Rapid identification by surface-enhanced Raman spectroscopy of cancer cells at low concentrations flowing in a microfluidic channel

Integration of a recyclable silver substrate for in situ surface-enhanced Raman spectroscopy in digital microfluidics

We report a new chip-integrated recyclable SERS substrate, achieved by photochemical deposition of silver nanoparticles onto titanium dioxide (TiO2) thin film. Facilitated by the photocatalytic activity of titanium dioxide the SERS substrate can be recycled for multiple analysis. This enables quasi-real time detection of various compounds in an automated and reusable DMF-SERS platform.

Raman and Surface-Enhanced Raman Scattering Detection in Flowing Solutions for Complex Mixture Analysis

Raman scattering provides a chemical-specific and label-free method for identifying and quantifying molecules in flowing solutions. This review provides a comprehensive examination of the application of Raman spectroscopy and surface-enhanced Raman scattering (SERS) to flowing liquid samples. We summarize developments in online and at-line detection using Raman and SERS analysis, including the design of microfluidic devices, the development of unique SERS substrates, novel sampling interfaces, and coupling these approaches to fluid-based chemical separations (e.g., chromatography and electrophoresis). The article highlights the challenges and limitations associated with these techniques and provides examples of their applications in a variety of fields, including chemistry, biology, and environmental science. Overall, this review demonstrates the utility of Raman and SERS for analysis of complex mixtures and highlights the potential for further development and optimization of these techniques.

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.

SERS sensing for cancer biomarker: Approaches and directions

These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Integrated Electrochemical and Optical Biosensing in Organs-on-Chip

Demand for biocompatible, non-invasive, and continuous real-time monitoring of organs-on-chip has driven the development of a variety of novel sensors. However, highest accuracy and sensitivity can arguably be achieved by integrated biosensing, which enables in situ monitoring of the in vitro microenvironment and dynamic responses of tissues and miniature organs recapitulated in organs-on-chip. This paper reviews integrated electrical, electrochemical, and optical sensing methods within organ-on-chip devices and platforms. By affording precise detection of analytes and biochemical reactions, these methods expand and advance the monitoring capabilities and reproducibility of organ-on-chip technology. The integration of these sensing techniques allows a deeper understanding of organ functions, and paves the way for important applications such as drug testing, disease modeling, and personalized medicine. By consolidating recent advancements and highlighting challenges in the field, this review aims to foster further research and innovation in the integration of biosensing in organs-on-chip.

Multiplexed Surface Protein Detection and Cancer Classification Using Gap-Enhanced Magnetic-Plasmonic Core-Shell Raman Nanotags and Machine Learning Algorithm

Cancer is the second leading cause of death attributed to disease worldwide. Current standard detection methods often rely on a single cancer marker, which can lead to inaccurate results, including false negatives, and an inability to detect multiple cancers simultaneously. Here, we developed a multiplex method that can effectively detect and classify surface proteins associated with three distinct types of breast cancer by utilizing gap-enhanced Raman scattering nanotags and machine learning algorithm. We synthesized anisotropic magnetic core-gold shell gap-enhanced Raman nanotags incorporating three different Raman reporters. These multicolor Raman nanotags were employed to distinguish specific surface protein markers in breast cancer cells. The acquired signals were deconvoluted and analyzed using classical least-squares regression to generate a surface protein profile and characterize the breast cancer cells. Furthermore, computational data obtained via finite-difference time-domain and discrete dipole approximation showed the amplification of the electric fields within the gap region due to plasmonic coupling between the two gold layers. Finally, a random forest classifier achieved an impressive classification and profiling accuracy of 93.9%, enabling effective distinguishing between the three different types of breast cancer cell lines in a mixed solution. With the combination of immunomagnetic multiplex target specificity and separation, gap-enhancement Raman nanotags, and machine learning, our method provides an accurate and integrated platform to profile and classify different cancer cells, giving implications for identification of the origin of circulating tumor cells in the blood system.

Communicating Supraparticles to Enable Perceptual, Information-Providing Matter

Materials are the fundament of the physical world, whereas information and its exchange are the centerpieces of the digital world. Their fruitful synergy offers countless opportunities for realizing desired digital transformation processes in the physical world of materials. Yet, to date, a perfect connection between these worlds is missing. From the perspective, this can be achieved by overcoming the paradigm of considering materials as passive objects and turning them into perceptual, information-providing matter. This matter is capable of communicating associated digitally stored information, for example, its origin, fate, and material type as well as its intactness on demand. Herein, the concept of realizing perceptual, information-providing matter by integrating customizable (sub-)micrometer-sized communicating supraparticles (CSPs) is presented. They are assembled from individual nanoparticulate and/or (macro)molecular building blocks with spectrally differentiable signals that are either robust or stimuli-susceptible. Their combination yields functional signal characteristics that provide an identification signature and one or multiple stimuli-recorder features. This enables CSPs to communicate associated digital information on the tagged material and its encountered stimuli histories upon signal readout anywhere across its life cycle. Ultimately, CSPs link the materials and digital worlds with numerous use cases thereof, in particular fostering the transition into an age of sustainability.

Nanoparticulate pollutants in the environment: Analytical methods, formation, and transformation

The wide application of nanomaterials and plastic products generates a substantial number of nanoparticulate pollutants in the environment. Nanoparticulate pollutants are quite different from their bulk counterparts because of their unique physicochemical properties, which may pose a threat to environmental organisms and human beings. To accurately predict the environmental risks of nanoparticulate pollutants, great efforts have been devoted to developing reliable methods to define their occurrence and track their fate and transformation in the environment. Herein, we summarized representative studies on the preconcentration, separation, formation, and transformation of nanoparticulate pollutants in environmental samples. Finally, some perspectives on future research directions are proposed.

A flexible wearable device coupled with injectable Fe3O4 nanoparticles for capturing circulating tumor cells and triggering their deaths

Elimination of circulating tumor cells (CTCs) in the blood can be an effective therapeutic approach to disrupt metastasis. Here, a strategy is proposed to implement flexible wearable electronics and injectable nanomaterials to disrupt the hematogenous transport of CTCs. A flexible device containing an origami magnetic membrane is used to attract Fe₃O₄@Au nanoparticles (NPs) that are surface modified with specific aptamers and intravenously injected into blood vessels, forming an invisible hand and fishing line/bait configuration to specifically capture CTCs through bonding with aptamers. Thereafter, thinned flexible AlGaAs LEDs in the device offer an average fluence of 15.75 mW mm^-2 at a skin penetration depth of 1.5 mm, causing a rapid rise of temperature to 48 °C in the NPs and triggering CTC death in 10 min. The flexible device has been demonstrated for intravascular isolation and enrichment of CTCs with a capture efficiency of 72.31% after 10 cycles in a simulated blood circulation system based on a prosthetic upper limb. The fusion of nanomaterials and flexible electronics reveals an emerging field that utilizes wearable and flexible stimulators to activate biological effects offered by nanomaterials, leading to improved therapeutical effects and postoperative outcomes of diseases.

Optical Detection of Cancer Cells Using Lab-on-a-Chip

The global need for accurate and efficient cancer cell detection in biomedicine and clinical diagnosis has driven extensive research and technological development in the field. Precision, high-throughput, non-invasive separation, detection, and classification of individual cells are critical requirements for successful technology. Lab-on-a-chip devices offer enormous potential for solving biological and medical problems and have become a priority research area for microanalysis and manipulating cells. This paper reviews recent developments in the detection of cancer cells using the microfluidics-based lab-on-a-chip method, focusing on describing and explaining techniques that use optical phenomena and a plethora of probes for sensing, amplification, and immobilization. The paper describes how optics are applied in each experimental method, highlighting their advantages and disadvantages. The discussion includes a summary of current challenges and prospects for cancer diagnosis.

Lab-on-a-chip systems for cancer biomarker diagnosis

Lab-on-a-chip (LOC) or micro total analysis system is one of the microfluidic technologies defined as the adaptation, miniaturization, integration, and automation of analytical laboratory procedures into a single instrument or "chip". In this article, we review developments over the past five years in the application of LOC biosensors for the detection of different types of cancer. Microfluidics encompasses chemistry and biotechnology skills and has revolutionized healthcare diagnosis. Superior to traditional cell culture or animal models, microfluidic technology has made it possible to reconstruct functional units of organs on chips to study human diseases such as cancer. LOCs have found numerous biomedical applications over the past five years, including integrated bioassays, cell analysis, metabolomics, drug discovery and delivery systems, tissue and organ physiology and disease modeling, and personalized medicine. This review provides an overview of the latest developments in microfluidic-based cancer research, with pros, cons, and prospects.

Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.

Characterizing viral samples using machine learning for Raman and absorption spectroscopy

Machine learning methods can be used as robust techniques to provide invaluable information for analyzing biological samples in pharmaceutical industries, such as predicting the concentration of viral particles of interest in biological samples. Here, we utilized both convolutional neural networks (CNNs) and random forests (RFs) to predict the concentration of the samples containing measles, mumps, rubella, and varicella-zoster viruses (ProQuad®) based on Raman and absorption spectroscopy. We prepared Raman and absorption spectra data sets with known concentration values, then used the Raman and absorption signals individually and together to train RFs and CNNs. We demonstrated that both RFs and CNNs can make predictions with R² values as high as 95%. We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Additionally, we further verified the advantage of using joint Raman-absorption with principal component analysis. Furthermore, our method can be extended to characterize properties other than concentration, such as the type of viral particles.

Effect of the Combination of Gold Nanoparticles and Polyelectrolyte Layers on SERS Measurements

In this study, polyelectrolyte (PE) layers are deposited on substrates made by glass covered with an array of gold nanoparticles (GNPs). In particular, the samples studied have 0 PE layers (GGPE0), 3 PE layers (GGPE3), 11 PE layers (GGPE11), and 21 PE layers (GGPE21). All samples have been studied by micro-Raman spectroscopy. An acetic acid solution (10% v/v) has been used as a standard solution in order to investigate the SERS effect induced by different numbers of PE layers in each sample. The Surface Enhancement Raman Spectroscopy (SERS) effect correlating to the number of PE layers deposited on the samples has been shown. This effect is explained in terms of an increase in the interaction between the photon of the laser source and the plasmonic band of the GNPs due to a change of the permittivity of the surrounding medium around the GNPs. The trends of the ratios of the intensities of the Raman bands of the acetic acid solution (acetic acid and water molecules) on the band at 1098 cm-1 ascribed to the substrates increase, and the number of PE layers increases.

Combination of Live Cell Surface-Enhanced Raman Scattering Imaging with Chemometrics to Study Intracellular Nanoparticle Dynamics

Surface-enhanced Raman scattering (SERS)-encoded nanoparticles are used for bioimaging, on account of their well-defined Raman spectra and biocompatibility, which allow long incubation times with high signal stability and no cytotoxicity. However, reliable analysis of SERS bioimaging requires quantification of the amount of encoded nanoparticles that have been taken up by cells and the effect of subsequent dilution due to cellular division (mitosis). Although methods such as elemental analysis and flow cytometry can be used to quantify nanoparticle uptake, these are both end-point measurements in which a cell population is screened rather than looking at individual cells. In contrast, SERS imaging can be applied at multiple timepoints to the same individual cells without damaging the biological sample. We present the application of both supervised and unsupervised multivariate analyses, to quantify the intracellular amount of SERS tags in individual MCF7 living cells, toward the characterization of cellular uptake in vitro. The obtained results from both methodologies were validated by standard elemental analysis techniques.

Review on combining surface-enhanced Raman spectroscopy and electrochemistry for analytical applications

The discovery of surface enhanced Raman scattering (SERS) from an electrochemical (EC)-SERS experiment is known as a historic breakthrough. Five decades have passed and Raman spectroelectrochemistry (SEC) has developed into a common characterization tool that provides information about the electrode-electrolyte interface. Recently, this technique has been successfully explored for analytical purposes. EC was found to highly improve the performances of SERS sensors, providing, among others, controlled adsorption of analytes and increased reproducibility. In this review, we highlight the potential of EC-SERS sensors to be implemented for point-of-need (PON) analyses as miniaturized devices, and their ability to revolutionize fields like quality control, diagnosis or environmental and food safety. Important developments have been achieved in Raman spectroelectrochemistry, which now represents a promising alternative to conventional analytical methods and interests more and more researchers. The studies included in this review open endless possibilities for real-life EC-SERS analytical applications.

Microfluidics and surface-enhanced Raman spectroscopy, a win-win combination?

With the continuous development in nanoscience and nanotechnology, analytical techniques like surface-enhanced Raman spectroscopy (SERS) render structural and chemical information of a variety of analyte molecules in ultra-low concentration. Although this technique is making significant progress in various fields, the reproducibility of SERS measurements and sensitivity towards small molecules are still daunting challenges. In this regard, microfluidic surface-enhanced Raman spectroscopy (MF-SERS) is well on its way to join the toolbox of analytical chemists. This review article explains how MF-SERS is becoming a powerful tool in analytical chemistry. We critically present the developments in SERS substrates for microfluidic devices and how these substrates in microfluidic channels can improve the SERS sensitivity, reproducibility, and detection limit. We then introduce the building materials for microfluidic platforms and their types such as droplet, centrifugal, and digital microfluidics. Finally, we enumerate some challenges and future directions in microfluidic SERS. Overall, this article showcases the potential and versatility of microfluidic SERS in overcoming the inherent issues in the SERS technique and also discusses the advantage of adding SERS to the arsenal of microfluidics.

Reliable SERS detection of pesticides with a large-scale self-assembled Au@4-MBA@Ag nanoparticle array

The fabrication of sensitive and reliable interfacial plasmonic platform for measuring chemical contaminants in various phases is an exciting topic in the food industry and for environment monitoring. In this study, a high-performance surface-enhanced Raman spectroscopy (SERS) analytic platform was developed through self-assembly of the gold@4-mercaptobenzoic acid@silver nanoparticles (Au@4-MBA@Ag NPs) at the cyclohexane/water interface. By addition of the inducer ethanol, the Au@4-MBA@Ag NPs in aqueous phase was effectively migrated to the biphasic interface, forming a large-scale close-packed nanoparticle array. The average gap between adjacent nanoparticles was smaller than 3 nm, where intensive SERS "hot spots" were created for high-sensitive detection. Furthermore, using the sandwiched 4-MBA molecule as the internal standard to correct the Raman signal fluctuations, the point-to-point and batch-to-batch reproducibility of Au@4-MBA@Ag array were improved with lower relative standard deviation (RSD) values of 8.84% and 14.97%, respectively, and pesticides (thiram and thiabendazole) analysis in both aqueous and organic phases were achieved with higher accuracy (R² of 0.986 and 0.990) as compared with those without 4-MBA correction (R² of 0.867 and 0.974). The high-throughput fabrication of the self-assembled nanoparticle array is a promising approach for development of a sensitive and reliable SERS platform for chemical contaminants monitoring in multiphase.

Interpreting convolutional neural network for real-time volatile organic compounds detection and classification using optical emission spectroscopy of plasma

This study presents the investigation of optical emission spectroscopy of plasma using interpretable convolutional neural network (CNN) for real-time volatile organic compounds (VOCs) classification. A microplasma-generation platform was developed to efficiently collect 64 k spectra from various types of VOCs at different concentrations, as training and testing sets for machine learning. A CNN model was trained to classify VOCs with accuracy of 99.9%. To interpret the CNN model and its predictions, the spectral processing mechanism of the CNN was visualized by feature maps and the critical spectral features were identified by gradient-weighted class activation mapping. Such approaches brought insights on how CNN analyzes the spectra and enables the CNN operation to be explainable. Finally, the CNN model was incorporated with the microplasma platform to demonstrate the application of real-time VOC monitoring. The type of VOCs can be identified and reported via messages within 10 s once the microplasma is ignited. We believe that using CNN brings a novel route for plasma spectroscopy analysis for VOC classification and impacts the fields of plasma, spectroscopy, and environmental monitoring.

Raman tweezers as an alternative diagnostic tool for paroxysmal nocturnal hemoglobinuria

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare disease characterized by hemolysis of red blood cells (RBC) and venous thrombosis. The gold standard method for the diagnosis of this disease is flow cytometry. Here, we propose a combined optical tweezers and Raman spectral (Raman tweezers) approach to analyze blood samples from volunteers with or without PNH conditions. Raman spectroscopy is a well-known method for investigating a material's chemical structure and is also used in molecular analysis of biological compounds. In this study, we trap individual RBCs found in whole blood samples drawn from PNH patients and the control group. Evaluation of the Raman spectra of these cells by band component analysis and machine learning shows a significant difference between the two groups. The specificity and the sensitivity of the training performed by support vector machine (SVM) analysis were found to be 81.8% and 78.3%, respectively. This study shows that an immediate and high accuracy test result is possible for PNH disease by employing Raman tweezers and machine learning.

A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier

Direct serological detection, due to its clinical facility and testing economy, affords prominent clinical values to the early detection of cancer. Surface-enhanced Raman spectroscopy (SERS)-based sensors have shown great promise in realizing this form of detection. Detecting liver cancer early with such a form, especially in terms of monitoring the pathogenic progression from hepatic inflammations to cancer, is the most effective clinical path to reducing the mortality rate. However, the methodology investigation for this purpose remains a formidable challenge. We fabricated a SERS-based sensor, consisting of Au-Ag nanocomplex-decorated ZnO nanopillars on paper. The sensor has an analytic enhancement factor of 1.02 × 107, which is enough to sense the biomolecular information of liver diseases through direct serum SERS analysis. A convolutional neural network (CNN) classifier for recognizing serum SERS spectra was constructed by deep learning. Integrating this sensor with the CNN, we established an intelligent biosensing method and realized direct serological detection of liver diseases within 1 min. As a proof-of-concept, the method achieved a prediction accuracy of 97.78% on an independent test dataset randomly sampled from 30 normal controls, 30 hepatocellular carcinoma (HCC) cases, and 30 hepatitis B (HB) patients. The results suggest this method can be developed for detecting liver diseases clinically and is worthy of exploration as a means of liver cancer surveillance. The presented sensor holds potential for clinical translation to the direct serological detection of diseases.

3D Printed Microfluidic Device for Magnetic Trapping and SERS Quantitative Evaluation of Environmental and Biomedical Analytes

Surface-enhanced Raman scattering (SERS) is an ideal technique for environmental and biomedical sensor devices due to not only the highly informative vibrational features but also to its ultrasensitive nature and possibilities toward quantitative assays. Moreover, in these areas, SERS is especially useful as water hinders most of the spectroscopic techniques such as those based on IR absorption. Despite its promising possibilities, most SERS substrates and technological frameworks for SERS detection are still restricted to research laboratories, mainly due to a lack of robust technologies and standardized protocols. We present herein the implementation of Janus magnetic/plasmonic Fe3O4/Au nanostars (JMNSs) as SERS colloidal substrates for the quantitative determination of several analytes. This multifunctional substrate enables the application of an external magnetic field for JMNSs retention at a specific position within a microfluidic channel, leading to additional amplification of the SERS signals. A microfluidic device was devised and 3D printed as a demonstration of cheap and fast production, with the potential for large-scale implementation. As low as 100 μL of sample was sufficient to obtain results in 30 min, and the chip could be reused for several cycles. To show the potential and versatility of the sensing system, JMNSs were exploited with the microfluidic device for the detection of several relevant analytes showing increasing analytical difficulty, including the comparative detection of p-mercaptobenzoic acid and crystal violet and the quantitative detection of the herbicide flumioxazin and the anticancer drug erlotinib in plasma, where calibration curves within diagnostic concentration intervals were obtained.

Plasmonic Nanotweezers and Nanosensors for Point-of-Care Applications

The capabilities of manipulating and analyzing biological cells, bacteria, viruses, DNAs, and proteins at high resolution are significant in understanding biology and enabling early disease diagnosis. We discuss progress in developments and applications of plasmonic nanotweezers and nanosensors where the plasmon-enhanced light-matter interactions at the nanoscale improve the optical manipulation and analysis of biological objects. Selected examples are presented to illustrate their design and working principles. In the context of plasmofluidics, which merges plasmonics and fluidics, the integration of plasmonic nanotweezers and nanosensors with microfluidic systems for point-of-care (POC) applications is envisioned. We provide our perspectives on the challenges and opportunities in further developing and applying the plasmofluidic POC devices.

Advanced Devices for Tumor Diagnosis and Therapy

At present, tumor diagnosis is performed using common procedures, which are slow, costly, and still presenting difficulties in diagnosing tumors at their early stage. Tumor therapeutic methods also mainly rely on large-scale equipment or non-intelligent treatment approaches. Thus, an early and accurate tumor diagnosis and personalized treatment may represent the best treatment option for a successful result, and the efforts in finding them are still in progress and mainly focusing on non-destructive, integrated, and multiple technologies. These objectives can be achieved with the development of advanced devices and smart technology that represent the topic of the current investigations. Therefore, this review summarizes the progress in tumor diagnosis and therapy and briefly explains the advantages and disadvantages of the described microdevices, finally proposing advanced micro smart devices as the future development trend for tumor diagnosis and therapy.

Role of plasmonics in detection of deadliest viruses: a review

Viruses have threatened animal and human lives since a long time ago all over the world. Some of these tiny particles have caused disastrous pandemics that killed a large number of people with subsequent economic downturns. In addition, the quarantine situation itself encounters the challenges like the deficiency in the online educational system, psychiatric problems and poor international relations. Although viruses have a rather simple protein structure, they have structural heterogeneity with a high tendency to mutation that impedes their study. On top of the breadth of such worldwide worrying issues, there are profound scientific gaps, and several unanswered questions, like lack of vaccines or antivirals to combat these pathogens. Various detection techniques like the nucleic acid test, immunoassay, and microscopy have been developed; however, there is a tradeoff between their advantages and disadvantages like safety in sample collecting, invasiveness, sensitivity, response time, etc. One of the highly resolved techniques that can provide early-stage detection with fast experiment duration is plasmonics. This optical technique has the capability to detect viral proteins and genomes at the early stage via highly sensitive interaction between the biological target and the plasmonic chip. The efficiency of this technique could be proved using commercialized techniques like reverse transcription polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA) techniques. In this study, we aim to review the role of plasmonic technique in the detection of 11 deadliest viruses besides 2 common genital viruses for the human being. This is a rapidly moving topic of research, and a review article that encompasses the current findings may be useful for guiding strategies to deal with the pandemics. By investigating the potential aspects of this technique, we hope that this study could open new avenues toward the application of point-of-care techniques for virus detection at early stage that may inhibit the progressively hygienic threats.

Microfluidic Chip-Based Cancer Diagnosis and Prediction of Relapse by Detecting Circulating Tumor Cells and Circulating Cancer Stem Cells

Detecting circulating tumor cells (CTCs) has been considered one of the best biomarkers in liquid biopsy for early diagnosis and prognosis monitoring in cancer. A major challenge of using CTCs is detecting extremely low-concentrated targets in the presence of high noise factors such as serum and hematopoietic cells. This review provides a selective overview of the recent progress in the design of microfluidic devices with optical sensing tools and their application in the detection and analysis of CTCs and their small malignant subset, circulating cancer stem cells (CCSCs). Moreover, discussion of novel strategies to analyze the differentiation of circulating cancer stem cells will contribute to an understanding of metastatic cancer, which can help clinicians to make a better assessment. We believe that the topic discussed in this review can provide brief guideline for the development of microfluidic-based optical biosensors in cancer prognosis monitoring and clinical applications.

Microfluidics-Based Sensing of Biospecies

Over the past decades, microfluidic devices based on many advanced techniques have aroused widespread attention in the fields of chemical, biological, and analytical applications. Integration of microdevices with a variety of chip designs will facilitate promising functionality. Notably, the combination of microfluidics with functional nanomaterials may provide creative ideas to achieve rapid and sensitive detection of various biospecies. In this review, focused on the microfluids and microdevices in terms of their fabrication, integration, and functions, we summarize the up-to-date developments in microfluidics-based analysis of biospecies, where biomarkers, small molecules, cells, and pathogens as representative biospecies have been explored in-depth. The promising applications of microfluidic biosensors including clinical diagnosis, food safety control, and environmental monitoring are also discussed. This review aims to highlight the importance of microfluidics-based biosensors in achieving high throughput, highly sensitive, and low-cost analysis and to promote microfluidics toward a wider range of applications.

Recent progress of microfluidics in surface-enhanced Raman spectroscopic analysis

Surface-enhanced Raman spectroscopy is a significant analytical tool capable of fingerprint identification of molecule in a rapid and ultrasensitive manner. However, it is still hard to meet the requirements of practical sample analysis. The introduction of microfluidics can effectively enhance the performance of surface-enhanced Raman spectroscopy in complex sample analysis including reproducibility, selectivity, sensitivity, and speed. This review summarizes the recent progress of microfluidics in surface-enhanced Raman spectroscopic analysis through four combination approaches. First, microfluidic synthetic techniques offer uniform nano-/microparticle fabrication approaches for reproductive surface-enhanced Raman spectroscopic analysis. Second, the integration of microchip and surface-enhanced Raman spectroscopic substrate provides advanced devices for sensitive and efficient detection. Third, microfluidic sample preparations enable rapid separation and preconcentration of analyte prior to surface-enhanced Raman spectroscopic detection. Fourth, highly integrated microfluidic devices can be employed to realize multistep surface-enhanced Raman spectroscopic analysis containing material fabrication, sample preparation, and detection processes. Furthermore, the challenges and outlooks of the application of microfluidics in surface-enhanced Raman spectroscopic analysis are discussed.

Surface-enhanced Raman scattering holography

Nanometric probes based on surface-enhanced Raman scattering (SERS) are promising candidates for all-optical environmental, biological and technological sensing applications with intrinsic quantitative molecular specificity. However, the effectiveness of SERS probes depends on a delicate trade-off between particle size, stability and brightness that has so far hindered their wide application in SERS imaging methodologies. In this Article, we introduce holographic Raman microscopy, which allows single-shot three-dimensional single-particle localization. We validate our approach by simultaneously performing Fourier transform Raman spectroscopy of individual SERS nanoparticles and Raman holography, using shearing interferometry to extract both the phase and the amplitude of wide-field Raman images and ultimately localize and track single SERS nanoparticles inside living cells in three dimensions. Our results represent a step towards multiplexed single-shot three-dimensional concentration mapping in many different scenarios, including live cell and tissue interrogation and complex anti-counterfeiting applications.

Recent advances in surface-enhanced Raman spectroscopy based liquid biopsy for colorectal cancer (Review)

As colorectal cancer (CRC) is one of the forms of cancer with the highest prevalence globally and with a high mortality, screening and early detection remains a major issue. Colonoscopy is still the gold standard for detecting premalignant lesions, but it is burdened by some complications. For instance, it is laborious, with some difficulties of acceptance for some patients, and is ultimately an imperfect standard, given that some premalignant lesions or incipient malignancies can be missed by colonoscopic evaluation. In this context, new non-invasive approaches such as surface-enhanced Raman spectroscopy (SERS) based liquid biopsy have gained ground in recent years, showing promising results in oncological pathology diagnosis. These new methods have enabled the detection of subtle molecular profile alterations prior to any macroscopic morphological changes, thus providing a useful tool for early CRC detection. In the present review, we provide a summary of published studies applying SERS in CRC detection, along with our personal experience in using SERS in the diagnosis of different oncological pathologies, including CRC.

Efficient Capture and Raman Analysis of Circulating Tumor Cells by Nano-Undulated AgNPs-rGO Composite SERS Substrates

The analysis of circulating tumor cells (CTCs) in the peripheral blood of cancer patients is critical in clinical research for further investigation of tumor progression and metastasis. In this study, we present a novel surface-enhanced Raman scattering (SERS) substrate for the efficient capture and characterization of cancer cells using silver nanoparticles-reduced graphene oxide (AgNPs-rGO) composites. A pulsed laser reduction of silver nanowire-graphene oxide (AgNW-GO) mixture films induces hot-spot formations among AgNPs and artificial biointerfaces consisting of rGOs. We also use in situ electric field-assisted fabrication methods to enhance the roughness of the SERS substrate. The AgNW-GO mixture films, well suited for the proposed process due to its inherent electrophoretic motion, is adjusted between indium tin oxide (ITO) transparent electrodes and the nano-undulated surface is generated by applying direct-current (DC) electric fields during the laser process. As a result, MCF7 breast cancer cells are efficiently captured on the AgNPs-rGO substrates, about four times higher than the AgNWs-GO films, and the captured living cells are successfully analyzed by SERS spectroscopy. Our newly designed bifunctional substrate can be applied as an effective system for the capture and characterization of CTCs.

SERS studies on normal epithelial and cancer cells derived from clinical breast cancer specimens

Surface-enhanced Raman scattering (SERS) spectroscopy of single-cell suspensions obtained from fresh specimens of breast cancer tissue and normal breast tissue by mechanical enzymatic digestion was obtained and analysed, which is different from most Raman studies using breast cancer cell lines. Random forest classification was implemented to develop effective diagnostic algorithms for the classification of SERS of different typed cells. We first examined the SERS spectra of the primary breast cancer single cell and normal epithelial single cell obtained by flow sorting cytometry due to their biomarkers of CD326+/CD45-. Comparison analyses on their SERS spectra disclose that the nucleic acid and protein levels of the primary breast cancer single cell are higher than those of the normal epithelial single cell, while the lipids are at a relatively lower level. An important finding is that the cholesterol, palmitic acid, and sphingomyelin in the cancer cell profiles exhibit stronger than those of normal cells, while the glycans are at a relatively lower level. Furthermore, the standard deviation (SD) of the normal epithelial single cell is larger than that of the breast cancer cell, and the SD of the primary breast cancer single cell is more obvious than that of the normal epithelial cells. In addition, the prospective application of an algorithm to the dataset results in an accuracy of 78.2%, a precision of 75.5%, and a recall of 66.7%. The breast cancer diagnostic model laid a solid foundation for judgment of breast-conserving surgical margins and early diagnosis of breast cancer.

Hydrophobic Plasmonic Nanoacorn Array for a Label-Free and Uniform SERS-Based Biomolecular Assay

A surface-enhanced Raman scattering (SERS) aptasensor based on a hydrophobic assembled nanoacorn (HANA) was developed with improved reproducibility and reduced nonspecific binding effect. In the fabrication process, a hexagonal-packed gold film over nanosphere (AuFON) arrays was first obtained and used as a hydrophobic plasmonic substrate. Then, a uniform sub-3 nm molecular spacer array (containing Raman reporters) was prepared by patterning nanometric hydrophilic ultrathin patches onto the hydrophobic AuFON, in which the hydrophilic thin layer is composed of polymers and aptamers. During the sensing process, the HANA aptasensor smartly impedes the adsorption of SERS probes as Au@Ag nanocubes (Au@Ag NCs) in the absence of targets. In the presence of targets, the displacement of aptamers occurs due to the specific interaction between the targets and the aptamers, and the Au@Ag NCs can be assembled onto the hydrophilic patches on AuFON through electrostatic interactions with polymers. Thus, SERS signals of reporter molecules inside the spacer can be dramatically enhanced due to the formation of a nanoparticle-on-mirror (NPoM) array. In such a SERS aptasensor, the well-ordered distribution of SERS probes ensures excellent repeatability, while the precise subnanometer junctions guarantee high sensitivity. More importantly, since the hydrophobic surface can greatly reduce nonspecific adsorption, the tedious process of nonspecific blocking that is employed in traditional biosensors is no longer needed. Using such a SERS HANA platform, human epidermal growth factor receptor 2 (HER2) and three exosomal proteins were analyzed with high sensitivity and good reproducibility (RSD < 7%) in whole-blood samples.

Bioinspired Superwettable Microspine Chips with Directional Droplet Transportation for Biosensing

Directional droplet transportation without extra energy input remains a challenge in microfluidic biochips for clinical detections. Herein, inspired by the water-collecting behaviors on the cactus spine, we fabricate nanomaterial-based superwettable microspine (SMS) chips. The bioinspired SMS chips are capable of spontaneous and directional droplet transportation by synergistically combining geometric asymmetry and surface superhydrophilicity. Based on theoretical models, the gradient of the Laplace pressure arising from the geometric asymmetry of the SMS chip can dominate the directional transportation of the droplet, and the superhydrophilicity of the nanomaterial-based microspine can also contribute to the droplet self-transportation. The multimicrochannel SMS chips provide a simple and energy efficient technology to realize accurate detection of serum prostate-specific antigen (PSA) from prostate cancer patients, showing great potential as a biosensing platform for clinical applications. We believe that our bioinspired superwettable two-dimensional conical surface will offer effective means for the design of smart microfluidic devices and have great potential applications in multicomponent biosensing and clinical detection.

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.

Design of a Multiplexed Analyte Biosensor using Digital Barcoded Particles and Impedance Spectroscopy

Multiplexing allows quantifying multiple analytes in a single step, providing advantages over individual testing through shorter processing time, lower sample volume, and reduced cost per test. Currently, flow cytometry is the gold standard for biomedical multiplexing, but requires technical training, extensive data processing, and expensive operational and capital costs. To solve this challenge, we designed digital barcoded particles and a microfluidic architecture for multiplexed analyte quantification. In this work, we simulate and model non-fluorescence-based microfluidic impedance detection with a single excitation and detection scheme using barcoded polymer microparticles. Our barcoded particles can be designed with specific coding regions and generate numerous distinct patterns enabling digital barcoding. We found that signals based on adhered microsphere position and relative orientation were evaluated and separated based on their associated electrical signatures and had a 7 µm microsphere limit of detection. Our proposed microfluidic system can enumerate micron-sized spheres in a single assay using barcoded particles of various configurations. As representation of blood cells, the microsphere concentrations may provide useful information on disease onset and progression. Such sensors may be used for diagnostic and management of common critical care diseases like sepsis, acute kidney injury, urinary tract infections, and HIV/AIDS.

Simulation and practice of particle inertial focusing in 3D-printed serpentine microfluidic chips via commercial 3D-printers

Inertial focusing of particles in serpentine microfluidic chips has been studied over the past decade. Here, a study to investigate the particle inertial focusing in 3D-printed serpentine microfluidic chips was conducted by simulation and practice. A test model was designed and printed using four commercial 3D-printers. Commercial inkjet 3D-printers have shown the best printing channel resolution of up to 0.1 mm. The force analysis of particle inertial focusing in 3D-printed microfluidic chips with large cross-sectional channels was discussed. Important parameters such as the channel curvature and flow velocity were studied by simulation. The optimal channel curvature and flow velocity are 5.9 mm and 480 μL min^-1 (Re: 29.8 and De: 4.49) in the 3D-printed microfluidic chips with 0.2 mm × 0.4 mm cross-sectional channels. Under these optimal conditions, particles were well focused in the middle of the channel. Furthermore, two kinds of cancer cells were focused in these 3D-printed serpentine microfluidic chips under the optimal conditions. We envision that this improved study would provide helpful insights into simulating particle inertial focusing in 3D-printed microfluidic chips and promoting 3D-printed microfluidic chips to commercial production.

rGO nanomaterial-mediated cancer targeting and photothermal therapy in a microfluidic co-culture platform

We developed the microfluidic co-culture platform to study photothermal therapy applications. We conjugated folic acid (FA) to target breast cancer cells using reduced graphene oxide (rGO)-based functional nanomaterials. To characterize the structure of rGO-based nanomaterials, we analyzed the molecular spectrum using UV-visible and Fourier-transform infrared spectroscopy (FT-IR). We demonstrated the effect of rGO-FA-based nanomaterials on photothermal therapy of breast cancer cells in the microfluidic co-culture platform. From the microfluidic co-culture platform with breast cancer cells and human umbilical vein endothelial cells (HUVECs), we observed that the viability of breast cancer cells treated with rGO-FA-based functional nanomaterials was significantly decreased after near-infrared (NIR) laser irradiation. Therefore, this microfluidic co-culture platform could be a potentially powerful tool for studying cancer cell targeting and photothermal therapy.

Construction of Raman spectroscopic fingerprints for the detection of Fusarium wilt of banana in Taiwan

Banana (Musa sp.) is cultivated worldwide and is one of the most popular fruits. The soil-borne fungal disease Fusarium wilt of banana (FWB), commonly known as Panama disease, is caused by Fusarium oxysporum f. sp. cubense (Foc) and is a highly lethal vascular fungal disease in banana plants. Raman spectroscopy, an emerging laser-based technology based on Raman scattering, has been used for the qualitative characterization of biological tissues such as foodborne pathogens, cancer cells, and melamine. In this study, we describe a Raman spectroscopic technique that could potentially be used as a method for diagnosing FWB. To that end, the Raman fingerprints of Foc (including mycelia and conidia) and Foc-infected banana pseudostems with varying levels of symptoms were determined. Our results showed that eight, eleven, and eleven characteristic surface-enhanced Raman spectroscopy peaks were observed in the mycelia, microconidia, and macroconidia of Foc, respectively. In addition, we constructed the Raman spectroscopic fingerprints of banana pseudostem samples with varying levels of symptoms in order to be able to differentiate Foc-infected bananas from healthy bananas. The rate at which FWB was detected in asymptomatic Foc-infected samples by using the spectral method was 76.2%, which was comparable to the rates previously reported for other FWB detection methods based on real-time PCR assays, suggesting that the spectral method described herein could potentially serve as an alternative tool for detecting FWB in fields. As such, we hope that the developed spectral method will open up new possibilities for the on-site diagnosis of FWB.

A Tunable Freeform-Segmented Reflector in a Microfluidic System for Conventional and Surface-Enhanced Raman Spectroscopy

We present a freeform-segmented reflector-based microfluidic system for conventional Raman and Surface-Enhanced Raman Scattering (SERS) analysis. The segmented reflector is directly designed by a numerical approach. The polymer-based Raman system strongly suppresses the undesirable background because it enables confocal detection of Raman scattering through the combination of a freeform reflector and a microfluidic chip. We perform systematic simulations using non-sequential ray tracing with the Henyey-Greenstein model to assess the Raman scattering behavior of the substance under test. We fabricate the freeform reflector and the microfluidic chip by means of ultra-precision diamond turning and laser cutting respectively. We demonstrate the confocal behavior by measuring the Raman spectrum of ethanol. Besides, we calibrate the setup by performing Raman measurements on urea and potassium nitrate solutions with different concentrations. The detection limit of our microfluidic system is approximately 20 mM according to the experiment. Finally, we implement a SERS microfluidic chip and discriminate 100 µM urea and potassium nitrate solutions.

iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the "intelligent" Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells.

Present and Future of Surface-Enhanced Raman Scattering

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.