Highly Sensitive Aptasensor for Detecting Cancerous Exosomes Based on Clover-like Gold Nanoclusters

Automatic Electrochemiluminescence Method for the Detection of Cancerous Exosomes Incorporating Specific Aptamer-Magnetic Beads and Signal Nanoprobes

Exosomes, as an emerging biomarker, have exhibited remarkable promise in early cancer diagnosis. Here, a highly sensitive, selective, and automatic electrochemiluminescence (ECL) method for the detection of cancerous exosomes was developed. Specific aptamer-(EK)4 peptide-tagged magnetic beads (MBs-(EK)4-aptamer) were designed as a magnetic capture probe in which the (EK)4 peptide was used to reduce the steric binding hindrance of cancerous exosomes with a specific aptamer. One new universal ECL signal nanoprobe (CD9 Ab-PEG@SiO2ϵRu(bpy)32+) was designed and synthesized by using microporous SiO2 nanoparticles as the carrier for loading ECL reagent Ru(bpy)32+, polyethylene glycol (PEG) layer, and anticluster of differentiation 9 antibody (CD9 Ab). A "sandwich" biocomplex was formed on the surface of the magnetic capture probe after mixing the capture probe, target exosomes, and ECL signal nanoprobe, and then it was introduced into an automated ECL analyzer for rapid and automatic ECL measurement. It was found that the designed signal nanoprobe shows a 270-fold improvement in the signal-to-noise ratio than that of the ruthenium complex-labeled CD9 antibody signal probe. The relative ECL intensity was proportional to MCF-7 exosomes as a model in the range of 102 to 104 particle/μL, with a detection limit of 11 particle/μL. Furthermore, the ECL method was employed to discriminate cancerous exosomes based on fingerprint responses using the designed multiple magnetic capture probes and the universal ECL signal nanoprobe. This work demonstrates that the utilization of a designed automated ECL tactic using the MBs-(EK)4-aptamer capture probe and the CD9 Ab-PEG@SiO2ϵRu(bpy)32+ signal nanoprobe will provide a unique and robust method for the detection and discrimination of cancerous exosomes.

Role of aptamer technology in extracellular vesicle biology and therapeutic applications

Extracellular vesicles (EVs) are cell-derived nanosized membrane-bound vesicles that are important intercellular signalling regulators in local cell-to-cell and distant cell-to-tissue communication. Their inherent capacity to transverse cell membranes and transfer complex bioactive cargo reflective of their cell source, as well as their ability to be modified through various engineering and modification strategies, have attracted significant therapeutic interest. Molecular bioengineering strategies are providing a new frontier for EV-based therapy, including novel mRNA vaccines, antigen cross-presentation and immunotherapy, organ delivery and repair, and cancer immune surveillance and targeted therapeutics. The revolution of EVs, their diversity as biocarriers and their potential to contribute to intercellular communication, is well understood and appreciated but is ultimately dependent on the development of methods and techniques for their isolation, characterization and enhanced targeting. As single-stranded oligonucleotides, aptamers, also known as chemical antibodies, offer significant biological, chemical, economic, and therapeutic advantages in terms of their size, selectivity, versatility, and multifunctional programming. Their integration into the field of EVs has been contributing to the development of isolation, detection, and analysis pipelines associated with bioengineering strategies for nano-meets-molecular biology, thus translating their use for therapeutic and diagnostic utility.

Recent advances in the applications of DNA frameworks in liquid biopsy: A review

Cancer is one of the serious threats to public life and health. Early diagnosis, real-time monitoring, and individualized treatment are the keys to improve the survival rate and prolong the survival time of cancer patients. Liquid biopsy is a potential technique for cancer early diagnosis due to its non-invasive and continuous monitoring properties. However, most current liquid biopsy techniques lack the ability to detect cancers at the early stage. Therefore, effective detection of a variety of cancers is expected through the combination of various techniques. Recently, DNA frameworks with tailorable functionality and precise addressability have attracted wide spread attention in biomedical applications, especially in detecting cancer biomarkers such as circulating tumor cells (CTCs), exosomes and circulating tumor nucleic acid (ctNA). Encouragingly, DNA frameworks perform outstanding in detecting these cancer markers, but also face some challenges and opportunities. In this review, we first briefly introduced the development of DNA frameworks and its typical structural characteristics and advantages. Then, we mainly focus on the recent progress of DNA frameworks in detecting commonly used cancer markers in liquid-biopsy. We summarize the advantages and applications of DNA frameworks for detecting CTCs, exosomes and ctNA. Furthermore, we provide an outlook on the possible opportunities and challenges for exploiting the structural advantages of DNA frameworks in the field of cancer diagnosis. Finally, we envision the marriage of DNA frameworks with other emerging materials and technologies to develop the next generation of disease diagnostic biosensors.

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.

Exosomes as Powerful Biomarkers in Cancer: Recent Advances in Isolation and Detection Techniques

Exosomes, small extracellular vesicles derived from cells, are known to carry important bioactive molecules such as proteins, nucleic acids, and lipids. These bioactive components play crucial roles in cell signaling, immune response, and tumor metastasis, making exosomes potential diagnostic biomarkers for various diseases. However, current methods for detecting tumor exosomes face scientific challenges including low sensitivity, poor specificity, complicated procedures, and high costs. It is essential to surmount these obstacles to enhance the precision and dependability of diagnostics that rely on exosomes. Merging DNA signal amplification techniques with the signal boosting capabilities of nanomaterials presents an encouraging strategy to overcome these constraints and improve exosome detection. This article highlights the use of DNA signal amplification technology and nanomaterials' signal enhancement effect to improve the detection of exosomes. This review seeks to offer valuable perspectives for the enhancement of amplification methods applied in practical cancer diagnosis and prognosis by providing an overview of how these novel technologies are utilized in exosome-based diagnostic procedures.

Methylene Blue-Stained Single-Stranded DNA Aptamers as a Highly Efficient Electronic Switch for Quasi-Reagentless Exosomes Detection: An Old Dog with New Tricks

Improving the convenience, sensitivity, and cost-effectiveness of electrochemical biosensors is crucial for advancing their clinical diagnostic applications. Herein, we presented an elegant approach to construct electrochemical aptasensors for tumor-derived exosome detection by harnessing the alterable interaction between methylene blue (MB) and DNA aptamer. In detail, the anti-EpCAM aptamer, named SYL3C, was found to exhibit a strong affinity toward MB due to the specific interaction between MB and unbound guanine bases. Thereby, SYL3C could be stained with MB to arouse a strong electrochemical signal on a gold electrode (AuE). Upon binding to EpCAM-positive exosomes, SYL3C underwent a conformational transformation. The resulting conformation, or exosomes-SYL3C complex, not only reduced the accumulation of MB on SYL3C by obstructing the accessibility of guanines to MB but also impeded the transfer of electrons from the bound MB to AuE, leading to a notable decrease in the electrochemical signal. Using MB-stained SYL3C as an electronic switch, an electrochemical aptasensor was readily established for the detection of EpCAM-positive exosome detection. Without the need for signal amplification strategies, expensive auxiliary reagents, and complex operation, this unique signal transduction mechanism alone could endow the aptasensor with ultrahigh sensitivity. A limit of detection (LOD) of 234 particles mL-1 was achieved, surpassing the performance of most reported methods. As a proof of concept, the aptasensor was applied to analyze clinical serum samples and effectively distinguish non-small-cell lung cancer (NSCLC) patients from healthy individuals. As EpCAM exhibits broad expression in exosomes derived from different tumor sources, the developed aptasensor holds promise for diagnosing other tumor types.