Biofluids Metabolic Profiling Based on PS@Fe3O4-NH2 Magnetic Beads-Assisted LDI-MS for Liver Cancer Screening

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.

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.

Profiling of carbonyl metabolic fingerprints in urine of Graves' disease patients based on atmospheric ionization mass spectrometry

Graves' disease (GD) is considered among the organ autoimmune diseases and is somewhat linked to other autoimmune and secondary diseases. Commonly used detection methods rely on identifying characteristic clinical features and abnormal biochemical markers, but they have certain limitations and may be affected by patient medication. In this study, a desorption separation ionization (DSI) device coupled with a linear ion trap mass spectrometer is introduced for effective detection and screening of urine from GD patients. To enhance the sensitivity of MS analysis, derivatization reagent is utilized as a labeling method. The MS signal is used for metabolic profiling, through which differential metabolites and pathways are identified. Subsequently, processing the acquired spectra with a machine learning algorithm enables successful differentiation of GD patients and healthy individuals. This method is believed to provide versatile and powerful technical support for effective detection on the scene. Notably, this method offers the advantage of achieving early and rapid diagnosis of thyroid-related diseases.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

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.

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.

Polyurea-magnetic hierarchical porous composites for profiling of anionic metabolites

Combining powerful adsorption capacity, simple preparation, rapid separation as well as superior stability and recyclability, a polyurea-magnetic hierarchical porous composite has been prepared. It demonstrates efficient physisorption for anionic metabolites in less than one minute and is promising for application to the analysis of a broad range of anionic metabolites in complex matrices.

Host-Guest Self-Assembled Interfacial Nanoarrays for Precise Metabolic Profiling

Accurate and rapid metabolic profiling of cerebrospinal fluid (CSF) is urgently needed but remains challenging for clinical diagnosis of central nervous system diseases and biomarker discovery. Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) holds promise for metabolic analysis. Its low signal reproducibility, however, severely restricts acquisition of quantitative MS data in clinical practice. Herein, a multifunctional self-assembled AuNPs array (MSANA)-based LDI-MS platform for direct amino acids analysis and metabolic profiling in patient CSF samples is developed. MSANA featuring a highly ordered and closely packed two-dimensional nanostructure permits capture and direct analysis of aromatic amino acids by LDI-MS with high selectivity and micromolar sensitivity. Meanwhile, the MSANA-based LDI-MS platform exhibits excellent reproducibility (RSD < 10%), largely outperforming the direct matrix spotting approach widely used now (RSD < 44%). The platform is successfully used in metabolic profiling of CSF (1 µL) within minutes for discrimination of medulloblastoma patients from non-tumor controls. Taken together, the MSANA-based LDI-MS platform shows potential clinical values toward large-scale metabolic diagnostics and pathogenic mechanism study.

"Iridium Signature" Mass Spectrometric Probes: New Tools Integrated in a Liquid Chromatography-Mass Spectrometry Workflow for Routine Profiling of Nitric Oxide and Metabolic Fingerprints in Cells

Nitric oxide (NO) is a highly reactive signaling molecule involved in diverse biological processes. Simultaneous profiling of NO and associated metabolic fingerprints in a single assay allows more accurate assessments of cell states and offers the possibility to better understand its exact biological roles. Herein, a multiplexing LC-MS workflow was established for simultaneous detection of intracellular NO and various metabolites based on a novel "iridium signature" mass spectrometric probe (Ir-MSP841). This Ir-MSP841 can convert highly liable NO to a stable permanently charged triazole product (Ir-TP852), enabling direct MS detection of NO. This ^191/193Ir-signature mass spectrometric probe-based approach is endowed with overwhelming advantages of interference-free, high quantitative accuracy, and great sensitivity (limit of detection down to 0.14 nM). It also reveals good linearity over a wide concentration range 12.5-500 nM and has been successfully employed for exploring the release behaviors of three representative NO donors in cells. Meanwhile, metabolic profiling results reveal that varying the concentrations of NO has distinct effects on various cellular metabolites. This study provides a robust, sensitive, and versatile method for simultaneous detection of NO and numerous metabolites in a single LC-MS run and expands its applications in biomedical research.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.