A Fluid Multivalent Magnetic Interface for High-Performance Isolation and Proteomic Profiling of Tumor-Derived Extracellular Vesicles

Target proteins-regulated DNA nanomachine-electroactive substance complexes enable separation-free electrochemical detection of clinical exosome

Simple and reliable profiling of tumor-derived exosomes (TDEs) holds significant promise for the early detection of cancer. Nonetheless, this remains challenging owing to the substantial heterogeneity and low concentration of TDEs. Herein, we devised an accurate and highly sensitive electrochemical sensing strategy for TDEs via simultaneously targeting exosomal mucin 1 (MUC1) and programmed cell death ligand 1 (PD-L1). This approach employs high-affinity aptamers as specific recognition elements, utilizes rolling circle amplification and DNA nanospheres as effective bridges and signal amplifiers, and leverages methylene blue (MB) and doxorubicin (DOX) as robust signal reporters. The crux of this separation- and label-free method is the specific response of MB and DOX to G-quadruplex structures and DNA nanospheres, respectively. Quantifying TDEs using this strategy enabled precise discrimination of lung cancer patients (n = 25) from healthy donors (n = 12), showing 100% specificity (12/12), 92% sensitivity (23/25), and an overall accuracy of 94.6% (35/37), with an area under the receiver operating characteristic curve (AUC) of 0.97. Furthermore, the assay results strongly correlated with findings from computerized tomography and pathological analyses. Our approach could facilitate the early diagnosis of lung cancer through TDEs-based liquid biopsy.

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.

Lab-on-a-chip: an advanced technology for the modernization of traditional Chinese medicine

Benefiting from the complex system composed of various constituents, medicament portions, species, and places of origin, traditional Chinese medicine (TCM) possesses numerous customizable and adaptable efficacies in clinical practice guided by its theories. However, these unique features are also present challenges in areas such as quality control, screening active ingredients, studying cell and organ pharmacology, and characterizing the compatibility between different Chinese medicines. Drawing inspiration from the holistic concept, an integrated strategy and pattern more aligned with TCM research emerges, necessitating the integration of novel technology into TCM modernization. The microfluidic chip serves as a powerful platform for integrating technologies in chemistry, biology, and biophysics. Microfluidics has given rise to innovative patterns like lab-on-a-chip and organoids-on-a-chip, effectively challenging the conventional research paradigms of TCM. This review provides a systematic summary of the nature and advanced utilization of microfluidic chips in TCM, focusing on quality control, active ingredient screening/separation, pharmaceutical analysis, and pharmacological/toxicological assays. Drawing on these remarkable references, the challenges, opportunities, and future trends of microfluidic chips in TCM are also comprehensively discussed, providing valuable insights into the development of TCM.

Recent Advances in Microfluidic-Based Extracellular Vesicle Analysis

Extracellular vesicles (EVs) serve as vital messengers, facilitating communication between cells, and exhibit tremendous potential in the diagnosis and treatment of diseases. However, conventional EV isolation methods are labor-intensive, and they harvest EVs with low purity and compromised recovery. In addition, the drawbacks, such as the limited sensitivity and specificity of traditional EV analysis methods, hinder the application of EVs in clinical use. Therefore, it is urgent to develop effective and standardized methods for isolating and detecting EVs. Microfluidics technology is a powerful and rapidly developing technology that has been introduced as a potential solution for the above bottlenecks. It holds the advantages of high integration, short analysis time, and low consumption of samples and reagents. In this review, we summarize the traditional techniques alongside microfluidic-based methodologies for the isolation and detection of EVs. We emphasize the distinct advantages of microfluidic technology in enhancing the capture efficiency and precise targeting of extracellular vesicles (EVs). We also explore its analytical role in targeted detection. Furthermore, this review highlights the transformative impact of microfluidic technology on EV analysis, with the potential to achieve automated and high-throughput EV detection in clinical samples.

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.

Fluid Multivalent Recognition Accelerating and Boosting Upconversion Luminescence-Activated DNA Nanomachines for Rapid and Sensitive In Vivo Imaging

By integrating near-infrared (NIR) light-dependent optical control and DNA walkers-based signal amplification, upconversion luminescence-activated DNA nanomachines hold great potential in conducting an in vivo analysis. For the typical DNA nanomachines, the immobile multivalent recognition interface greatly compromised the reaction kinetics and amplification efficiency due to the cleavage-dependent response mode. In this work, novel upconversion luminescence-activated DNA nanomachines with a fluid multivalent recognition interface were reported for rapid and sensitive in vivo imaging. As a proof-of-concept study, the photolocked DNAzyme-based walker system was anchored on the surface of phospholipid membrane-coated upconversion nanoparticles through the cholesterol-phospholipid interaction to acquire a fluid multivalent recognition interface. Upon sequential inputs of NIR light and metal ions, the formed DNA nanomachines were autonomously initiated and generated a cascade of amplified signal. Relative to the typical DNA nanomachines, the proposed ones possess an accelerated reaction rate and an improved amplification capability owing to a higher local concentration by the lateral mobility. The present work provides a versatile alternative for performing precise and highly efficient in vivo analysis.

A Magnetically Driven Tandem Chip Enables Rapid Isolation and Multiplexed Profiling of Extracellular Vesicles

The protein phenotypes of extracellular vesicles (EVs) have emerged as promising biomarkers for cancer diagnosis and treatment monitoring. However, the technical challenges in rapid isolation and multiplexed molecular detection of EVs have limited their clinical practice. Herein, we developed a magnetically driven tandem chip to achieve streamlined rapid isolation and multiplexed profiling of surface protein biomarkers of EVs. Driven by magnetic force, the magnetic nanomixers not only act as tiny stir bars to promote mass transfer and enhance reaction efficiency of EVs, but also transport on communicating vessels of the tandem chip continuously and expedite the assay workflow. We designed cyclic surface enhancement of Raman scattering (SERS) tags to bind with target EVs and then release them by exonuclease I, eliminating steric hindrance and amplifying the SERS signal of multiple protein biomarkers on EVs. Due to the excellent assay performance, six breast cancer biomarkers were detected simultaneously on EVs using only 10 μL plasma within 1.5 h. The unweighted SUM signature offers great accuracy in discriminating breast cancer patients from healthy donors. Overall, the dynamic magnetic driving tandem chip offers a new avenue to advance the clinical application of EV-based liquid biopsy.

Multivalent DNA Flowers for High-Performance Isolation, Detection, and Release of Tumor-Derived Extracellular Vesicles

Tumor-derived extracellular vesicles (T-EVs) hold great promise for understanding cancer biology and improving cancer diagnostics and therapeutics. Herein, we developed multivalent DNA flowers (DFs) containing repeated and equidistant EpCAM aptamers for the efficient isolation of T-EVs. The multivalent aptamer chains in DFs had good flexibility to adapt to the surface morphology of T-EVs and achieved multivalent ligand-receptor interactions, thus showing enhanced isolation ability compared to monovalent aptamers. Compared with other materials for isolation of EVs, DFs were generated by rolling circle amplification (RCA) and self-assembled into microspheres in a one-pot reaction, and the recognition molecules (aptamers) were directly replicated and assembled during the RCA reaction instead of chemical modification and immobilization on the surface of solid materials. Moreover, as optically transparent biomaterials, the content of EpCAM+ EVs could be directly reflected via membrane-based hydrophobic assembly of signaling modules in DFs@EpCAM+ EVs complex, and we found that the amount of EpCAM+ EVs showed greater accuracy in cancer diagnosis than total EVs (88.3 vs 69.7%) and was also higher than the clinically commonly used marker carcinoembryonic antigen (CEA) (88.3 vs 76.7%). In addition, T-EVs could be released by lysis of DFs with the nuclease, gently and easily, keeping high intact and activity of EVs for downstream biological function studies. These results demonstrated that DFs are efficient and nondestructive tools for isolation, detection, and release of T-EVs.

Microfluidic Enrichment of Intact SARS-CoV-2 Viral Particles by Stoichiometric Balanced DNA Computation

Health diagnostic tools for community safety and environmental monitoring require selective and quantitatively accurate active viral load assessment. Herein, we report a microfluidic enrichment strategy to separate intact SARS-CoV-2 particles by AND logic gate with inputs of cholesterol oligonucleotides for the envelope and aptamers for the spike viral proteins. Considering the unequal quantity of endogenous spikes and lipid membranes on SARS-CoV-2, a dual-domain binding strategy, with two aptamers targeting different spike domains, was applied to balance the spike-envelope stoichiometric ratio. By balancing the stoichiometric with DNA computation and promoting microscale mass transfer of the herringbone chip, the developed strategy enabled high sensitivity detection of pseudotyped SARS-CoV-2 with a limit of detection as low as 37 active virions/μL while distinguishing it from inactive counterparts, other nontarget viruses, and free spike protein. Moreover, the captured viral particles can be released through DNase I treatment with up to 90% efficiency, which is fully compatible with virus culture and sequencing. Overall, the developed strategy not only identified SARS-CoV-2-infected patients (n = 14) with 100% identification from healthy donors (n = 8) but also provided a fresh perspective on the regulation of stoichiometric ratio to achieve a more biologically relevant DNA computation.