Molecular Identification of Tumor-Derived Extracellular Vesicles Using Thermophoresis-Mediated DNA Computation

CRISPR-based dual-aptamer proximity ligation coupled hybridization chain reaction for precise detection of tumor extracellular vesicles and cancer diagnosis

Tumor cell-derived extracellular vesicles (TEVs) contain numerous cellular molecules and are considered potential biomarkers for non-invasive liquid biopsy. However, due to the low abundance of TEVs secreted by tumor cells and their phenotypic heterogeneity, there is a lack of sensitive and specific methods to quantify TEVs. Here, we developed a dual-aptamer proximity ligation-coupled hybridization chain reaction (HCR) method for tracing TEVs, exploiting CRISPR to achieve highly sensitive detection. Taking advantage of the high binding affinity of aptamers, the two aptamers (AptEpCAM, AptHER2) exhibited the high selectivity for TEVs recognition. HCR generated long-repeated sequence containing multiple crRNA targetable barcodes, and the signals were further amplified by CRISPR upon recognizing the HCR sequences, thereby enhancing the sensitivity. Under optimal conditions, the developed method demonstrated a favorable linear relationship in the range of 2 × 103-107 particles/μL, with a limit of detection (LOD) of 3.3 × 102 particles/μL. We directly applied our assay to clinical plasma analysis, achieving 100 % accuracy in cancer diagnosis, thus demonstrating the potential clinical applications of TEVs. Due to its simplicity and rapidity, excellent sensitivity and specificity, this method has broad applications in clinical medicine.

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.

Highly Sensitive Detection of Tumor Cell-Derived Exosomes Using Solid-State Nanopores Assisted with a Slight Salt Gradient

As an important biomarker, tumor cell-derived exosomes have substantial application prospects in early cancer screening and diagnosis. However, the unsatisfactory sensitivity and complicated sample pretreatment processes of conventional detection approaches have limited their use in clinical diagnosis. Nanopore sensors, as a highly sensitive, label-free, single-molecule technology, are widely utilized in molecule and bioparticle detection. Nevertheless, the exosome capture rate through nanopores is extremely low due to the low surface charge densities of exosomes and the effects of electrolyte concentration on their structural stability, thereby reducing the detection throughput. Here, we report an approach to improve the capture rate of exosome translocations using silicon nitride (SiNx) nanopores assisted by a slight salt electrolyte gradient. Improvements in exosome translocation event frequency are assessed in electrolyte solutions with different concentration gradients. In the case of asymmetric electrolytes (cis1× PBS and trans0.2 M NaCl, 1× PBS), the event frequency of tumor cell (HepG2)-derived exosome translocations is enhanced by nearly 2 orders of magnitude while maintaining vesicle structure stability. Furthermore, benefiting from the salt gradient effect, tumor cell (AsPC-1 and HCT116)-derived exosome translocations could be discriminated from those of HepG2 cell-derived exosomes. The developed highly sensitive detection method for tumor cell-derived exosomes at the single-particle level provides an approach for early cancer diagnosis.

Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks

DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited. Here, a molecular learning vector quantization neural network (LVQNN) model based on DNA strand displacement (DSD) technology for breast tumor diagnosis is developed. Compared to previous work, the molecular LVQNN boasts powerful computing abilities, handling high-dimensional data for intelligent cancer diagnosis. To verify the feasibility and versatility of the network, two distinct typical datasets are selected: one from a single source with cell morphology data from 569 cases, and a more extensive one spanning different populations and ages, with miRNA gene expression data from 1881 cases. By using the molecular LVQNN, diagnostic experiments are conducted on 50 and 120 public individuals from these two datasets, respectively, achieving accuracy rates of 94% and 97.5%. This study demonstrates that the LVQNN model exhibits significant advantages in breast cancer diagnosis and enhances diagnostic accuracy while introducing new approaches for intelligent cancer diagnosis, anticipated to bring significant breakthroughs and application prospects to precise cancer medicine.

DNA Nanomaterial-Empowered Surface Engineering of Extracellular Vesicles

Extracellular vesicles (EVs) are cell-secreted biological nanoparticles that are critical mediators of intercellular communication. They contain diverse bioactive components, which are promising diagnostic biomarkers and therapeutic agents. Their nanosized membrane-bound structures and innate ability to transport functional cargo across major biological barriers make them promising candidates as drug delivery vehicles. However, the complex biology and heterogeneity of EVs pose significant challenges for their controlled and actionable applications in diagnostics and therapeutics. Recently, DNA molecules with high biocompatibility emerge as excellent functional blocks for surface engineering of EVs. The robust Watson-Crick base pairing of DNA molecules and the resulting programmable DNA nanomaterials provide the EV surface with precise structural customization and adjustable physical and chemical properties, creating unprecedented opportunities for EV biomedical applications. This review focuses on the recent advances in the utilization of programmable DNA to engineer EV surfaces. The biology, function, and biomedical applications of EVs are summarized and the state-of-the-art achievements in EV isolation, analysis, and delivery based on DNA nanomaterials are introduced. Finally, the challenges and new frontiers in EV engineering are discussed.

Enhancing RNA detection and breast cancer subtyping with a universal 3D-hybridization chain reaction system

Breast cancer, a globally prevalent malignancy, is characterized by pronounced heterogeneity. Accurate subtyping requires the simultaneous detection of different biomarkers, which is crucial for personalized treatment strategies. However, existing methodologies are hindered by limited versatility and sensing performance. To overcome these hurdles, this study presents a universal 3D-Hybridization Chain Reaction (3D-HCR) system for RNA detection and subtype-specific diagnosis of breast cancer. The system integrated a universal trigger for HCR, thereby circumventing the need for complex sequence design and enabling the analysis of various RNA targets. Leveraging the spatial-confinement effect offered by DNA nanocarriers, this system exhibited superior amplification efficiency, achieving detection limits of 3.83 pM and 4.96 pM for PD-L1 mRNA and miR-21, respectively. Importantly, the system could differentiate between triple-negative breast cancer and estrogen receptor-positive breast cancer in both living cells and clinical tissues. These findings underscore the potential of the universal 3D-HCR system as a promising tool in clinical diagnostics. With its proven proficiency in breast cancer diagnostics and versatility in RNA analysis, this system holds the promise of broadening the horizons of precision medicine.

Exosomal Thomsen-Friedenreich Glycoantigen: A New Liquid Biopsy Biomarker for Lung and Breast Cancer Diagnoses

Exosomes are nanosized extracellular vesicles released by cells to transport biomolecules such as proteins and RNAs for intercellular communication. Exosomes play important roles in cancer development and metastasis; therefore, they have emerged as potential liquid biopsy biomarkers for cancer screening, diagnosis, and management. Many exosome cargos, including proteins, RNAs, and lipids, have been extensively investigated as biomarkers for cancer liquid biopsy. However, carbohydrates, an important type of biomolecule, have not yet been explored for this purpose. In this study, we reported a new exosomal carbohydrate biomarker, α-linked Thomsen-Friedenreich glycoantigen (TF-Ag-α; Galβ1-3GalNAc-α). To translate our discovery into clinical settings, we developed a surface plasmon resonance-based assay which utilized a unique mAb, JAA-F11, with high specificity to measure the levels of exosomal TF-Ag-α in blood. To the best of our knowledge, we are the first to demonstrate that exosomes carry TF-Ag-α. We detected exosomal TF-Ag-α in as low as 10 μL serum samples from patients with cancer, but in contrast, levels were negligible in those from normal controls. With a total of 233 patients with cancer and normal controls, we showed that exosomal TF-Ag-α detected lung cancer (n = 60) and breast cancer (n = 95) from normal controls (n = 78) with ≥95% and ≥97% accuracy, respectively. These results demonstrated that exosomal TF-Ag-α is a potential liquid biopsy biomarker for cancer diagnosis.

SIGNIFICANCE

Exosomes or small extracellular vesicles have emerged as potent biomarkers of cancer liquid biopsy. We discovered a new exosomal carbohydrate marker, TF-Ag-α (Galβ1-3GalNAc-α), and showed that exosomal TF-Ag-α detected both lung and breast cancers with >95% accuracy. Our findings demonstrated that exosomal TF-Ag-α is a promising liquid biopsy biomarker for cancer screening and early detection.

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.

Ultrasensitive and Wash-Free Detection of Tumor Extracellular Vesicles by Aptamer-Proximity-Ligation-Activated Rolling Circle Amplification Coupled to Single Particle ICP-MS

Tumor-derived extracellular vesicles (TEVs) are rich in cellular information and hold great promise as a biomarker for noninvasive cancer diagnosis. However, accurate measurement of TEVs presents challenges due to their low abundance and potential interference from a high number of EVs derived from normal cells. Herein, an aptamer-proximity-ligation-activated rolling circle amplification (RCA) method for EV membrane recognition, coupled with single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) for the quantification of TEVs, is developed. When DNA-labeled ultrasmall gold nanoparticle (AuNP) probes bind to the long chains formed by RCA, they aggregate to form large particles. Notably, small AuNPs scarcely produce pulse signals in sp-ICP-MS, thereby detecting TEVs in a wash-free manner. By leveraging the strong binding affinity of aptamers, dual aptamers for EpCAM and PD-L1 recognition, and the sp-ICP-MS technique, this method offers remarkable sensitivity and selectivity in tracing TEVs. Under optimized conditions, the present method shows a favorable linear relationship between the pulse signal frequency of sp-ICP-MS and TEV concentration within the range of 105-107 particles/mL, along with a detection limit of 1.1 × 104 particles/mL. The pulse signals from sp-ICP-MS combined with machine learning algorithms are used to discriminate cancer patients from healthy donors with 100% accuracy. Due to its simple and fast operation and excellent sensitivity and accuracy, this approach holds significant potential for diverse applications in life sciences and personalized medicine.

In Situ Array Microextraction and Metabolic Profiling of Small Extracellular Vesicles to Guide and Monitor Maternal Delivery

The advantages of small extracellular vesicles (sEV) in disease management have become increasingly prominent, with the main challenge lying in meeting the demands of large-scale extraction and high-throughput analysis, a crucial aspect in the realm of precision medicine. To overcome this challenge, an engineered on-plate aptamer array (16×24 spots) is developed for continuous scale-up microextraction of plasma sEV and their in situ metabolic analysis using mass spectrometry. With this integrated array strategy, metabolic profiles of sEV are acquired from the plasma of 274 antenatal or postpartum women, reducing analysis time by half (7.5 h) and sample volume by 95% (only 0.125 µL usage) compared to the traditional suspension method. Moreover, using machine learning algorithms on sEV metabolic profiles, a risk score system is constructed that accurately assesses the need for epidural analgesia during childbirth and the likelihood of post-administration fever. The system, based on admission samples, achieves an impressive 94% accuracy. Furthermore, post-administration fever can be identified from delivery samples, reaching an overall accuracy rate of 88%. This work offers real-time monitoring of the childbirth process that can provide timely guidance for maternal delivery, underscoring the significance of sEV detection in large-scale clinical samples for medicine innovation and advancement.

Scaling up of a Self-Confined Catalytic Hybridization Circuit for Robust microRNA Imaging

The precise regulation of cellular behaviors within a confined, crowded intracellular environment is highly amenable in diagnostics and therapeutics. While synthetic circuitry system through a concatenated chemical reaction network has rarely been reported to mimic dynamic self-assembly system. Herein, a catalytic self-defined circuit (CSC) for the hierarchically concatenated assembly of DNA domino nanostructures is engineered. By incorporating pre-sealed symmetrical fragments into the preying hairpin reactants, the CSC system allows the hierarchical DNA self-assembly via a microRNA (miRNA)-powered self-sorting catalytic hybridization reaction. With minimal strand complexity, this self-sustainable CSC system streamlined the circuit component and achieved localization-intensified cascaded signal amplification. Profiting from the self-adaptively concatenated hybridization reaction, a reliable and robust method has been achieved for discriminating carcinoma tissues from the corresponding para-carcinoma tissues. The CSC-sustained self-assembly strategy provides a comprehensive and smart toolbox for organizing various hierarchical DNA nanostructures, which may facilitate more insights for clinical diagnosis and therapeutic assessment.

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.

Logic Signal Amplification System for Sensitive Electrochemiluminescence Detection and Subtype Identification of Cancer Cells

Achieving sensitive detection and accurate identification of cancer cells is vital for diagnosing and treating the disease. Here, we developed a logic signal amplification system using DNA tetrahedron-mediated three-dimensional (3D) DNA nanonetworks for sensitive electrochemiluminescence (ECL) detection and subtype identification of cancer cells. Specially designed hairpins were integrated into DNA tetrahedral nanostructures (DTNs) to perform a catalytic hairpin assembly (CHA) reaction in the presence of target microRNA, forming hyperbranched 3D nanonetworks. Benefiting from the "spatial confinement effect," the DNA tetrahedron-mediated catalytic hairpin assembly (DTCHA) reaction displayed significantly faster kinetics and greater cycle conversion efficiency than traditional CHA. The resulting 3D nanonetworks could load a large amount of Ru(phen)32+, significantly enhancing its ECL signal, and exhibit detection limits for both miR-21 and miR-141 at the femtomolar level. The biosensor based on modular logic gates facilitated the distinction and quantification of cancer cells and normal cells based on miR-21 levels, combined with miR-141 levels, to further identify different subtypes of breast cancer cells. Overall, this study provides potential applications in miRNA-related clinical diagnostics.

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.

DNA-Based Nanomaterials for Analysis of Extracellular Vesicles

Extracellular vesicles (EVs) are cell-derived nanovesicles comprising a myriad of molecular cargo such as proteins and nucleic acids, playing essential roles in intercellular communication and physiological and pathological processes. EVs have received substantial attention as noninvasive biomarkers for disease diagnosis and prognosis. Owing to their ability to recognize protein and nucleic acid targets, DNA-based nanomaterials with excellent programmability and modifiability provide a promising tool for the sensitive and accurate detection of molecular cargo carried by EVs. In this perspective, recent advancements in EV analysis using a variety of DNA-based nanomaterials are summarized, which can be broadly classified into three categories: linear DNA probes, DNA nanostructures, and hybrid DNA nanomaterials. The design, construction, advantages, and disadvantages of different types of DNA nanomaterials, as well as their performance for detecting EVs are reviewed. The challenges and opportunities in the field of EV analysis by DNA nanomaterials are also discussed.

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.

DNA Logic Gates Integrated on DNA Substrates in Molecular Computing

Due to nucleic acid's programmability, it is possible to realize DNA structures with computing functions, and thus a new generation of molecular computers is evolving to solve biological and medical problems. Pioneered by Milan Stojanovic, Boolean DNA logic gates created the foundation for the development of DNA computers. Similar to electronic computers, the field is evolving towards integrating DNA logic gates and circuits by positioning them on substrates to increase circuit density and minimize gate distance and undesired crosstalk. In this minireview, we summarize recent developments in the integration of DNA logic gates into circuits localized on DNA substrates. This approach of all-DNA integrated circuits (DNA ICs) offers the advantages of biocompatibility, increased circuit response, increased circuit density, reduced unit concentration, facilitated circuit isolation, and facilitated cell uptake. DNA ICs can face similar challenges as their equivalent circuits operating in bulk solution (bulk circuits), and new physical challenges inherent in spatial localization. We discuss possible avenues to overcome these obstacles.

An intelligent ratiometric fluorescent assay based on MOF nanozyme-mediated tandem catalysis that guided by contrary logic circuit for highly sensitive sarcosine detection and smartphone-based portable sensing application

As the well-known test-indicator for early prostate cancer (PCa), sarcosine (SA) is closely related to the differential pathological process, which makes its accurate determination increasingly significant. Herein, we for the first time expanded the peroxidase (POD)-like property of facile-synthesized Zn-TCPP(Fe) MOF to fluorescent substrates and exploited it to ratiometric fluorescent (RF) sensing. By harnessing the effective catalytic oxidation of MOF nanozyme toward two fluorescent substrates (Scopoletin, SC; Amplex Red, AR) with contrary changes, and target-responsive (SA + SOx)/MOF/(SC + AR) tandem catalytic reaction, we constructed the first MOF nanozyme-based RF sensor for the quantitative determination of SA. Superior to previous works, the operation of this RF sensor is under the guidance of AND-(AND^NAND) contrary logic circuit. The dual-channel binary output changes (from 1/0 to 0/1) not only enable the intelligent logical recognition of SA, bringing strengthened reliability and accuracy, but also manifest the proof-of-concept discrimination of PCa individuals and healthy ones. Through recording the fluorescence alterations of SC (F465) and AR (F585), two segments of linear relationships between ratiometric values (F585/F465) and varied contents of SA are realized successfully. The LOD for SA could reach to as low as 39.98 nM, which outperforms all nanozyme-originated SA sensors reported till now. Moreover, this sensor also demonstrates high selectivity and satisfactory performance in human serum samples. Furthermore, the portable sensing of SA is realized under the assistance of smartphone-based RGB analysis, demonstrating the potential of point-of-care diagnostics of PCa in the future.

Isolation and Enrichment of Extracellular Vesicles with Double-Positive Membrane Protein for Subsequent Biological Studies

The isolation and enrichment of specific extracellular vesicle (EV) subpopulations are essential in the context of precision medicine. However, the current methods predominantly rely on a single-positive marker and are susceptible to interference from soluble proteins or impurities. This limitation represents a significant obstacle to the widespread application of EVs in biological research. Herein, a novel approach that utilizes proximity ligation assay (PLA) and DNA-RNA hybridization are proposed to facilitate the binding of two proteins on the EV membrane in advance enabling the isolation and enrichment of intact EVs with double-positive membrane proteins followed by using functionalized magnetic beads for capture and enzymatic cleavage for isolated EVs release. The isolated subpopulations of EVs can be further utilized for cellular uptake studies, high-throughput small RNA sequencing, and breast cancer diagnosis. Hence, developing and implementing a specialized system for isolating and enriching a specific subpopulation of EVs can enhance basic and clinical research in this field.

Thermophoretic Aggregation Imaging of Tumor-Derived Exosomes by CRISPR-Cas13a-Based Nanoprobes

The inherent heterogeneity of tumor-derived exosomes holds great promise for enhancing the precision of cancer diagnostics. MicroRNAs (miRNAs) encapsulated in tumor-associated exosomes have emerged as valuable biomarkers for the early detection of cancers. Nevertheless, the flexible structure and inherent instability of RNA limit its application in biological diagnostics. The CRISPR-Cas13a system, distinguished by its target-responsive "collateral effect", represents a powerful tool for advancing cancer diagnostics. In this study, we harness the CRISPR-Cas13a system as an innovative signal amplification tool to image cancer-related exosomal miRNA in situ. Furthermore, we capitalize on the thermophoretic aggregation effect exhibited by gold nanoparticles (Au NPs) to consolidate the fluorescent signals generated by the CRISPR-Cas13a system. Subsequently, the developed nanoprobe is applied to detect lung cancer-related exosomal miRNA from human serum, enabling the aggregated visualization of low-abundance cancer exosomes in individuals with lung cancer compared with healthy individuals. This sensitive thermophoretic aggregation assay provides a diagnostic tool for lung cancer in the clinic.

A Mode-Coupling Model of Colloid Thermophoresis in Aqueous Systems: Temperature and Size Dependencies of the Soret Coefficient

Thermophoresis allows for the manipulation of colloids in systems containing a temperature gradient. A deep understanding of the phenomena at the molecular level allows for increased control and manipulation strategies. We developed a microscopic model revealing different coupling mechanisms for colloid thermophoresis under local thermodynamic equilibrium conditions. The model has been verified through comparison with a variety of previously published experimental data and shows good agreement across significantly different systems. We found five different temperature-dependent contributions to the Soret coefficient, two from bulk properties and three from interfacial interactions between the fluid medium and the colloid. Our analysis shows that the Soret coefficient for nanosized particles is governed by the competition between the electrostatic and hydration interfacial interactions, while bulk contributions become more pronounced for protein systems. This theory can be used as a guide to design thermophoretic transport, which is relevant for sensing, focusing, and separation at the microscale.

Point-Of-Care Ultra-Portable Single-Molecule Bioassays for One-Health

Screening asymptomatic organisms (humans, animals, plants) with a high-diagnostic accuracy using point-of-care-testing (POCT) technologies, though still visionary holds great potential. Convenient surveillance requires easy-to-use, cost-effective, ultra-portable but highly reliable, in-vitro-diagnostic devices that are ready for use wherever they are needed. Currently, there are not yet such devices available on the market, but there are a couple more promising technologies developed at readiness-level 5: the Clustered-Regularly-Interspaced-Short-Palindromic-Repeats (CRISPR) lateral-flow-strip tests and the Single-Molecule-with-a-large-Transistor (SiMoT) bioelectronic palmar devices. They both hold key features delineated by the World-Health-Organization for POCT systems and an occurrence of false-positive and false-negative errors <1-5% resulting in diagnostic-selectivity and sensitivity >95-99%, while limit-of-detections are of few markers. CRISPR-strip is a molecular assay that, can detect down to few copies of DNA/RNA markers in blood while SiMoT immunometric and molecular test can detect down to a single oligonucleotide, protein marker, or pathogens in 0.1mL of blood, saliva, and olive-sap. These technologies can prospectively enable the systematic and reliable surveillance of asymptomatic ones prior to worsening/proliferation of illnesses allowing for timely diagnosis and swift prognosis. This could establish a proactive healthcare ecosystem that results in effective treatments for all living organisms generating diffuse and well-being at efficient costs.

DNA Logical Device Combining an Entropy-Driven Catalytic Amplification Strategy for the Simultaneous Detection of Exosomal Multiplex miRNAs In Situ

Exosomal miRNAs are considered promising biomarkers for cancer diagnosis, but their accuracy is severely compromised by the low content of miRNAs and the large amount of exosomal miRNAs released from normal cells. Here, we presented a dual-specific miRNA's logical recognition triggered by an entropy-driven catalysis (EDC)-enhanced system in exosomes for accurate detection of liver cancer-cell-derived exosomal miR-21 and miR-122. Taking advantage of the accurate analytical performance of the logic device, the excellent membrane penetration of gold nanoparticles, and the outstanding amplification ability of the EDC reaction, this method exhibits high sensitivity and selectivity for the detection of tumor-derived exosomal miRNAs in situ. Moreover, due to its excellent performance, this logic device can effectively distinguish liver cancer patients from healthy donors by determining the amount of cancer-cell-derived exosomal miRNAs. Overall, this strategy has great potential for analyzing various types of exosomes and provides a viable tool to improve the accuracy of cancer diagnosis.

DNA Gate-Based CRISPR-Cas Exponential Amplification System for Ultrasensitive Small Extracellular Vesicle Detection to Enhance Breast Cancer Diagnosis

Tumor-derived small extracellular vesicles (tEVs) as potential biomarkers possess abundant surface proteins closely related to parent cells, which are crucial for noninvasive cancer diagnosis. However, tEVs exhibit phenotype heterogeneity and low abundance, posing a significant challenge for multiplex detection with a high sensitivity. Herein, we developed a DNA gate-based exponential amplification CRISPR-Cas (DGEAC) system for accurate and ultrasensitive detection of tEVs, which can greatly improve the accuracy of breast cancer (BC) diagnosis. Based on the coexpression of CD63 and vascular endothelial growth factor (VEGF) on BC-derived tEVs, we developed a dual-aptamer-based AND gate fluorescent probe by proximity hybridization. By integrating the target recognition and trans-cleavage activity of Cas12a, an autocatalysis-driven exponential amplification circuit was developed for ultrasensitive detection of CD63 and VEGF proteins on tEVs, which could avoid false negative signals from single protein or other interfering proteins. We achieved highly sensitive detection of tEVs over a linear range from 1.75 × 103 to 3.5 × 108 particles/mL with a detection limit as low as 1.02 × 103 particles/mL. Furthermore, the DGEAC system can distinguish tEVs from tEVs derived from different BC cell lines, including MDA-MB-231, MCF-7, SKBR3, and MCF-10A. Compared to linear amplification (AUC 90.0%), the DGEAC system effectively differentiates BC in different stages (AUC 98.3%).

An Aptamer-Based Nanoflow Cytometry Method for the Molecular Detection and Classification of Ovarian Cancers through Profiling of Tumor Markers on Small Extracellular Vesicles

Molecular profiling of protein markers on small extracellular vesicles (sEVs) is a promising strategy for the precise detection and classification of ovarian cancers. However, this strategy is challenging owing to the lack of simple and practical detection methods. In this work, using an aptamer-based nanoflow cytometry (nFCM) detection strategy, a simple and rapid method for the molecular profiling of multiple protein markers on sEVs was developed. The protein markers can be easily labeled with aptamer probes and then rapidly profiled by nFCM. Seven cancer-associated protein markers, including CA125, STIP1, CD24, EpCAM, EGFR, MUC1, and HER2, on plasma sEVs were profiled for the molecular detection and classification of ovarian cancers. Profiling these seven protein markers enabled the precise detection of ovarian cancer with a high accuracy of 94.2 %. In addition, combined with machine learning algorithms, such as linear discriminant analysis (LDA) and random forest (RF), the molecular classifications of ovarian cancer cell lines and subtypes were achieved with overall accuracies of 82.9 % and 55.4 %, respectively. Therefore, this simple, rapid, and non-invasive method exhibited considerable potential for the auxiliary diagnosis and molecular classification of ovarian cancers in clinical practice.

Accumulation and Stretching of DNA Molecules in Temperature-Induced Concentration Gradients

Temperature fields provide a noninvasive approach for manipulating individual macromolecules in solution. Utilizing thermophoresis and other secondary effects resulting from the inhomogeneous distribution of crowding agents, one may gain valuable insights into the interactions of molecular mixtures. In this report, we examine the steady-state concentration distribution and dynamics of DNA molecules in a poly(ethylene glycol) (PEG)/water solution when exposed to localized temperature gradients generated by optical heating of a thin chrome layer at a liquid-solid boundary. This allowed us to experimentally investigate the interplay between DNA thermophoresis and PEG-induced entropic depletion effects. Our quantitative analysis demonstrates that the depletion effects dominate over DNA thermophoresis, causing the DNA polymers to migrate toward the heat source. Additionally, we explore the transient stretching of individual DNA molecules in thermally induced PEG gradients and estimate the contributing forces.

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.

Ultrasound-enhanced catalytic hairpin assembly capable of ultrasensitive microRNA biosensing for the early screening of Alzheimer's disease

Catalytic hairpin assembly (CHA) is a promising enzyme-free, isothermal signal amplification strategy, but the relatively time-consuming strand replacement limits its application scenarios. Here, we developed an ultrasound-enhanced catalytic hairpin assembly (UECHA) biosensing platform for early screening of Alzheimer's disease by introducing a portable acoustic-drive platform with functionalized microspheres for effective biomarkers enrichment and fluorescence enhancement. By constructing a gradient ultrasonic field in a microcavity, the platform concentrates the functionalized microspheres in a central position, accompanied by an enhanced fluorescence signal with a specific release. In addition, the programmable frequency modulation can also modify the acoustic potential well and effectively promote non-equilibrium chemical reactions such as CHA (25 min). Compared with the conventional catalytic hairpin assembly (CHA), UECHA allows for direct and quantitative measurement of AD miRNAs down to 3.55 × 10-15 M in 1 μL samples. This visual analysis of ultra-trace biomarkers based on acoustic enrichment and promotion provides a new perspective for the rapid and highly sensitive clinical detection of Alzheimer's disease.

Accurate and noninvasive diagnosis of epithelial cancers through AND gate photoluminescence on tumor-derived small extracellular vesicles

Noninvasive detection of small extracellular vesicles (sEVs) has become one of the most promising liquid biopsy methodologies for effective and timely cancer diagnosis and prognostic monitoring. Currently, accurate and sensitive detection of tumor-derived sEVs is compromised by their heterogeneous nature, and the tissue origin and parent cell cycle change may significantly affect the tumor-associated information (e.g., phenotypic proteins) of sEVs. Accordingly, many of the single-marker dependent detections on sEVs may not provide comprehensive information about the tumor, and their reliability and clinical applicability cannot be guaranteed. Herein, a strategy for constructing AND gate photoluminescence on tumor-derived sEVs is proposed. Briefly, only after co-recognition of the two epithelial phenotypic proteins (EpCAM and MUC1) on tumor-derived sEVs simultaneously, can our designed lanthanide luminescence probe precursors then assemble to form the AND gate for photoluminescence detection. Consequently, the generated AND gate photoluminescence provided time-resolved luminescence for a wide cancerous sEV linear detection range of 6.0 × 104-6.0 × 109 particles per mL, with a calculated detection limitation of 1.42 × 102 particles per mL. Furthermore, the AND gate photoluminescence can significantly distinguish epithelial cancer patients from healthy controls, displaying its great potential for accurate and noninvasive cancer diagnosis.

Dual-Recognition Triggered Proximity Ligation Combined with a Rolling Circle Amplification Strategy for Analysis of Exosomal Protein-Specific Glycosylation

Exosomal surface glycan reveals the biological function and molecular information on the protein, especially in indicating the pathogenesis of certain diseases through monitoring of specific protein glycosylation accurately. However, in situ and nondestructive measurement techniques for certain Exosomal glycoproteins are still lacking. In this work, combined with on-chip purification, we designed a proximity ligation assay-induced rolling circle amplification (RCA) strategy for highly sensitive identification of Exosomal protein-specific glycosylation based on a couple of proximity probes to target Exosomal protein and the protein-specific glycosylation site. Benefiting from efficient separation, scalable dual-recognition, and proximity-triggered RCA amplification, the proposed strategy could convert different protein-specific glycan levels to prominent changes in absorbance signals, resulting in accurate quantification of specific glycosylated Exosomal protein. When detecting the glycosylated PD-L1 on MDA-MB-231 exosomes and glycosylated PTK7 on HepG2 exosomes, the detection limits were calculated to be as low as 1.04 × 104 and 2.759 × 103 particles/mL, respectively. In addition, we further expand the dual-recognition site to investigate the potential correlation of Exosomal glycosylation with polarization of THP-1 cells toward the tumor-suppressive M1 phenotype. Overall, this strategy provides a universal tool for multiple analyses of diverse protein-specific glycosylated exosomes, exhibiting enormous potential to explore exosome function and search for new early diagnosis markers.

Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding

The clinical potential of miRNA-based liquid biopsy has been largely limited by the heterogeneous sources in plasma and tedious assay processes. Here, we develop a precise and robust one-pot assay called dual-surface-protein-guided orthogonal recognition of tumor-derived exosomes and in situ profiling of microRNAs (SORTER) to detect tumor-derived exosomal miRNAs and enhance the diagnostic accuracy of prostate cancer (PCa). The SORTER uses two allosteric aptamers against exosomal marker CD63 and tumor marker EpCAM to create an orthogonal labeling barcode and achieve selective sorting of tumor-specific exosome subtypes. Furthermore, the labeled barcode on tumor-derived exosomes initiated targeted membrane fusion with liposome probes to import miRNA detection reagents, enabling in situ sensitive profiling of tumor-derived exosomal miRNAs. With a signature of six miRNAs, SORTER differentiated PCa and benign prostatic hyperplasia with an accuracy of 100%. Notably, the diagnostic accuracy reached 90.6% in the classification of metastatic and nonmetastatic PCa. We envision that the SORTER will promote the clinical adaptability of miRNA-based liquid biopsy.

Advanced extracellular vesicle bioinformatic nanomaterials: from enrichment, decoding to clinical diagnostics

Extracellular vesicles (EVs) are membrane nanoarchitectures generated by cells that carry a variety of biomolecules, including DNA, RNA, proteins and metabolites. These characteristics make them attractive as circulating bioinformatic nanocabinets for liquid biopsy. Recent advances on EV biology and biogenesis demonstrate that EVs serve as highly important cellular surrogates involved in a wide range of diseases, opening up new frontiers for modern diagnostics. However, inefficient methods for EV enrichment, as well as low sensitivity of EV bioinformatic decoding technologies, hinder the use of EV nanocabinet for clinical diagnosis. To overcome these challenges, new EV nanotechnology is being actively developed to promote the clinical translation of EV diagnostics. This article aims to present the emerging enrichment strategies and bioinformatic decoding platforms for EV analysis, and their applications as bioinformatic nanomaterials in clinical settings.

Application of Single Extracellular Vesicle Analysis Techniques

Extracellular vesicles (EVs) are lipid containers that are actively released by cells and contain complex molecular cargoes. These cargoes include abundant material such as genomes and proteins from cells of origin. They are involved in intercellular communication and various pathological processes, showing excellent potential for diagnosing and treating diseases. Given the significant heterogeneity of EVs in complex physiopathological processes, unveiling their composition is essential to understanding their function. Bulk detection methods have been previously used to analyze EVs, but they often mask their heterogeneity, leading to the loss of valuable information. To overcome this limitation, single extracellular vesicle (SEV) analysis techniques have been developed and advanced. These techniques allow for analyzing EVs' physical information and biometric molecules at the SEV level. This paper reviews recent advances in SEV detection methods and summarizes some clinical applications for SEV detection strategies.

Unraveling the Possibilities: Recent Progress in DNA Biosensing

Due to the advantages of its numerous modification sites, predictable structure, high thermal stability, and excellent biocompatibility, DNA is the ideal choice as a key component of biosensors. DNA biosensors offer significant advantages over existing bioanalytical techniques, addressing limitations in sensitivity, selectivity, and limit of detection. Consequently, they have attracted significant attention from researchers worldwide. Here, we exemplify four foundational categories of functional nucleic acids: aptamers, DNAzymes, i-motifs, and G-quadruplexes, from the perspective of the structure-driven functionality in constructing DNA biosensors. Furthermore, we provide a concise overview of the design and detection mechanisms employed in these DNA biosensors. Noteworthy advantages of DNA as a sensor component, including its programmable structure, reaction predictility, exceptional specificity, excellent sensitivity, and thermal stability, are highlighted. These characteristics contribute to the efficacy and reliability of DNA biosensors. Despite their great potential, challenges remain for the successful application of DNA biosensors, spanning storage and detection conditions, as well as associated costs. To overcome these limitations, we propose potential strategies that can be implemented to solve these issues. By offering these insights, we aim to inspire subsequent researchers in related fields.

Isolation of PD-L1 Extracellular Vesicle Subpopulations Using DNA Computation Mediated Microfluidic Tandem Separation

Accurate isolation of targeted extracellular vesicle (EV) is challenging due to the antigenic heterogeneity of EV subpopulations which are from different cell origins. Most EV subpopulations lack a single marker whose expression cleanly distinguishes them from mixed populations of closely related EVs. Here, a modular platform capable of taking multiple binding events as input, performing logic computations, and producing two independent outputs for tandem microchips for EV subpopulation isolation, is developed. Taking advantages of the excellent selectivity of dual-aptamer recognition and the sensitivity of tandem microchips, this method achieves, for the first time, sequential isolation of tumor PD-L1 EVs and non-tumor PD-L1 EVs. As a result, the developed platform can not only effectively distinguish cancer patients from healthy donors but also provides new clues for assessing immune heterogeneity. Moreover, the captured EVs can be released through a DNA hydrolysis reaction with high efficiency, which is compatible with downstream mass spectrometry for EV proteome profiling. Overall, this strategy is expected to isolate different EV subpopulations, translate EVs into reliable clinical biomarkers, and accurately investigate the biological functions of different EV subsets.

Functional Nucleic Acid Probes Based on Two-Photon for Biosensing

Functional nucleic acid (FNA) probes have been widely used in environmental monitoring, food analysis, clinical diagnosis, and biological imaging because of their easy synthesis, functional modification, flexible design, and stable properties. However, most FNA probes are designed based on one-photon (OP) in the ultraviolet or visible regions, and the effectiveness of these OP-based FNA probes may be hindered by certain factors, such as their potential for photodamage and limited light tissue penetration. Two-photon (TP) is characterized by the nonlinear absorption of two relatively low-energy photons of near-infrared (NIR) light with the resulting emission of high-energy ultraviolet or visible light. TP-based FNA probes have excellent properties, including lower tissue self-absorption and autofluorescence, reduced photodamage and photobleaching, and higher spatial resolution, making them more advantageous than the conventional OP-based FNA probes in biomedical sensing. In this review, we summarize the recent advances of TP-excited and -activated FNA probes and detail their applications in biomolecular detection. In addition, we also share our views on the highlights and limitations of TP-based FNA probes. The ultimate goal is to provide design approaches for the development of high-performance TP-based FNA probes, thereby promoting their biological applications.

DNA-Guided Extracellular-Vesicle Metallization with High Catalytic Activity for Accurate Diagnosis of Pulmonary Nodules

Sensitive and specific analysis of extracellular vesicles (EVs) offers a promising minimally invasive way to identify malignant pulmonary nodules from benign lesions. However, accurate analysis of EVs is subject to free target proteins in blood samples, which compromises the clinical diagnosis value of EVs. Here a DNA-guided extracellular-vesicle metallization (DEVM) strategy is described for ultrasensitive and specific analysis of EV protein biomarkers and classification of pulmonary nodules. The facile DEVM process mainly includes the incorporation of DNA labeled with cholesterol and thiol groups into EV membranes and subsequent deposition of Au3+ and Pt4+ to allow the DNA-functionalized EVs to be encapsulated with AuPt nanoshells. It is found that the synthesized AuPt-metallized EVs possess extrinsic peroxidase-like activity. Utilizing the feature of the catalytic metal nanoshells just growth on the EV membranes, the DEVM method enables multiparametric recognition of target proteins and EV membranes and can produce an amplified colorimetric signal, avoiding the interference of free proteins. By profiling four surface proteins of EVs from 48 patients with pulmonary nodules, the highest area under the receiver operating characteristic curve (0.9983) is obtained. Therefore, this work provides a feasible EVs analysis tool for accurate pulmonary nodules management.

Combination of specific proteins as markers for accurate detection of extracellular vesicles using proximity ligation-mediated bHCR amplification

As the molecular characteristics of extracellular vesicles (EVs) are closely related to the occurrence and progression of cancer, the detection of tumor-derived EVs provides a promising non-invasive tool for the early diagnosis and treatment of cancer. However, it would be difficult for most of the existing methods to avoid false positives because the obtained result declares the amounts of proteins, but cannot accurately reflect the protein sources, including EV proteins and interfering proteins, in the actual samples. In this manuscript, a robust, accurate, and sensitive fluorescent strategy for profiling EV proteins is developed by using the combination of specific proteins as markers (Co-marker). Our strategy relies on the Co-marker recognition-activated cascade bHCR amplification, which forms numerous G-quadruplex structures that are integrated with fluorescent dyes for signal transduction. Notably, the detection accuracy can be improved owing to the effective avoidance of false positives from interfering proteins or single protein markers. Moreover, by using the double-positive protein recognition mode, unpurified detection can be achieved that avoids time-consuming EVs purification procedures. With its capacities of accuracy, portability, sensitivity, high throughput, and non-purification, the developed strategy might provide a practical tool for EV identification and the related early diagnosis and treatment of cancer.

Sensitive microscale thermophoresis assay for rapid ochratoxin A detection with fluorescently labeled engineered aptamer

Ochratoxin A (OTA) is a widespread mycotoxin that causes contamination in a variety of foodstuffs and environments, inducing great health risks to humans and animals. Rapid and sensitive detection of OTA is necessary for food safety, environmental health, and risk assessment. Herein, we report an aptamer microscale thermophoresis (MST) assay for OTA, with the unique merits of ratiometric analysis, rapid measurement, simple operation, high sensitivity, low sample consumption, and high throughput. A fluorescein (FAM)-labeled high-affinity DNA aptamer with a G-quadruplex and duplex structure was used as the recognition element for OTA, and MST, which measures the fluorescence responses of the sample solution inside capillaries to a mild temperature increase generated by infrared laser heating, was employed for signal generation. Upon OTA binding, the FAM-labeled aptamer probe underwent changes in conformation and stability, and the bound and unbound aptamer probes showed significant differences in their MST signals. To achieve sensitive detection of OTA with a large signal change, we systematically characterized aptamers with different stem lengths, which had large effects on the MST responses of the aptamer probes to OTA. We found that a 32-mer aptamer with FAM label at the 3' end gave a sensitive MST response to OTA, allowing OTA detection within seconds with a detection limit of 0.98 nM under optimal experimental conditions. This aptamer MST assay shows potential in real sample analysis and broad applications.

A DNA-based hydrogel for exosome separation and biomedical applications

Exosomes (EXOs) have been proven as biomarkers for disease diagnosis and agents for therapeutics. Great challenge remains in the separation of EXOs with high-purity and low-damage from complex biological media, which is critical for the downstream applications. Herein, we report a DNA-based hydrogel to realize the specific and nondestructive separation of EXOs from complex biological media. The separated EXOs were directly utilized in the detection of human breast cancer in clinical samples, as well as applied in the therapeutics of myocardial infarction in rat models. The materials chemistry basis of this strategy involved the synthesis of ultralong DNA chains via an enzymatic amplification, and the formation of DNA hydrogels through complementary base-pairing. These ultralong DNA chains that contained polyvalent aptamers were able to recognize and bind with the receptors on EXOs, and the specific and efficient binding ensured the selective separation of EXOs from media into the further formed networked DNA hydrogel. Based on this DNA hydrogel, rationally designed optical modules were introduced for the detection of exosomal pathogenic microRNA, which achieved the classification of breast cancer patients versus healthy donors with 100% precision. Furthermore, the DNA hydrogel that contained mesenchymal stem cell-derived EXOs was proved with significant therapeutic efficacy in repairing infarcted myocardium of rat models. We envision that this DNA hydrogel-based bioseparation system is promising as a powerful biotechnology, which will promote the development of extracellular vesicles in nanobiomedicine.

A cell-surface-anchored DNA probe coupled with hybridization chain reaction enzyme-free dual signal amplification for sensitive electrochemical detection of the cellular microenvironment

The cellular microenvironment plays key roles in regulating physiological processes. However, it is still a challenge to detect it with quantification. Here, a simple, biocompatible, and universal strategy based on cell surface-anchored specific DNAzymes and hybridization chain reaction enzyme-free signal amplification for cellular microenvironment electrochemical detection is presented. In this strategy, the cell could be captured on the surface of the electrode via aptamer-target recognition. On the other hand, the DNAzyme hybridized with the substrate strand as a metal ion probe was anchored on the surface of the cell. In the presence of metal ions, the substrate strand could be cleaved into two fragments by the DNAzyme and released from the cell surface. Then, the DNA modified gold nanoparticles (AuNPs) could be captured on the electrode. Subsequently, an alternative hybridization reaction of two hairpin probes was triggered by the carried initiators forming nicked double helices. For signal readout, hemin could be inserted into the double-helix DNA long chain via electrostatic interaction, which could electro-reduce hydrogen peroxide to generate an electrochemical signal. Based on the intrinsic advantages of DNAzymes, including rapid kinetics, high sensitivity, and high selectivity, and the signal amplification strategy, this method should be able to monitor and semi-quantify target metal ions in the cellular microenvironment. Furthermore, this method shows potential for various targets by employing different DNA probes in the cellular microenvironment, providing a platform for bioanalysis.

Real-Time Monitoring of Exosomes Secretion from Single Cell Using Dual-Nanopore Biosensors

Exosomes secreted from cells carry rich information from their parent cells, representing a promising biomarker for investigation of diseases. We develop a dual-nanopore biosensor using DNA aptamers to specifically recognize CD63 protein on the exosome's surface, which enables label-free exosome detection based on ionic current change. The sensor allows for sensitive detection of exosomes with a detection limit of 3.4 × 10⁶ particles/mL. The dual-nanopore biosensor was able to form an intrapipette electric circuit for ionic current measurement due to its unique structure, which is crucial to achieve detection of exosome secretion from a single cell. We utilized a microwell array chip to entrap a single cell into a confined microwell with small volume, enabling the accumulation of exosomes with high concentration. The dual-nanopore biosensor was positioned into the microwell with a single cell, and monitoring of exosome secretion from a single cell in different cell lines and under different stimulations has been achieved. Our design may provide a useful platform for developing nanopore biosensors for detecting cell secretions from a single living cell.

Polymerase-Driven Logic Signal Amplification for the Detection of Small Extracellular Vesicle Surface Proteins and the Identification of Breast Cancer

Small extracellular vesicles (sEVs) derived from tumors contain a vast amount of cellular information and are regarded as a potential diagnostic biomarker for noninvasive cancer diagnosis. Nevertheless, it remains challenging to accurately measure sEVs from clinical samples due to the low abundance of these vesicles as well as their phenotypic heterogeneity. Herein, a polymerase-driven logic signal amplification system (PLSAS) was developed for the high-sensitivity detection of sEV surface proteins and breast cancer (BC) identification. Aptamers were introduced to serve as sensing modules to specifically recognize target proteins. By changing the input DNA sequences, two polymerase-driven primer exchange reaction systems were rationally designed for DNA logic computing. This allows for autonomous targeting of a limited number of targets using "OR" and "AND" logic, leading to a significant increase in fluorescence signals and enabling the specific and ultrasensitive detection of sEV surface proteins. In this work, we investigated surface proteins of mucin 1 (MUC1) and the epithelial cell adhesion molecule (EpCAM) as model proteins. When MUC1 or EpCAM proteins were used as single signal input in the "OR" DNA logic system, the detection limit of sEVs was 24 or 58 particles/μL, respectively. And MUC1 and EpCAM proteins of sEVs can be simultaneously detected in the AND logic method, which can significantly reduce the effect of phenotypic heterogeneity of sEVs to distinguish the source of sEVs derived from various mammary cell lines, such as MCF-7, MDA MB 231, SKBR3, and MCF-10A. The approach has achieved high discrimination in serologically tested positive BC samples (AUC 98.1%) and holds significant potential in advancing the early diagnosis and prognostic assessments of BC.

Catalytic hairpin assembly-assisted dual-signal amplification platform for ultrasensitive detection of tumor markers and intelligent diagnosis of gastric cancer

Quantification of extracellular tumor markers has shown great promise for non-invasive cancer diagnosis. Combined detection of multiple tumor markers instead of a single one is valuable for accurate diagnosis. Here, we integrate CRISPR-Cas12a with DNA catalytic hairpin assembly (CHA) to doubly amplify the output signal for detecting microRNA-182 (miR-182), which is overexpressed by gastric cancer patients. Additionally, we develop a CHA system with self-replicating capacity (SRCHA) to realize dual-signal amplification for the detection of carcinoembryonic antigen (CEA), a broad-spectrum tumor marker. The proposed cascade amplification strategies enable ultrasensitive detection of miR-182 and CEA with low LODs of 0.063 fM and 4.8 pg mL^-1, respectively. Moreover, we design a ternary "AND" logic gate using different concentrations of miR-182 and CEA as inputs, which demonstrates intelligent diagnosis of gastric cancer staging with a high accuracy of 93.3% in a clinical cohort of 30 individuals. Overall, our study expands the application of CRISPR-Cas12a in biosensing and provides a new diagnostic strategy for non-invasive liquid biopsy of gastric cancer before resorting to a traumatic tissue biopsy.

Proteomic Research of Extracellular Vesicles in Clinical Biofluid

Extracellular vesicles (EVs), the lipid bilayer membranous structures of particles, are produced and released from almost all cells, including eukaryotes and prokaryotes. The versatility of EVs has been investigated in various pathologies, including development, coagulation, inflammation, immune response modulation, and cell-cell communication. Proteomics technologies have revolutionized EV studies by enabling high-throughput analysis of their biomolecules to deliver comprehensive identification and quantification with rich structural information (PTMs, proteoforms). Extensive research has highlighted variations in EV cargo depending on vesicle size, origin, disease, and other features. This fact has sparked activities to use EVs for diagnosis and treatment to ultimately achieve clinical translation with recent endeavors summarized and critically reviewed in this publication. Notably, successful application and translation require a constant improvement of methods for sample preparation and analysis and their standardization, both of which are areas of active research. This review summarizes the characteristics, isolation, and identification approaches for EVs and the recent advances in EVs for clinical biofluid analysis to gain novel knowledge by employing proteomics. In addition, the current and predicted future challenges and technical barriers are also reviewed and discussed.

Construction of DNA-based molecular circuits using normally open and normally closed switches driven by lambda exonuclease

Building synthetic molecular circuits is an important way to realize ion detection, information processing, and molecular computing. However, it is still challenging to implement the NOT logic controlled by a single molecule input in synthetic molecular circuits wherein the presence or absence of the molecule represents the ON or OFF state of the input. Here, based on lambda exonuclease (λ exo), for the first time, we propose the normally open (NO) and normally closed (NC) switching strategy with a unified signal transmission mechanism to build molecular circuits. Specifically, the opposite logic can be output with or without a single signal, and the state of the switch can be adjusted by the addition order and time interval of the upstream signal and switch signal, which endows the switch with time-responsive characteristics. In addition, a time-delay relay with the function of delayed disconnection is developed to realize quantitative control of outputs, which has the potential to meet the automation control need of the system. Finally, digital square and square root circuits are constructed by cascading the NO and NC switches, which demonstrates the versatility of switches. Our design can be extended to time logic and complex digital computing circuits for use in information processing and nanomachines.

Machine Learning-Based Label-Free SERS Profiling of Exosomes for Accurate Fuzzy Diagnosis of Cancer and Dynamic Monitoring of Drug Therapeutic Processes

Exosomes are a class of extracellular vesicles secreted by cells, which can be used as promising noninvasive biomarkers for the early diagnosis and treatment of diseases, especially cancer. However, due to the heterogeneity of exosomes, it remains a grand challenge to distinguish accurately and reliably exosomes from clinical samples. Herein, we achieve accurate fuzzy discrimination of exosomes from human serum samples for accurate diagnosis of breast cancer and cervical cancer through machine learning-based label-free surface-enhanced Raman spectroscopy (SERS), by using "hot spot" rich 3D plasmonic AuNPs nanomembranes as substrates. Due to the existence of some weak distinguishable SERS fingerprint signals and the high sensitivity of the method, the machine learning-based SERS analysis can precisely identify three (normal and cancerous) cell lines, two of which are different types of cancer cells, without specific labeling of biomarkers. The prediction accuracy based on the machine learning algorithm was up to 91.1% for the discrimination of different cell lines (H8, HeLa, and MCF-7 cell)-derived exosomes. Our model trained with SERS spectra of cell-derived exosomes could reach 93.3% prediction accuracy for clinical samples. Furthermore, the action mechanism of the chemotherapeutic process of MCF-7 cells can be revealed by dynamic monitoring of SERS profiling of the exosomes secreted. The method would be useful for noninvasive and accurate diagnosis and postoperative assessment of cancer or other diseases in the future.

Compute-and-Release Logic-Gated DNA Cascade Circuit for Accurate Cancer Cell Imaging

Accurate identification of cancer cells is an essential prerequisite for cancer diagnosis and subsequent effective curative interventions. The logic-gate-assisted cancer imaging system that allows a comparison of expression levels between biomarkers, rather than just reading biomarkers as inputs, returns a more comprehensive logical output, improving its accuracy for cell identification. To fulfill this key criterion, we develop a compute-and-release logic-gated double-amplified DNA cascade circuit. This novel system, CAR-CHA-HCR, consists of a compute-and-release (CAR) logic gate, a double-amplified DNA cascade circuit (termed CHA-HCR), and a MnO₂ nanocarrier. CAR-CHA-HCR, a novel adaptive logic system, is designed to logically output the fluorescence signals after computing the expression levels of intracellular miR-21 and miR-892b. Only when miR-21 is present and its expression level is above the threshold C_miR-21 > C_miR-892b, the CAR-CHA-HCR circuit performs a compute-and-release operation on free miR-21, thereby outputting enhanced fluorescence signals to accurately image positive cells. It is capable of comparing the relative concentrations of two biomarkers while sensing them, thus allowing accurate identification of positive cancer cells, even in mixed cell populations. Such an intelligent system provides an avenue for highly accurate cancer imaging and is potentially envisioned to perform more complex tasks in biomedical studies.

Simultaneous Detection of Two Extracellular Vesicle Subpopulations in Saliva Assisting Tumor T Staging of Oral Squamous Cell Carcinoma

Extracellular vesicles (EVs), acting as important mediators of intercellular communication, play an essential role in physiological processes, which have unique potential in the medical field. However, the heterogeneity of EVs limits their development for disease diagnosis and therapy, making the EV subpopulation analysis extremely valuable. In this article, a simple microfluidic approach was presented for the on-chip specific isolation and detection of two phenotypes of EVs (Annexin V⁺ EGFR⁺ EVs and Annexin V^- EGFR⁺ EVs) based on different biomolecule-modified magnetic nanospheres and a fluorescence labeling technique. Combined with the control of the magnetic field in the microzone and fluid flow, it was easy to form two separate functional regions in the chip to capture different EV subpopulations. This method was successfully applied to the tests of clinical saliva samples in 75 oral squamous cell carcinoma (OSCC) patients and 10 healthy people. The results showed that the total level of EGFR⁺ EVs was much higher in OSCC patients that in healthy people. Meantime, the ratio of Annexin V⁺ EGFR⁺ EVs to Annexin V^- EGFR⁺ EVs was found to be negatively correlated with tumor T stage of OSCC patients with a statistical difference, which suggested the ratio as a clinical index for monitoring the progression of OSCC in real time based on a noninvasive method. The approach provided a novel idea for evaluating the tumor T stage of OSCC and a powerful tool for clinical application.

One-step multiplex analysis of breast cancer exosomes using an electrochemical strategy assisted by gold nanoparticles

Exosomes, as a non-invasive biomarker, perform an important role in breast cancer screening and prognosis monitoring. However, establishing a simple, sensitive, and reliable exosome analysis technique remains challenging. Herein, a one-step multiplex analysis electrochemical aptasensor based on a multi-probe recognition strategy was constructed to analyze breast cancer exosomes. HER2-positive breast cancer cell (SK-BR-3) exosomes were selected as the model targets and three aptamers including CD63, HER2 and EpCAM aptamers were used as the capture units. Methylene blue (MB) functionalized HER2 aptamer and ferrocene (Fc) functionalized EpCAM aptamer, which were modified on gold nanoparticles (Au NPs), i.e. MB-HER2-Au NPs and Fc-EpCAM-Au NPs, were used as signal units. When the mixture of target exosomes, MB-HER2-Au NPs and Fc-EpCAM-Au NPs were added on the CD63 aptamer modified gold electrode, two Au NPs modified by MB and Fc could be specifically captured on the electrode by the recognition of three aptamers with target exosomes. Then one-step multiplex analysis of exosomes was achieved by detecting two independent electrochemical signals. This strategy can not only distinguish breast cancer exosomes from other exosomes (including normal exosomes and other tumor exosomes) but also HER2-positive breast cancer exosomes and HER2-negative breast cancer exosomes. Besides, it had high sensitivity and can detect SK-BR-3 exosomes with a concentration as low as 3.4 × 10³ particles mL^-1. Crucially, this method can be applicable to the examination of exosomes in complicated samples, which is anticipated to afford assistance for the screening and prognosis of breast cancer.