Beehive-Inspired Macroporous SERS Probe for Cancer Detection through Capturing and Analyzing Exosomes in Plasma

Three-dimensional urchin-like K2Ti8O17 / Ag NPs composite as a SERS substrate for detecting folic acid and thiram

The three-dimensional (3D) semiconductor/noble metal composite substrates for surface-enhanced Raman scattering (SERS) have garnered increasing interest due to their excellent optical and chemical properties, as well as the capacity to trigger both electromagnetic mechanism (EM) and chemical mechanism (CM) simultaneously. In this work, a facile 3D urchin-like K2Ti8O17/Ag nanoparticles (Ag NPs) composite substrate is designed for multi-purpose SERS sensing. K2Ti8O17, as a dielectric medium, improves the electric field environment around Ag NPs, which is consistent with finite-different time domain (FDTD) results, and enhances the SERS performance of the K2Ti8O17/Ag composite substrate. Besides, the efficient "donor-bridge-acceptor" charge transfer mode, explored through energy level calculations and enhanced utilization of incident light, further strengthens the SERS performance. Results show that the prepared K2Ti8O17/Ag NPs substrate exhibits high detection sensitivity, with 10-11 and 10-12 M limits in detecting Methylene Blue (MB) and Crystal Violet (CV), and the enhancement factors (EFs) of 2.66 × 109 and 6.07 × 109, respectively. At the same time, the composite substrate also possesses good signal uniformity (RSD = 10.5 %) and promising photocatalytic ability. For practical applications, the prepared K2Ti8O17/Ag NPs substrate can detect folic acid of 10-7 M in the diluted serum environment and thiram of 10-8 M in lake water, respectively. The urchin-like K2Ti8O17/Ag NPs substrate expands the range of 3D semiconductor composite SERS substrates, which is expected to be used for biosensing and trace analysis of harmful substances.

Harnessing Photo-Energy Conversion in Nanomaterials for Precision Theranostics

The rapidly advancing field of theranostics aims to integrate therapeutic and diagnostic functionalities into a single platform for precision medicine, enabling the simultaneous treatment and monitoring of diseases. Photo-energy conversion-based nanomaterials have emerged as a versatile platform that utilizes the unique properties of light to activate theranostics with high spatial and temporal precision. This review provides a comprehensive overview of recent developments in photo-energy conversion using nanomaterials, highlighting their applications in disease theranostics. The discussion begins by exploring the fundamental principles of photo-energy conversion in nanomaterials, including the types of materials used and various light-triggered mechanisms, such as photoluminescence, photothermal, photoelectric, photoacoustic, photo-triggered SERS, and photodynamic processes. Following this, the review delves into the broad spectrum of applications of photo-energy conversion in nanomaterials, emphasizing their role in the diagnosis and treatment of major diseases, including cancer, neurodegenerative disorders, retinal degeneration, and osteoarthritis. Finally, the challenges and opportunities of photo-energy conversion-based technologies for precision theranostics are discussed, aiming to advance personalized medicine.

An All-in-One Nanohole Array for Size-Exclusive Trapping and High-Throughput Digital Counting of Single Extracellular Vesicles for Non-Invasive Cancer Screening

The analysis of single small extracellular vesicles (sEVs) could distinguish the heterogeneity of sEVs thus better extract tumor-related signatures. Current protocols for the analysis of single sEV rely mainly on the advanced techniques and require lengthy isolation procedures, limiting applications in clinical diagnosis. Herein, we developed a one-step procedure for rapid isolation of single sEVs from urine, along with an analytical pipeline for the diagnosis of early bladder cancer (BCa). Single sEVs are isolated by an EV-imprinted gold nanohole (EI-AuNH) array that selectively traps individual sEVs and spatially enhances their Raman spectra. After the invalid spectral data from incomplete or absent sEVs was eliminated using Smart-Filter, a convolutional neural network model identifies the origin of the spectra and generates a digital count matrix for each patient. By integrating the digital count data of both tumor-associated and normal sEVs, our model achieves an accuracy of 97.37% in early diagnosis of BCa. Feature extraction using explainable AI identified nine BCa-related signatures, with noticeable reduction on cholesterol and lipids in BCa-associated sEVs. These signatures could further distinguish BCa from other cancers. Overall, the present non-invasive and highly accurate diagnosis platform may revolutionize clinical disease diagnostics through simplified single sEV isolation and advanced modeling.

Comprehensive Analysis of Advancement in Optical Biosensing Techniques for Early Detection of Cancerous Cells

This investigation presents an overview of various optical biosensors utilized for the detection of cancer cells. It covers a comprehensive range of technologies, including surface plasmon resonance (SPR) sensors, which exploit changes in refractive index (RI) at the sensor surface to detect biomolecular interactions. Localized surface plasmon resonance (LSPR) sensors offer high sensitivity and versatility in detecting cancer biomarkers. Colorimetric sensors, based on color changes induced via specific biochemical reactions, provide a cost-effective and simple approach to cancer detection. Sensors based on fluorescence work using the light emitted from fluorescent molecules detect cancer-specific targets with specificity and high sensitivity. Photonics and waveguide sensors utilize optical waveguides to detect changes in light propagation, offering real-time and label-free detection of cancer biomarkers. Raman spectroscopy-based sensors utilize surface-enhanced Raman scattering (SERS) to provide molecular fingerprint information for cancer diagnosis. Lastly, fiber optic sensors offer flexibility and miniaturization, making them suitable for in vivo and point-of-care applications in cancer detection. This study provides insights into the principles, applications, and advancements of these optical biosensors in cancer diagnostics, highlighting their potential in improving early detection and patient outcomes.

Small extracellular vesicles: crucial mediators for prostate cancer

Small extracellular vesicles (sEVs) play a critical role in the progression, diagnosis, and treatment of prostate cancer (PCa), particularly within the tumor microenvironment (TME). Acting as novel biomarkers and agents for targeted biological therapy, sEVs contribute significantly to improving patient survival. These vesicles transport a variety of biomolecules, including proteins, nucleic acids, and lipids, which are instrumental in remodeling the TME, facilitating intercellular communication, and influencing key processes such as tumor growth, metastasis, and therapy resistance. A thorough understanding of sEV heterogeneity, including their biogenesis, characteristics, and potential applications, is essential. Recent advances have illuminated the origins, formation processes, and molecular cargo of PCa-derived sEVs (PCa-sEVs), enhancing our understanding of their role in disease progression. Furthermore, sEVs show promise as diagnostic markers, with potential applications in early detection and prognostic assessment in PCa. Therapeutically, natural and engineered sEVs offer versatile applications, including drug delivery, gene therapy, and immunomodulation, underscoring their potential in PCa management. This review delves into the substantial potential of sEVs in clinical practices for PCa.

Surface-Enhanced Raman Scattering (SERS) for exosome detection

BACKGROUND

Exosomes, nanoscale extracellular vesicles secreted by various cells, are abundantly present in biological fluids. They have been identified as carriers of specific molecules, suggesting their potential role in early disease detection. However, their clinical application is hindered by several challenges, including the need for large sample volumes for enrichment, limitations of traditional detection methods, and the complexity involved in phenotype analysis and separation.

OBJECTIVE

This review aims to explore the application of Surface-Enhanced Raman Scattering (SERS) technology in exosome detection. SERS, known for its unique photonic properties and high sensitivity, offers a promising solution for detecting exosomes without the need for large sample volumes or extensive phenotypic analysis. This review focuses on the real-time and non-invasive assessment capabilities of SERS in exosome detection, providing insights into its potential for early disease diagnosis.

CONCLUSION

The review concludes by emphasizing the potential of SERS-based exosome detection in advancing early disease diagnosis. By overcoming existing challenges, SERS technology offers a promising approach for the development of sensitive and specific diagnostic assays, contributing to better patient outcomes and personalized medicine.

Raman spectroscopy in extracellular vesicles analysis: Techniques, applications and advancements

Raman spectroscopy provides a robust approach for detailed analysis of the chemical and molecular profiles of extracellular vesicles (EVs). Recent advancements in Raman techniques have significantly enhanced the sensitivity and accuracy of EV characterization, enabling precise detection and profiling of molecular components within EV samples. This review introduces and compares various Raman-based techniques for EV characterization. These include Raman spectroscopy (RS), which provides fundamental molecular information; Raman trapping analysis (RTA), which combines optical trapping with Raman scattering for the manipulation and analysis of individual EVs; surface-enhanced Raman spectroscopy (SERS), which enhances the Raman signal through the use of metallic nanostructures, significantly improving sensitivity; and microfluidic SERS, which integrates SERS with microfluidic platforms to allow high-throughput, label-free analysis of EVs in biological fluids. In addition to comparing various Raman techniques, this review provides a comprehensive analysis that includes comparisons of machine learning methods, EV isolation techniques, and characterization strategies. By integrating these approaches, the review presents a holistic perspective on Raman-based EV analysis, covering profiling, purity, heterogeneity and size analysis as well as imaging. The combined assessment of Raman technologies with advanced computational and experimental methodologies supports the development of more robust diagnostic and therapeutic applications involving EVs.

Recent advances in SERS assays for detection of multiple extracellular vesicles biomarkers for cancer diagnosis

As the prevalence of cancer is escalating, there is an increased demand for early and sensitive diagnostic tools. A major challenge in early detection is the lack of specific biomarkers, and a readily accessible, sensitive and rapid detection method. To meet these challenges, cancer-derived small extracellular vesicles (sEVs) have been discovered as a new promising cancer biomarker due to the high abundance of sEVs in body fluids and their extensive cargo of biomarkers. Additionally, surface-enhanced Raman scattering (SERS) presents a sensitive, multiplexed, and rapid method that has gained attraction with recent studies showing promising results from patient samples for the multiplex detection of cancer sEVs. Various label-based SERS multiplex assays have been developed in the field of SERS including bead assays, lateral flow immunoassays, microfluidic devices, and artificial intelligence (AI)-based label-free SERS chips, targeting multiple surface proteins to ensure comprehensive multiplex diagnostics. These assays hold promise for enabling early detection, quantification, and subtyping of cancer-derived sEVs for cancer diagnostic applications. This review aims to provide a summary of the recent advances in the field of SERS multiplex assays for detection, quantification, and subtyping of sEVs to facilitate cancer diagnosis. This review further provides unique insights into the use of sEVs as a biomarker and aims to address the issues surrounding their translation from laboratories to clinics.

Label-Free SERS Analysis of Biological and Physical Information Heterogeneity of Nanoscale Extracellular Vesicle by Matching Specific Sizes of Enhanced Particles

The heterogeneity of extracellular vesicles (EVs) surface information represents different functions, which is neglected in previous studies. In this study, a label-free SERS analysis approach is demonstrated to study fundamental EV biological and physical information heterogeneity by matching specific sizes of nano-enhanced particles. This strategy reveals informative, comprehensive, and high-quality SERS spectra of the overall exosome surface, and effectively circumvents the key information loss caused by the spatial resistance of NPs binding to the 293 exosomes' concave structure. The classification of normal and cancerous cell-derived exosomes by PCA method, the accuracy is improved from 91.2% to 95.1% by optimizing sizes of nano-enhanced particles. In addition, stem cell-derived EVs of diverse sizes and morphologies similarly show acuity of spectrum variation to NPs size, which is conductive to qualitative studies. This new strategy will offer a widened in-depth understanding of the surface information, size, and morphology of EVs, which can be applied to the study of biological functions.

Advancements in molecular imaging probes for precision diagnosis and treatment of prostate cancer

Prostate cancer is the second most common cancer in men, accounting for 14.1% of new cancer cases in 2020. The aggressiveness of prostate cancer is highly variable, depending on its grade and stage at the time of diagnosis. Despite recent advances in prostate cancer treatment, some patients still experience recurrence or even progression after undergoing radical treatment. Accurate initial staging and monitoring for recurrence determine patient management, which in turn affect patient prognosis and survival. Classical imaging has limitations in the diagnosis and treatment of prostate cancer, but the use of novel molecular probes has improved the detection rate, specificity, and accuracy of prostate cancer detection. Molecular probe-based imaging modalities allow the visualization and quantitative measurement of biological processes at the molecular and cellular levels in living systems. An increased understanding of tumor biology of prostate cancer and the discovery of new tumor biomarkers have allowed the exploration of additional molecular probe targets. The development of novel ligands and advances in nano-based delivery technologies have accelerated the research and development of molecular probes. Here, we summarize the use of molecular probes in positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and ultrasound imaging, and provide a brief overview of important target molecules in prostate cancer.

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.

Advances in exosome plasmonic sensing: Device integration strategies and AI-aided diagnosis

Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, due to their label-free, real-time, and high-sensitivity features. Their advantages in multiplex immunoassays of minimal liquid samples establish the leading position in various diagnostic studies. This review delineates the application principles of plasmonic sensing technologies, highlighting the importance of exosomes-based spectrum and image signals in disease diagnostics. It also introduces advancements in miniaturizing plasmonic biosensing platforms of exosomes, which can facilitate point-of-care testing for future healthcare. Nowadays, inspired by the surge of artificial intelligence (AI) for science and technology, more and more AI algorithms are being adopted to process the exosome spectrum and image data from plasmonic detection. Using representative algorithms of machine learning has become a mainstream trend in plasmonic biosensing research for exosome liquid biopsy. Typically, these algorithms process complex exosome datasets efficiently and establish powerful predictive models for precise diagnosis. This review further discusses critical strategies of AI algorithm selection in exosome-based diagnosis. Particularly, we categorize the AI algorithms into the interpretable and uninterpretable groups for exosome plasmonic detection applications. The interpretable AI enhances the transparency and reliability of diagnosis by elucidating the decision-making process, while the uninterpretable AI provides high diagnostic accuracy with robust data processing by a "black-box" working mode. We believe that AI will continue to promote significant progress of exosome plasmonic detection and mobile healthcare in the near future.

Electroactivated SERS Nanoplatform for Rapid and Sensitive Detection and Identification of Tumor-Derived Exosome miRNA

Exosomal miRNA expression derived from tumor cells provides a valuable and promising noninvasive modality for the early diagnosis and assessment of the efficacy of cancer treatment. However, accurate detection and identification of miRNA within exosomes have been challenging due to its low abundance and the complexity and tedious extraction with large sample volumes in the separation process. Here, we developed an electrically activated nanoplatform for rapid and sensitive detection and identification of exosome miRNA, through triggering miRNA release by opening exosomes that were captured on the electrode surface using a slightly applied electric field (50 mV), and simultaneously detected them with surface-enhanced Raman spectroscopy (SERS) in situ. The method possessed superior specificity and sensitivity for exosomal miRNA detection, with a low detection concentration of 0.5 nM. The SERS sensor chips also showed a superior sensing performance of exosomal miRNA in complex body fluids such as urine and blood. We found that exosomal miRNA contents derived from tumor cells were significantly higher than those in normal cells, and importantly, the concentrations of exosomes secreted from three different cell lines were distinctly augmented after mild electrical stimulation (ES) treatment. Furthermore, the miRNA expression within exosomes was upregulated after the ES treatment of cells. The developed approach and SERS detection platform for exosomal miRNA are promising for noninvasive and precise screening, classification, and monitoring of cancer.

Integrating All-rounder TiO2 Accelerated Electrochemiluminescence with Dual-Quenching PDA@COF Probes for Sensitive Quantification and Protein Profiling of Tumorous Exosomes

Exosomes have been perceived as promising biomarkers for noninvasive cancer diagnosis and treatment monitoring. However, the sensitive and accurate quantification and phenotyping of exosomes remains challenging. Herein, a versatile electrochemiluminescence (ECL) aptasensor was proposed for the sensitive analysis of tumorous exosomes. Specifically, a ternary nanohybrid (Ru-HAuTiO2), by covalently linking ECL luminophore Ru(dcbpy)32+ with gold nanoparticles (AuNPs)-decorated hollow urchin-like TiO2 (HTiO2), was ingeniously designed as a highly luminescent and self-enhanced ECL nanoemitter. Notably, the porous HTiO2 played an "all-rounder" role, including the carrier for ECL luminophores and AuNPs, coreaction accelerator, and specific exosome capturing scaffold through Ti-phosphate coordination interaction. On the other hand, a polydopamine modified covalent organic framework (PDA@COF) was employed as a quencher to remarkably attenuate the ECL of Ru-HAuTiO2 through a dual-quenching mechanism, and further labeled with a specific aptamer (Apt) of exosomal surface protein. Based on forming a Ru-HAuTiO2/exosome/Apt-PDA@COF sandwich structure on the electrode, a "signal on-off" ECL platform for tumorous exosomes was constructed, realizing sensitive detection within the range of 3.1 × 103 particles/mL to 1 × 108 particles/mL and a low limit of detection of 1.41 × 103 particles/mL, achieving phenotypic profiling of surface proteins on different tumorous exosomes. This work provides a promising alternative method for the detection and analysis of exosomes.

Exosomes to exosome-functionalized scaffolds: a novel approach to stimulate bone regeneration

Bone regeneration is a complex biological process that relies on the orchestrated interplay of various cellular and molecular events. Bone tissue engineering is currently the most promising method for treating bone regeneration. However, the immunogenicity, stable and cell quantity of seed cells limited their application. Recently, exosomes, which are small extracellular vesicles released by cells, have been found to effectively address these problems and better induce bone regeneration. Meanwhile, a growing line of research has shown the cargos of exosomes may provide effective therapeutic and biomarker tools for bone repair, including miRNA, lncRNA, and proteins. Moreover, engineered scaffolds loaded with exosomes can offer a cell-free bone repair strategy, addressing immunogenicity concerns and providing a more stable functional performance. Herein, we provide a comprehensive summary of the role played by scaffolds loaded with exosomes in bone regeneration, drawing on a systematic analysis of relevant literature available on PubMed, Scopus, and Google Scholar database.

SERS-Based Droplet Microfluidic Platform for Sensitive and High-Throughput Detection of Cancer Exosomes

Exosomes, nanosized extracellular vesicles containing biomolecular cargo, are increasingly recognized as promising noninvasive biomarkers for cancer diagnosis, particularly for their role in carrying tumor-specific molecular information. Traditional methods for exosome detection face challenges such as complexity, time consumption, and the need for sophisticated equipment. This study addresses these challenges by introducing a novel droplet microfluidic platform integrated with a surface-enhanced Raman spectroscopy (SERS)-based aptasensor for the rapid and sensitive detection of HER2-positive exosomes from breast cancer cells. Our approach utilized an on-chip salt-induced gold nanoparticles (GNPs) aggregation process in the presence of HER2 aptamers and HER2-positive exosomes, enhancing the hot spot-based SERS signal amplification. This platform achieved a limit of detection of 4.5 log10 particles/mL with a sample-to-result time of 5 min per sample. Moreover, this platform has been successfully applied for HER2 status testing in clinical samples to distinguish HER2-positive breast cancer patients from HER2-negative breast cancer patients. High sensitivity, specificity, and the potential for high-throughput screening of specific tumor exosomes make this SERS-based droplet system a potential liquid biopsy technology for early cancer diagnosis.

Nanoplasmonic sensors for extracellular vesicles and bacterial membrane vesicles

Extracellular vesicles (EVs) are promising tools for the early diagnosis of diseases, and bacterial membrane vesicles (MVs) are especially important in health and environment monitoring. However, detecting EVs or bacterial MVs presents significant challenges for the clinical translation of EV-based diagnostics. In this Review, we provide a comprehensive discussion on the basics of nanoplasmonic sensing and emphasize recent developments in nanoplasmonics-based optical sensors to effectively identify EVs or bacterial MVs. We explore various nanoplasmonic sensors tailored for EV or bacterial MV detection, emphasizing the application of localized surface plasmon resonance through gold nanoparticles and their multimers. Additionally, we highlight advanced EV detection techniques based on surface plasmon polaritons using plasmonic thin film and nanopatterned structures. Furthermore, we evaluate the improved detection capability of surface-enhanced Raman spectroscopy in identifying and classifying these vesicles, aided by plasmonic nanostructures. Nanoplasmonic sensing techniques have remarkable precision and sensitivity, making them a potential tool for accurate EV detection in clinical applications, facilitating point-of-care molecular diagnostics. Finally, we summarize the challenges associated with nanoplasmonic EV or bacterial MV sensors and offer insights into potential future directions for this evolving field.

Label-free single-vesicle based surface enhanced Raman spectroscopy: A robust approach for investigating the biomolecular composition of small extracellular vesicles

Small extracellular vesicles (sEVs) are cell-released vesicles ranging from 30-150nm in size. They have garnered increasing attention because of their potential for both the diagnosis and treatment of disease. The diversity of sEVs derives from their biological composition and cargo content. Currently, the isolation of sEV subpopulations is primarily based on bio-physical and affinity-based approaches. Since a standardized definition for sEV subpopulations is yet to be fully established, it is important to further investigate the correlation between the biomolecular composition of sEVs and their physical properties. In this study, we employed a platform combining single-vesicle surface-enhanced Raman spectroscopy (SERS) and machine learning to examine individual sEVs isolated by size-exclusion chromatography (SEC). The biomolecular composition of each vesicle examined was reflected by its corresponding SERS spectral features (biomolecular "fingerprints"), with their roots in the composition of their collective Raman-active bonds. Origins of the SERS spectral features were validated through a comparative analysis between SERS and mass spectrometry (MS). SERS fingerprinting of individual vesicles was effective in overcoming the challenges posed by EV population averaging, allowing for the possibility of analyzing the variations in biomolecular composition between the vesicles of similar and/or different sizes. Using this approach, we uncovered that each of the size-based fractions of sEVs contained particles with predominantly similar SERS spectral features. Indeed, more than 84% of the vesicles residing within a particular group were clearly distinguishable from that of the other EV sub-populations, despite some spectral variations within each sub-population. Our results suggest the possibility that size-based EV fractionation methods produce samples where similarly eluted sEVs are correlated with their respective biochemical contents, as reflected by their SERS spectra. Our findings therefore highlight the possibility that the biogenesis and respective biological functionalities of the various sEV fractions may be inherently different.

Effective enrichment of trace exosomes for the label-free SERS detection via low-cost thermophoretic profiling

Exosome-based liquid biopsies possess great potential in monitoring cancer development However, current exosome detection biosensors require large exosome volumes, showing the weak detection sensitivity. Besides, these methods pay little attention to in situ analysis of exosomes, hence limiting the provision of more accurate clinically-relevant information. Herein, we develop an innovative label-free biosensor combining the low-cost thermophoretic enrichment method with the surface-enhanced Raman spectroscopy (SERS) detection. Based on the thermophoretic enrichment strategy, exosomes and gold nanoparticles can be enriched together into a small area with a scale of 500 μm within 10 min. The Raman signals of various exosomes derived from normal, cancerous cell lines and human serum are dynamically monitored in situ, with the limit of detection of 102-103 particles per microliter, presenting higher sensitivity compared with the similar label-free SERS detection. The spectral data set of different exosomes is applied to train for multivariate classification of cell types and to estimate how the normal exosome data resemble cancer cell exosome. The reliable classification and identification of different exosomes can be realized. The current biosensor is convenient, low-cost and requires small exosome volumes (∼3 μL), and if validated in larger cohorts may contribute to the tumor prediction and diagnosis.

Increasing the sensitivity and accuracy of detecting exosomes as biomarkers for cancer monitoring using optical nanobiosensors

The advancement of nanoscience and material design in recent times has facilitated the creation of point-of-care devices for cancer diagnosis and biomolecule sensing. Exosomes (EXOs) facilitate the transfer of bioactive molecules between cancer cells and diverse cells in the local and distant microenvironments, thereby contributing to cancer progression and metastasis. Specifically, EXOs derived from cancer are likely to function as biomarkers for early cancer detection due to the genetic or signaling alterations they transport as payload within the cancer cells of origin. It has been verified that EXOs circulate steadily in bodily secretions and contain a variety of information that indicates the progression of the tumor. However, acquiring molecular information and interactions regarding EXOs has presented significant technical challenges due to their nanoscale nature and high heterogeneity. Colorimetry, surface plasmon resonance (SPR), fluorescence, and Raman scattering are examples of optical techniques utilized to quantify cancer exosomal biomarkers, including lipids, proteins, RNA, and DNA. Many optically active nanoparticles (NPs), predominantly carbon-based, inorganic, organic, and composite-based nanomaterials, have been employed in biosensing technology. The exceptional physical properties exhibited by nanomaterials, including carbon NPs, noble metal NPs, and magnetic NPs, have facilitated significant progress in the development of optical nanobiosensors intended for the detection of EXOs originating from tumors. Following a summary of the biogenesis, biological functions, and biomarker value of known EXOs, this article provides an update on the detection methodologies currently under investigation. In conclusion, we propose some potential enhancements to optical biosensors utilized in detecting EXO, utilizing various NP materials such as silicon NPs, graphene oxide (GO), metal NPs, and quantum dots (QDs).

Raman Spectroscopic Insights of Phase-Separated Insulin Aggregates

Phase-separated protein accumulation through the formation of several aggregate species is linked to the pathology of several human disorders and diseases. Our current investigation envisaged detailed Raman signature and structural intricacy of bovine insulin in its various forms of aggregates produced in situ at an elevated temperature (60 °C). The amide I band in the Raman spectrum of the protein in its native-like conformation appeared at 1655 cm-1 and indicated the presence of a high content of α-helical structure as prepared freshly in acidic pH. The disorder content (turn and coils) also was predominately present in both the monomeric and oligomeric states and was confirmed by the presence shoulder amide I maker band at ∼1680 cm-1. However, the band shifted to ∼1671 cm-1 upon the transformation of the protein solution into fibrillar aggregates as produced for a longer time of incubation. The protein, however, maintained most of its helical conformation in the oligomeric phase; the low-frequency backbone α-helical conformation signal at ∼935 cm-1 was similar to that of freshly prepared aqueous protein solution enriched in helical conformation. The peak intensity was significantly weak in the fibrillar aggregates, and it appeared as a good Raman signature to follow the phase separation and the aggregation behavior of insulin and similar other proteins. Tyrosine phenoxy moieties in the protein may maintained its H-bond donor-acceptor integrity throughout the course of fibril formation; however, it entered in more hydrophobic environment in its journey of fibril formation. In addition, it was noticed that oligomeric bovine insulin maintained the orientation/conformation of the disulfide bonds. However, in the fibrillar state, the disulfide linkages became more strained and preferred to maintain a single conformation state.

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.

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.

Application of microfluidic technology based on surface-enhanced Raman scattering in cancer biomarker detection: A review

With the continuous discovery and research of predictive cancer-related biomarkers, liquid biopsy shows great potential in cancer diagnosis. Surface-enhanced Raman scattering (SERS) and microfluidic technology have received much attention among the various cancer biomarker detection methods. The former has ultrahigh detection sensitivity and can provide a unique fingerprint. In contrast, the latter has the characteristics of miniaturization and integration, which can realize accurate control of the detection samples and high-throughput detection through design. Both have the potential for point-of-care testing (POCT), and their combination (lab-on-a-chip SERS (LoC-SERS)) shows good compatibility. In this paper, the basic situation of circulating proteins, circulating tumor cells, exosomes, circulating tumor DNA (ctDNA), and microRNA (miRNA) in the diagnosis of various cancers is reviewed, and the detection research of these biomarkers by the LoC-SERS platform in recent years is described in detail. At the same time, the challenges and future development of the platform are discussed at the end of the review. Summarizing the current technology is expected to provide a reference for scholars engaged in related work and interested in this field.

Extracellular vesicle-based liquid biopsy biomarkers and their application in precision immuno-oncology

While the field of precision oncology is rapidly expanding and more targeted options are revolutionizing cancer treatment paradigms, therapeutic resistance particularly to immunotherapy remains a pressing challenge. This can be largely attributed to the dynamic tumor-stroma interactions that continuously alter the microenvironment. While to date most advancements have been made through examining the clinical utility of tissue-based biomarkers, their invasive nature and lack of a holistic representation of the evolving disease in a real-time manner could result in suboptimal treatment decisions. Thus, using minimally-invasive approaches to identify biomarkers that predict and monitor treatment response as well as alert to the emergence of recurrences is of a critical need. Currently, research efforts are shifting towards developing liquid biopsy-based biomarkers obtained from patients over the course of disease. Liquid biopsy represents a unique opportunity to monitor intercellular communication within the tumor microenvironment which could occur through the exchange of extracellular vesicles (EVs). EVs are lipid bilayer membrane nanoscale vesicles which transfer a plethora of biomolecules that mediate intercellular crosstalk, shape the tumor microenvironment, and modify drug response. The capture of EVs using innovative approaches, such as microfluidics, magnetic beads, and aptamers, allow their analysis via high throughput multi-omics techniques and facilitate their use for biomarker discovery. Artificial intelligence, using machine and deep learning algorithms, is advancing multi-omics analyses to uncover candidate biomarkers and predictive signatures that are key for translation into clinical trials. With the increasing recognition of the role of EVs in mediating immune evasion and as a valuable biomarker source, these real-time snapshots of cellular communication are promising to become an important tool in the field of precision oncology and spur the recognition of strategies to block resistance to immunotherapy. In this review, we discuss the emerging role of EVs in biomarker research describing current advances in their isolation and analysis techniques as well as their function as mediators in the tumor microenvironment. We also highlight recent lung cancer and melanoma studies that point towards their application as predictive biomarkers for immunotherapy and their potential clinical use in precision immuno-oncology.

The therapeutic potential of exosomes in lung cancer

BACKGROUND

Lung cancer (LC) is one of the most common malignancies globally. Besides early detection and surgical resection, there is currently no effective curative treatment for metastatic advanced LC. Exosomes are endogenous nano-extracellular vesicles produced by somatic cells that play an important role in the development and maintenance of normal physiology. Exosomes can carry proteins, peptides, lipids, nucleic acids, and various small molecules for intra- and intercellular material transport or signal transduction. LC cells can maintain their survival, proliferation, migration, invasion, and metastasis, by producing or interacting with exosomes. Basic and clinical data also show that exosomes can be used to suppress LC cell proliferation and viability, induce apoptosis, and enhance treatment sensitivity. Due to the high stability and target specificity, good biocompatibility, and low immunogenicity of exosomes, they show promise as vehicles of LC therapy.

CONCLUSION

We have written this comprehensive review to communicate the LC treatment potential of exosomes and their underlying molecular mechanisms. We found that overall, LC cells can exchange substances or crosstalk with themselves or various other cells in the surrounding TME or distant organs through exosomes. Through this, they can modulate their survival, proliferation, stemness, migration, and invasion, EMT, metastasis, and apoptotic resistance.

Casting liquid PDMS on self-assembled bilayer polystyrene nanospheres to prepare a SERS substrate with two layers of nanopits for detection of p-nitrophenol

p-Nitrophenol (PNP) is widely used in pesticides, pharmaceuticals, and dyestuffs. It is vital to detect trace PNP in the environment, because it poses significant environmental hazards due to its high toxicity. In this paper, a new method was reported for preparing a SERS substrate with excellent SERS activity by combining self-assembly techniques and flexible materials. First, the three-dimensional (3D) polystyrene (PS) photonic crystal (PC) structural master was fabricated by stacking two layers of self-assembled PS nanospheres with different diameters. Polydimethylsiloxane (PDMS) with a complementary structure to the master was obtained by casting, curing and peeling off. Finally, the PDMS-Ag substrate was fabricated by sputtering a thin Ag layer on the PDMS structure. The enhancement factor (EF) of the PDMS-Ag substrate was calculated to be 2.90 × 109 by using 4-amino thiophenol (ATP) as the probe molecule, and the limit of detection (LOD) for ATP can reach 10-11 M. And the RSD of the SERS intensity for the peak at 1078 cm-1 on the PDMS-Ag substrates from batch to batch was within 2%, indicating the high reproducibility of the as-prepared substrate. The quantitative analysis of PNP was achieved with a LOD of 10-8 M. Therefore, the PDMS-Ag substrate exhibits high sensitivity and reproducibility, and it can detect PNP in trace amounts, with great potential for detecting other contaminants.

Renal Clearable Magnetic Nanoreporter for Colorimetric Urinalysis of Tumor

The convenience and availability are of great significance for the early screening of cancer. Herein, a magnetic nanoreporter with renal clearable capability and activatable catalytic activity was developed for colorimetric urinalysis of tumors. The magnetic nanoreporters were prepared by loading 3.2 nm Fe3O4 nanoparticles (NPs) and glucose oxidase (GOD) into macrophage cell-derived microvesicles (MVs) through electroporation, and these compositions serve as renal clearable catalytic reporters, synergistic catalysts, and targeted delivery carriers, respectively. The magnetic nanoreporters can convert the H2O2 in the mildly acidic tumor microenvironment into hydroxyl radicals through the synergistic catalysis of Fe3O4 NPs and GOD. Then the MVs can be disintegrated by the radicals, and ultrasmall Fe3O4 NPs will be released from the MVs at the tumor site, enabling rapid clearance of the Fe3O4 NPs into urine and a direct colorimetric urinalysis of the tumor within 4 h. The magnetic nanoreporters had good biocompatibility, and the released Fe3O4 NPs were rapidly excreted from the body, avoiding the potential toxicity. We envision that the magnetic nanoreporters can be used for convenient and rapid cancer screening.

Application of Metal-Based Nanomaterials in In Vitro Diagnosis of Tumor Markers: Summary and Prospect

Cancer, which presents with high incidence and mortality rates, has become a significant health threat worldwide. However, there is currently no effective solution for rapid screening and high-quality treatment of early-stage cancer patients. Metal-based nanoparticles (MNPs), as a new type of compound with stable properties, convenient synthesis, high efficiency, and few adverse reactions, have become highly competitive tools for early cancer diagnosis. Nevertheless, challenges such as the difference between the microenvironment of detected markers and the real-life body fluids remain in achieving widespread clinical application of MNPs. This review provides a comprehensive review of the research progress made in the field of in vitro cancer diagnosis using metal-based nanoparticles. By delving into the characteristics and advantages of these materials, this paper aims to inspire and guide researchers towards fully exploiting the potential of metal-based nanoparticles in the early diagnosis and treatment of cancer.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

A New Detection Method of Oral and Oropharyngeal Squamous Cell Carcinoma Based on Multivariate Analysis of Surface Enhanced Raman Spectra of Salivary Exosomes

Raman spectroscopy recently proved a tremendous capacity to identify disease-specific markers in various (bio)samples being a non-invasive, rapid, and reliable method for cancer detection. In this study, we first aimed to record vibrational spectra of salivary exosomes isolated from oral and oropharyngeal squamous cell carcinoma patients and healthy controls using surface enhancement Raman spectroscopy (SERS). Then, we assessed this method's capacity to discriminate between malignant and non-malignant samples by means of principal component-linear discriminant analysis (PC-LDA) and we used area under the receiver operating characteristics with illustration as the area under the curve to measure the power of salivary exosomes SERS spectra analysis to identify cancer presence. The vibrational spectra were collected on a solid plasmonic substrate developed in our group, synthesized using tangential flow filtered and concentrated silver nanoparticles, capable of generating very reproducible spectra for a whole range of bioanalytes. SERS examination identified interesting variations in the vibrational bands assigned to thiocyanate, proteins, and nucleic acids between the saliva of cancer and control groups. Chemometric analysis indicated discrimination sensitivity between the two groups up to 79.3%. The sensitivity is influenced by the spectral interval used for the multivariate analysis, being lower (75.9%) when the full-range spectra were used.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.

Shape-Preserving Transformation of Electrodeposited Macroporous Microparticles for Single-Particle SERS Applications

Microparticles composed of bicontinuous and ordered macropores are important in many applications. However, rational integration of ordered macropores into a single crystalline microparticle remains a challenge. Here, we report a method to prepare three-dimensionally ordered macroporous (3DOM) Ag₇O₈NO₃ micropyramids via selectively cementing the colloidal crystal templates via an electrochemical method and their shape-preserving transformation into 3DOM Ag micropryamids formed by Ag nanoparticles via a chemical reduction process. The interconnected macropores facilitated the transportation and enrichment of the analyte molecules into the 3DOM Ag micropyramids. The dense Ag nanoparticles on the skeletons of the 3DOM Ag micropyramids provided strong electromagnetic fields. Taken together, a 3DOM Ag micropyramid as a kind of single-particle surface-enhanced Raman scattering (SERS) sensing substrate demonstrated high SERS sensitivity and outstanding SERS signal reproducibility. We explored the application of 3DOM Ag micropyramids in SERS detection of biomolecules (e.g., adenosine, adenine, hemoglobin bovine, and lysozyme) and proved their potentials in distinguishing exosomes from tumor and non-tumor cells. The method can be extended to prepared 3DOM structures of other materials with promising applications in sensing, separation, and catalytic fields.

Metasurface supporting quasi-BIC for optical trapping and Raman-spectroscopy of biological nanoparticles

Optical trapping combined with Raman spectroscopy have opened new possibilities for analyzing biological nanoparticles. Conventional optical tweezers have proven successful for trapping of a single or a few particles. However, the method is slow and cannot be used for the smallest particles. Thus, it is not adapted to analyze a large number of nanoparticles, which is necessary to get statistically valid data. Here, we propose quasi-bound states in the continuum (quasi-BICs) in a silicon nitride (Si₃N₄) metasurface to trap smaller particles and many simultaneously. The quasi-BIC metasurface contains multiple zones with high field-enhancement ('hotspots') at a wavelength of 785 nm, where a single nanoparticle can be trapped at each hotspot. We numerically investigate the optical trapping of a type of biological nanoparticles, namely extracellular vesicles (EVs), and study how their presence influences the resonance behavior of the quasi-BIC. It is found that perturbation theory and a semi-analytical expression give good estimates for the resonance wavelength and minimum of the potential well, as a function of the particle radius. This wavelength is slightly shifted relative to the resonance of the metasurface without trapped particles. The simulations show that the Q-factor can be increased by using a thin metasurface. The thickness of the layer and the asymmetry of the unit cell can thus be used to get a high Q-factor. Our findings show the tight fabrication tolerances necessary to make the metasurface. If these can be overcome, the proposed metasurface can be used for a lab-on-a-chip for mass-analysis of biological nanoparticles.

Nanostructures and Nanotechnologies for the Detection of Extracellular Vesicle

Liquid biopsy has been taken as a minimally invasive examination and a promising surrogate to the clinically applied tissue-based test for the diagnosis and molecular analysis of cancer. Extracellular vesicles (EVs) carry complex molecular information from the tumor, allowing for the multicomponent analysis of cancer and would be beneficial to personalized medicine. In this review, the advanced nanomaterials and nanotechniques for the detection and molecular profiling of EVs, highlight the advantages of nanotechnology in the high-purity isolation and the high-sensitive and high-specific identification of EVs, are summarized. An outlook on the clinical application of nanotechnology-based liquid biopsy in the diagnosis, prognostication, and surveillance of cancer is also provided. It provides information for developing liquid biopsy based on EVs by discussing the advantages and challenges of functionalized nanomaterials and various nanotechnologies.

CNN-assisted SERS enables ultra-sensitive and simultaneous detection of Scr and BUN for rapid kidney function assessment

Kidney disease is highly prevalent and may result in severe clinical outcomes. Serum creatinine (Scr) and blood urea nitrogen (BUN) are the most widely used biomarkers for kidney function assessment, yet when measured alone, the result can be affected by a variety of parameters such as age, gender, protein consumption, etc. Measuring Scr and BUN simultaneously can eliminate most of the external influences and greatly improve the assessment of kidney function. In this study, a real-time kidney function assessment system based on dual biomarker detection was proposed. Scr and BUN were determined using surface-enhanced Raman scattering (SERS) within the concentration range of 10^-1 to 10^-6 M and 0.28 to 100 mg dl^-1, respectively. A one-dimensional convolutional neural network (1D-CNN) model was employed to quantitatively analyze the concentration of biomarkers from the SERS spectral measurements. Moreover, we simulated a variety of kidney health conditions with 16 groups of mixed Scr and BUN in serum. The proposed CNN-assisted SERS method was used to quantify both biomarkers and provide diagnostic results. The Au core-Ag shell nanoprobes provided ultra-sensitive SERS detection and the CNN model achieved excellent regression results with an R² of 0.9871 in the testing dataset. The system demonstrated a rapid and robust evaluation for the assessment of kidney function, providing a promising idea for medical diagnosis with the help of spectroscopy and deep learning methods.

Convenient exosome separation by phosphatidylserine targeting polymer brush materials

We report phosphatidylserine targeting polymer brush materials to selectively separate exosomes. This method provides an efficient separation strategy with ordinary centrifuge force, which improves the integrity and purity of the exosomes. Compared with the common methods, the content of contaminated lipoprotein in the resulting exosomes decreased obviously.

Nanotechnology-Inspired Extracellular Vesicles Theranostics for Diagnosis and Therapy of Central Nervous System Diseases

Shuttling various bioactive substances across the blood-brain barrier (BBB) bidirectionally, extracellular vesicles (EVs) have been opening new frontiers for the diagnosis and therapy of central nervous system (CNS) diseases. However, clinical translation of EV-based theranostics remains challenging due to difficulties in effective EV engineering for superior imaging/therapeutic potential, ultrasensitive EV detection for small sample volume, as well as scale-up and standardized EV production. In the past decade, continuous advancement in nanotechnology provided extensive concepts and strategies for EV engineering and analysis, which inspired the application of EVs for CNS diseases. Here we will review the existing types of EV-nanomaterial hybrid systems with improved diagnostic and therapeutic efficacy for CNS diseases. A summary of recent progress in the incorporation of nanomaterials and nanostructures in EV production, separation, and analysis will also be provided. Moreover, the convergence between nanotechnology and microfluidics for integrated EV engineering and liquid biopsy of CNS diseases will be discussed.

Microfluidic Platform for Profiling of Extracellular Vesicles from Single Breast Cancer Cells

Extracellular vesicles (EVs) are considered as valuable biomarkers to discriminate healthy from diseased cells such as cancer. Passing cytosolic and plasma membranes before their release, EVs inherit the biochemical properties of the cell. Here, we determine protein profiles of single EVs to understand how much they represent their cell of origin. We use a microfluidic platform which allows to immobilize EVs from completely isolated single cells, reducing heterogeneity of EVs as strongly seen in cell populations. After immunostaining, we employ four-color total internal reflection fluorescence microscopy to enumerate EVs and determine their biochemical fingerprint encoded in membranous or cytosolic proteins. Analyzing single cells derived from pleural effusions of two different human adenocarcinoma as well as from human embryonic kidney (SkBr3, MCF-7 and HEK293, respectively), we observed that a single cell secretes enough EVs to extract the respective tissue fingerprint. We show that overexpressed integral plasma membrane proteins are also found in EV membranes, which together with populations of colocalized proteins, provide a cell-specific, characteristic pattern. Our method highlights the potential of EVs as a diagnostic marker and can be directly employed for fundamental studies of EV biogenesis.

Plasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis

Nanophotonics has been widely utilized in enhanced molecularspectroscopy or mediated chemical reaction, which has major applications in the field of enhancing sensing and enables opportunities in developing healthcare monitoring. This review presents an updated overview of the recent exciting advances of plasmonic biosensors in the healthcare area. Manufacturing, enhancements and applications of plasmonic biosensors are discussed, with particular focus on nanolisted main preparation methods of various nanostructures, such as chemical synthesis, lithography, nanosphere lithography, nanoimprint lithography, etc., and describing their respective advances and challenges from practical applications of plasmon biosensors. Based on these sensing structures, different types of plasmonic biosensors are summarized regarding detecting cancer biomarkers, body fluid, temperature, gas and COVID-19. Last, the existing challenges and prospects of plasmonic biosensors combined with machine learning, mega data analysis and prediction are surveyed.

Analysis and Biomedical Applications of Functional Cargo in Extracellular Vesicles

Extracellular vesicles (EVs) can facilitate essential communication among cells in a range of pathophysiological conditions including cancer metastasis and progression, immune regulation, and neuronal communication. EVs are membrane-enclosed vesicles generated through endocytic origin and contain many cellular components, including proteins, lipids, nucleic acids, and metabolites. Over the past few years, the intravesicular content of EVs has proven to be a valuable biomarker for disease diagnostics, involving cancer, cardiovascular diseases, and central nervous system diseases. This review aims to provide insight into EV biogenesis, composition, function, and isolation, present a comprehensive overview of emerging techniques for EV cargo analysis, highlighting their major technical features and limitations, and summarize the potential role of EV cargos as biomarkers in disease diagnostics. Further, progress and remaining challenges will be discussed for clinical diagnostic outlooks.

Nanomaterials assisted exosomes isolation and analysis towards liquid biopsy

Exosomes has attracted tremendous research interests as they are emerging as a new paradigm of liquid biopsy. Although the concentration of exosomes in blood is relatively abundant, there still exists various vesicle-like nanoparticles, such as microvesicles, apoptotic bodies. It's an urgent need to isolate and enrich exosomes from the complex contaminants in biofluid samples. Moreover, the expressing level of exosomal biomarkers varies a lot, which make the sensitive molecular detection of exosomes in high demand. Unfortunately, the efficient isolation and sensitive molecular quantification of exosomes is still a major obstacle hindering the further development and clinical application of exosome-based liquid biopsy. Nanomaterials, with unique physiochemical properties, have been widely used in biosensing and analysis aspects, thus they are thought as powerful tools for effective purification and molecular analysis of exosomes. In this review, we summarized the most recent progresses in nanomaterials assisted exosome isolation and analysis towards liquid biopsy. On the one hand, nanomaterials can be used as capture substrates to afford large binding area and specific affinity to exosomes. Meanwhile, nanomaterials can also be served as promising signal transducers and amplifiers for molecular detection of exosomes. Furthermore, we also pointed out several potential and promising research directions in nanomaterials assisted exosome analysis. It's envisioned that this review will give the audience a complete outline of nanomaterials in exosome study, and further promote the intersection of nanotechnology and bio-analysis.

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.

Recent progress in surface-enhanced Raman spectroscopy-based biosensors for the detection of extracellular vesicles

Extracellular vesicles (EVs) are a type of mediator that enables intercellular communication. Moreover, EVs carry critical molecular information from parental cells, making them ideal biomarkers for clinical screening and diagnosis. Currently, several sensing technologies have been established to sensitively detect EVs. Among them, surface-enhanced Raman spectroscopy (SERS) has become a powerful analytical tool with high sensitivity and low detection limits. In this review, we first cover the biological characteristics of EVs and the principle of SERS amplification. Then, we describe the recent progress in SERS technology applied to detect EVs, including direct label-free methods and indirect labeling strategies, in which substrate fabrication and nanoprobe assembly were emphasized. Furthermore, SERS technology could also be used to characterize or monitor the behavior of programmable EVs. Finally, we discuss the prospects and issues to be addressed for the development of SERS technology for EV analysis.

Recent Progress on Solid Substrates for Surface-Enhanced Raman Spectroscopy Analysis

Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy technique with distinguished features of non-destructivity, ultra-sensitivity, rapidity, and fingerprint characteristics for analysis and sensors. The SERS signals are mainly dependent on the engineering of high-quality substrates. Recently, solid SERS substrates with diverse forms have been attracting increasing attention due to their promising features, including dense hot spot, high stability, controllable morphology, and convenient portability. Here, we comprehensively review the recent advances made in the field of solid SERS substrates, including their common fabrication methods, basic categories, main features, and representative applications, respectively. Firstly, the main categories of solid SERS substrates, mainly including membrane substrate, self-assembled substrate, chip substrate, magnetic solid substrate, and other solid substrate, are introduced in detail, as well as corresponding construction strategies and main features. Secondly, the typical applications of solid SERS substrates in bio-analysis, food safety analysis, environment analysis, and other analyses are briefly reviewed. Finally, the challenges and perspectives of solid SERS substrates, including analytical performance improvement and largescale production level enhancement, are proposed.

Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment

Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for label-free spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.

Silica Inverse Opal Nanostructured Sensors for Enhanced Immunodetection of Extracellular Vesicles by Quartz Crystal Microbalance with Dissipation Monitoring

Extracellular vesicles (EVs) are nanosized circulating assemblies that contain biomarkers considered promising for early diagnosis within neurology, cardiology, and oncology. Recently, acoustic wave biosensors, in particular based on quartz crystal microbalance with dissipation monitoring (QCM-D), have emerged as a sensitive, label-free, and selective EV characterization platform. A rational approach to further improving sensing detection limits relies on the nanostructuration of the sensor surfaces. To this end, inorganic inverse opals (IOs) derived from colloidal self-assembly present a highly tunable and scalable nanoarchitecture of suitable feature sizes and surface chemistry. This work systematically investigates their use in two-dimensional (2D) and three-dimensional (3D) for enhanced QCM-D EV detection. Precise tuning of the architecture parameters delivered improvements in detection performance to sensitivities as low as 6.24 × 10⁷ particles/mL. Our findings emphasize that attempts to enhance acoustic immunosensing via increasing the surface area by 3D nanostructuration need to be carefully analyzed in order to exclude solvent and artifact entrapment effects. Moreover, the use of 2D nanostructured electrodes to compartmentalize analyte anchoring presents a particularly promising design principle.

Gold Nanopyramid Arrays for Non-Invasive Surface-Enhanced Raman Spectroscopy-Based Gastric Cancer Detection via sEVs

Gastric cancer (GC) is one of the most common and lethal types of cancer affecting over one million people, leading to 768,793 deaths globally in 2020 alone. The key for improving the survival rate lies in reliable screening and early diagnosis. Existing techniques including barium-meal gastric photofluorography and upper endoscopy can be costly and time-consuming and are thus impractical for population screening. We look instead for small extracellular vesicles (sEVs, currently also referred as exosomes) sized ⌀ 30-150 nm as a candidate. sEVs have attracted a significantly higher level of attention during the past decade or two because of their potentials in disease diagnoses and therapeutics. Here, we report that the composition information of the collective Raman-active bonds inside sEVs of human donors obtained by surface-enhanced Raman spectroscopy (SERS) holds the potential for non-invasive GC detection. SERS was triggered by the substrate of gold nanopyramid arrays we developed previously. A machine learning-based spectral feature analysis algorithm was developed for objectively distinguishing the cancer-derived sEVs from those of the non-cancer sub-population. sEVs from the tissue, blood, and saliva of GC patients and non-GC participants were collected (n = 15 each) and analyzed. The algorithm prediction accuracies were reportedly 90, 85, and 72%. "Leave-a-pair-of-samples out" validation was further performed to test the clinical potential. The area under the curve of each receiver operating characteristic curve was 0.96, 0.91, and 0.65 in tissue, blood, and saliva, respectively. In addition, by comparing the SERS fingerprints of individual vesicles, we provided a possible way of tracing the biogenesis pathways of patient-specific sEVs from tissue to blood to saliva. The methodology involved in this study is expected to be amenable for non-invasive detection of diseases other than GC.