Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Future embracing: exosomes driving a revolutionary approach to the diagnosis and treatment of idiopathic membranous nephropathy

Membranous nephropathy (MN) is a leading cause of nephrotic syndrome in adults and is associated with high rates of end-stage renal disease. Early detection and precise interventions are crucial for improving patient prognosis and quality of life. However, the current diagnosis primarily relies on renal biopsies and traditional biomarkers, which have limitations. Additionally, targeted therapeutic strategies are lacking. Exosomes, small vesicles that facilitate intercellular communication, have emerged as potential noninvasive diagnostic markers due to their stability, diverse cargo, and rapid detectability. They also hold promise as carriers for gene and drug delivery, presenting innovative opportunities in renal disease prognosis and treatment. However, research on exosomes in the context of idiopathic membranous nephropathy (IMN) remains limited, with a focus on exploring urinary exosomes as IMN markers. In this review, we summarize the current status of MN diagnosis and treatment, highlight the fundamental characteristics of exosomes, and discuss recent advancements in their application to IMN diagnosis and therapy. We provide insights into the clinical prospects of exosomes in IMN and acknowledge potential challenges. This article aims to offer forward-looking insights into the future of exosome-mediated IMN diagnosis and treatment, indicating a revolutionary transformation in this field.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022

The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.

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.

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.

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.

Urinary Exosomal Metabolites: Overlooked Clue for Predicting Cardiovascular Risk

Over the last decade, increasing research has focused on urinary exosomes (UEs) in biological fluids and their relationship with physiological and pathological processes. UEs are membranous vesicles with a size of 40-100 nm, containing a number of bioactive molecules such as proteins, lipids, mRNAs, and miRNAs. These vesicles are an inexpensive non-invasive source that can be used in clinical settings to differentiate healthy patients from diseased patients, thereby serving as potential biomarkers for the early identification of disease. Recent studies have reported the isolation of small molecules called exosomal metabolites from individuals' urine with different diseases. These metabolites could utilize for a variety of purposes, such as the discovery of biomarkers, investigation of mechanisms related to disease development, and importantly prediction of cardiovascular diseases (CVDs) risk factors, including thrombosis, inflammation, oxidative stress, hyperlipidemia as well as homocysteine. It has been indicated that alteration in urinary metabolites of N1-methylnicotinamide, 4-aminohippuric acid, and citric acid can be valuable in predicting cardiovascular risk factors, providing a novel approach to evaluating the pathological status of CVDs. Since the UEs metabolome has been clearly and precisely so far unexplored in CVDs, the present study has specifically addressed the role of the mentioned metabolites in the prediction of CVDs risk factors.

Nanomaterials assisted exosomes isolation and analysis towards liquid biopsy

Exosomes has attracted tremendous research interests as they are emerging as a new paradigm of liquid biopsy. Although the concentration of exosomes in blood is relatively abundant, there still exists various vesicle-like nanoparticles, such as microvesicles, apoptotic bodies. It's an urgent need to isolate and enrich exosomes from the complex contaminants in biofluid samples. Moreover, the expressing level of exosomal biomarkers varies a lot, which make the sensitive molecular detection of exosomes in high demand. Unfortunately, the efficient isolation and sensitive molecular quantification of exosomes is still a major obstacle hindering the further development and clinical application of exosome-based liquid biopsy. Nanomaterials, with unique physiochemical properties, have been widely used in biosensing and analysis aspects, thus they are thought as powerful tools for effective purification and molecular analysis of exosomes. In this review, we summarized the most recent progresses in nanomaterials assisted exosome isolation and analysis towards liquid biopsy. On the one hand, nanomaterials can be used as capture substrates to afford large binding area and specific affinity to exosomes. Meanwhile, nanomaterials can also be served as promising signal transducers and amplifiers for molecular detection of exosomes. Furthermore, we also pointed out several potential and promising research directions in nanomaterials assisted exosome analysis. It's envisioned that this review will give the audience a complete outline of nanomaterials in exosome study, and further promote the intersection of nanotechnology and bio-analysis.

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

Gastric cancer (GC) presents high mortality worldwide because of delayed diagnosis. Currently, exosome-based liquid biopsy has been applied in diagnosis and monitoring of diseases including cancers, whereas disease detection based on exosomes at the metabolic level is rarely reported. Herein, the specific aptamer-coupled Au-decorated polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of urinary exosomes from early GC patients and healthy controls (HCs) and the subsequent exosome metabolic pattern profiling without extra elution process. Combining with machine learning algorithm on all exosome metabolic patterns, the early GC patients are excellently discriminated from HCs, with an accuracy of 100% for both the discovery set and blind test. Ulteriorly, three key metabolic features with clear identities are determined as a biomarker panel, obtaining a more than 90% diagnostic accuracy for early GC in the discovery set and validation set. Moreover, the change law of the key metabolic features along with GC development is revealed through making a comparison among HCs and GC at early stage and advanced stage, manifesting their monitoring ability toward GC. This work illustrates the high specificity of exosomes and the great prospective of exosome metabolic analysis in disease diagnosis and monitoring, which will promote exosome-driven precision medicine toward practical clinical application.

In Vitro Diagnostic Examination and Prognosis Surveillance by Hierarchical Heterojunction-Assisted Metabolic Analysis

High-throughput metabolic analysis based on laser desorption/ionization mass spectrometry exhibits broad prospects in the field of large-scale precise medicine, for which the assisted ionization ability of the matrix becomes a determining step. In this work, the gold-decorated hierarchical metal oxide heterojunctions (dubbed Au/HMOHs) are proposed as a matrix for extracting urine metabolic fingerprints (UMFs) of primary nephrotic syndrome (PNS). The hierarchical heterojunctions are simply derived from metal-organic framework (MOF)-on-MOF hybrids, and the native built-in electric field from heterojunctions plus the extra Au decoration provides remarkable ionization efficiency, attaining high-quality UMFs. These UMFs are employed to realize precise diagnosis, subtype classification, and effective prognosis evaluation of PNS by appropriate machine learning, all with 100% accurate ratios. Moreover, a high-confidence marker panel for PNS diagnosis is constructed. Interestingly, all panel metabolite markers present obviously uniform downregulation in PNS compared to healthy controls, shedding light on mechanism exploration and pathway analysis. This work drives the application of metabolomics toward precision medicine.