MoS2-Plasmonic Nanocavities for Raman Spectra of Single Extracellular Vesicles Reveal Molecular Progression in Glioblastoma

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.

Surface-Enhanced Raman Scattering Nanosensing and Imaging in Neuroscience

Monitoring neurochemicals and imaging the molecular content of brain tissues in vitro, ex vivo, and in vivo is essential for enhancing our understanding of neurochemistry and the causes of brain disorders. This review explores the potential applications of surface-enhanced Raman scattering (SERS) nanosensors in neurosciences, where their adoption could lead to significant progress in the field. These applications encompass detecting neurotransmitters or brain disorders biomarkers in biofluids with SERS nanosensors, and imaging normal and pathological brain tissues with SERS labeling. Specific studies highlighting in vitro, ex vivo, and in vivo analysis of brain disorders using fit-for-purpose SERS nanosensors will be detailed, with an emphasis on the ability of SERS to detect clinically pertinent levels of neurochemicals. Recent advancements in designing SERS-active nanomaterials, improving experimentation in biofluids, and increasing the usage of machine learning for interpreting SERS spectra will also be discussed. Furthermore, we will address the tagging of tissues presenting pathologies with nanoparticles for SERS imaging, a burgeoning domain of neuroscience that has been demonstrated to be effective in guiding tumor removal during brain surgery. The review also explores future research applications for SERS nanosensors in neuroscience, including monitoring neurochemistry in vivo with greater penetration using surface-enhanced spatially offset Raman scattering (SESORS), near-infrared lasers, and 2-photon techniques. The article concludes by discussing the potential of SERS for investigating the effectiveness of therapies for brain disorders and for integrating conventional neurochemistry techniques with SERS sensing.

Single extracellular vesicle research: From cell population to a single cell

Extracellular vesicles (EVs) are secreted by cells with a membrane structure and complex components such as DNA, RNA and proteins. These biomolecules play an important role in cell communication, cell proliferation, cell migration, vascularization, immune response and other physiological and pathological processes. Most current research on EVs focused on populations of EVs. Heterogeneity of EVs is neglected. Considering the heterogeneity of single EVs may offer critical molecular insights into cell-cell interactions, it is necessary to enhance our understanding about molecular characteristics from EVs derived from cell population to a single EV of derived from a single cell. This transformation is expected to provide a new insight into the understanding of cellular biology and the accurate description of the law of disease progress. In this article, we review the current research progress of single EV analysis technology for single EVs derived from cell population (SECP) and discuss its main applications in biological and clinical medicine research. After that, we propose the development direction, main difficulties and application prospect of single EV analysis technology for single EVs derived from single cells (SESC) according to our own research work, to provide new perspectives for the field of EV research.

Ultrasensitive quantification of PD-L1+ extracellular vesicles in melanoma patient plasma using a parallelized high throughput droplet digital assay

The expression of programmed death-ligand 1 (PD-L1) on extracellular vesicles (EVs) is an emerging biomarker for cancer, and has gained particular interest for its role mediating immunotherapy. However, precise quantification of PD-L1+ EVs in clinical samples remains challenging due to their sparse concentration and the enormity of the number of background EVs in human plasma, limiting applicability of conventional approaches. In this study, we develop a high-throughput droplet-based extracellular vesicle analysis (DEVA) assay for ultrasensitive quantification of EVs in plasma that are dual positive for both PD-L1 and tetraspanin (CD81) known to be expressed on EVs. We achieve a performance that significantly surpasses conventional approaches, demonstrating 360× enhancement in the limit of detection (LOD) and a 750× improvement in the limit of quantitation (LOQ) compared to conventional plate enzyme-linked immunoassay (ELISA). Underlying this performance is DEVA's high throughput analysis of individual EVs one at a time and the high specificity to targeted EVs versus background. We achieve a 0.006% false positive rate per droplet by leveraging avidity effects that arise from EVs having multiple copies of their target ligands on their surface. We use parallelized optofluidics to rapidly process 10 million droplets per minute, ∼100× greater than conventional approaches. A validation study on a cohort of 14 patients with melanoma confirms DEVA's ability to match conventional ELISA measurements with reduced plasma sample volume and without the need for prior EV purification. This proof-of-concept study demonstrates DEVA's potential for clinical utility to enhance prognosis as well as guide treatment for cancer.

Revisiting the advances and challenges in the clinical applications of extracellular vesicles in cancer

Extracellular vesicles (EVs) have been the subject of an exponentially growing number of studies covering their biogenesis mechanisms, isolation and analysis techniques, physiological and pathological roles, and clinical applications, such as biomarker and therapeutic uses. Nevertheless, the heterogeneity of EVs both challenges our understanding of them and presents new opportunities for their potential application. Recently, the EV field experienced a wide range of advances. However, the challenges also remain huge. This review focuses on the recent progress and difficulties encountered in the practical use of EVs in clinical settings. In addition, we also explored the concept of EV heterogeneity to acquire a more thorough understanding of EVs and their involvement in cancer, specifically focusing on the fundamental nature of EVs.

SERS-based microdevices for use as in vitro diagnostic biosensors

Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.

A Panorama of Extracellular Vesicle Applications: From Biomarker Detection to Therapeutics

Extracellular vesicles (EVs) secreted by all cell types are involved in the cell-to-cell transfer of regulatory factors that influence cell and tissue phenotypes in normal and diseased tissues. EVs are thus a rich source of biomarker targets for assays that analyze blood and urinary EVs for disease diagnosis. Sensitive biomarker detection in EVs derived from specific cell populations is a key major hurdle when analyzing complex biological samples, but innovative approaches surveyed in this Perspective can streamline EV isolation and enhance the sensitivity of EV detection procedures required for clinical application of EV-based diagnostics and therapeutics, including nanotechnology and microfluidics, to achieve EV characterizations. Finally, this Perspective also outlines opportunities and challenges remaining for clinical translation of EV-based assays.

Label-free nonlinear optical signatures of extracellular vesicles in liquid and tissue biopsies of human breast cancer

Extracellular vesicles (EVs) have been implicated in metastasis and proposed as cancer biomarkers. However, heterogeneity and small size makes assessments of EVs challenging. Often, EVs are isolated from biofluids, losing spatial and temporal context and thus lacking the ability to access EVs in situ in their native microenvironment. This work examines the capabilities of label-free nonlinear optical microscopy to extract biochemical optical metrics of EVs in ex vivo tissue and EVs isolated from biofluids in cases of human breast cancer, comparing these metrics within and between EV sources. Before surgery, fresh urine and blood serum samples were obtained from human participants scheduled for breast tumor surgery (24 malignant, 6 benign) or healthy participants scheduled for breast reduction surgery (4 control). EVs were directly imaged both in intact ex vivo tissue that was removed during surgery and in samples isolated from biofluids by differential ultracentrifugation. Isolated EVs and freshly excised ex vivo breast tissue samples were imaged with custom nonlinear optical microscopes to extract single-EV optical metabolic signatures of NAD(P)H and FAD autofluorescence. Optical metrics were significantly altered in cases of malignant breast cancer in biofluid-derived EVs and intact tissue EVs compared to control samples. Specifically, urinary isolated EVs showed elevated NAD(P)H fluorescence lifetime in cases of malignant cancer, serum-derived isolated EVs showed decreased optical redox ratio in stage II cancer, but not earlier stages, and ex vivo breast tissue showed an elevated number of EVs in cases of malignant cancer. Results further indicated significant differences in the measured optical metabolic signature based on EV source (urine, serum and tissue) within individuals.

Roles of extracellular vesicles in glioblastoma: foes, friends and informers

Glioblastoma (GB) tumors are one of the most insidious cancers which take over the brain and defy therapy. Over time and in response to treatment the tumor and the brain cells in the tumor microenvironment (TME) undergo many genetic/epigenetic driven changes in their phenotypes and this is reflected in the cellular contents within the extracellular vesicles (EVs) they produce. With the result that some EVs try to subdue the tumor (friends of the brain), while others participate in the glioblastoma takeover (foes of the brain) in a dynamic and ever changing process. Monitoring the contents of these EVs in biofluids can inform decisions based on GB status to guide therapeutic intervention. This review covers primarily recent research describing the different cell types in the brain, as well as the tumor cells, which participate in this EV deluge. This includes EVs produced by the tumor which manipulate the transcriptome of normal cells in their environment in support of tumor growth (foes), as well as responses of normal cells which try to restrict tumor growth and invasion, including traveling to cervical lymph nodes to present tumor neo-antigens to dendritic cells (DCs). In addition EVs released by tumors into biofluids can report on the status of living tumor cells via their cargo and thus serving as biomarkers. However, EVs released by tumor cells and their influence on normal cells in the tumor microenvironment is a major factor in immune suppression and coercion of normal brain cells to join the GB "band wagon". Efforts are being made to deploy EVs as therapeutic vehicles for drugs and small inhibitory RNAs. Increasing knowledge about EVs in the TME is being utilized to track tumor progression and response to therapy and even to weaponize EVs to fight the tumor.

Extracellular Vesicles: Techniques and Biomedical Applications Related to Single Vesicle Analysis

Extracellular vesicles (EVs) are extensively dispersed lipid bilayer membrane vesicles involved in the delivery and transportation of molecular payloads to certain cell types to facilitate intercellular interactions. Their significant roles in physiological and pathological processes make EVs outstanding biomarkers for disease diagnosis and treatment monitoring as well as ideal candidates for drug delivery. Nevertheless, differences in the biogenesis processes among EV subpopulations have led to a diversity of biophysical characteristics and molecular cargos. Additionally, the prevalent heterogeneity of EVs has been found to substantially hamper the sensitivity and accuracy of disease diagnosis and therapeutic monitoring, thus impeding the advancement of clinical applications. In recent years, the evolution of single EV (SEV) analysis has enabled an in-depth comprehension of the physical properties, molecular composition, and biological roles of EVs at the individual vesicle level. This review examines the sample acquisition tactics prior to SEV analysis, i.e., EV isolation techniques, and outlines the current state-of-the-art label-free and label-based technologies for SEV identification. Furthermore, the challenges and prospects of biomedical applications based on SEV analysis are systematically discussed.

Additive manufacturing leveraged microfluidic setup for sample to answer colorimetric detection of pathogens

Colorimetric readout for the detection of infectious diseases is gaining traction at the point of care/need owing to its ease of analysis and interpretation, and integration potential with highly specific loop-mediated amplification (LAMP) assays. However, coupling colorimetric readout with LAMP is rife with challenges including, rapidity, inter-user variability, colorimetric signal quantification, and user involvement in sequential steps of the LAMP assay, hindering its application. To address these challenges, for the first time, we propose a remotely smartphone-operated automated setup consisting of (i) an additively manufactured microfluidic cartridge, (ii) a portable reflected-light imaging setup with controlled epi-illumination (PRICE) module, and (iii) a control and data analysis module. The microfluidic cartridge facilitates sample collection, lysis, mixing of amplification reagents stored on-chip, and subsequent isothermal heating for initiation of amplification in a novel way by employing tunable elastomeric chambers and auxiliary components (heaters and linear actuators). PRICE offers a new imaging setup that captures the colorimetric change of the amplification media over a plasmonic nanostructured substrate in a controlled and noise-free environment for rapid minute-scale nucleic acid detection. The control and data analysis module employs microprocessors to automate cartridge operation in tandem with the imaging module. The different device components were characterized individually and finally, as a proof of concept, SARS-CoV-2 wild-type RNA was detected with a turnaround time of 13 minutes, showing the device's clinical feasibility. The suggested automated device can be adopted in future iterations for other detection and molecular assays that require sequential fluid handling steps.