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

A cavity induced mode hybridization plasmonic sensor for portable detection of exosomes

Exosomes have been considered as promising biomarkers for cancer diagnosis due to their abundant information from originating cells. However, sensitive and reliable detection of exosomes is still facing technically challenges due to the lack of a sensing platform with high sensitivity and reproducibility. To address the challenges, here we propose a portable surface plasmon resonance (SPR) sensing of exosomes with a three-layer Au mirror/SiO2 spacer/Au nanohole sensor, fabricated by an economical polystyrene nanosphere self-assembly method. The SiO2 spacer can act as an optical cavity and induce mode hybridization, leading to excellent optimization of both sensitivity and full width at half maximum compared with normal single layer Au nanohole sensors. When modified with CD63 or EpCAM aptamers, a detection of limit (LOD) of as low as 600 particles/μL was achieved. The sensors showed good capability to distinguish between non-tumor derived L02 exosomes and tumor derived HepG2 exosomes. Additionally, high reproducibility was also achieved in detection of artificial serum samples with RSD as low as 2%, making it feasible for clinical applications. This mode hybridization plasmonic sensor provides an effective approach to optimize the detection sensitivity of exosomes, pushing SPR sensing one step further towards cancer diagnosis.

Surface-enhanced Raman scattering-based identification of breast cancer progression using extracellular vesicles-derived integrin α6β4

Detection of progression is of great importance to breast cancer treatment and can benefit patients. Limited by current detection technologies and biomarkers, early breast cancer progression diagnosis remains challenging. Researchers have found blood extracellular vesicles (EVs)-derived integrin α6β4 directly facilitate progression in breast cancer, enabling cancer detection. However, EVs size and heterogeneity hinder protein detection, masked by abundant background EVs. Hence, novel tools for efficient detection of EVs with high selectivity and low interference are significantly desired. Here, a new silver-coated gold nanorods SERS probe, termed as Au@Ag@IDA-B/4MSTP, based on DNA aptamer was established for the detection of integrin α6β4 derived from EVs. Validation of the Au@Ag@IDA-B/4MSTP probes using cell-culture-derived EVs revealed a LOD of 23 particles/μL for EVs detection. This tool was further confirmed to mimic the real state of cancer with subcutaneous tumor model and lung metastasis model in mice. With 10 μL of blood plasma and simple Raman analysis process, the test achieved 85.7 % sensitivity and 83.3 % specificity. Moreover, our method achieves a simplified approach that expedites the detection process. These results demonstrate the good detection performance of Au@Ag@IDA-B/4MSTP probes for EVs integrin α6β4, and suggest that this non-invasive approach could be a promising tool for early detection of breast cancer progression.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Deep autoencoder as an interpretable tool for Raman spectroscopy investigation of chemical and extracellular vesicle mixtures

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.

SERS microscopy as a tool for comprehensive biochemical characterization in complex samples

Surface enhanced Raman scattering (SERS) spectra of biomaterials such as cells or tissues can be used to obtain biochemical information from nanoscopic volumes in these heterogeneous samples. This tutorial review discusses the factors that determine the outcome of a SERS experiment in complex bioorganic samples. They are related to the SERS process itself, the possibility to selectively probe certain regions or constituents of a sample, and the retrieval of the vibrational information in order to identify molecules and their interaction. After introducing basic aspects of SERS experiments in the context of biocompatible environments, spectroscopy in typical microscopic settings is exemplified, including the possibilities to combine SERS with other linear and non-linear microscopic tools, and to exploit approaches that improve lateral and temporal resolution. In particular the great variation of data in a SERS experiment calls for robust data analysis tools. Approaches will be introduced that have been originally developed in the field of bioinformatics for the application to omics data and that show specific potential in the analysis of SERS data. They include the use of simulated data and machine learning tools that can yield chemical information beyond achieving spectral classification.

Extracellular Vesicle Preparation and Analysis: A State-of-the-Art Review

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state-of-the-art technologies that employ both microfluidic and non-microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting-edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential.

The involvement and application potential of exosomes in breast cancer immunotherapy

Breast cancer has a high incidence and a heightened propensity for metastasis. The absence of precise targets for effective intervention makes it imperative to devise enhanced treatment strategies. Exosomes, characterized by a lipid bilayer and ranging in size from 30 to 150 nm, can be actively released by various cells, including those in tumors. Exosomes derived from distinct subsets of immune cells have been shown to modulate the immune microenvironment within tumors and influence breast cancer progression. In addition, tumor-derived exosomes have been shown to contribute to breast cancer development and progression and may become a new target for breast cancer immunotherapy. Tumor immunotherapy has become an option for managing tumors, and exosomes have become therapeutic vectors that can be used for various pathological conditions. Edited exosomes can be used as nanoscale drug delivery systems for breast cancer therapy, contributing to the remodeling of immunosuppressive tumor microenvironments and influencing the efficacy of immunotherapy. This review discusses the regulatory role of exosomes from different cells in breast cancer and the latest applications of exosomes as nanoscale drug delivery systems and immunotherapeutic agents in breast cancer, showing the development prospects of exosomes in the clinical treatment of breast cancer.

Advanced Nanoencapsulation-Enabled Ultrasensitive Analysis: Unraveling Tumor Extracellular Vesicle Subpopulations for Differential Diagnosis of Hepatocellular Carcinoma via DNA Cascade Reactions

Tumor-derived extracellular vesicles (tEVs) hold immense promise as potential biomarkers for the precise diagnosis of hepatocellular carcinoma (HCC). However, their clinical translation is hampered by their inherent characteristics, such as small size and high heterogeneity and complex environment, including non-EV particles and normal cell-derived EVs, which prolong separation procedures and compromise detection accuracy. In this study, we devised a DNA cascade reaction-triggered individual EV nanoencapsulation (DCR-IEVN) strategy to achieve the ultrasensitive and specific detection of tEV subpopulations via routine flow cytometry in a one-pot, one-step fashion. DCR-IEVN enables the direct and selective packaging of multiple tEV subpopulations in clinical serum samples into flower-like particles exceeding 600 nm. This approach bypasses the need for EV isolation, effectively reducing interference from non-EV particles and nontumor EVs. Compared with conventional analytical technologies, DCR-IEVN exhibits superior efficacy in diagnosing HCC owing to its high selectivity for tEVs. Integration of machine learning algorithms with DCR-IEVN resulted in differential diagnosis accuracy of 96.7% for the training cohort (n = 120) and 93.3% for the validation cohort (n = 30), effectively distinguishing HCC, cirrhosis, and healthy donors. This strategy offers a streamlined workflow and rapid assay completion and requires only small-volume serum samples and routine clinical devices, facilitating the clinical translation of tEV-based tumor diagnosis.

Exosome, a Rising Biomarkers in Liquid Biopsy: Advances of Label-Free and Label Strategy for Diagnosis of Cancer

Cancer is commonly considered as one of the most severe diseases, posing a significant threat to human health and society due to various serious challenges. These challenges include difficulties in accurate diagnosis and a high propensity to form metastasis. Tissue biopsy remains the gold standard for diagnosing and subtyping cancer. However, concerns arise from its invasive nature and the potential risk of metastasis during these complex diagnostic procedures. Meanwhile, liquid biopsy has recently witnessed the rapid advancements with the emergence of three prominent detection biomarkers: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Whereas, the very low abundance of CTCs combined with the instability of ctDNA intensify the challenges and decrease the accuracy of these two biomarkers for cancer diagnosis. While exosomes have gained widespread recognition as a promising biomarker in liquid biopsy due to their relatively low-invasive detection method, excellent biostability, rich resources, high abundance, and ability to provide valuable information about cancer. Therefore, it is crucial to systematically summarize recent advancements mainly in exosome-based detection methods for early cancer diagnosis. Specifically, this review will primarily focus on label-based and label-free strategies for detecting cancer using exosomes. We anticipate that this comprehensive analysis will enhance readers' understanding of the significance and value of exosomes in the fields of cancer diagnosis and therapy.

Highly Accurate Profiling of Exosome Phenotypes Using Super-resolution Tricolor Fluorescence Co-localization

Exosomes contain a wealth of proteomic information, presenting promising biomarkers for the noninvasive early diagnosis of diseases, especially cancer. However, it remains a great challenge to accurately and reliably distinguish exosomes secreted from different types of cell lines. Fluorescence immunoassay is frequently used for exosome detection. Nonspecific adsorption in immunoassays is unavoidable and affects the reliability of assay results. Despite the fact that various methods have been proposed to reduce nonspecific adsorption, a more effective method that can eliminate the influence of nonspecific adsorption is still lacking. Here, we report a more convenient way (named SR-TFC) to remove the artifacts caused by nonspecific adsorption, which combines tricolor fluorescence labeling of target exosomes, tricolor super-resolution imaging, and pixel counting. The pixel counting method (named CFPP) is realized by MATLAB and can eliminate nonspecific binding sites at the single-pixel level, which has never been achieved before and could improve the reliability of detection to the maximum extent. Furthermore, as a proof-of-concept, profiling of exosomal membrane proteins and identification of breast cancer subpopulations are demonstrated. To enable multiplex breast cancer phenotypic analysis, three kinds of specific proteins are labeled to obtain the 3D phenotypic information on various exosomes. Breast cancer subtypes can be accurately identified according to the super-resolution images of some clinically relevant exosomal proteins. Worth mentioning is that, by selecting other biomarkers, classification of other cancers could also be realized using SR-TFC. Hence, the present work holds great potential in clinical cancer diagnosis and precision medicine.

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.

Monitoring of Medical Wastewater by Sensitive, Convenient, and Low-Cost Determination of Small Extracellular Vesicles Using a Glycosyl-Imprinted Sensor

The monitoring of small extracellular vesicles (sEVs) in medical waste is of great significance for the prevention of the spread of infectious diseases and the treatment of environmental pollutants in medical waste. Highly sensitive and selective detection methods are urgently needed due to the low content of sEVs in waste samples and the complex sample composition. Herein, a glycosyl-imprinted electrochemical sensor was constructed and a novel strategy for rapid, sensitive, and selective sEVs detection was proposed. The characteristic trisaccharide at the end of the glycosyl chain of the glycoprotein carried on the surface of the sEVs was used as the template molecule. The glycosyl-imprinted polymer films was then prepared by electropolymerization with o-phenylenediamine (o-PD) and 3-aminophenylboronic acid (m-APBA) as functional monomers. sEVs were captured by the imprinted cavities through the recognition and adsorption of glycosyl chains of glycoproteins on sEVs. The m-APBA molecule also acted as a signal probe and was then attached on the immobilized glycoprotein on the surface of sEVs by boric acid affinity. The electrochemical signal of m-APBA was amplificated due to the abundant glycoproteins on the surface of sEVs. The detection range of the sensor was 2.1 × 104 to 8.7 × 107 particles/mL, and the limit of detection was 1.7 × 104 particles/mL. The sensor was then applied to the determination of sEVs in medical wastewater and urine, which showed good selectivity, low detection cost, and good sensitivity.

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.

Standardization Approaches for Extracellular Vesicle Loading with Oligonucleotides and Biologics

Extracellular vesicles (EVs) are widely recognized for their potential as drug delivery systems. EVs are membranous nanoparticles shed from cells. Among their natural features are their ability to shield cargo molecules against degradation and enable their functional internalization into target cells. Especially biological or bio-inspired large molecules (LMs), like nucleic acids, proteins, peptides, and others, may profit from encapsulation in EVs for drug delivery purposes. In the last years, a variety of loading protocols are explored for different LMs. The lack of standardization in the EV drug delivery field has impeded their comparability so far. Currently, the first reporting frameworks and workflows for EV drug loading are proposed. The aim of this review is to summarize these evolving standardization approaches and set recently developed methods into context. This will allow for enhanced comparability of future work on EV drug loading with LMs.

In situ synthesis and dynamic simulation of molecularly imprinted polymeric nanoparticles on a micro-reactor system

Current practices in synthesizing molecularly imprinted polymers face challenges-lengthy process, low-productivity, the need for expensive and sophisticated equipment, and they cannot be controlled in situ synthesis. Herein, we present a micro-reactor for in situ and continuously synthesizing trillions of molecularly imprinted polymeric nanoparticles that contain molecular fingerprints of bovine serum albumin in a short period of time (5-30 min). Initially, we performed COMSOL simulation to analyze mixing efficiency with altering flow rates, and experimentally validated the platform for synthesizing nanoparticles with sizes ranging from 52-106 nm. Molecular interactions between monomers and protein were also examined by molecular docking and dynamics simulations. Afterwards, we benchmarked the micro-reactor parameters through dispersity and concentration of molecularly imprinted polymers using principal component analysis. Sensing assets of molecularly imprinted polymers were examined on a metamaterial sensor, resulting in 81% of precision with high selectivity (4.5 times), and three cycles of consecutive use. Overall, our micro-reactor stood out for its high productivity (48-288 times improvement in assay-time and 2 times improvement in reagent volume), enabling to produce 1.4-1.5 times more MIPs at one-single step, and continuous production compared to conventional strategy.

Engineered extracellular vesicles (EVs): Promising diagnostic/therapeutic tools for pediatric high-grade glioma

Diffuse intrinsic pontine glioma (DIPG) is a highly malignant brain tumor that mainly occurs in children with extremely low overall survival. Traditional therapeutic strategies, such as surgical resection and chemotherapy, are not feasible mostly due to the special location and highly diffused features. Radiotherapy turns out to be the standard treatment method but with limited benefits of overall survival. A broad search for novel and targeted therapies is in the progress of both preclinical investigations and clinical trials. Extracellular vesicles (EVs) emerged as a promising diagnostic and therapeutic candidate due to their distinct biocompatibility, excellent cargo-loading-delivery capacity, high biological barrier penetration efficiency, and ease of modification. The utilization of EVs in various diseases as biomarker diagnoses or therapeutic agents is revolutionizing modern medical research and practice. In this review, we will briefly talk about the research development of DIPG, and present a detailed description of EVs in medical applications, with a discussion on the application of engineered peptides on EVs. The possibility of applying EVs as a diagnostic tool and drug delivery system in DIPG is also discussed.

Manifold Learning Enables Interpretable Analysis of Raman Spectra from Extracellular Vesicle and Other Mixtures

Extracellular vesicles (EVs) have emerged as promising diagnostic and therapeutic candidates in many biomedical applications. However, EV research continues to rely heavily on in vitro cell cultures for EV production, where the exogenous EVs present in fetal bovine (FBS) or other required serum supplementation can be difficult to remove entirely. Despite this and other potential applications involving EV mixtures, there are currently no rapid, robust, inexpensive, and label-free methods for determining the relative concentrations of different EV subpopulations within a sample. In this study, we demonstrate that surface-enhanced Raman spectroscopy (SERS) can biochemically fingerprint fetal bovine serum-derived and bioreactor-produced EVs, and after applying a novel manifold learning technique to the acquired spectra, enables the quantitative detection of the relative amounts of different EV populations within an unknown sample. We first developed this method using known ratios of Rhodamine B to Rhodamine 6G, then using known ratios of FBS EVs to breast cancer EVs from a bioreactor culture. In addition to quantifying EV mixtures, the proposed deep learning architecture provides some knowledge discovery capabilities which we demonstrate by applying it to dynamic Raman spectra of a chemical milling process. This label-free characterization and analytical approach should translate well to other EV SERS applications, such as monitoring the integrity of semipermeable membranes within EV bioreactors, ensuring the quality or potency of diagnostic or therapeutic EVs, determining relative amounts of EVs produced in complex co-culture systems, as well as many Raman spectroscopy applications.

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.