Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer

TiO2-coated honeycomb-like super-macroporous silica for high-purity extraction of exosomes from human plasma

Screening biomarkers from exosomes has emerged as a new strategy for non-invasive early diagnosis of diseases. Nevertheless, the present screening efficiency and accuracy of biomarkers are limited by the low extraction efficiency and purity of exosomes. To address this issue, a highly selective adsorbent, which integrates size-exclusion and chemisorption, was created by coating TiO2 on honeycomb-like super-macroporous silica. Cell culture medium and human plasma were employed to investigate the enrichment performance, and the results indicate that the super-macropores ranging from 63.5 to 147.5 nm together with thin pore walls allow exosomes to enter and be adsorbed by TiO2 in the pores, enhancing the available surface area for exosomes meanwhile physically excluding the large-sized cell debris and vesicles. Taking advantages of these properties, the prepared adsorbent achieves a higher extraction efficiency, recovery and purity of exosomes compared with the normal adsorbents and ultracentrifugation (UC) method. Combining this method with proteomic analysis, a total of 392 proteins were identified in exosomes from healthy human plasma, which is significantly higher than the number obtained by UC (200 proteins). For clinical samples, 59 upregulated and 124 downregulated proteins were identified in the plasma from colorectal cancer (CRC) patients, of which 44 upregulated proteins and 69 downregulated proteins are strongly associated with the progression of CRC. These findings suggest that this adsorbent possesses considerable potential in the extraction of exosomes for screening biomarkers and diagnosing tumor progress.

Accelerated Exosomal Metabolic Profiling Enabled by Robust On-Target Array Sintering with Metal-Organic Frameworks

Pancreatic cancer is highly lethal, and survival chances improve only with early detection at a precancerous stage. However, there remains a significant gap in developing tools for large-scale, rapid screening. To this end, a high-throughput On-Target Array Extraction Platform (OTAEP) by direct sintering of a series of metal-organic frameworks (MOFs) for dual in situ extraction, encompassing both exosomes and their metabolic profiles, is developed. Based on the principle of geometry-dependent photothermal conversion efficiency and standard testing, the appropriate MOF functional unit is identified. This unit enables exosome enrichment within 10 min and metabolic fingerprint extraction in under 1 s of laser irradiation, with over five reuse. To further accelerate and enhance the quality of metabolic profile analysis, the application of Surrogate Variable Analysis to eliminate hidden confounding factors within the profiles is proposed, and five biomarkers demonstrated by MS/MS experiments are identified. These biomarkers enable early diagnosis, risk stratification, and staging of pancreatic cancer simultaneously, with sensitivity of 94.1%, specificity of 98.8%, and precision of 94.9%. This work represents a breakthrough for overcoming throughput challenges in large-scale testing and for addressing confounding factors in big data analysis.

Bifunctional nanomaterial enabled high-specific isolation of urinary exosomes for cervical cancer metabolomics analysis and biomarker discovery

Cervical cancer (CC) remains a critical public health issue, highlighting the importance of early detection. However, current methods such as cytological and HPV testing face challenges of invasiveness and low patient compliance. Exosomes, emerging as crucial in cancer diagnosis, offer promise due to their noninvasive, highly specificity, and abundant biomarkers. However, isolating exosomes efficiently remains challenging. In this study, we designed and synthesized a bifunctional affinity nanomaterial Fe3O4 @CD63-CLIKKPF, based on the synergistic interaction between its modified aptamer CD63 and peptide CLIKKPF, and CD63 protein and PS of exosomes which can achieve high specificity and high yield separation of urinary exosomes. Notably, the co-modified aptamer CD63 and peptide CLIKKPF not only enable efficient exosome isolation by leveraging dual-affinity mechanisms through a synergistic "AND" logic analysis, but also could be achieved on the Fe3O4 in one-step reaction at room temperature via Fe-S bonding. Combined with LC-MS/MS, we conducted exosome metabolomics analysis in healthy individuals and CC patients across various stages, and machine learning models demonstrated accurate classification (accuracy >0.822) and prediction capabilities for CC. Furthermore, six key metabolites indicative of CC progression were identified and validated in additional patient samples, highlighting their potential as biomarkers. Overall, this study establishes a novel method for exosome metabolomics in CC, offering insights for non-invasive early diagnosis and progression prediction on a large scale.

Molecular engineering of functional DNA molecules toward point-of-care diagnostic devices

The pursuit of rapid, sensitive, and specific diagnostic methodologies is imperative across diverse applications, including the detection of pathogens and disease biomarkers, food safety testing and environmental monitoring. Point-of-care testing (POCT) is characterized by its portability, ease of use, rapidity, and affordability, emerging as an attractive alternative for traditional diagnostics. Over recent years, the incorporation of functional DNA (fDNA) into POC diagnostic devices has emerged as a groundbreaking advancement, significantly enhancing sensitivity, specificity, and user-friendliness. In this review, we explore the innovative applications of fDNA in POC devices, highlighting its potential to revolutionize diagnostics by providing rapid, portable, and precise solutions. We discuss the unique advantages of fDNA, including its stability in complex biological matrices and its ability to recognize a wide range of targets. Furthermore, we explore the potential synergy between fDNA and cutting-edge technologies, such as nanotechnology and artificial intelligence (AI), to forge a path toward more personalized and accessible healthcare solutions. Despite significant progress, challenges remain in translating these innovations from the bench to the clinic. This review aims to provide a comprehensive overview of the current status of fDNA-based POCT devices and future directions for their development, emphasizing their critical role in meeting the global demand for accessible, efficient, and precise diagnostic solutions.

Efficient extraction via titanium organic frameworks facilitates in-depth profiling of urinary exosome metabolite fingerprints

Urinary exosome metabolite analysis has demonstrated notable advantages in uncovering disease status, yet its potential in decoding the intricacies of clear cell renal cell carcinoma (ccRCC) remains untapped. To address this, a core-shell magnetic titanium organic framework was designed to capture urinary exosomes and assist laser desorption/ionization mass spectrometry (LDI MS) to decipher the exosomal metabolic profile of ccRCC, with high sensitivity, throughput, and speed. A total of 492 urinary exosome metabolite fingerprints (UEMFs) from 176 samples were extracted for exploring the differences between ccRCC and healthy individuals. Leveraging machine learning algorithms, the exosomal metabolic profile was disclosed, achieving accurate differentiation and prediction of ccRCC patients versus healthy individuals, with an accuracy exceeding 97.3%. Furthermore, an optimized algorithm panel comprising five key features demonstrated consistent and high diagnosing accuracy rates of over 94.0% both in the training and blind test sets for ccRCC, underscoring the remarkable effectiveness and superiority of this strategy in ccRCC detection. This study not only refines the LDI MS method for metabolite analysis in urinary exosomes but also introduces a promising technical approach for unraveling the mysteries of ccRCC.

Porous Silicon Particle-Assisted Mass Spectrometry Technology Unlocks Serum Metabolic Fingerprints in the Progression From Chronic Hepatitis B to Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy and generally develops from liver cirrhosis (LC), which is primarily caused by the chronic hepatitis B (CHB) virus. Reliable liquid biopsy methods for HCC screening in high-risk populations are urgently needed. Here, we establish a porous silicon-assisted laser desorption ionization mass spectrometry (PSALDI-MS) technology to profile metabolite information hidden in human serum in a high throughput manner. Serum metabolites can be captured in the pore channel of APTES-modified porous silicon (pSi) particles and well-preserved during storage or transportation. Furthermore, serum metabolites captured in the APTES-pSi particles can be directly detected on the LDI-MS without the addition of an organic matrix, thus greatly accelerating the acquisition of metabolic fingerprints of serum samples. The PSALDI-MS displays the capability of high throughput (5 min per 96 samples), high reproducibility (coefficient of variation <15%), high sensitivity (LOD ∼ 1 pmol), and high tolerance to background salt and proteins. In a multicenter cohort study, 1433 subjects including healthy controls (HC), CHB, LC, and HCC volunteers were enrolled and nontargeted serum metabolomic analysis was performed on the PSALDI-MS platform. After the selection of feature metabolites, a stepwise diagnostic model for the classification of different liver disease stages was constructed by the machine learning algorithm. In external testing, the accuracy of 91.2% for HC, 71.4% for CHB, 70.0% for LC, and 95.3% for HCC was achieved by chemometrics. Preliminary studies indicated that the diagnostic model constructed from serum metabolic fingerprint also displays good predictive performance in a prospective observation. We believe that the combination of PSALDI-MS technology and machine learning may serve as an efficient tool in clinical practice.

Targeting Tetraspanins at Cell Interfaces: Functional Modulation and Exosome-Based Drug Delivery for Precise Disease Treatment

Tetraspanins are key players in various physiological and pathological processes, including malignancy, immune response, fertilization, and infectious disease. Affinity ligands targeting the interactions between tetraspanins and partner proteins are promising for modulating downstream signaling pathways, thus emerging as attractive candidates for interfering related biological functions. Due to the involvement in vesicle biogenesis and cargo trafficking, tetraspanins are also regarded as exosome markers, and become molecular targets for drug loading and delivery. Given the rapid development in these areas, this minireview focuses on recent advances in design and engineering of affinity binders toward tetraspanins including CD63, CD81, and CD9. Their mechanism of actions in modulating protein interactions at cell interfaces and treatment of malignant diseases are discussed. Strategies for constructing exosome-based drug delivery platforms are also reviewed, with emphasis on the important roles of tetraspanins and the affinity ligands. Finally, challenges and future development of tetraspanin-targeting therapy and exosomal drug delivery platforms are also discussed.

Deep Learning-Enabled Rapid Metabolic Decoding of Small Extracellular Vesicles via Dual-Use Mass Spectroscopy Chip Array

The increasing focus of small extracellular vesicles (sEVs) in liquid biopsy has created a significant demand for streamlined improvements in sEV isolation methods, efficient collection of high-quality sEV data, and powerful rapid analysis of large data sets. Herein, we develop a high-throughput dual-use mass spectroscopic chip array (DUMSCA) for the rapid isolation and detection of plasma sEVs. The DUMSCA realizes more than a 50% increase in speed compared to traditional method and confirms proficiency in robust storage, reuse, high-efficiency desorption/ionization, and metabolite quantification. With the collected metabolic data matrix of sEVs, a deep learning model achieves high-performance diagnosis of Crohn's disease. Furthermore, discovered biomarkers by feature sparsification and tandem mass spectrometry experiments also exhibited remarkable performance in diagnosis. This work demonstrates the rapidity and validity of DUMSCA for disease diagnosis, enabling the diagnosis of diseases without the necessity for prior knowledge and providing a high-throughput technology for sEV-based liquid biopsy that will empower its vigorous development.

Time-Lapse Acquisition of Both Freely Secreted Proteome and Exosome Encapsulated Proteome in Live Organoids' Microenvironment

Proteomic communications in neighboring microenvironments during early organ development is a dynamic process that continuously reshapes human embryonic stem cells (hESCs) developmental fate. Such dynamic proteomic alteration in the microenvironment consists of both freely secreted proteome and exosome-encapsulated proteome. Simultaneous monitoring of the time-lapse shift of both proteomes with live organoids remains technically challenging. Here, a continuous organoid secretion/encapsulation proteome tandem LC-MS/MS (COSEP-LCM) is introduced, which permits time-lapse monitoring of proteomic alterations both in free secretion form and in exosome encapsulated form at live organoids' microenvironment. Continuous growth of human cerebral organoids (COs) and free-secretion/exosome-encapsulation proteomics acquisition with COSEP-LCM for 60 days is demonstrated. SERPINF1, F5, and EFNB1 are initially enriched inside exosomes as encapsulated excretion and then gradually enriched outside exosomes as freely secreted excretion, while C3 is initially enriched outside exosomes as freely secreted excretion and gradually enriched inside exosomes as encapsulated excretion. Such dynamic excretion pattern paradigm shift may imply critical developmental strategy evolution during early human cerebral development. COSEP-LCM offers a platform technique for continuous inside/outside exosome proteomics co-analysis in live organoids' microenvironment.

Diagnosis and Biomarker Screening of Endometrial Cancer Enabled by a Versatile Exosome Metabolic Fingerprint Platform

Exosomes have emerged as a revolutionary tool for liquid biopsy (LB), as they carry specific cargo from cells. Profiling the metabolites of exosomes is crucial for cancer diagnosis and biomarker discovery. Herein, we propose a versatile platform for exosomal metabolite assay of endometrial cancer (EC). The platform is based on a nanostructured composite material comprising gold nanoparticle-coated magnetic COF with aptamer modification (Fe3O4@COF@Au-Apt). The unique design and novel synthesis strategy of Fe3O4@COF@Au-Apt provide the material with a large specific surface area, enabling the efficient and specific isolation of exosomes. The exosomes captured Fe3O4@COF@Au-Apt can be directly used as the laser desorption/ionization mass spectrometry (LDI-MS) matrix for rapid exosomal metabolic patterns. By integrating these functionalities into a single platform, the analytical process is simplified, eliminating the need for additional elution steps and minimizing potential sample loss, resulting in large-scale exosomal metabolic fingerprints. Combining with machine learning algorithms on the metabolic patterns, accurate discrimination between endometrial patients (EGs) and benign controls (CGs) was achieved, and the area under the receiver operating characteristic curve of the blind test cohort was 0.924. Confusion matrix analysis of important metabolic fingerprint features further demonstrates the high accuracy of the proposed approach toward EC diagnosis, with an overall accuracy of 94.1%. Moreover, four metabolites, namely, hydroxychalcone, l-acetylcarnitine, elaidic acid, and glutathione, have been identified as potential biomarkers of EC. These results highlight the great value of the integrated exosome metabolic fingerprint platform in facilitating low-cost and high-throughput characterization of exosomal metabolites for cancer diagnosis and biomarker discovery.

Nucleic Acid Amplification Circuit-Based Hydrogel (NACH) Assay for One-Step Detection of Metastatic Gastric Cancer-Derived Exosomal miRNA

Gastric cancer (GC) is recognized as the fifth most prevalent malignant tumor worldwide. It is characterized by diverse clinical symptoms, treatment responses, and prognoses. In GC prognosis, the promotion of epithelial-mesenchymal transition (EMT) fosters cancer cell invasion and metastasis, thereby triggering the dissemination of tumor cells. This study proposes a nucleic acid amplification circuit-based hydrogel (NACH) assay for identifying exosomal miRNA derived from metastatic GC. The NACH assay employs the rolling circle amplification method and targets miRNA-21, a tumor-related oncogene, and miRNA-99a, which promotes EMT. Specific amplification probes for each target are immobilized within the hydrogel, enabling a streamlined, one-step amplification reaction. The NACH assay exhibits a detection limit of 1 fm for miRNA-21 and miRNA-99a, thereby enabling rapid and highly sensitive on-site detection. Performance evaluation using exosomal miRNA extracted from cell culture media, mouse plasma, and human plasma revealed fluorescence intensity patterns similar to those obtained in qRT-PCR. Furthermore, deploying a custom-developed portable fluorometer for the NACH assay allows for diagnostic performance assessment and point-of-care testing using clinical samples from GC patients. These findings emphasize the potential of the NACH assay to be used as a robust tool for the genetic diagnosis of GC based on exosome detection.

Preparation of Dysprosium(III)-Metal Organic Framework Nanofiber for Exosome Capture and Biomarker Discovery toward Liver Disease

As an emerging source for liquid biopsy, exosomes hold significant promise for clinical diagnosis. However, commonly used exosome isolation methods (e.g., ultracentrifugation) suffer from low throughput for a large number of clinical samples. Herein, a dysprosium-metal organic framework was synthesized and doped with nanofibers by electrospinning for efficient capture of exosomes from body fluid. With the integration of multichannel of pipet or robot automatic workstation, high throughput exosome isolation can be achieved with clinical samples with high reproducibility. To evaluate the clinical value of the developed method, urinary exosomes were enriched from 34 liver disease samples of different stages for the profiling of metabolites by mass spectrometry. The results showed that HCC, cirrhosis, and healthy controls can be significantly differentiated by the Random Forest classification model. The dysprosium-metal organic framework has promising applications in exosome-based liquid biopsy for large-scale clinical disease diagnosis.

The new advance of exosome-based liquid biopsy for cancer diagnosis

Liquid biopsy is a minimally invasive method that uses biofluid samples instead of tissue samples for cancer diagnosis. Exosomes are small extracellular vesicles secreted by donor cells and act as mediators of intercellular communication in human health and disease. Due to their important roles, exosomes have been considered as promising biomarkers for liquid biopsy. However, traditional methods for exosome isolation and cargo detection methods are time-consuming and inefficient, limiting their practical application. In the past decades, many new strategies, such as microfluidic chips, nanowire arrays and electrochemical biosensors, have been proposed to achieve rapid, accurate and high-throughput detection and analysis of exosomes. In this review, we discussed about the new advance in exosome-based liquid biopsy technology, including isolation, enrichment, cargo detection and analysis approaches. The comparison of currently available methods is also included. Finally, we summarized the advantages and limitations of the present strategies and further gave a perspective to their future translational use.

Advances in Extracellular-Vesicles-Based Diagnostic and Therapeutic Approaches for Ocular Diseases

Extracellular vesicles (EVs) are nanoscale membrane vesicles of various sizes that can be secreted by most cells. EVs contain a diverse array of cargo, including RNAs, lipids, proteins, and other molecules with functions of intercellular communication, immune modulation, and regulation of physiological and pathological processes. The biofluids in the eye, including tears, aqueous humor, and vitreous humor, are important sources for EV-based diagnosis of ocular disease. Because the molecular cargos may reflect the biology of their parental cells, EVs in these biofluids, as well as in the blood, have been recognized as promising candidates as biomarkers for early diagnosis of ocular disease. Moreover, EVs have also been used as therapeutics and targeted drug delivery nanocarriers in many ocular disorders because of their low immunogenicity and superior biocompatibility in nature. In this review, we provide an overview of the recent advances in the field of EV-based studies on the diagnosis and therapeutics of ocular disease. We summarized the origins of EVs applied in ocular disease, assessed different methods for EV isolation from ocular biofluid samples, highlighted bioengineering strategies of EVs as drug delivery systems, introduced the latest applications in the diagnosis and treatment of ocular disease, and presented their potential in the current clinical trials. Finally, we briefly discussed the challenges of EV-based studies in ocular disease and some issues of concern for better focusing on clinical translational studies of EVs in the future.

Portable Aptasensor Based on Parallel Rolling Circle Amplification for Tumor-Derived Exosomes Liquid Biopsy

Here, a separation-free and label-free portable aptasensor is developed for rapid and sensitive analysis of tumor-derived exosomes (TEXs). It integrated a parallel rolling circle amplification (RCA) reaction, selective binding of metal ions or small molecules to nucleic acid-specific conformations, and a low-cost, highly sensitive handheld fluorometer. Lung cancer, for example, is targeted with two typical biomarkers (mucin 1 and programmed cell death ligand 1 (PD-L1)) on its exosomes. The affinity of aptamers to the targets modulated the amount of RCA products (T-Hg2+-T and cytosine (C)-rich single-stranded DNA), which in turn affected the fluorescence intensity of quantum dots (QDs) and methylene blue (MB). The results revealed that the limit of detection (LOD) of the handheld fluorometer for cell-derived exosomes can be as low as 30 particles mL-1. Moreover, its specificity, sensitivity, and area under the curve (AUC) are 93% (14/15), 92% (23/25), and 0.956, as determined by the analysis of 40 clinical samples. Retesting 16 of these samples with the handheld fluorometer yielded strong concordance between the fluorometer results and those acquired from clinical computed tomography (CT) and pathology.

Recent progress in Surface-Enhanced Raman Spectroscopy detection of biomarkers in liquid biopsy for breast cancer

Breast cancer is the most commonly diagnosed cancer in women globally and a leading cause of cancer-related mortality. However, current detection methods, such as X-rays, ultrasound, CT scans, MRI, and mammography, have their limitations. Recently, with the advancements in precision medicine and technologies like artificial intelligence, liquid biopsy, specifically utilizing Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a promising approach to detect breast cancer. Liquid biopsy, as a minimally invasive technique, can provide a temporal reflection of breast cancer occurrence and progression, along with a spatial representation of overall tumor information. SERS has been extensively employed for biomarker detection, owing to its numerous advantages such as high sensitivity, minimal sample requirements, strong multi-detection ability, and controllable background interference. This paper presents a comprehensive review of the latest research on the application of SERS in the detection of breast cancer biomarkers, including exosomes, circulating tumor cells (CTCs), miRNA, proteins and others. The aim of this review is to provide valuable insights into the potential of SERS technology for early breast cancer diagnosis.

Empowering Exosomes with Aptamers for Precision Theranostics

As information messengers for cell-to-cell communication, exosomes, typically small membrane vesicles (30-150 nm), play an imperative role in the physiological and pathological processes of living systems. Accumulating studies have demonstrated that exosomes are potential biological candidates for theranostics, including liquid biopsy-based diagnosis and drug delivery. However, their clinical applications are hindered by several issues, especially their unspecific detection and insufficient targeting ability. How to upgrade the accuracy of exosome-based theranostics is being widely explored. Aptamers, benefitting from their admirable characteristics, are used as excellent molecular recognition elements to empower exosomes for precision theranostics. With high affinity against targets and easy site-specific modification, aptamers can be incorporated with platforms for the specific detection of exosomes, thus providing opportunities for advancing disease diagnostics. Furthermore, aptamers can be tailored and functionalized on exosomes to enable targeted therapeutics. Herein, this review emphasizes the empowering of exosomes by aptamers for precision theranostics. A brief introduction of exosomes and aptamers is provided, followed by a discussion of recent progress in aptamer-based exosome detection for disease diagnosis, and the emerging applications of aptamer-functionalized exosomes for targeted therapeutics. Finally, current challenges and opportunities in this research field are presented.

Buoyant Metal-Organic Framework Corona-Driven Fast Isolation and Ultrasensitive Profiling of Circulating Extracellular Vesicles

Accurately assaying tumor-derived circulating extracellular vesicles (EVs) is fundamental in noninvasive cancer diagnosis and therapeutic monitoring but limited by challenges in efficient EV isolation and profiling. Here, we report a bioinspired buoyancy-driven metal-organic framework (MOF) corona that leverages on-bubble coordination and dual-encoded surface-enhanced Raman scattering (SERS) nanotags to streamline rapid isolation and ultrasensitive profiling of plasma EVs in a single assay for cancer diagnostics. This integrated bubble-MOF-SERS EV assay (IBMsv) allows barnacle-like high-density adhesion of MOFs on a self-floating bubble surface to enable fast isolation (2 min, near 90% capture efficiency) of tumor EVs via enhanced EV-MOF binding. Also, IBMsv harnesses four-plexed SERS nanotags to profile the captured EV surface protein markers at a single-particle level. Such a sensitive assay allows multiplexed profiling of EVs across five cancer types, revealing heterogeneous EV surface expression patterns. Furthermore, the IBMsv assay enables cancer diagnosis in a pilot clinical cohort (n = 55) with accuracies >95%, improves discrimination between cancer and noncancer patients via an algorithm, and monitors the surgical treatment response from hepatocellular carcinoma patients. This assay provides a fast, sensitive, streamlined, multiplexed, and portable blood test tool to enable cancer diagnosis and response monitoring in clinical settings.

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.

Aptamer-bivalent-cholesterol-mediated proximity entropy-driven exosomal protein reporter for tumor diagnosis

Exosomal proteins from the parental cells are considered to be promising biomarker sets for precise tumor diagnostics and monitoring. However, the accurate quantitative analysis of low-abundance exosomal proteins remains challenging due to the heterogeneity of clinical samples. Here, we standardized the exosomal concentration with a fluorogenic membrane probe and developed an aptamer-bivalent-cholesterol-mediated Proximity Entropy-driven Exosomal Protein Reporter (PEEPR). The proposed PEEPR enables the in-situ analysis of multiple exosomal proteins by integrating bivalent cholesterol anchor (exosomal lipid bilayer) and aptamer (exosomal proteins) with a proximity entropy-driven circuit. Based on this strategy, we successfully achieved detection limits of 3.9 pg/mL exosomal GPC-3 and 3.4 pg/mL exosomal PD-L1. Notably, the standardization of exosome concentrations is designed to avoid errors due to biological heterogeneity. The results showed that evaluating the levels of exosomal GPC-3 and PD-L1 in clinical samples via this strategy could accurately differentiate healthy individuals, hepatitis B patients, and hepatocellular carcinoma patients. In summary, PEEPR is a promising clinical diagnostic strategy for the quantitative analysis of a variety of tumor-associated exosomal proteins for the precise diagnosis and personalized treatment monitoring of tumors.

Exosome, a Rising Biomarkers in Liquid Biopsy: Advances of Label-Free and Label Strategy for Diagnosis of Cancer

Cancer is commonly considered as one of the most severe diseases, posing a significant threat to human health and society due to various serious challenges. These challenges include difficulties in accurate diagnosis and a high propensity to form metastasis. Tissue biopsy remains the gold standard for diagnosing and subtyping cancer. However, concerns arise from its invasive nature and the potential risk of metastasis during these complex diagnostic procedures. Meanwhile, liquid biopsy has recently witnessed the rapid advancements with the emergence of three prominent detection biomarkers: circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Whereas, the very low abundance of CTCs combined with the instability of ctDNA intensify the challenges and decrease the accuracy of these two biomarkers for cancer diagnosis. While exosomes have gained widespread recognition as a promising biomarker in liquid biopsy due to their relatively low-invasive detection method, excellent biostability, rich resources, high abundance, and ability to provide valuable information about cancer. Therefore, it is crucial to systematically summarize recent advancements mainly in exosome-based detection methods for early cancer diagnosis. Specifically, this review will primarily focus on label-based and label-free strategies for detecting cancer using exosomes. We anticipate that this comprehensive analysis will enhance readers' understanding of the significance and value of exosomes in the fields of cancer diagnosis and therapy.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Serological Exosome Metabolic Biopsy of Hepatocellular Carcinoma via Designed Core-Shell Nanoparticles

Exosome metabolite-based liquid biopsy is a promising strategy for large-scale application in practical clinics toward precise medicine. Given the current challenges in successive isolation and analysis of exosomes and their metabolites in this field, we established a low-cost, high-throughput, and rapid platform for serological exosome metabolic biopsy of hepatocellular carcinoma (HCC) via designed core-shell nanoparticles. It starts with the efficient extraction of high-quality serum exosomes and exosome metabolic features, based on which significantly obvious sample clusters are observed by unsupervised cluster analysis. The following integration of feature selection and supervised machine learning enables the identification of six key metabolites and achieves high-performance prediction between HCC, liver cirrhosis, and healthy controls. Specifically, both sensitivity and accuracy achieve 100% among any pairwise intergroup discrimination in a blind test. The quality and reliability of six key metabolites are further evaluated and validated by using different machine learning algorithms and pathway exploration. Our platform contributes to the future growth of new liquid biopsy technologies for precision diagnosis and real-time monitoring of HCC, among other conditions.

The Landscape of Biomimetic Nanovesicles in Brain Diseases

Brain diseases, such as brain tumors, neurodegenerative diseases, cerebrovascular diseases, and brain injuries, are caused by various pathophysiological changes, which pose a serious health threat. Brain disorders are often difficult to treat due to the presence of the blood-brain barrier (BBB). Biomimetic nanovesicles (BNVs), including endogenous extracellular vesicles (EVs) derived from various cells and artificial nanovesicles, possess the ability to penetrate the BBB and thus can be utilized for drug delivery to the brain. BNVs, especially endogenous EVs, are widely distributed in body fluids and usually carry various disease-related signal molecules such as proteins, RNA, and DNA, and may also be analyzed to understand the etiology and pathogenesis of brain diseases. This review covers the exhaustive classification and characterization of BNVs and pathophysiological roles involved in various brain diseases, and emphatically focuses on nanotechnology-integrated BNVs for brain disease theranostics, including various diagnosis strategies and precise therapeutic regulations (e.g., immunity regulation, disordered protein clearance, anti-neuroinflammation, neuroregeneration, angiogenesis, and the gut-brain axis regulation). The remaining challenges and future perspectives regarding the nanotechnology-integrated BNVs for the diagnosis and treatment of brain diseases are also discussed and outlined.

Plasmonic nanocavity-modulated electrochemiluminescence sensor for gastric cancer exosomal miRNA detection

Plasmonic nanocavity possessing highly light field confinement and electromagnetic field enhancement can concentrate and enhance the luminescence signal. The plasmonic nanocavity has the great potential value in biosensing research and improve analytical sensitivity. In this work, we constructed a plasmonic nanocavity between circular Au nanoplate-film and spherical Au nanoparticle with tetrahedral DNA nanostructures. The nanocavity structure can regulate the local density of optical states and provide the field restriction to enhance the spontaneous ECL radiation of PEDOT-S dots. Additionally, Au nanoparticle acted as nanoantenna which localized and modulated ECL to directional emission. Because the plasmonic nanocavity effectively collected and redistributed ECL signal, the emission was enhanced by 5.9 times with polarized characteristics. The proposed plasmonic nanocavity-based ECL sensor was further used to detect exosomal miRNA-223-3p in ascites. The detection results indicated the novel sensing strategy can assist early diagnosis of peritoneal metastasis of gastric cancer.

Investigation of rare earth-based magnetic nanocomposites for specific enrichment of exosomes from human plasma

Exosomes, also known as small extracellular vesicles, are widely present in a variety of body fluids (e.g., blood, urine, and saliva). Exosomes are becoming an alternative promising source of diagnostic markers for disease rich in cargo of metabolites, proteins, and nucleic acids. However, due to the low abundance and structure similarity with protein complex, the efficient isolation of exosomes is one of the most important issues for biomedical applications. With a higher order of f-orbitals in rare earth element, it will have strong adsorption toward the phosphate group on the surface of the phospholipid bilayer of exosomes. In this study, we systematically investigated the ability of various rare earths interacting with phosphate-containing molecules and plasma exosomes. One of the best binding europium was selected and used to synthesize core-shell magnetic nanomaterials (Fe3O4@SiO2@Eu2O3) for the enrichment of exosomes from human plasma. The developed nanomaterials exhibited higher enrichment capacity, less time consumption and more convenient handling compared to commonly used ultracentrifugation method. The nanomaterials were applied to separate exosomes from the plasma of patients with hepatocellular carcinoma and healthy controls for metabolomics study with high-resolution mass spectrometry, where 70 differentially expressed metabolites were identified, involving amino acid and lipid metabolic pathway. We anticipated the rare earth-based materials to be an alternative approach on exosome isolation for disease diagnosis or postoperative clinical monitoring.

Record Resolution of Nanometal Surface Energy Transfer Optical Nanoruler Projects 3D Spatial Configuration of Aptamers on a Living Cell Membrane

Decrypting the in situ three-dimensional spatial configuration of an aptamer is of considerable significance; however, suitable nanoscale resolution tools are lacking. Herein, we show that a new nanometal surface energy transfer (NSET) optical nanoruler has a record resolution, down to single-nucleobase levels. We labeled fluorophores on different T bases of XQ-2d, including 5', 3', 6T, 22T, 38T, and 52T positions. The NSET nanoruler in situ decrypted the base sequence-dependent distance projection on the nanogold surface, demonstrating that 5', 3', stem, and loop structures are symmetrical in three-dimensional spatial configuration. The orientation of the 5' and 3' stem was toward the antiCD71-binding site, whereas the loop was in the opposite direction at a considerable distance. Molecular docking simulation was performed to list all of the possible conformations; however, all base distance parameters projecting on the nanogold surface determined a single conformation of XQ-2d. The specific binding sites of XQ-2d were Lys477, Ser691, and Arg698 on the CD71 receptor.

Organic metal chalcogenide-assisted metabolic molecular diagnosis of central precocious puberty

Metabolic analysis in biofluids based on laser desorption/ionization mass spectrometry (LDI-MS), featuring rapidity, simplicity, small sample volume and high throughput, is expected to be a powerful diagnostic tool. Nevertheless, the signals of most metabolic biomarkers obtained by matrix-assisted LDI-MS are too limited to achieve a highly accurate diagnosis due to serious background interference. To address this issue, nanomaterials have been frequently adopted in LDI-MS as substrates. However, the "trial and error" approach still dominates the development of new substrates. Therefore, rational design of novel LDI-MS substrates showing high desorption/ionization efficiency and no background interference is extremely desired. Herein, four few-layered organic metal chalcogenides (OMCs) were precisely designed and for the first time investigated as substrates in LDI-MS, which allowed a favorable internal energy and charge transfer by changing the functional groups of organic ligands and metal nodes. As a result, the optimized OMC-assisted platform satisfyingly enhanced the mass signal by ≈10 000 fold in detecting typical metabolites and successfully detected different saccharides. In addition, a high accuracy diagnosis of central precocious puberty (CPP) with potential biomarkers of 12 metabolites was realized. This work is not only expected to provide a universal detection tool for large-scale clinical diagnosis, but also provides an idea for the design and selection of LDI-MS substrates.

The role and application of vesicles in triple-negative breast cancer: Opportunities and challenges

Extracellular vesicles (EVs) carry DNA, RNA, protein, and other substances involved in intercellular crosstalk and can be used for the targeted delivery of drugs. Triple-negative breast cancer (TNBC) is rich in recurrent and metastatic disease and lacks therapeutic targets. Studies have proved the role of EVs in the different stages of the genesis and development of TNBC. Cancer cells actively secrete various biomolecules, which play a significant part establishing the tumor microenvironment via EVs. In this article, we describe the roles of EVs in the tumor immune microenvironment, metabolic microenvironment, and vascular remodeling, and summarize the application of EVs for objective delivery of chemotherapeutic drugs, immune antigens, and cancer vaccine adjuvants. EVs-based therapy may represent the next-generation tool for targeted drug delivery for the cure of a variety of diseases lacking effective drug treatment.

Heterogeneous MXene Hybrid-Oriented Exosome Isolation and Metabolic Profiling for Early Screening, Subtyping and Follow-up Evaluation of Bladder Cancer

Exosome metabolite-based noninvasive liquid biopsy is an emerging research hotspot that tends to substitute current means in clinics. Nanostructure-based mass spectrometry enables continuous exosome isolation and metabolic profiling with superior analysis speed and high efficiency. Herein, we construct a heterogeneous MXene hybrid that possesses ternary binding sites for exosome capture and outstanding matrix performance for metabolite analysis. Upon optimizing experimental conditions, the average extraction of exosomes and their metabolic patterns from a 60 mL urine sample is completed within 45 s (40 samples per batch for 30 min). According to the exosomal metabolic patterns and the subsequently established biomarker panel, we distinguish early bladder cancer (BCa) from healthy controls with an area under the curve (AUC) value greater than 0.995 in model training and validation sets. As well, we realize subtype classification of BCa in the blind test on metabolic patterns, with an AUC value of 0.867. We also explore the significant biomarkers that are sensitive to follow-up patients, which indeed present reverse change levels compared with pathological progression. This study has the potential to guide the development of the liquid biopsy approach.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

OBJECTIVE

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

Metabolic profiling of urinary exosomes for systemic lupus erythematosus discrimination based on HPL-SEC/MALDI-TOF MS

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease which leads to the formation of immune complex deposits in multiple organs and has heterogeneous clinical manifestations. Currently, exosomes for liquid biopsy have been applied in diagnosis and monitoring of diseases, whereas SLE discrimination based on exosomes at the metabolic level is rarely reported. Herein, we constructed a protocol for metabolomic study of urinary exosomes from SLE patients and healthy controls (HCs) with high efficiency and throughput. Exosomes were first obtained by high-performance liquid size-exclusion chromatography (HPL-SEC), and then metabolic fingerprints of urinary exosomes were extracted by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with high throughput and high efficency. With the statistical analysis by orthogonal partial least-squares discriminant analysis (OPLS-DA) model, SLE patients were efficiently distinguished from HCs, the area under the curve (AUC) of the receiver characteristic curve (ROC) was 1.00, and the accuracy of the unsupervised clustering heatmap was 90.32%. In addition, potential biomarkers and related metabolic pathways were analyzed. This method, with the characteristics of high throughput, high efficiency, and high accuracy, will provide the broad prospect of exosome-driven precision medicine and large-scale screening in clinical applications.

One-pot preparation of bi-functional POSS-based hybrid monolith via photo-initiated polymerization for isolation of extracellular vesicles

Extracellular vesicles (EVs) are important participants in numerous pathophysiological processes, and could be used as valuable biomarkers to detect and monitor various diseases. However, facile EV isolation methods are the essential and preliminary issue for their downstream analysis and function investigation. In this work, a polyhedral oligomeric silsesquioxanes (POSS) based hybrid monolith combined metal affinity chromatography (MAC) and distearoyl phospholipid ethanolamine (DSPE) function was developed via photo-initiated thiol-ene polymerization. This synthesis process was facile, simple and convenient, and the obtained hybrid monolith could be applied to efficiently isolate EVs from bio-samples by taking advantages of the specific bond of Ti4+ and phosphate groups on the phospholipid membrane of EVs and the synergistic effect of DSPE insertion. Meanwhile, the eluted EVs could maintain their structural integrity and biological activity, suggesting they could be used for downstream application. Furthermore, 75 up-regulated proteins and 56 down-regulated proteins were identified by comparing the urinary EVs of colorectal cancer (CRC) patients and healthy donors, and these proteins might be used as potential biomarkers for early screening of CRC. These results demonstrated that this hybrid monolith could be used as a simple and convenient tool for isolating EVs from bio-samples and for wider applications in biomarker discovery.

Diverse applications and development of aptamer detection technology

Aptamers have received extensive attention in recent years because of their advantages of high specificity, high sensitivity and low immunogenicity. Aptamers can perform almost all functions of antibodies through the combination of spatial structure and target, which are called "chemical antibodies". At present, aptamers have been widely used in cell imaging, new drug development, disease treatment, microbial detection and other fields. Due to the diversity of modifications, aptamers can be combined with different detection technologies to construct aptasensors. This review focuses on the diversity of aptamers in the field of detection and the development of aptamer-based detection technology and proposes new challenges for aptamers in this field.

Plasmonic alloys for quantitative determination and reaction monitoring of biothiols

Biothiols participate in numerous physiological and pathological processes in an organism. Quantitative determination and reaction monitoring of biothiols have important implications for evaluating human health. Herein, we synthesized plasmonic alloys as the matrix to assist the laser desorption and ionization (LDI) process of biothiols in mass spectrometry (MS). Plasmonic alloys were constructed with mesoporous structures for LDI enhancement and trimetallic (PdPtAu) compositions for noble metal-thiol hybridization, toward enhanced detection sensitivity and selectivity, respectively. Plasmonic alloys enabled direct detection of biothiols from complex biosamples without any enrichment or separation. We introduced internal standards into the quantitative MS system, achieving accurate quantitation of methionine directly from serum samples with a recovery rate of 103.19% ± 6.52%. Moreover, we established a rapid monitoring platform for the oxidation-reduction reaction of glutathione, consuming trace samples down to 200 nL with an interval of seconds. This work contributes to the development of molecular tools based on plasmonic materials for biothiol detection toward real-case applications.

A sensitive and rapid electrochemical biosensor for sEV-miRNA detection based on domino-type localized catalytic hairpin assembly

Small extracellular-vesicule-associated microRNA (sEV-miRNA) is an important biomarker for cancer diagnosis. However, rapid and sensitive detection of low-abundance sEV-miRNA in clinical samples is challenging. Herein, a simple electrochemical biosensor that uses a DNA nanowire to localize catalytic hairpin assembly (CHA), also called domino-type localized catalytic hairpin assembly (DT-LCHA), has been proposed for sEV-miRNA1246 detection. The DT-LCHA offers triple amplification, (i). CHA system was localized in DNA nanowire, which shorten the distance between hairpin substrate, inducing the high collision efficiency of H1 and H2 and domino effect. Then, larger numbers of CHAs were triggered, capture probe bind DT-LCHA by exposed c sites. (ii) The DNA nanowire can load large number of electroactive substance RuHex as amplified electrochemical signal tags. (iii) multiple DT-LCHA was carried by the DNA nanowire, only one CHA was triggered, the DNA nanowire was trapped by the capture probe, which greatly improve the detection sensitivity, especially when the target concentration is extremely low. Owing to the triple signal amplification in this strategy, sEV-miRNA at a concentration of as low as 24.55 aM can be detected in 20 min with good specificity. The accuracy of the measurements was also confirmed using reverse transcription quantitative polymerase chain reaction. Furthermore, the platform showed good performance in discriminating healthy donors from patients with early gastric cancer (area under the curve [AUC]: 0.96) and was equally able to discriminate between benign gastric tumors and early cancers (AUC: 0.77). Thus, the platform has substantial potential in biosensing and clinical diagnosis.

microRNAs delivered by small extracellular vesicles in MSCs as an emerging tool for bone regeneration

Bone regeneration is a dynamic process that involves angiogenesis and the balance of osteogenesis and osteoclastogenesis. In bone tissue engineering, the transplantation of mesenchymal stem cells (MSCs) is a promising approach to restore bone homeostasis. MSCs, particularly their small extracellular vesicles (sEVs), exert therapeutic effects due to their paracrine capability. Increasing evidence indicates that microRNAs (miRNAs) delivered by sEVs from MSCs (MSCs-sEVs) can alter gene expression in recipient cells and enhance bone regeneration. As an ideal delivery vehicle of miRNAs, MSCs-sEVs combine the high bioavailability and stability of sEVs with osteogenic ability of miRNAs, which can effectively overcome the challenge of low delivery efficiency in miRNA therapy. In this review, we focus on the recent advancements in the use of miRNAs delivered by MSCs-sEVs for bone regeneration and disorders. Additionally, we summarize the changes in miRNA expression in osteogenic-related MSCs-sEVs under different microenvironments.

Hollow multishelled heterostructures with enhanced performance for laser desorption/ionization mass spectrometry based metabolic diagnosis

High-performance metabolic diagnosis-based laser desorption/ionization mass spectrometry (LDI-MS) improves the precision diagnosis of diseases and subsequent treatment. Inorganic matrices are promising for the detection of metabolites by LDI-MS, while the structure and component impacts of the matrices on the LDI process are still under investigation. Here, we designed a multiple-shelled ZnMn2O4/(Co, Mn)(Co, Mn)2O4 (ZMO/CMO) as the matrix from calcined MOF-on-MOF for detecting metabolites in LDI-MS and clarified the synergistic impacts of multiple-shells and the heterostructure on LDI efficiency. The ZMO/CMO heterostructure allowed 3-5 fold signal enhancement compared with ZMO and CMO with the same morphology. Furthermore, the ZMO/CMO heterostructure with a triple-shelled hollow structure displayed a 3-fold signal enhancement compared to its nanoparticle counterpart. Taken together, the triple-shelled hollow ZMO/CMO exhibits 102-fold signal enhancement compared to the commercial matrix products (e.g., DHB and DHAP), allowing for sensitive metabolic profiling in bio-detection. We directly extracted metabolic patterns by the optimized triple-shelled hollow ZMO/CMO particle-assisted LDI-MS within 1 s using 100 nL of serum and used machine learning as the readout to distinguish hepatocellular carcinoma from healthy controls with the area under the curve value of 0.984. Our approach guides us in matrix design for LDI-MS metabolic analysis and drives the development of a nanomaterial-based LDI-MS platform toward precision diagnosis.

Integrated analysis of single-cell and bulk RNA-seq establishes a novel signature for prediction in gastric cancer

BACKGROUND

Single-cell sequencing technology provides the capability to analyze changes in specific cell types during the progression of disease. However, previous single-cell sequencing studies on gastric cancer (GC) have largely focused on immune cells and stromal cells, and further elucidation is required regarding the alterations that occur in gastric epithelial cells during the development of GC.

AIM

To create a GC prediction model based on single-cell and bulk RNA sequencing (bulk RNA-seq) data.

METHODS

In this study, we conducted a comprehensive analysis by integrating three single-cell RNA sequencing (scRNA-seq) datasets and ten bulk RNA-seq datasets. Our analysis mainly focused on determining cell proportions and identifying differentially expressed genes (DEGs). Specifically, we performed differential expression analysis among epithelial cells in GC tissues and normal gastric tissues (NAGs) and utilized both single-cell and bulk RNA-seq data to establish a prediction model for GC. We further validated the accuracy of the GC prediction model in bulk RNA-seq data. We also used Kaplan–Meier plots to verify the correlation between genes in the prediction model and the prognosis of GC.

RESULTS

By analyzing scRNA-seq data from a total of 70707 cells from GC tissue, NAG, and chronic gastric tissue, 10 cell types were identified, and DEGs in GC and normal epithelial cells were screened. After determining the DEGs in GC and normal gastric samples identified by bulk RNA-seq data, a GC predictive classifier was constructed using the Least absolute shrinkage and selection operator (LASSO) and random forest methods. The LASSO classifier showed good performance in both validation and model verification using The Cancer Genome Atlas and Genotype-Tissue Expression (GTEx) datasets [area under the curve (AUC)_min = 0.988, AUC_1se = 0.994], and the random forest model also achieved good results with the validation set (AUC = 0.92). Genes TIMP1, PLOD3, CKS2, TYMP, TNFRSF10B, CPNE1, GDF15, BCAP31, and CLDN7 were identified to have high importance values in multiple GC predictive models, and KM-PLOTTER analysis showed their relevance to GC prognosis, suggesting their potential for use in GC diagnosis and treatment.

CONCLUSION

A predictive classifier was established based on the analysis of RNA-seq data, and the genes in it are expected to serve as auxiliary markers in the clinical diagnosis of GC.

MoS2 QDs-MXene heterostructure-based ECL sensor for the detection of miRNA-135b in gastric cancer exosomes

Exosomes play an important role in the proliferation, adhesion and migration of cancer cells. In this study, we have developed a novel electrochemiluminescence (ECL) sensor based on MoS2 QDs-MXene heterostructure and Au NPs@biomimetic lipid layer to detect exosomal miRNA. MoS2 QDs-MXene heterostructure had been prepared as the luminescence probe. Ti3C2Tx MXene nanosheets possessed the large specific surface area, excellent flexibility and superior conductivity. MoS2 QDs on the MXene nanosheets worked as the radiation center to generate strong ECL signal. Meanwhile, Au NPs with biomimetic lipid layer have been modified on the electrode, which retained the lipid dynamics and excellent antifouling property. When miRNA-135b was recognized on the Au NPs@biomimetic lipid layer, MoS2 QDs-MXene heterostructure was linked on the electrode and further extended the outer Helmholtz plane. As a result, the self-luminous Faraday cage-mode sensing system has been used to detect miRNA-135b from 30 fM to 20 nM with a detection limit of 10 fM. Furthermore, gastric cancer exosomal miRNA in the ascites of clinical patients has been detected successfully. The sensing system can be served as a versatile platform with huge application potential in the field of exosome detection.

Quantification of GPC1(+) Exosomes Based on MALDI-TOF MS In Situ Signal Amplification for Pancreatic Cancer Discrimination and Evaluation

Pancreatic cancer (PC) has a high mortality, with a fairly low five-year survival rate, because of its delayed diagnosis. Recently, liquid biopsy, especially based on exosomes, has attracted vast attention, thanks to its low invasiveness. Herein, we constructed a protocol for pancreatic cancer related Glypican 1 (GPC1) exosome quantification, based on in situ mass spectrometry signal amplification, by utilizing mass tag molecules on gold nanoparticles (AuNPs). Exosomes were extracted and purified by size-exclusion chromatography (SEC), captured by TiO₂ modified magnetic nanoparticles, and then targeted specifically by anti-GPC1 antibody modified on AuNPs. With matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), the signal of PC biomarker, GPC1, was converted to a mass tag signal and amplified. With addition of a certain amount of internal standard molecules modified on AuNPs, the relative intensity ratio of mass tag to internal standard was proportional to the concentration of GPC1(+) exosomes derived from pancreatic cancer cell lines, PANC-1, with good linearity (R² = 0.9945) in a wide dynamic range from 7.1 × 10 to 7.1 × 10⁶ particles/μL. This method was further applied to plasma samples from healthy control (HC) and pancreatic cancer patients with different tumor load, and exhibited a great potential in discriminating diagnosed PC patients from HC, and has the monitoring potential in PC progression.

Aptamer-based technology for gastric cancer theranostics

Gastric cancer is one of the most common causes of cancer death worldwide. This cancer exhibits high molecular and phenotype heterogeneity. The overall survival rate for gastric cancer is very low because it is always diagnosed in the advanced stages. Therefore, early detection and treatment are of great significance. Currently, biomedical studies have tapped the potential clinical applicability of aptamer-based technology for gastric cancer diagnosis and targeted therapy. Herein, we summarize the enrichment and evolution of relevant aptamers, followed by documentation of the recent developments in aptamer-based techniques for early diagnosis and precision therapy for gastric cancers.

Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy

Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.

Ratiometric electrochemical OR gate assay for NSCLC-derived exosomes

Non-small cell lung cancer (NSCLC) is the most common pathological type of LC and ranks as the leading cause of cancer deaths. Circulating exosomes have emerged as a valuable biomarker for the diagnosis of NSCLC, while the performance of current electrochemical assays for exosome detection is constrained by unsatisfactory sensitivity and specificity. Here we integrated a ratiometric biosensor with an OR logic gate to form an assay for surface protein profiling of exosomes from clinical serum samples. By using the specific aptamers for recognition of clinically validated biomarkers (EpCAM and CEA), the assay enabled ultrasensitive detection of trace levels of NSCLC-derived exosomes in complex serum samples (15.1 particles μL^-1 within a linear range of 10²-10⁸ particles μL^-1). The assay outperformed the analysis of six serum biomarkers for the accurate diagnosis, staging, and prognosis of NSCLC, displaying a diagnostic sensitivity of 93.3% even at an early stage (Stage I). The assay provides an advanced tool for exosome quantification and facilitates exosome-based liquid biopsies for cancer management in clinics.

Integrated SERS-Vertical Flow Biosensor Enabling Multiplexed Quantitative Profiling of Serological Exosomal Proteins in Patients for Accurate Breast Cancer Subtyping

Protein profiles of exosomes (EXOs) in clinical samples of cancer patients have become a promising diagnostic and therapeutic biomarker. However, simultaneous quantitative analysis of multiple exosomal proteins of interest remains challenging. To address the unmet need, we develop a paper-based surface-enhanced Raman spectroscopy (SERS)-vertical flow biosensor, named iREX (integrated Raman spectroscopic EXO) biosensor, for multiplexed quantitative profiling of exosomal proteins in clinical serum samples of patients. Utilizing this iREX biosensor, we are able to quantitatively profile MUC1, HER2 and CEA in EXO samples derived from various breast cancer cell subtypes. The results show discriminative expression profiles of the three exosomal proteins in these cell subtypes, which allows for accurate diagnosis and molecular subtyping of breast cancer. We further validate the clinical utility of the iREX biosensor for simultaneous quantitative analysis of MUC1, HER2 and CEA in patient's blood serums, thereby aiding in noninvasive breast cancer subtyping and longitudinal treatment monitoring. Our iREX biosensor integrating the SERS detection in a vertical flow diagnostic device offers great advantages of high sensitivity, molecular specificity, powerful multiplexing capability, and high diagnostic accuracy. We believe that the iREX biosensor could be a promising clinical tool for comprehensive analysis of exosomal proteins in clinical samples for personalized diagnosis and precise management of breast cancer.

Clinical application and detection techniques of liquid biopsy in gastric cancer

Gastric cancer (GC) is one of the most common tumors worldwide and the leading cause of tumor-related mortality. Endoscopy and serological tumor marker testing are currently the main methods of GC screening, and treatment relies on surgical resection or chemotherapy. However, traditional examination and treatment methods are more harmful to patients and less sensitive and accurate. A minimally invasive method to respond to GC early screening, prognosis monitoring, treatment efficacy, and drug resistance situations is urgently needed. As a result, liquid biopsy techniques have received much attention in the clinical application of GC. The non-invasive liquid biopsy technique requires fewer samples, is reproducible, and can guide individualized patient treatment by monitoring patients' molecular-level changes in real-time. In this review, we introduced the clinical applications of circulating tumor cells, circulating free DNA, circulating tumor DNA, non-coding RNAs, exosomes, and proteins, which are the primary markers in liquid biopsy technology in GC. We also discuss the current limitations and future trends of liquid biopsy technology as applied to early clinical biopsy technology.