Combined Morpho-Chemical Profiling of Individual Extracellular Vesicles and Functional Nanoparticles without Labels

Nanotherapeutic Heterogeneity: Sources, Effects, and Solutions

Nanomaterials have revolutionized medicine by enabling control over drugs' pharmacokinetics, biodistribution, and biocompatibility. However, most nanotherapeutic batches are highly heterogeneous, meaning they comprise nanoparticles that vary in size, shape, charge, composition, and ligand functionalization. Similarly, individual nanotherapeutics often have heterogeneously distributed components, ligands, and charges. This review discusses nanotherapeutic heterogeneity's sources and effects on experimental readouts and therapeutic efficacy. Among other topics, it demonstrates that heterogeneity exists in nearly all nanotherapeutic types, examines how nanotherapeutic heterogeneity arises, and discusses how heterogeneity impacts nanomaterials' in vitro and in vivo behavior. How nanotherapeutic heterogeneity skews experimental readouts and complicates their optimization and clinical translation is also shown. Lastly, strategies for limiting nanotherapeutic heterogeneity are reviewed and recommendations for developing more reproducible and effective nanotherapeutics provided.

Peptide-based capture-and-release purification of extracellular vesicles and statistical algorithm enabled quality assessment

Circulating extracellular vesicles (EVs) have gained significant attention for discovering tumor biomarkers. However, isolating EVs with well-defined homogeneous populations from complex biological samples is challenging. Different isolation methods have been found to derive different EV populations carrying different molecular contents, which confounds current investigations and hinders subsequent clinical translation. Therefore, standardizing and building a rigorous assessment of isolated EV quality associated with downstream molecular analysis is essential. To address this need, we introduce a statistical algorithm (ExoQuality Index, EQI) by integrating multiple EV characterizations (size, particle concentration, zeta potential, total protein, and RNA), enabling direct EV quality assessment and comparisons between different isolation methods. We also introduced a novel capture-release isolation approach using a pH-responsive peptide conjugated with NanoPom magnetic beads (ExCy) for simple, fast, and homogeneous EV isolation from various biological fluids. Bioinformatic analysis of next-generation sequencing (NGS) data of EV total RNAs from pancreatic cancer patient plasma samples using our novel EV isolation approach and quality index strategy illuminates how this approach improves the identification of tumor associated molecular markers. Results showed higher human mRNA coverage compared to existing isolation approaches in terms of both pancreatic cancer pathways and EV cellular component pathways using gProfiler pathway analysis. This study provides a valuable resource for researchers, establishing a workflow to prepare and analyze EV samples carefully and contributing to the advancement of reliable and rigorous EV quality assessment and clinical translation.

Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection

Due to its ability to record position, intensity, and intensity distribution information, camera-based monitoring of nanoparticles in optical traps can enable multi-parametric morpho-optical characterization at the single-particle level. However, blurring due to the relatively long (10s of microsecond) integration times and aliasing from the resulting limited temporal bandwidth affect the detected particle position when considering nanoparticles in traps with strong stiffness, leading to inaccurate size predictions. Here, we propose a ResNet-based method for accurate size characterization of trapped nanoparticles, which is trained by considering only simulated time series data of nanoparticles' constrained Brownian motion. Experiments prove the method outperforms state-of-art sizing algorithms such as adjusted Lorentzian fitting or CNN-based networks on both standard nanoparticles and extracellular vesicles (EVs), as well as maintains good accuracy even when measurement times are relatively short (<1s per particle). On samples of clinical EVs, our network demonstrates a well-generalized ability to accurately determine the EV size distribution, as confirmed by comparison with gold-standard nanoparticle tracking analysis (NTA). Furthermore, by combining the sizing network with still frame images from high-speed video, the camera-based optical tweezers have the unique capacity to quantify both the size and refractive index of bio-nanoparticles at the single-particle level. These experiments prove the proposed sizing network as an ideal path for predicting the morphological heterogeneity of bio-nanoparticles in optical potential trapping-related measurements.

Single Particle Chemical Characterisation of Nanoformulations for Cargo Delivery

Nanoparticles can encapsulate a range of therapeutics, from small molecule drugs to sensitive biologics, to significantly improve their biodistribution and biostability. Whilst the regulatory approval of several of these nanoformulations has proven their translatability, there remain several hurdles to the translation of future nanoformulations, leading to a high rate of candidate nanoformulations failing during the drug development process. One barrier is that the difficulty in tightly controlling nanoscale particle synthesis leads to particle-to-particle heterogeneity, which hinders manufacturing and quality control, and regulatory quality checks. To understand and mitigate this heterogeneity requires advancements in nanoformulation characterisation beyond traditional bulk methods to more precise, single particle techniques. In this review, we compare commercially available single particle techniques, with a particular focus on single particle Raman spectroscopy, to provide a guide to adoption of these methods into development workflows, to ultimately reduce barriers to the translation of future nanoformulations.

Towards artificial intelligence-enabled extracellular vesicle precision drug delivery

Extracellular Vesicles (EVs), particularly exosomes, recently exploded into nanomedicine as an emerging drug delivery approach due to their superior biocompatibility, circulating stability, and bioavailability in vivo. However, EV heterogeneity makes molecular targeting precision a critical challenge. Deciphering key molecular drivers for controlling EV tissue targeting specificity is in great need. Artificial intelligence (AI) brings powerful prediction ability for guiding the rational design of engineered EVs in precision control for drug delivery. This review focuses on cutting-edge nano-delivery via integrating large-scale EV data with AI to develop AI-directed EV therapies and illuminate the clinical translation potential. We briefly review the current status of EVs in drug delivery, including the current frontier, limitations, and considerations to advance the field. Subsequently, we detail the future of AI in drug delivery and its impact on precision EV delivery. Our review discusses the current universal challenge of standardization and critical considerations when using AI combined with EVs for precision drug delivery. Finally, we will conclude this review with a perspective on future clinical translation led by a combined effort of AI and EV research.

Three-dimensional deep regression-based light scattering imaging system for nanoscale exosome analysis

Exosomes are extracellular vesicles that serve as promising intrinsic nanoscale biomarkers for disease diagnosis and treatment. Nanoparticle analysis technology is widely used in the field of exosome study. However, the common particle analysis methods are usually complex, subjective, and not robust. Here, we develop a three-dimensional (3D) deep regression-based light scattering imaging system for nanoscale particle analysis. Our system solves the problem of object focusing in common methods and acquires light scattering images of label-free nanoparticles as small as 41 nm in diameter. We develop a new method for nanoparticle sizing with 3D deep regression, where the 3D time series Brownian motion data of single nanoparticles are input as a whole, and sizes are output automatically for both entangled and untangled nanoparticles. Exosomes from the normal and cancer liver cell lineage cells are observed and automatically differentiated by our system. The 3D deep regression-based light scattering imaging system is expected to be widely used in the field of nanoparticle analysis and nanomedicine.

Current Advances in Technologies for Single Extracellular Vesicle Analysis and Its Clinical Applications in Cancer Diagnosis

Extracellular vesicles (EVs) have been regarded as one of the most potential diagnostic biomarkers for different cancers, due to their unique physiological and pathological functions. However, it is still challenging to precisely analyze the contents and sources of EVs, due to their heterogeneity. Herein, we summarize the advances in technologies for a single EV analysis, which may provide new strategies to study the heterogeneity of EVs, as well as their cargo, more specifically. Furthermore, the applications of a single EV analysis on cancer early diagnosis are also discussed.

Hybrid Principal Component Analysis Denoising Enables Rapid, Label-Free Morpho-Chemical Quantification of Individual Nanoliposomes

Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles.

Comparative analysis of tangential flow filtration and ultracentrifugation, both combined with subsequent size exclusion chromatography, for the isolation of small extracellular vesicles

Small extracellular vesicles (sEVs) provide major promise for advances in cancer diagnostics, prognostics, and therapeutics, ascribed to their distinctive cargo reflective of pathophysiological status, active involvement in intercellular communication, as well as their ubiquity and stability in bodily fluids. As a result, the field of sEV research has expanded exponentially. Nevertheless, there is a lack of standardisation in methods for sEV isolation from cells grown in serum-containing media. The majority of researchers use serum-containing media for sEV harvest and employ ultracentrifugation as the primary isolation method. Ultracentrifugation is inefficient as it is devoid of the capacity to isolate high sEV yields without contamination of non-sEV materials or disruption of sEV integrity. We comprehensively evaluated a protocol using tangential flow filtration and size exclusion chromatography to isolate sEVs from a variety of human and murine cancer cell lines, including HeLa, MDA-MB-231, EO771 and B16F10. We directly compared the performance of traditional ultracentrifugation and tangential flow filtration methods, that had undergone further purification by size exclusion chromatography, in their capacity to separate sEVs, and rigorously characterised sEV properties using multiple quantification devices, protein analyses and both image and nano-flow cytometry. Ultracentrifugation and tangential flow filtration both enrich consistent sEV populations, with similar size distributions of particles ranging up to 200 nm. However, tangential flow filtration exceeds ultracentrifugation in isolating significantly higher yields of sEVs, making it more suitable for large-scale research applications. Our results demonstrate that tangential flow filtration is a reliable and robust sEV isolation approach that surpasses ultracentrifugation in yield, reproducibility, time, costs and scalability. These advantages allow for implementation in comprehensive research applications and downstream investigations.

Recent advances in optical label-free characterization of extracellular vesicles

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks

A critical study of CNN networks for Raman regression problems is presented. In evaluating performance on models where spectral information is missing, CNN performs as well as state-of-the-art methods, without the need for spectral pre-processing.

Progress in Nanomaterials-Based Optical and Electrochemical Methods for the Assays of Exosomes

Exosomes with diameters of 30-150 nm are small membrane-bound vesicles secreted by a variety of cells. They play an important role in many biological processes, such as tumor-related immune response and intercellular signal transduction. Exosomes have been considered as emerging and noninvasive biomarkers for cancer diagnosis. Recently, a large number of optical and electrochemical biosensors have been proposed for sensitive detection of exosomes. To meet the increasing demands for ultrasensitive detection, nanomaterials have been integrated with various techniques as powerful components. Because of their intrinsic merits of biological compatibility, excellent physicochemical features and unique catalytic ability, nanomaterials have significantly improved the analytical performances of exosome biosensors. In this review, we summarized the recent progress in nanomaterials-based biosensors for the detection of cancer-derived exosomes, including fluorescence, colorimetry, surface plasmon resonance spectroscopy, surface enhanced Raman scattering spectroscopy, electrochemistry, electrochemiluminescence and so on.

Advances in the analysis of single extracellular vesicles: A critical review

There is an ever-growing need for new cancer diagnostic approaches that provide earlier diagnosis as well as richer diagnostic, prognostic, and resistance information. Extracellular vesicles (EVs) recovered from a liquid biopsy have paradigm-shifting potential to offer earlier and more complete diagnostic information in the form of a minimally invasive liquid biopsy. However, much remains unknown about EVs, and current analytical approaches are unable to provide precise information about the contents and source of EVs. New approaches have emerged to analyze EVs at the single particle level, providing the opportunity to study biogenesis, correlate markers for higher specificity, and connect EV cargo with the source or destination. In this critical review we describe and analyze methods for single EV analysis that have emerged over the last five years. In addition, we note that current methods are limited in their adoption due to cost and complexity and we offer opportunities for the research community to address this challenge.

Antibodies Enhance the Suppressive Activity of Extracellular Vesicles in Mouse Delayed-Type Hypersensitivity

Previously, we showed that mouse delayed-type hypersensitivity (DTH) can be antigen-specifically downregulated by suppressor T cell-derived miRNA-150 carried by extracellular vesicles (EVs) that target antigen-presenting macrophages. However, the exact mechanism of the suppressive action of miRNA-150-targeted macrophages on effector T cells remained unclear, and our current studies aimed to investigate it. By employing the DTH mouse model, we showed that effector T cells were inhibited by macrophage-released EVs in a miRNA-150-dependent manner. This effect was enhanced by the pre-incubation of EVs with antigen-specific antibodies. Their specific binding to MHC class II-expressing EVs was proved in flow cytometry and ELISA-based experiments. Furthermore, by the use of nanoparticle tracking analysis and transmission electron microscopy, we found that the incubation of macrophage-released EVs with antigen-specific antibodies resulted in EVs’ aggregation, which significantly enhanced their suppressive activity in vivo. Nowadays, it is increasingly evident that EVs play an exceptional role in intercellular communication and selective cargo transfer, and thus are considered promising candidates for therapeutic usage. However, EVs appear to be less effective than their parental cells. In this context, our current studies provide evidence that antigen-specific antibodies can be easily used for increasing EVs’ biological activity, which has great therapeutic potential.

Biophotonics for diagnostic detection of extracellular vesicles

Extracellular Vesicles (EVs) are versatile carriers for biomarkers involved in the pathogenesis of multiple human disorders. Despite the increasing scientific and commercial interest in EV application in diagnostics, traditional biomolecular techniques usually require consistent sample amount, rely on operator-dependent and time- consuming procedures and cannot cope with the nano-size range of EVs, limiting both sensitivity and reproducibility of results. The application of biophotonics, i.e. light-based methods, for the diagnostic detection of EVs has brought to the development of innovative platforms with excellent sensitivity. In this review, we propose an overview of the most promising and emerging technologies used in the field of EV-related biomarker discovery. When tested on clinical samples, the reported biophotonic approaches in most cases have managed to discriminate between nanovesicles and contaminants, achieved much higher resolution compared to traditional procedures, and reached moderate to excellent diagnostic accuracy, thus demonstrating great potentialities for their clinical translation.

Using single-vesicle technologies to unravel the heterogeneity of extracellular vesicles

Extracellular vesicles (EVs) are heterogeneous lipid containers with a complex molecular cargo comprising several populations with unique roles in biological processes. These vesicles are closely associated with specific physiological features, which makes them invaluable in the detection and monitoring of various diseases. EVs play a key role in pathophysiological processes by actively triggering genetic or metabolic responses. However, the heterogeneity of their structure and composition hinders their application in medical diagnosis and therapies. This diversity makes it difficult to establish their exact physiological roles, and the functions and composition of different EV (sub)populations. Ensemble averaging approaches currently employed for EV characterization, such as western blotting or 'omics' technologies, tend to obscure rather than reveal these heterogeneities. Recent developments in single-vesicle analysis have made it possible to overcome these limitations and have facilitated the development of practical clinical applications. In this review, we discuss the benefits and challenges inherent to the current methods for the analysis of single vesicles and review the contribution of these approaches to the understanding of EV biology. We describe the contributions of these recent technological advances to the characterization and phenotyping of EVs, examination of the role of EVs in cell-to-cell communication pathways and the identification and validation of EVs as disease biomarkers. Finally, we discuss the potential of innovative single-vesicle imaging and analysis methodologies using microfluidic devices, which promise to deliver rapid and effective basic and practical applications for minimally invasive prognosis systems.

Fast confocal Raman imaging via context-aware compressive sensing

Raman hyperspectral imaging is a powerful method to obtain detailed chemical information about a wide variety of organic and inorganic samples noninvasively and without labels. However, due to the weak, nonresonant nature of spontaneous Raman scattering, acquiring a Raman imaging dataset is time-consuming and inefficient. In this paper we utilize a compressive imaging strategy coupled with a context-aware image prior to improve Raman imaging speed by 5- to 10-fold compared to classic point-scanning Raman imaging, while maintaining the traditional benefits of point scanning imaging, such as isotropic resolution and confocality. With faster data acquisition, large datasets can be acquired in reasonable timescales, leading to more reliable downstream analysis. On standard samples, context-aware Raman compressive imaging (CARCI) was able to reduce the number of measurements by ∼85% while maintaining high image quality (SSIM >0.85). Using CARCI, we obtained a large dataset of chemical images of fission yeast cells, showing that by collecting 5-fold more cells in a given experiment time, we were able to get more accurate chemical images, identification of rare cells, and improved biochemical modeling. For example, applying VCA to nearly 100 cells' data together, cellular organelles were resolved that were not faithfully reconstructed by a single cell's dataset.

Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies

Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.

Mixed-Dimensional Heterostructure Material-Based SERS for Trace Level Identification of Breast Cancer-Derived Exosomes

Raman spectroscopy has capability for fingerprint molecular identification with high sensitivity if weak Raman scattering signal can be enhanced by several orders of magnitudes. Herein, we report a heterostructure-based surface-enhanced Raman spectroscopy (SERS) platform using 2D graphene oxide (GO) and 0D plasmonic gold nanostar (GNS), with capability of Raman enhancement factor (EF) in the range of ∼1010 via light-matter and matter-matter interactions. The current manuscript reveals huge Raman enhancement for heterostructure materials occurring via both electromagnetic enhancement mechanism though plasmonic GNS nanoparticle (EF ∼107) and chemical enhancement mechanism through 2D-GO material (EF ∼102). Finite-difference time-domain (FDTD) simulation data and experimental investigation indicate that GNS allows light to be concentrated into nanoscale "hotspots" formed on the heterostructure surface, which significantly enhanced Raman efficiency via a plasmon-exciton light coupling process. Notably, we have shown that mixed-dimensional heterostructure-based SERS can be used for tracking of cancer-derived exosomes from triple-negative breast cancer and HER2(+) breast cancer with a limit of detection (LOD) of 3.8 × 102 exosomes/mL for TNBC-derived exosomes and 4.4 × 102 exosomes/mL for HER2(+) breast cancer-derived exosomes.