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.

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.

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.

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.

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.

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.

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.

Magnetic porous carbon-dependent platform for the determination of N-glycans from urine exosomes.

A magnetic porous carbon-dependent platform is established to separate and determine N-glycans from urine exosomes of healthy people and patients with gastric cancer. The results of the comparison reveal that 6 N-glycans shared by the two groups are downregulated, most of which present core fucose or bisecting N-acetylglucosamine (GlcNAc) type. In addition, five shared N-glycans including two of sialic acid type are upregulated. These obvious differences indicate the close relationship between glycans and gastric cancer thus permitting early diagnosis. A magnetic porous carbon material (FeMPC) from MIL-101(Fe) was employed to separate and analyze N-glycans from urine exosomes of healthy people and patients with gastric cancer.

Magnetic titanium dioxide nanomaterial modified with hydrophilic dicarboxylic ligand for effective enrichment and separation of phosphopeptides and glycopeptides.

A dual functionalized magnetic nanomaterial was fabricated by combining the magnetic core, titania shell, and hydrophilic molecules (denoted as Fe3O4@TiO2-IDA). Based on the mechanism of metal oxide affinity chromatography and hydrophilic interaction liquid chromatography, the sorbent shows excellent one-step separation capacity for both glycopeptides and phosphopeptides. For phosphopeptide enrichment, Fe3O4@TiO2-IDA exhibits high sensitivity and selectivity. The concentration of ß-casein in the digests can be as low as 0.0125 ng µL-1; the mass ratio of ß-casein and BSA digest can reach 1:800. For glycopeptide enrichment, Fe3O4@TiO2-IDA also exhibits good performance. The concentration of horseradish peroxidase (HRP) in the digested sample can be as low as 0.04 ng µL-1; the mass ratio of HRP and BSA digest can reach 1:100. Following the one-step enrichment, elution, and nano LC-MS/MS analysis, 550 unique phosphopeptides and 330 glycopeptides were identified from 100 µg mouse brain sample through a single run of LC-MS/MS. Graphical abstract Multipurpose Fe3O4@TiO2-IDA nanomaterials are employed in simultaneous enrichment and separation of glycopeptides and phosphopeptides.

Immobilization of titanium dioxide/ions on magnetic microspheres for enhanced recognition and extraction of mono- and multi-phosphopeptides.

The authors are presenting a novel strategy for global phosphoproteome recognition in practical samples. It integrates metal oxide affinity chromatography (MOAC) and immobilization metal ion affinity chromatography (IMAC). This resulted in a kind of titanium dioxide/ion-based multifunctional probe (dubbed T2M). The T2M combines the features of MOAC and IMAC including their recognition preferences towards mono- and multi-phosphorylated peptides. Hence, they exhibit an outstanding recognition capability towards global phosphoproteome, high sensitivity (the limit of detection of which is merely 10 fmol) and excellent specificity in MALDI-TOF MS detection. Their performance is further demonstrated by the identification of the phosphoproteome in non-fat milk and human saliva. By combining T2M with nano LC-MS/MS, remarkable results are obtained in the tryptic digestion of healthy eye lens and cataract lens phosphoproteomes. A total of 658 and 162 phosphopeptides, respectively, were identified. This indicates that phosphorylation and the appearance of cataract can be related to each other. Graphical abstract Schematic presentation of the preparation of titanium dioxide/ion-based multifunctional magnetic nanomaterials (T2M). The T2M based enrichment protocol exhibits outstanding recognition capability towards global phosphoproteome. This protocol shows great prospect for clarifying mechanism of phosphorylation-related diseases via further information acquisition.

Magnetite nanoparticles coated with mercaptosuccinic acid-modified mesoporous titania as a hydrophilic sorbent for glycopeptides and phosphopeptides prior to their quantitation by LC-MS/MS.

A hydrophilic material consisting of a magnetite core coated with mercaptosuccinic acid modified mesoporous titania (denoted as Fe3O4@mTiO2-MSA) has been fabricated. It is shown to be a viable sorbent for capturing glycopeptides and phosphopeptides. The sorbent combines the features of metal oxide-based affinity chromatography and of hydrophilic interaction liquid chromatography (HILIC) with the advantages of using mesoporous titania. The use of magnetic microspheres provides magnetic response and simplifies separation. Following elution with 10% ammonia, the peptides were submitted to LC-MS/MS analysis. The method enabled 327 phosphopeptides and 65 glycopeptides to be identified in three isolated replicates of merely 5 µL samples of human saliva. Among them, the phosphorylation sites and glycosylation sites were detected in 20 peptide segments. Graphical abstract Schematic presentation of preparation of novel hydrophilic magnetic mesoporous titania nanomaterials (Fe3O4@mTiO2-MSA). This specific sorbent exhibits highly selective and efficient simultaneous adsorption ability for both glycopeptides and phosphopeptides from biosamples by mass spectrometric analysis.

Rapid synthesis of titanium(IV)-immobilized magnetic mesoporous silica nanoparticles for endogenous phosphopeptides enrichment.

The MALDI-TOF MS has already been a main platform for phosphoproteome analysis. However, there are some weaknesses in direct analysis of endogenous phosphopeptides by MALDI-TOF MS because of the serious suppression effect and poor ionization efficiency, which is brought by the excess of nonphosphopeptides and protein. It is essential to enrich endogenous phosphopeptides from complex biosamples efficiently prior to MALDI-TOF MS analysis. Herein, we present a time-saving and detailed protocol for the synthesis of titanium(iv)-immobilized magnetic mesoporous silica nanoparticles (denoted as Fe3 O4 @mSiO2 -Ti4+ ), the subsequent enrichment process, and MALDI-TOF MS analysis. We tested the LOD, size-exclusive effect, reproducibility, and stability of Fe3 O4 @mSiO2 -Ti4+ nanoparticles. Furthermore, the ability of this protocol for identifying endogenous phosphopeptides in healthy human serum and saliva was investigated.

Glucose-6-Phosphate-Functionalized Magnetic Microsphere as Novel Hydrophilic Probe for Specific Capture of N-Linked Glycopeptides.

Developing cost-effective approaches based on hydrophilic interaction liquid chromatography (HILIC) has been the main tendency for low-abundance glycopeptides capture before LC-MS/MS analysis. Carbohydrates with outstanding biocompatibility and hydrophilicity are ubiquitous in the kingdoms of animal and plant and could be a wonderful choice as functional groups for glycopeptides enrichment. In this work, glucose-6-phosphate, as one of the indispensable cogs in pivotal metabolic wheels of life, was chosen as functionalized groups to be grafted onto the surface of Fe3O4 microspheres via one-step surface fabrication strategy. The acquired hydrophilic Fe3O4@G6P microspheres showed superior enrichment performance for glycopeptides with high sensitivity (0.5 fmol/µL) and high selectivity (1:100) and good repeatability (10 times at least). Furthermore, the Fe3O4@G6P microspheres also exhibited enrichment ability for glycopeptides in different biosamples. A total of 243 glycopeptides assigned to 92 glycoproteins and 183 glycopeptides corresponding to 74 different glycoproteins was identified from merely 2 µL of serum and saliva, respectively.

Hydrophilic Mesoporous Silica Materials for Highly Specific Enrichment of N-Linked Glycopeptide.

Hydrophilic interaction liquid chromatography (HILIC) is a significant enrichment strategy in glycoproteomics profiling. In this report, hydrophilic magnetic mesoporous silica materials (denoted as Fe3O4@mSiO2-IDA) were designed and synthesized as an outstanding enrichment platform for glycopeptide analysis. By taking advantage of their merits, such as large surface area, excellent hydrophilicity and unbiased affinity toward all types of glycopeptides, the Fe3O4@mSiO2-IDA nanomaterials were successfully applied to capture glycopeptides from complex samples. A total of 25 glycopeptides from horseradish peroxidase (HRP) digests and 33 glycopeptides from IgG were identified, respectively. Especially, as a result, 424 glycopeptides assigned to 140 glycoproteins were identified from only 2 µL human serum.

Highly selective enrichment of N-linked glycan by carbon-functionalized ordered graphene/mesoporous silica composites.

Abnormal protein glycosylation has been demonstrated to be associated with many diseases; therefore, it is very important to conduct a comprehensive structure analysis of glycan for prognosis and diagnosis of diseases, such as cancer. In this work, for the first time, carbon-functionalized ordered graphene/mesoporous silica composites (denoted as C-graphene@mSiO2) with large surface area and uniform pore size were designed and synthesized. By taking advantage of the special interaction between the carbon and glycans as well as size-exclusion ability, 25 N-linked glycans released from ovalbumin were observed clearly with strong MS signals and increased signal-to-noise (S/N) ratio. In addition, after enrichment with the C-graphene@mSiO2 composites, 48 N-linked glycans (S/N > 10) with sufficient peak intensities were obtained from only 400 nL of healthy pristine human serum. The facile and low-cost synthesis method as well as high selective enrichment ability of the novel C-graphene@mSiO2 composite makes it a promising tool for glycosylation research.

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.

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.

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.

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.