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

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

Straightforward Creation of Multishell Hollow Hybrids for an Integrated Metabolic Monitoring System in Disease Management.

Multidimensional metabolic analysis has become a new trend in establishing efficient disease monitoring systems, as the constraints associated with relying solely on a single dimension in refined monitoring are increasingly pronounced. Here, coordination polymers are employed as derivative precursors to create multishell hollow hybrids, developing an integrated metabolic monitoring system. Briefly, metabolic fingerprints are extracted from hundreds of serum samples and urine samples, encompassing not only membranous nephropathy but also related diseases, using high-throughput mass spectrometry. With optimized algorithm and initial feature selection, the established combined panel demonstrates enhanced accuracy in both subtype differentiation (over 98.1%) and prognostic monitoring (over 95.6%), even during double blind test. This surpasses the serum biomarker panel (≈90.7% for subtyping, ≈89.7% for prognosis) and urine biomarker panel (≈94.4% for subtyping, ≈76.5% for prognosis). Moreover, after attempting to further refine the marker panel, the blind test maintains equal sensitivity, specificity, and accuracy, showcasing a comprehensive improvement over the single-fluid approach. This underscores the remarkable effectiveness and superiority of the integrated strategy in discriminating between MN and other groups. This work has the potential to significantly advance diagnostic medicine, leading to the establishment of more effective strategies for patient management.

Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma.

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.

A non-targeting magnetic metal-organic framework probe for highly specific peptide-mediated precise disease monitoring.

Alzheimer's disease (AD) is a universal neurodegenerative disease in older adults with incurable and progressive properties, urging for precise monitoring to perform timely treatment to delay its progression. Herein, we introduced a non-targeting magnetic metal-organic framework probe coupled with high-throughput mass spectrometry, creating a rapid screening strategy for highly specific peptides associated with AD. Notably, an elution-free extraction process was proposed, significantly reducing sample preprocessing time while simultaneously ensuring the efficient detection of captured peptides. Using this elution-free extraction process, high-quality peptide profiles were rapidly extracted from the hundreds of samples from both diseased and healthy individuals. By integrating machine learning algorithms, LC-MS/MS, and Uniprot database searching, we identified three specific serum endogenous peptides (m/z = 4215.41, 2884.77 and 2704.61) closely associated with AD. Remarkably, with the use of any single specific peptide, the AUC (Area Under the Curve) values can reach approximately 0.9 during monitoring AD. Moreover, integrating three specific biomarkers provides a robust basis for machine learning algorithms to build monitoring models, with AUC value up to 1.000. This work represents a substantial advancement in the development of peptide-specific precise monitoring approaches for complex diseases, serving as a catalyst for increased dedication to the molecular detection field.

Designed Directional Growth of Ti-Metal-Organic Frameworks for Decoding Alzheimer's Disease-Specific Exosome Metabolites.

Exosomes, a growing focus for liquid biopsies, contain diverse molecular cargos. In particular, exosome metabolites with valuable information have exhibited great potential for improving the efficiency of liquid biopsies for addressing complex medical conditions. In this work, we design the directional growth of Ti-metal-organic frameworks on polar-functionalized magnetic particles. This design facilitates the rapid synergistic capture of exosomes with the assistance of an external magnetic field and additionally synergistically enhances the ionization of their metabolites during mass spectrometry detection. Benefiting from this dual synergistic effect, we identified three high-performance exosome metabolites through the differential comparison of a large number of serum samples from individuals with Alzheimer's disease (AD) and normal cognition. Notably, the accuracy of AD identification ranges from 93.18 to 100% using a single exosome metabolite and reaches a flawless 100% with three metabolites. These findings emphasize the transformative potential of this work to enhance the precision and reliability of AD diagnosis, ushering in a new era of improved diagnostic accuracy.

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.

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.

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.

Zwitterionic mesoporous engineering aids peptide-dependent pattern profiling for identification of different liver diseases.

Liver disease remains a global health challenge, with its incidence steadily increasing worldwide. Herein, zwitterionic mesoporous engineering was developed for the identification of different liver diseases including liver cirrhosis and liver cancer. Based on this engineering, a total of 2633 m/z signals were observed to be enriched. Notably, three key peptides were identified and showed high accuracy and precision for distinguishing the healthy and disease states, propelling the field of nanomedicine toward genuine personalized medicine.

Construction of Porous Perovskite Oxide Microrods with Au Nanoparticle Anchor for Precise Metabolic Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive illness, and early diagnosis and treatment can help delay its progression. However, clinics still lack high-throughput, low-invasive, precise, and objective diagnostic strategies. Herein, the Au nanoparticles anchored porous perovskite oxide microrods (CTO@Au) with designed superior properties is developed to construct a high-throughput detection platform. Specifically, a single metabolic fingerprinting is obtained from only 30 nL of serum within seconds, enabling the rapid acquisition of 239 × 8 high-quality fingerprints in ≈ 2 h. AD is distinguish from health controls and Parkinson's disease with an area under the curve (AUC) of 1.000. Moreover, eight specific metabolites are identified as a biomarker panel, based on which precise diagnosis of AD is achieved, with an AUC of 1.000 in blind test. The possible relevant pathways and potential mechanism involved in these biomarkers are investigated and discussed. This work provides a high-performance platform for metabolic diagnostic analysis.

Resol/triblock copolymer composite-guided smart fabrication of carbonized mesopores for efficiently decoding exosomal glycans.

Soft-template carbonized mesopores were developed for the purpose of enriching urinary exosomal glycans through organic-organic self-assembly using block copolymers and resol precursors. With a high surface area of 229 m2 g-1, a small pore size of 3.1 nm, and a significant amount of carbon that specifically interacts with oligosaccharides in glycans, this carbonized mesopore material exhibits high selectivity and low limits of detection (5 ng µL-1) towards glycans. Our analysis of complex urine samples from healthy volunteers and bladder carcinoma patients successfully profiled 48 and 56 exosomal glycans, respectively, and 16 of them were significantly changed. Moreover, one upregulated bisecting N-acetylglucosamine (GlcNAc)-type glycan with core fucose, two upregulated and two downregulated terminal-sialylated glycans were revealed to be linked to bladder carcinoma. This approach is of significant importance for understanding diseases that arise from protein glycosylation mutations, and it may contribute to the development of novel diagnostic and therapeutic strategies for bladder carcinoma.

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.

A protocol of carbonized on-column enrichment for urinary exosomal N-glycans profiling.

As a widely present vesicle, exosome plays an important role in lots of biological processes due to its inclusive cargos. In particular, exosome glycan cargo is attracting attentions since its aberrant alteration is closely related to many progressions in diseases. In this work, a novel carbonized packing capillary trap column for urinary exosomal N-glycan enrichment was proposed. The carbonized packing exhibited large specific surface area, mesoporous structure with narrow pore size distribution and abundant carbon for specially interacting with oligosaccharides. Benefitting from all these advantages, the N-glycans deriving from standard glycoproteins or complex human urine exosomes could be identified with high sensitivity and selectivity. Finally, from the glycans identified in healthy volunteers and patients with bladder carcinoma, we observed that 10 of glycans shared by two groups were obvious downregulation and the 18 were upregulation. These results show great potential of capillary trap column as a tool for the enrichment and detection of glycans in exosomal, attracting more attention on disease progression monitoring and biomarker discovery.

Multiscale Element-Doped Nanowire Array-Coupled Machine Learning Reveals Metabolic Fingerprints of Nonreversible Liver Diseases.

Timely detection of nonreversible liver diseases contributes greatly to reasonable therapy and quality of life. Given the current situation, minimally invasive high-specificity molecular diagnosis based on body fluid can be a good choice. Herein, a mesoporous superstructure is designed using silicon atom-doped nanowire arrays to uniformly load Pt nanoparticles on the surface to produce a desirable ionization effect. We apply the multiscale element-doped nanowire arrays to efficiently assist extraction of high-quality metabolic fingerprints from only 35 nL of serum within seconds. Using different machine learning algorithms, we establish specific biomarker panels to distinguish different liver diseases from the healthy control, with more than 90% accuracy, sensitivity, and specificity. Moreover, from established biomarker panels, we further determine key metabolites of significant difference (p < 0.01) via group comparison to realize the discrimination of different liver diseases with 100% sensitivity. Our work confirms the design protocol of an advanced diagnosis tool and lays a robust foundation for metabolic molecular diagnosis in large-scale clinical application.

Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis.

Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 µL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R2Y(cum) = 0.744, Q2 (cum) = 0.880), as well, from patients with other tumors (R2Y(cum) = 0.748, Q2(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R2Y(cum) = 0.620, Q2(cum) = 0.656), and the staging of RCC (R2Y(cum) = 0.755, Q2(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R2Y(cum) = 0.710, Q2(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.

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.

Precise Detection of Cataracts with Specific High-Risk Factors by Layered Binary Co-Ionizers Assisted Aqueous Humor Metabolic Analysis.

Diabetes and high myopia as well-known high-risk factors can aggravate cataracts, yet clinical coping strategy remains a bottleneck. Metabolic analysis tends to be powerful for precisely detection and mechanism exploration since most of diseases including cataracts are accompanied by metabolic disorder. Herein, a layered binary co-ionizers assisted aqueous humor metabolic analysis tool is proposed for potentially etiological typing and detection of cataracts, including age-related cataracts (ARC), cataracts with diabetes mellitus (CDM), and cataracts with high myopia (CHM). Startlingly, taking advantage of the optimal machine learning algorithm and all metabolic fingerprints, 100% of accuracy, precision, and recall rates are achieved for arbitrary comparison between groups. Moreover, 11, 9, and 7 key metabolites with explicit identities are confirmed as markers of discriminating CDM from ARC, CHM from ARC, and CDM from CHM, and the corresponding area under the curve values of validation cohorts are 0.985, 1.000, and 1.000. Finally, the critical impact of diabetes/high myopia on cataracts is revealed by excavating the change levels and metabolic pathways of key metabolites. This work updates the insights of prevention and treatment about cataracts at metabolic level and throws out huge surprises and progresses metabolic diagnosis toward a reality.

Simultaneous analysis of cellular glycoproteome and phosphoproteome in cervical carcinoma by one-pot specific enrichment.

Cervical carcinoma is a kind of common gynecological cancer, the physiopathology of which is consanguineously connected with protein glycosylation and phosphorylation. Here, the magnetic binary metal oxide composites with the functionalization of hydrophilic tripeptide is designed and synthesized for simultaneous enrichment of glycopeptide and phosphopeptide in HeLa cells. Multiple kinds of metal sites realize more comprehensive enrichment of phosphopeptides compared with single metal, and ultra-hydrophilic property of glutathione further endows the composites with excellent affinity towards glycopeptides. The composites exhibited low detection limit (0.5 fmol/µL) and high selectivity (digests of ß-casein and BSA, 1:500, m/m) for phosphopeptides, also showed good enrichment efficiency for glycopeptide including detection limit (0.1 fmol/µL) and selectivity (digests of HRP and BSA, 1:50, m/m). Meanwhile, the composites still possess outstanding enrichment capability towards phosphopeptide and glycopeptide after stored for two months or six consecutive times reuse. Eventually, 1177 phosphopeptides and 438 glycopeptides are identified simultaneously from 100 µg HeLa cell digests. As anticipated, potential biomarkers, such as heat shock protein beta-1, DNA topoisomerase 2-alpha, proliferation marker protein Ki-67 and 60 kDa heat shock protein are detected, suggesting its promising application in discovery and screening of cervical carcinoma.