Nanocomposite-Based Matrices in Laser Desorption/Ionization Mass Spectrometry for Small-Molecule Analysis

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome

Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.

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.

Construction of a ternary component chip with enhanced desorption efficiency for laser desorption/ionization mass spectrometry based metabolic fingerprinting

Introduction: In vitro metabolic fingerprinting encodes diverse diseases for clinical practice, while tedious sample pretreatment in bio-samples has largely hindered its universal application. Designed materials are highly demanded to construct diagnostic tools for high-throughput metabolic information extraction. Results: Herein, a ternary component chip composed of mesoporous silica substrate, plasmonic matrix, and perfluoroalkyl initiator is constructed for direct metabolic fingerprinting of biofluids by laser desorption/ionization mass spectrometry. Method: The performance of the designed chip is optimized in terms of silica pore size, gold sputtering time, and initiator loading parameter. The optimized chip can be coupled with microarrays to realize fast, high-throughput (∼second/sample), and microscaled (∼1 μL) sample analysis in human urine without any enrichment or purification. On-chip urine fingerprints further allow for differentiation between kidney stone patients and healthy controls. Discussion: Given the fast, high throughput, and easy operation, our approach brings a new dimension to designing nano-material-based chips for high-performance metabolic analysis and large-scale diagnostic use.

Titania Nanosheet as a Matrix for Surface-Assisted Laser Desorption/Ionization Mass Spectrometry Analysis and Imaging

Surface-assisted laser desorption/ionization (SALDI) acts as a soft desorption/ionization technique, which has been widely recognized in small-molecule analysis owing to eliminating the requirement of the organic matrix. Herein, titania nanosheets (TiO₂ NSs) were applied as novel substrates for simultaneous analysis and imaging of low-mass molecules and lipid species. A wide variety of representative analytes containing amino acids, bases, drugs, peptides, endogenous small molecules, and saccharide-spiked urine were examined by the TiO₂ NS-assisted LDI mass spectrometry (MS). Compared with conventional organic matrices and substrates [Ag nanoparticles (NPs), Au NPs, carbon nanotubes, carbon NPs, CeO₂ microparticles, and P25 TiO₂], the TiO₂ NS-assisted LDI MS method shows higher sensitivity and less spectral interference. Repeatability was evaluated with batch-to-batch relative standard deviations for 5-hydroxytryptophan, glucose-spiked urine, and glucose with addition of internal standard, which were 17.4, 14.9, and 2.8%, respectively. The TiO₂ NS-assisted LDI MS method also allows the determination of blood glucose levels in mouse serum with a linear range of 0.5-10 mM. Owing to the nanoscale size and uniform deposition of the TiO₂ NS matrix, spatial distributions of 16 endogenous small molecules and 16 lipid species from the horizontal section of the mouse brain tissue can be visualized at a 50 μm spatial resolution. These successful applications confirm that the TiO₂-assisted LDI MS method has promising prospects in the field of life science.

Inorganic Matrices Assisted Laser Desorption/Ionization Mass Spectrometry for Metabolic Analysis in Bio-fluids

Metabolic analysis in bio-fluids interprets the end products in the bio-process, emerging as an irreplaceable disease diagnosis and monitoring platform. Laser desorption/ionization mass spectrometry (LDI MS) based metabolic analysis exhibits great potential for clinical applications in terms of high throughput, rapid signal readout, and minimal sample preparation. There are two essential elements to construct the LDI MS-based metabolic analysis: 1) well-designed nanomaterials as matrices; 2) machine learning algorithms for data analysis. This review highlights the development of various inorganic matrices to comprehend the advantages of LDI MS in metabolite detection and the recent diagnostic applications based on target metabolite detection and untargeted metabolic fingerprints in biological fluids.

MALDI Matrices for the Analysis of Low Molecular Weight Compounds: Rational Design, Challenges and Perspectives

The analysis of low molecular weight (LMW) compounds is of great interest to detect small pharmaceutical drugs rapidly and sensitively, or to trace and understand metabolic pathways. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) plays a central role in the analysis of high molecular weight (bio)molecules. However, its application for LMW compounds is restricted by spectral interferences in the low m/z region, which are produced by conventional organic matrices. Several strategies regarding sample preparation have been investigated to overcome this problem. A different rationale is centred on developing new matrices which not only meet the fundamental requirements of good absorption and high ionization efficiency, but are also vacuum stable and "MALDI silent", i. e., do not give matrix-related signals in the LMW area. This review gives an overview on the rational design strategies used to develop matrix systems for the analysis of LMW compounds, focusing on (i) the modification of well-known matrices, (ii) the search for high molecular weight matrices, (iii) the development of binary, hybrid and nanomaterial-based matrices, (iv) the advance of reactive matrices and (v) the progress made regarding matrices for negative or dual polarity mode.