful tool for understanding the complexities of the TME and optimizing immunotherapy. Further advancements in imaging technologies, combined with nanomaterial-based approaches, could pave the way for enhanced diagnostic accuracy and therapeutic efficacy in cancer treatment.

SERS Analysis Platform Based on Aptamer Recognition-Release Strategy for Efficient and Sensitive Diagnosis of Colorectal Precancerous Lesions

Background

Colorectal cancer (CRC) has become a significant global public health challenge, demanding immediate attention due to its high incidence and mortality rates. Regular CRC screening is essential for the early detection of precancerous lesions and CRC.

Methods

: We developed a novel surface-enhanced Raman scattering (SERS) analysis platform that employs high-throughput microarray chips as carriers and Au/SnO2 nanoring arrays (Au/SnO2 NRAs) as substrates. This platform utilizes an aptamer recognition-release strategy to achieve efficient and sensitive detection of protein tumor markers. In the detection process, the strong affinity and high specificity between the aptamer and the target protein result in competitive replacement of the SERS nanoprobes originally bound to the substrate surface. As a result, the SERS nanoprobes carrying Raman reporter genes are dislodged, leading to a reduction in the SERS signal intensity.

Results

The platform demonstrated excellent detection performance, with rapid detection completed within 15 minutes and limits of detection (LOD) as low as 6.2×10-12 g/mL for hnRNP A1 and 6.51×10-12 g/mL for S100P. Clinical samples analyzed using the SERS platform showed high consistency with enzyme-linked immunosorbent assay (ELISA) results.

Conclusion

This platform offers strong support for the early detection, risk assessment, and treatment monitoring of colorectal cancer precancerous lesions, with broad potential for clinical applications.

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.

Surface-Enhanced Raman Spectroscopy (SERS)-Based Sensors for Deoxyribonucleic Acid (DNA) Detection

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful technique for the detection and analysis of biomolecules due to its high sensitivity and selectivity. In recent years, SERS-based sensors have received significant attention for the detection of deoxyribonucleic acid (DNA) molecules, offering promising applications in fields such as medical diagnostics, forensic analysis, and environmental monitoring. This paper provides a concise overview of the principles, advancements, and potential of SERS-based sensors for DNA detection. First, the fundamental principles of SERS are introduced, highlighting its ability to enhance the Raman scattering signal by several orders of magnitude through the interaction between target molecules with metallic nanostructures. Then, the fabrication technologies of SERS substrates tailored for DNA detection are reviewed. The performances of SERS substrates previously reported for DNA detection are compared and analyzed in terms of the limit of detection (LOD) and enhancement factor (EF) in detail, with respect to the technical parameters of Raman spectroscopy (e.g., laser wavelength and power). Additionally, strategies for functionalizing the sensor surfaces with DNA-specific capture probes or aptamers are outlined. The collected data can be of help in selecting and optimizing the most suitable fabrication technology considering nucleotide sensing applications with Raman spectroscopy.

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Detection of lung cancer through SERS analysis of serum

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

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.

Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing

Circulating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically comprises thousands of gene expression reads per cell, which artificial intelligence algorithms can accurately analyze. This work presents machine-learning-based classifiers that differentiate CTCs from peripheral blood mononuclear cells (PBMCs) based on single cell RNA sequencing data. We developed four tree-based models and we trained and tested them on a dataset consisting of Smart-Seq2 sequenced data from primary tumor sections of breast cancer patients and PBMCs and on a public dataset with manually annotated CTC expression profiles from 34 metastatic breast patients, including triple-negative breast cancer. Our best models achieved about 95% balanced accuracy on the CTC test set on per cell basis, correctly detecting 133 out of 138 CTCs and CTC-PBMC clusters. Considering the non-invasive character of the liquid biopsy examination and our accurate results, we can conclude that our work has potential application value.

Targeted-activation superparamagnetic spherical nucleic acid nanomachine for ultrasensitive SERS detection of lysozyme based on a bienzymatic-mediated in situ amplification strategy

Lysozyme (LYS) is a widely used bacteriostatic enzyme. In this paper, we built a sensitive and accurate Raman biosensing platform to detect trace amounts of LYS. The method is based on magnetic spherical nucleic acid formed by a combination of LYS aptamer (Apt) and magnetic beads (MBs). Meanwhile, this method utilizes a dual enzyme-assisted nucleic acid amplification circuit and surface-enhanced Raman scattering (SERS). In this sensing strategy, which is based on the specific recognition of Apt, magnetic spherical nucleic acids were associated with SERS through a nucleic acid amplification circuit, and the low abundance of LYS was converted into a high-specificity Raman signal. Satellite-like MB@AuNPs were formed in the presence of the target, which separated specifically in a magnetic field, effectively avoided the interference of complex sample environment. Under the optimal sensing conditions, the concentration of LYS exhibited a good linear relationship between 1.0 × 10-14 and 5.0 × 10-12 M and the limit of detection was as low as 8.3 × 10-15 M. In addition, the sensor strategy shows excellent accuracy and sensitivity in complex samples, providing a new strategy for the specific detection of LYS.

An outlier removal method based on PCA-DBSCAN for blood-SERS data analysis

Surface-enhanced Raman spectroscopy (SERS) has shown promising potential in cancer screening. In practical applications, Raman spectra are often affected by deviations from the spectrometer, changes in measurement environments, and anomalies in spectrum characteristic peak intensities due to improper sample storage. Previous research has overlooked the presence of outliers in categorical data, leading to significant impacts on model learning outcomes. In this study, we propose a novel method, called Principal Component Analysis and Density Based Spatial Clustering of Applications with Noise (PCA-DBSCAN) to effectively remove outliers. This method employs dimensionality reduction and spectral data clustering to identify and remove outliers. The PCA-DBSCAN method introduces adjustable parameters (Eps and MinPts) to control the clustering effect. The effectiveness of the proposed PCA-DBSCAN method is verified through modeling on outlier-removed datasets. Further refinement of the machine learning model and PCA-DBSCAN parameters resulted in the best cancer screening model, achieving 97.41% macro-average recall and 97.74% macro-average F1-score. This paper introduces a new outlier removal method that significantly improves the performance of the SERS cancer screening model. Moreover, the proposed method serves as inspiration for outlier detection in other fields, such as biomedical research, environmental monitoring, manufacturing, quality control, and hazard prediction.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.

Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm-1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Two-dimensional substrate assisted SERS immunosensor for accurate detection of carcinoembryonic antigen

Accurate and sensitive detection of carcinoembryonic antigen (CEA) is essential for the detection of various diseases in healthcare and the medical field. Currently, due to the high false negative rate of CEA assay in clinical setting and its use as a common indicator for early cancer screening, a novel CEA detection method with high sensitivity, increased specificity and the lower cost has become a clinical challenge. Here, a facile sandwich type immunosensor based on surface-enhanced Raman scattering (SERS) was presented including 4-mercaptobenzonitrile (4MBN) labeled gold core-silver shell nanoparticles (Au@4MBN@Ag NPs) as SERS tag and 4-mercaptophenylboronic acid (4-MPBA) functionalized two-dimensional (2D) silver nanoparticle film (Ag FM) as SERS capture substrate for CEA detection. A linearity of 10-9-10-14M was observed with high sensitivity and excellent selectivity for the detection of CEA. Additionally, the spiking experiment yielded 105.33-127.00% recovery with variation coefficients below 10% demonstrating high assay accuracy and precision. The immunosensor we proposed here is a promising approach to quantify CEA in liquid biopsy samples with high sensitivity, which could be further developed for early cancer screening.

Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells

The lack of specific and sensitive early diagnostic options for pancreatic cancer (PC) results in patients being largely diagnosed with late-stage disease, thus inoperable and burdened with high mortality. Molecular spectroscopic methodologies, such as Raman or infrared spectroscopies, show promise in becoming a leader in screening for early-stage cancer diseases, including PC. However, should such technology be introduced, the identification of differentiating spectral features between various cancer types is required. This would not be possible without the precise extraction of spectra without the contamination by necrosis, inflammation, desmoplasia, or extracellular fluids such as mucous that surround tumor cells. Moreover, an efficient methodology for their interpretation has not been well defined. In this study, we compared different methods of spectral analysis to find the best for investigating the biomolecular composition of PC cells cytoplasm and nuclei separately. Sixteen PC tissue samples of main PC subtypes (ductal adenocarcinoma, intraductal papillary mucinous carcinoma, and ampulla of Vater carcinoma) were collected with Raman hyperspectral mapping, resulting in 191,355 Raman spectra and analyzed with comparative methodologies, specifically, hierarchical cluster analysis, non-negative matrix factorization, T-distributed stochastic neighbor embedding, principal components analysis (PCA), and convolutional neural networks (CNN). As a result, we propose an innovative approach to spectra classification by CNN, combined with PCA for molecular characterization. The CNN-based spectra classification achieved over 98% successful validation rate. Subsequent analyses of spectral features revealed differences among PC subtypes and between the cytoplasm and nuclei of their cells. Our study establishes an optimal methodology for cancer tissue spectral data classification and interpretation that allows precise and cognitive studies of cancer cells and their subcellular components, without mixing the results with cancer-surrounding tissue. As a proof of concept, we describe findings that add to the spectroscopic understanding of PC.

Emerging ctDNA detection strategies in clinical cancer theranostics

Circulating tumor DNA (ctDNA) is naked DNA molecules shed from the tumor cells into the peripheral blood circulation. They contain tumor-specific gene mutations and other valuable information. ctDNA is considered to be one of the most significant analytes in liquid biopsies. Over the past decades, numerous researchers have developed various detection strategies to perform quantitative or qualitative ctDNA analysis, including PCR-based detection and sequencing-based detection. More and more studies have illustrated the great value of ctDNA as a biomarker in the diagnosis, prognosis and heterogeneity of tumor. In this review, we first outlined the development of digital PCR (dPCR)-based and next generation sequencing (NGS)-based ctDNA detection systems. Besides, we presented the introduction of the emerging ctDNA analysis strategies based on various biosensors, such as electrochemical biosensors, fluorescent biosensors, surface plasmon resonance and Raman spectroscopy, as well as their applications in the field of biomedicine. Finally, we summarized the essentials of the preceding discussions, and the existing challenges and prospects for the future are also involved.

A novel dual-function SERS-based identification strategy for preliminary screening and accurate diagnosis of circulating tumor cells

Non-specific adsorption of bioprobes based on surface-enhanced Raman spectroscopy (SERS) technology inevitably endows white blood cells (WBC) in the peripheral blood with Raman signals, which greatly interfere the identification accuracy of circulating tumor cells (CTCs). In this study, an innovative strategy was proposed to effectively identify CTCs by using SERS technology assisted by a receiver operating characteristic (ROC) curve. Firstly, a magnetic Fe3O4-Au complex SERS bioprobe was developed, which could effectively capture the triple negative breast cancer (TNBC) cells and endow the tumor cells with distinct SERS signals. Then, the ROC curve obtained based on the comparison of SERS intensity of TNBC cells and WBC was used to construct a tumor cell identification model. The merit of the model was that the detection sensitivity and specificity could be intelligently switched according to different identification purposes such as accurate diagnosis or preliminary screening of tumor cells. Finally, the difunctional recognition ability of the model for accurate diagnosis and preliminary screening of tumor cells was further validated by using the healthy human blood added with TNBC cells and blood samples of real tumor patients. This novel difunctional identification strategy provides a new perspective for identification of CTCs based on the SERS technology.

ctDNA as a novel and promising approach for cancer diagnosis: a focus on hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent forms of cancer worldwide. Therefore, it is essential to diagnose and treat HCC patients promptly. As a novel discovery, circulating tumor DNA (ctDNA) can be used to analyze the tumor type and the cancer location. Additionally, ctDNA assists the cancer stage determination, which enables medical professionals to provide patients with the most appropriate treatment. This review will discuss the HCC-related mutated genes diagnosed by ctDNA. In addition, we will introduce the different and the most appropriate ctDNA diagnosis approaches based on the facilities.

Microfluidics engineering towards personalized oncology-a review

Identifying and monitoring the presence of cancer metastasis and highlighting inter-and intratumoral heterogeneity is a central tenet of targeted precision oncology medicine (POM). This process of relocation of cancer cells is often referred to as the missing link between a tumor and metastasis. In recent years, microfluidic technologies have been developed to isolate a plethora of different biomarkers, such as circulating tumor cells (CTCs), tumor-derived vesicles (exosomes), or cell/free nucleic acids and proteins directly from patients' blood samples. With the advent of microfluidic developments, minimally invasive and quantitative assessment of different tumors is becoming a reality. This short review article will touch briefly on how microfluidics at early-stage achievements can be combined or developed with the active vs passive microfluidic technologies, depending on whether they utilize external fields and forces (active) or just microchannel geometry and inherent fluid forces (passive) from the market to precision oncology research and our future prospectives in terms of the emergence of ultralow cost and rapid prototyping of microfluidics in precision oncology.

Simple and Ultrasensitive Detection of Glioma-Related ctDNAs in Mice Serum by SERS-Based Catalytic Hairpin Assembly Signal Amplification Coupled with Magnetic Aggregation

Purpose

Circulating tumor DNA (ctDNA) is more representative and accurate than biopsy and is also conducive to dynamic monitoring, facilitating accurate diagnosis and prognosis of glioma. Therefore, the present study aimed to establish and validate a novel amplified method for the detection of IDH1 R132H and BRAF V600E, which were associated with the genetic diagnosis of glioma.

Patients and Methods

A dual-signal amplification method based on magnetic aggregation and catalytic hairpin assembly (CHA) was constructed for the simultaneous detection of ctDNAs. When target ctDNAs are present, the CHA reaction is initiated and leads to the assembly of Au-Ag nanoshuttles (Au-Ag NSs) onto magnetic beads (MBs). Further enrichment of MBs under an external magnetic field facilitated the dual-signal amplification of SERS.

Results

The limit of detection (LOD) for IDH1 R132H and BRAF V600E in serum was as low as 6.01 aM and 5.48 aM. The reproducibility and selectivity of the proposed SERS analysis platform was satisfactory. Finally, the platform was applied to quantify IDH1 R132H and BRAF V600E in the serum of subcutaneous-tumor‑bearing nude mice, and the results obtained by SERS were consistent with those from quantitative real-time polymerase chain reaction (qRT-PCR).

Conclusion

The present study showed that the dual-signal amplification method is a simple and ultrasensitive strategy for gliomas-associated ctDNAs detection, which is crucial for early diagnosis and dynamic monitoring.

Emerging integrated SERS-microfluidic devices for analysis of cancer-derived small extracellular vesicles

Cancer-derived small extracellular vesicles (sEVs) are specific subgroups of lipid bilayer vesicles secreted from cancer cells to the extracellular environment. They carry distinct biomolecules (e.g., proteins, lipids and nucleic acids) from their parent cancer cells. Therefore, the analysis of cancer-derived sEVs can provide valuable information for cancer diagnosis. However, the use of cancer-derived sEVs in clinics is still limited due to their small size, low amounts in circulating fluids, and heterogeneous molecular features, making their isolation and analysis challenging. Recently, microfluidic technology has gained great attention for its ability to isolate sEVs in minimal volume. In addition, microfluidics allows the isolation and detection of sEVs to be integrated into a single device, offering new opportunities for clinical application. Among various detection techniques, surface-enhanced Raman scattering (SERS) has emerged as a promising candidate for integrating with microfluidic devices due to its ultra-sensitivity, stability, rapid readout, and multiplexing capability. In this tutorial review, we start with the design of microfluidics devices for isolation of sEVs and introduce the key factors to be considered for the design, and then discuss the integration of SERS and microfluidic devices by providing descriptive examples of the currently developed platforms. Lastly, we discuss the current limitations and provide our insights for utilising integrated SERS-microfluidics to isolate and analyse cancer-derived sEVs in clinical settings.

Rapid and precise detection of cancers via label-free SERS and deep learning

Early, express, and reliable detection of cancer can provide a favorable prognosis and decrease mortality. Tumor biomarkers have been proven to be closely related to tumor occurrence and development. Conventional tumor biomarker detection based on genomic, proteomic, and metabolomic methods is time and equipment-consuming and always needs a specific target marker. Surface-enhanced Raman scattering (SERS), as a non-invasive ultrasensitive and label-free vibrational spectroscopy technique, can detect cancer-related biomedical changes in biofluids. In this paper, 110 serum samples were collected from 30 healthy controls and 80 cancer patients (including 30 bladder cancer (BC), 30 adrenal cancer (AC), and 20 acute myeloid leukemia (AML)). One microliter of blood serum was mixed with 1 μl silver colloid and then was air-dried for SERS measurements. After spectral data augmentation, one-dimensional convolutional neural network (1D-CNN) was proposed for precise and rapid identification of healthy and three different cancers with high accuracy of 98.27%. After gradient-weighted class activation mapping (Grad-CAM) based spectral interpretation, the contributions of SERS peaks corresponding to biochemical substances indicated the most potential biomarkers, i.e., L-tyrosine in bladder cancer; acetoacetate and riboflavin in adrenal cancer and phospholipids, amide-I, and α-Helix in acute myeloid leukemia, which might provide an insight into the mechanism of intelligent diagnosis of different cancers based on label-free serum SERS. The integration of label-free SERS and deep learning has great potential for the rapid, reliable, and non-invasive detection of cancers, which may significantly improve the precise diagnosis in clinical practice.

Highly selective and quantitative in situ monitoring of cell surface proteins by SERS immunoassay system

Due to their pivotal roles in many biological functions, cell surface proteins (CSPs) are often used for cancer prognosis, as evidenced by a number of studies that have reported significant changes in the expression levels of specific surface proteins depending on the stage of tumorigenesis and selection/variety of reprogrammed cells during cell fate conversion. Current CSP detection strategies suffer from poor selectivity and lack the ability for in situ analysis but maintain the spatial information between cells. Here, we have fabricated nanoprobes for surface-enhanced Raman scattering (SERS) immunoassays by conjugating a specific antibody onto silica-coated gold nanoparticles incorporating an individual Raman reporter (Au-tag@SiO₂-Ab NPs) for highly sensitive and selective in situ detection in different types of cells. When multiple HEK293 cell lines stably expressing different levels of the CSP, ACE2, were investigated by the SERS immunoassay, we demonstrated that the level of ACE2 expression in each cell line could be statistically distinguished from that in the other cell lines, indicating the quantitative feature of this biosensing system. When detecting living cells without cell lysis or fixation, as well as fixed cells, the levels of the epithelial CSPs, EpCAM (epithelial cell adhesion molecule) and E-cadherin, were successfully determined using our Au-tag@SiO₂-Ab NPs and SERS immunoassay system in a highly selective and quantitative manner without significant cytotoxicity. Hence, our work provides technical insight into the development of a biosensing platform for a variety of biomedical applications, such as cancer metastasis prognosis and the in situ monitoring of stem cell reprogramming and differentiation.

The Potential Use of Exosomes in Anti-Cancer Effect Induced by Polarized Macrophages

The rapid development of aberrant cells outgrowing their normal bounds, which can subsequently infect other body parts and spread to other organs-a process known as metastasis-is one of the significant characteristics of cancer. The main reason why cancer patients die is because of widespread metastases. This abnormal cell proliferation varies in cancers of over a hundred types, and their response to treatment can vary substantially. Several anti-cancer drugs have been discovered to treat various tumors, yet they still have harmful side-effects. Finding novel, highly efficient targeted therapies based on modifications in the molecular biology of tumor cells is essential to reduce the indiscriminate destruction of healthy cells. Exosomes, an extracellular vesicle, are promising as a drug carrier for cancer therapy due to their good tolerance in the body. In addition, the tumor microenvironment is a potential target to regulate in cancer treatment. Therefore, macrophages are polarized toward M1 and M2 phenotypes, which are involved in cancer proliferation and are malignant. It is evident from recent studies that controlled macrophage polarization might contribute to cancer treatment, by the direct way of using miRNA. This review provides an insight into the potential use of exosomes to develop an 'indirect', more natural, and harmless cancer treatment through regulating macrophage polarization.

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.

Toward a New Era of SERS and TERS at the Nanometer Scale: From Fundamentals to Innovative Applications

Surface-enhanced Raman scattering (SERS) and tip-enhanced Raman scattering (TERS) have opened a variety of exciting research fields. However, although a vast number of applications have been proposed since the two techniques were first reported, none has been applied to real practical use. This calls for an update in the recent fundamental and application studies of SERS and TERS. Thus, the goals and scope of this review are to report new directions and perspectives of SERS and TERS, mainly from the viewpoint of combining their mechanism and application studies. Regarding the recent progress in SERS and TERS, this review discusses four main topics: (1) nanometer to subnanometer plasmonic hotspots for SERS; (2) Ångström resolved TERS; (3) chemical mechanisms, i.e., charge-transfer mechanism of SERS and semiconductor-enhanced Raman scattering; and (4) the creation of a strong bridge between the mechanism studies and applications.

Application of SERS in In-Vitro Biomedical Detection

Surface-enhanced Raman scattering (SERS), as a rapid and nondestructive biological detection method, holds great promise for clinical on spot and early diagnosis. In order to address the challenging demands of on spot detection of biomedical samples, a variety of strategies has been developed. These strategies include substrate structural and component engineering, data processing techniques, as well as combination with other analytical methods. This report summarizes the recent SERS developments for biomedical detection, and their promising applications in cancer detection, virus or bacterial infection detection, miscarriage spotting, neurological disease screening et al. The first part discusses the frequently used SERS substrate component and structures, the second part reports on the detection strategies for nucleic acids, proteins, bacteria, and virus, the third part summarizes their promising applications in clinical detection in a variety of illnesses, and the forth part reports on recent development of SERS in combination with other analytical techniques. The special merits, challenges, and perspectives are discussed in both introduction and conclusion sections.

Advances in the application of Raman spectroscopy in haematological tumours

Hematologic malignancies are a diverse collection of cancers that affect the blood, bone marrow, and organs. They have a very unpredictable prognosis and recur after treatment. Leukemia, lymphoma, and myeloma are the most prevalent symptoms. Despite advancements in chemotherapy and supportive care, the incidence rate and mortality of patients with hematological malignancies remain high. Additionally, there are issues with the clinical diagnosis because several hematological malignancies lack defined, systematic diagnostic criteria. This work provided an overview of the fundamentals, benefits, and limitations of Raman spectroscopy and its use in hematological cancers. The alterations of trace substances can be recognized using Raman spectroscopy. High sensitivity, non-destructive, quick, real-time, and other attributes define it. Clinicians must promptly identify disorders and keep track of analytes in biological fluids. For instance, surface-enhanced Raman spectroscopy is employed in diagnosing gene mutations in myelodysplastic syndromes due to its high sensitivity and multiple detection benefits. Serum indicators for multiple myeloma have been routinely used for detection. The simultaneous observation of DNA strand modifications and the production of new molecular bonds by tip-enhanced Raman spectroscopy is of tremendous significance for diagnosing lymphoma and multiple myeloma with unidentified diagnostic criteria.

In situ detection of exosomal RNAs for cancer diagnosis

Exosomes are considered as biomarkers reflecting the physiological state of the human body. Studies have revealed that the expression levels of specific exosomal RNAs are closely associated with certain cancers. Thus, detection of exosomal RNA offers a new avenue for liquid biopsy of cancers. Many exosomal RNA detection methods based on various principles have been developed, and most of the methods detect the extracted RNAs after lysing exosomes. Besides complex and time-consuming extraction steps, a major drawback of this approach is the degradation of the extracted RNAs in the absence of plasma membrane and cytosol. In addition, there is considerable loss of RNAs during their extraction. In situ detection of exosomal RNAs can avoid these drawbacks, thus allowing higher diagnostic reliability. In this paper, in situ detection of exosomal RNAs was systematically reviewed from the perspectives of detection methods, transport methods of the probe systems, probe structures, signal amplification strategies, and involved functional materials. Furthermore, the limitations and possible improvements of the current in situ detection methods for exosomal RNAs towards the clinical diagnostic application are discussed. This review aims to provide a valuable reference for the development of in situ exosomal RNA detection strategies for non-invasive diagnosis of cancers. STATEMENT OF SIGNIFICANCE: Certain RNAs have been identified as valuable biomarkers for some cancers, and sensitive detection of cancer-related RNAs is expected to achieve better diagnostic efficacy. Currently, the detection of exosomal RNAs is receiving increasing attention due to their high stability and significant concentration differences between patients and healthy individuals. In situ detection of exosomal RNAs has greater diagnostic reliability due to the avoidance of RNA degradation and loss. However, this mode is still limited by some factors such as detection methods, transport methods of the probe systems, probe structures, signal amplification strategies, etc. This review focuses on the progress of in situ detection of exosomal RNAs and aims to promote the development of this field.

Nano-diagnostics as an emerging platform for oral cancer detection: Current and emerging trends

Globally, oral cancer kills an estimated 150,000 individuals per year, with 300,000 new cases being diagnosed annually. The high incidence rate of oral cancer among the South-Asian and American populations is majorly due to overuse of tobacco, alcohol, and poor dental hygiene. Additionally, socio-economic issues and lack of general awareness delay the primary screening of the disease. The availability of early screening techniques for oral cancer can help in carving out a niche for accurate disease prognosis and also its prevention. However, conventional diagnostic approaches and therapeutics are still far from optimal. Thus, enhancing the analytical performance of diagnostic platforms in terms of specificity and precision can help in understanding the disease progression paradigm. Fabrication of efficient nanoprobes that are sensitive, noninvasive, cost-effective, and less labor-intensive can reduce the global cancer burden. Recent advances in optical, electrochemical, and spectroscopy-based nano biosensors that employ noble and superparamagnetic nanoparticles, have been proven to be extremely efficient. Further, these sensitive nanoprobes can also be employed for predicting disease relapse after chemotherapy, when the majority of the biomarker load is eliminated. Herein, we provide the readers with a brief summary of conventional and new-age oral cancer detection techniques. A comprehensive understanding of the inherent challenges associated with conventional oral cancer detection techniques is discussed. We also elaborate on how nanoparticles have shown tremendous promise and effectiveness in radically transforming the approach toward oral cancer detection. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices Diagnostic Tools > In Vitro Nanoparticle-Based Sensing.

Single-step controlled synthesis of flower-like gold nanoparticles stabilized by chitosan for sensitive detection of heparin using a surface-enhanced Raman scattering method

A novel single-step and template-free procedure, including controlled synthesis of gold flowers (AuNFs), conjugation to a 4-MBA reporter, and stabilization with chitosan, is proposed to develop the SERS tags-based nanoparticles for trace detection of heparin. This SERS detection assay is based on the aggregation/non-aggregation balance of AuNFs-4-MBA@chitosan nanoparticles, which was induced by adding a very low concentration of heparin in the as-synthesized colloidal solutions. SERS-tag colloids are prepared by mixing chitosan with HAuCl4 and 4-mercapto benzoic acid before being reduced with ascorbic acid under appropriate pH conditions. The formed AuNFs-4-MBA@chitosan nanoparticles were positively charged with high stability and well-dispersed in aqueous media. Based on understanding each reaction component's role in the preparation of the SERS tag colloid, we aim to simplify the controlled synthesis and Raman probe conjugation process. The average size of AuNFs is below 90 nm, fine-tuned in shape and effectively conjugated to the Raman reporter molecules 4-MBA. These as-prepared SERS tag-based AuNFs have good biocompatibility and are virtually non-toxic, as studied with fibroblast and MCF-7 cells. Through these SERS-tag colloids, the trace detection of heparin is improved, with a wide detection window (0.01 to 100 ppm), high reproducibility (RSD value of 3.56%), limit of detection (LOD) of 0.054 ppm, and limit of quantification (LOQ) of 0.17 ppm. Comparison experiments show that the SERS-tag colloids possess good selectivity over other ions, and organic and amino acid substances. The results provide the capability and the potential for application under complex biological conditions and future biosensing based on SERS signal amplification.

Evaluation of classification ability of logistic regression model on SERS data of miRNAs

Logistic regression (LR) is a supervised multiple linear regression model, which uses linear weighted calculation for input to obtain weight coefficients of model. The surface enhanced Raman spectroscopy (SERS) technology greatly enhances the Raman signal of analyte. LR model was used to analyze the data of seven types of pancreatic cancer-related miRNAs obtained from commercial SERS substrate. The classification ability of the model on such data was observed under the configurations of different key parameters (classification mode, regularization method and loss function optimization way), and the effect of the two types of data formats were also evaluated. The results showed that though LR model used to classify this data did not perform well as expected, miRNA-191 and miRNA-4306 still had high recalls (sensitivity), which laid a theoretical foundation for the purpose of using LR model to identify these two miRNAs to jointly diagnose of pancreatic cancer at miRNA level.

Rapid and sensitive detection of esophageal cancer by FTIR spectroscopy of serum and plasma

Fourier transform infrared (FTIR) spectroscopy, as a platform technology for cancer detection, must be up to the challenge of clinical transformation. To this end, detection of esophageal squamous cell carcinoma (ESCC) was hereby explored using serum and plasma scrape-coated on barium fluoride (BaF₂) disk by transmission FTIR method, and the classification model was built using six multivariate statistical analyses, including support vector machine (SVM), principal component linear discriminant analysis (PC-LDA), decision tree (DT), k-nearest neighbor (KNN) classification, ensemble algorithms (EA) and partial least squares for discriminant analysis (PLS-DA). All statistical analyses methods demonstrated that late-stage cancer could be well classified from healthy people employing either serum or plasma with different anticoagulants. Resulting PC-LDA model differentiated late-stage cancer from normal group with an accuracy of 99.26%, a sensitivity of 98.53%, and a specificity of 100%. The accuracy and sensitivity reached 97.08% and 91.43%, respectively for early-stage cancer discrimination from normal group. This pilot exploration demonstrated that transmission FTIR provided a rapid, cost effective and sensitive method for ESCC diagnosis using either serum or plasma.

Microneedle Array Encapsulated with Programmed DNA Hydrogels for Rapidly Sampling and Sensitively Sensing of Specific MicroRNA in Dermal Interstitial Fluid

Author: Please verify that the changes made to improve the English still retain your original meaning.Detection of microRNA (miRNA) in dermal interstitial fluid (ISF) has emerged as clinically useful in health status monitoring. However, it remains a great challenge owing to the difficult sampling and low abundance. Here, we report a DNA hydrogel microneedles (MNs) array to realize rapid enrichment and sensitive detection of miRNA in ISF. The MNs' patch consists of methacrylate hyaluronic acid (MeHA) equipped with a smart DNA circuit hydrogels' system (MeHA/DNA), in which an appropriate miRNA input enables triggering a cascading toehold-mediated DNA displacement reaction to catalytically cleave cross-linking points to generate amplified fluorescence (FL) for miRNA detection. The MeHA/DNA-MNs patch with high mechanical strength can extract adequate ISF in a short time (0.97 ± 0.2 mg in 5 min) in vivo because of its supreme water affinity. Additionally, the cascading toehold-mediated DNA displacement signal amplification reaction allows for sensitive detection of the low-abundant miRNAs down to 241.56 pM. The DNA hydrogels' MNs present potential for minimally invasive personalized diagnosis and real-time health monitoring in clinical applications.

Label-Free Optical Spectroscopy for Early Detection of Oral Cancer

Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.

Diagnosis of urogenital cancer combining deep learning algorithms and surface-enhanced Raman spectroscopy based on small extracellular vesicles

PURPOSE

To identify and compare the capacities of serum and serum-derived small extracellular vesicles (EV) in diagnosis of common urogenital cancer combining Surface-enhanced Raman spectroscopy (SERS) and Convolutional Neural Networks (CNN).

MATERIALS AND METHODS

We collected serum samples from 32 patients with prostate cancer (PCa), 33 patients with renal cell cancer (RCC) and 30 patients with bladder cancer (BCa) as well as 35 healthy control (HC), which were thereafter used to enrich extracellular vesicles by ultracentrifuge. Label-free SERS was utilized to collect Raman spectra from serum and matched EV samples. We constructed CNN models to process SERS data for classification of malignant patients and healthy controls (HCs).

RESULTS

We collected 650 and 1206 spectra from serum and serum-derived EV, respectively. CNN models of EV spectra revealed high testing accuracies of 79.3%, 78.7% and 74.2% in diagnosis of PCa, RCC and BCa, respectively. In comparison, serum SERS-based CNN model had testing accuracies of 73.0%, 71.1%, 69.2% in PCa, RCC and BCa, respectively. Moreover, CNN models based on EV SERS data show significantly higher diagnostic capacities than matched serum CNN models with the area under curve (AUC) of 0.80, 0.88 and 0.74 in diagnosis of PCa, RCC and BCa, respectively.

CONCLUSION

Deep learning-based SERS analysis of EV has great potentials in diagnosis of urologic cancer outperforming serum SERS analysis, providing a novel tool in cancer screening.

Emerging SERS biosensors for the analysis of cells and extracellular vesicles

Cells and their derived extracellular vesicles (EVs) or exosomes contain unique molecular signatures that could be used as biomarkers for the detection of severe diseases such as cancer, as well as monitoring the treatment response. Revealing these molecular signatures requires developing non-invasive ultrasensitive tools to enable single molecule/cell-level detection using a small volume of sample with low signal-to-noise ratio background and multiplex capability. Surface-enhanced Raman scattering (SERS) can address the current limitations in studying cells and EVs through two main mechanisms: plasmon-enhanced electric field (the so-called electromagnetic mechanism (EM)), and chemical mechanism (CM). In this review, we first highlight these two SERS mechanisms and then discuss the nanomaterials that have been used to develop SERS biosensors based on each of the aforementioned mechanisms as well as the combination of these two mechanisms in order to take advantage of the synergic effect between electromagnetic enhancement and chemical enhancement. Then, we review the recent advances in designing label-aided and label-free SERS biosensors in both colloidal and planar systems to investigate the surface biomarkers on cancer cells and their derived EVs. Finally, we discuss perspectives of emerging SERS biosensors in future biomedical applications. We believe this review article will thus appeal to researchers in the field of nanobiotechnology including material sciences, biosensors, and biomedical fields.

Tetraplex Immunophenotyping of Cell Surface Proteomes via Synthesized Plasmonic Nanotags and Portable Raman Spectroscopy

Multiplex immunophenotyping of cell surface proteomes is useful for cell characterization as well as providing valuable information on a patient's physiological or pathological state. Current methods for multiplex immunophenotyping of cell surface proteomes still have associated technical pitfalls in terms of limited multiplexing capability, challenging result interpretation, and large equipment footprint limited to use in a laboratory setting. Herein, we presented a portable surface-enhanced Raman spectroscopy (SERS) assay for multiplex cell surface immunophenotyping. We synthesized and functionalized customizable SERS nanotags for cell labeling and subsequent signal measurement using a portable Raman spectrometer. We extensively evaluated and validated the analytical assay performance of the portable SERS immunophenotyping assay in two different cellular models (red blood cells and breast cancer cells). In terms of analytical specificity, the cell surface immunophenotyping of both red blood cells and breast cancer cells correlated well with flow cytometry. The portable SERS immunophenotyping assay also has comparable analytical repeatability to flow cytometry, with coefficient of variation values of 21.89-23.33% and 6.88-17.32% for detecting red blood cells and breast cancer cells, respectively. The analytical detection limits were 77 cells/mL for red blood cells and 1-17 cells/mL for breast cancer cells. As an alternative to flow cytometry, the portable SERS immunophenotyping assay demonstrated excellent analytical assay performance and possessed advantages such as quick sample-to-result turnaround time, multiplexing capability, and small equipment footprint.