Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications

Engineered Bimetallic MOF-Crafted Bullet Aids in Penetrating Serum Metabolic Traits of Chronic Obstructive Pulmonary Disease

Metabolomics analysis based on body fluids, combined with high-throughput laser desorption and ionization mass spectrometry (LDI-MS), holds great potential and promising prospects for disease diagnosis and screening. On the other hand, chronic obstructive pulmonary disease (COPD) currently lacks innovative and powerful diagnostic and screening methods. In this work, CoFeNMOF-D, a metal-organic framework (MOF)-derived metal oxide nanomaterial, was synthesized and utilized as a matrix to assist LDI-MS for extracting serum metabolic fingerprints of COPD patients and healthy controls (HC). Through machine learning algorithms, successful discrimination between the COPD and HC was achieved. Furthermore, four potential biomarkers significantly downregulated in COPD were screened out. The disease diagnostic models based on the biomarkers demonstrated excellent diagnostic performance across different algorithms, with area under the curve (AUC) values reaching 0.931 and 0.978 in the training and validation sets, respectively. Finally, the potential metabolic pathways and disease mechanisms associated with the identified markers were explored. This work advances the application of LDI-based molecular diagnostics in clinical settings.

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.

Pt/NiFe-LDH hybrids for quantification and qualification of polyphenols

Polyphenols with antioxidant properties are of significant interest in medical and pharmaceutical applications. Given the diverse range of activities of polyphenols in vivo, accurate detection of these compounds plays a crucial role in nutritional surveillance and pharmaceutical development. Yet, the efficient quantitation of polyphenol contents and qualification of monomer compositions present a notable challenge when studying polyphenol bioavailability. In this study, platinum-modified nickel-iron layered double hydroxide (Pt/NiFe-LDH hybrids) were designed to mimic peroxidases for colorimetric analysis and act as enhanced matrices for laser desorption/ionization mass spectrometry (LDI MS) to quantify and qualify polyphenols. The hybrids exhibited an enzymatic activity of 33.472 U/mg for colorimetric assays, facilitating the rapid and direct quantitation of total tea polyphenols within approximately 1 min. Additionally, the heterogeneous structure and exposed hydroxyl groups on the hybrid surface contributed to photoelectric enhancement and in-situ enrichment of polyphenols in LDI MS. This study introduces an innovative approach to detect polyphenols using advanced materials, potentially inspiring the future development and applications of other photoactive nanomaterials.

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.

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.

Novel Ag-modified vanadate nanosheets for determination of small organic molecules with laser desorption ionization mass spectrometry

Laser desorption ionization mass spectrometry (LDI-MS) aroused intensive concerns for the merits of label-free and high-throughput analysis. Here, we designed a silver nanoparticles (AgNP)-modified indium vanadate nanosheets with doping samarium (AgNP@InVO4:Sm) nanosheets. The developed AgNP@InVO4:Sm nanosheets (AIVON) were synthesized based on the microemulsion-mediated solvothermal method and ultraviolet-assisted in situ formation of AgNP, then for the first time applied as a matrix in LDI-MS analysis. With the advantages including enhanced MS signal, little matrix-related background, high reproducibility, and good salt tolerance, AIVON exhibited much better prospect than non-modified indium vanadate nanosheets with doping samarium (IVON) and traditional organic matrix, thus allowing sensitive MS detection for a wide range of low-molecular-weight (LMW) molecules. Moreover, by coupling with headspace sampling thin-film microextraction (TFME), a kind of representative pollutant chlorophenols were identified and quantified via AIVON-assisted LDI-MS in environmental and biological samples. Volatile LMW pollutants could be preconcentrated after TFME, hence a sensitive and rapid assay with negligible sample matrix effect was realized by using AIVON-assisted LDI-MS. It is anticipated that this novel nano-matrix AIVON and the proposed TFME coupling detection strategy were of competitive merits for LDI-MS analysis in the fields of environment, biomedicine, and agriculture.

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.

Research progress on nanomaterial-based matrices for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis

Matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a novel soft ionization bio-mass spectrometry technology emerging in the 1980s, which can realize rapid detection of non-volatile, highly polar, and thermally unstable macromolecules. However, the analysis of small molecular compounds has been a major problem for MALDI-TOF MS all the time. In the MALDI analysis process based on traditional matrices, large numbers of interference peaks in the low molecular weight area and "sweet spots" phenomenon are produced, so the detection method needs to be further optimized. The promotion of matrix means the improvement of MALDI performance. In recent years, many new nanomaterial-based matrices have been successfully applied to the analysis of small molecular compounds, which makes MALDI applicable to a wider range of detection and useful in more fields such as pharmacy and environmental science. In this paper, the newly developed MALDI matrix categories in recent years are reviewed initially. Meanwhile, the potential applications, advantages and disadvantages of various matrices are analyzed. Finally, the future development prospects of nanomaterial-based matrices are also prospected.

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.

Plasmonic alloys for quantitative determination and reaction monitoring of biothiols

Biothiols participate in numerous physiological and pathological processes in an organism. Quantitative determination and reaction monitoring of biothiols have important implications for evaluating human health. Herein, we synthesized plasmonic alloys as the matrix to assist the laser desorption and ionization (LDI) process of biothiols in mass spectrometry (MS). Plasmonic alloys were constructed with mesoporous structures for LDI enhancement and trimetallic (PdPtAu) compositions for noble metal-thiol hybridization, toward enhanced detection sensitivity and selectivity, respectively. Plasmonic alloys enabled direct detection of biothiols from complex biosamples without any enrichment or separation. We introduced internal standards into the quantitative MS system, achieving accurate quantitation of methionine directly from serum samples with a recovery rate of 103.19% ± 6.52%. Moreover, we established a rapid monitoring platform for the oxidation-reduction reaction of glutathione, consuming trace samples down to 200 nL with an interval of seconds. This work contributes to the development of molecular tools based on plasmonic materials for biothiol detection toward real-case applications.

Hollow multishelled heterostructures with enhanced performance for laser desorption/ionization mass spectrometry based metabolic diagnosis

High-performance metabolic diagnosis-based laser desorption/ionization mass spectrometry (LDI-MS) improves the precision diagnosis of diseases and subsequent treatment. Inorganic matrices are promising for the detection of metabolites by LDI-MS, while the structure and component impacts of the matrices on the LDI process are still under investigation. Here, we designed a multiple-shelled ZnMn2O4/(Co, Mn)(Co, Mn)2O4 (ZMO/CMO) as the matrix from calcined MOF-on-MOF for detecting metabolites in LDI-MS and clarified the synergistic impacts of multiple-shells and the heterostructure on LDI efficiency. The ZMO/CMO heterostructure allowed 3-5 fold signal enhancement compared with ZMO and CMO with the same morphology. Furthermore, the ZMO/CMO heterostructure with a triple-shelled hollow structure displayed a 3-fold signal enhancement compared to its nanoparticle counterpart. Taken together, the triple-shelled hollow ZMO/CMO exhibits 102-fold signal enhancement compared to the commercial matrix products (e.g., DHB and DHAP), allowing for sensitive metabolic profiling in bio-detection. We directly extracted metabolic patterns by the optimized triple-shelled hollow ZMO/CMO particle-assisted LDI-MS within 1 s using 100 nL of serum and used machine learning as the readout to distinguish hepatocellular carcinoma from healthy controls with the area under the curve value of 0.984. Our approach guides us in matrix design for LDI-MS metabolic analysis and drives the development of a nanomaterial-based LDI-MS platform toward precision diagnosis.

Effect of Silane Monolayers and Nanoporous Silicon Surfaces on the Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Detection of Sepsis Metabolites Biomarkers Mixed in Solution

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-ToF MS) is a promising strategy for clinical diagnosis based on metabolite detection. However, several bottlenecks (such as the lack of reproducibility in analysis, the presence of an important background in low-mass range, and the lack of organic matrix for some molecules) prevent its transfer to clinical cases. These limitations can be addressed by using nanoporous silicon surfaces chemically functionalized with silane monolayers. In the present study, sepsis metabolite biomarkers were used to investigate the effects of silane monolayers and porous silicon substrates on MALDI-ToF MS analysis (signal-to-noise value (S/N), relative standard deviation of the S/N of triplicate samples (STDmean), and intra-substrates uniformity). Also, the impact of the physicochemical properties of metabolites, with different isoelectric points and hydrophobic-hydrophilic balances, was assessed. Four different silane molecules, with various alkyl chain lengths and head-group charges, were self-assembled in monolayers on plane and porous silicon surfaces. Their surface coverage and conformity were investigated by X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The seven metabolites detected on the stainless-steel target plate (lysophosphatidylcholine, caffeine, phenylalanine, creatinine, valine, arginine, and glycerophosphocholine) are also detected on the silanized and bare, plane and porous silicon surfaces. Moreover, two metabolites, glycine and alanine, which are not detected on the stainless-steel target plate, are detected on all silanized surfaces, except glycine which is not detected on CH3 short-modified porous silicon and on the bare plane silicon substrate. In addition, whatever the metabolites (except phenylalanine and valine), at least one of the silicon surfaces allows to increase the S/N value in comparison with the stainless-steel target plate. Also, the heterogeneity of matrix crystallization features is linked to the STDmean which is poor on the NH3+ monolayer on plane substrate and better on the NH3+ monolayer on porous substrate, for most of the metabolites. Nevertheless, matrix crystallization features are not sufficient to systematically get high STDmean and uniformity in MALDI-ToF MS analysis. Indeed, the physicochemical properties of metabolites and surfaces, limitations in metabolite extraction from the pores, and improvement in metabolite desorption due to the pores are shown to significantly impact MS analysis. In particular, in the case of the most hydrophobic metabolites studied, the highest S/N values and the best STDmean and uniformity (the lowest values) are reached by using porous substrates, while in the case of the most hydrophilic metabolites studied, plane substrates demonstrated the highest S/N and the lowest STDmean. No clear trend of surface chemistry was evidenced.

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.

Profiling aromatic constituents of Chimonanthus nitens Oliv. leaf granule using mass spectrometry

RATIONALE

The chemical constituents of Chinese patent medicine are usually different from those of crude medicine because of specific preparation processes. Chimonanthus nitens Oliv. leaf granule is widely used for prevention against COVID-19 in China. However, no research has been reported on the chemical constituents of the granule and their variation during the preparation process.

METHODS

Fragmentation patterns of reference compounds were investigated using electrospray ionization mass spectrometry, and the new gas-phase reaction was demonstrated by electronic and steric effects and calculated chemistry. Then, a strategy based on new fragmentation patterns was used to profile aromatic constituents. In addition, based on untargeted metabolomics analytical workflow, a comparison was made on the chemical constituents of the leaf and granule.

RESULTS

New fragmentation patterns related to two competing reactions, ring-opening and ring-closing reactions for coumarin, have been proposed and investigated in depth. The newly established diagnostic ion at m/z 81.0331 worked strongly in the assignment of OH-7 and substituent at C-8 of coumarin. McLafferty rearrangement occurring in coumarin glycoside while sugar group locating at C-4 was first observed, and new diagnostic ions at m/z 147.0440, 119.0488, and 91.0543 were constructed.

CONCLUSIONS

Aromatic constituents of the granule were first profiled. A total of 114 aromatic compounds were identified; of these 85 compounds were identified first. Kaempferol-7-O-neohesperidoside and its homologues were mostly enriched in the granule. Considering their reported bioactivities, these analogues possibly contribute greatly to clinical efficacy. Our results provided a new fragmentation theory for coumarins and a new material basis for the quality control of the granule.

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.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Single-Step Capture and Targeted Metabolomics of Alkyl-Quinolones in Outer Membrane Vesicles (OMVs) of Pseudomonas Aeruginosa

Outer membrane vesicles (OMVs), also called as bacterial membrane vesicles (BMVs), are secreted by many Gram-negative bacterial pathogens. These nanoscale vesicles traffic discrete arrays of virulence factors that can often induce complex pathologies far from the infection sites. The OMVs of P. aeruginosa, often regarded as the gold standard of BMVs are known to traffic a battery of specific small MW alkyl-quinolones (AQs). These AQs function like primordial hormones by modulating intra-species and inter-species bacterial interactions. They can also perform cross-kingdom signaling with the human host and directly exacerbate pathogenesis. The discrete isotopic signatures of AQs enjoy potential in the mass spectrometry-based diagnosis P. aeruginosa infections. Matrix-free laser desorption/ionization mass spectrometry (LDI-MS) presents a robust, cost-effective platform to fit this demand. We describe a LDI-MS system using inert ceramic filters that performs dual role of single-step enrichment of OMVs and matrix-free ionization/identification of AQs in situ.

Covalent Bonding and Coulomb Repulsion-Guided AuNP Array: A Tunable and Reusable Substrate for Metabolomic Characterization of Lung Cancer Patient Sera

Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has gained increased attention in the metabolic characterization of human biofluids. However, the stability and reproducibility of nanoparticle-based substrates remain two of the biggest challenges in high-salt environments. Here, by controlling the extent of Coulomb repulsion of 26 nm positively charged AuNPs, a homogeneous layer of covalently bonded AuNPs on a coverslip with tunable interparticle distances down to 16 nm has been successfully fabricated to analyze small biomolecules in human serum. Compared with the self-assembled AuNP array, the covalently bonded AuNP array showed superior performances on stability, reproducibility, and sensitivity in high-salt environments. The stable attachment of AuNPs maintained a detection reproducibility with a RSD less than 12% and enabled the reusability of the array for 10 experiments without significant signal deterioration (<15%) and carryover effects. Moreover, the closely positioned AuNPs allowed the coupling of photoinduced plasmons to generate an enhanced electric field, which promotes the generation of excited electrons to facilitate the desorption/ionization processes instead of the heat dissipation, thus enhancing the detection sensitivity with detection limits down to the femtomole level. Combined with machine learning methods, the AuNP array has been successfully applied to discover seven biomarkers for differentiating early-stage lung cancer patients from healthy controls. It is anticipated that this simple approach of developing robust AuNP arrays can also be extended to other types of NP arrays for wider applications of SALDI-MS technology.

MALDI-TOF Mass Spectrometry in Clinical Analysis and Research

In the decade after being awarded the Nobel Prize in Chemistry in 2002, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used as an analytical chemistry tool for the detection of large and small molecules (e.g., polymers, proteins, peptides, nucleic acids, amino acids, lipids, etc.) and for clinical analysis and research (e.g., pathogen identification, genetic disorders screening, cancer diagnosis, etc.). In view of the fast development of MALDI-TOF MS in clinical usage, this review systematically summarizes the most important applications of MALDI-TOF MS in clinical analysis and research by analyzing MALDI TOF MS-related reviews collected in the Web of Science database. On the basis of the analysis of keyword co-occurrence of over 2000 review articles, four themes consisting of "pathogen identification", "disease diagnosis", "nucleic acids analysis", and "small molecules analysis" were found. For each theme, the review further outlined their application implications, analytical methods, and systems as well as limitations that need to be addressed. Overall, the review summarizes and elaborates on the clinical applications of MALDI-TOF MS, providing a comprehensive picture for researchers embarking on MALDI TOF MS-related clinical analysis and research.

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.

Laccase-mimicking Mn-Cu hybrid nanoflowers for paper-based visual detection of phenolic neurotransmitters and rapid degradation of dyes

Background

Laccase-based biosensors are efficient for detecting phenolic compounds. However, the instability and high cost of laccases have hindered their practical utilization.

Results

In this study, we developed hierarchical manganese dioxide–copper phosphate hybrid nanoflowers (H–Mn–Cu NFs) as excellent laccase-mimicking nanozymes. To synthesize the H–Mn–Cu NFs, manganese dioxide nanoflowers (MnO₂ NFs) were first synthesized by rapidly reducing potassium permanganate using citric acid. The MnO₂ NFs were then functionalized with amine groups, followed by incubation with copper sulfate for three days at room temperature to drive the coordination interaction between the amine moieties and copper ions and to induce anisotropic growth of the petals composed of copper phosphate crystals, consequently yielding H–Mn–Cu NFs. Compared with those of free laccase, at the same mass concentration, H–Mn–Cu NFs exhibited lower K_m (~ 85%) and considerably higher V_max (~ 400%), as well as significantly enhanced stability in the ranges of pH, temperature, ionic strength, and incubation periods evaluated. H–Mn–Cu NFs also catalyzed the decolorization of diverse dyes considerably faster than the free laccase. Based on these advantageous features, a paper microfluidic device incorporating H–Mn–Cu NFs was constructed for the convenient visual detection of phenolic neurotransmitters, including dopamine and epinephrine. The device enabled rapid and sensitive quantification of target neurotransmitters using an image acquired using a smartphone.

Conclusions

These results clearly show that H–Mn–Cu NFs could be potential candidates to replace natural laccases for a wide range of applications in biosensing, environmental protection, and biotechnology.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-022-01560-0.

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.

Bevacizumab-Laden Nanofibers Simulating an Antiangiogenic Niche to Improve the Submuscular Stability of Stem Cell Engineered Cartilage

Bone marrow stem cells (BMSCs) engineered cartilage (BEC) is prone to endochondral ossification in a submuscular environment due to the process of vascular infiltration, which limits its application in repairing tracheal cartilage defects. Bevacizumab, an antitumor drug with pronounced antiangiogenic activity, is successfully laden into a poly(L-lactide-co-caprolactone) system to prepare bevacizumab-laden nanofiber (BevNF) characterized by 5% and 10% bevacizumab concentrations. The in vitro results reveal that a sustained release of bevacizumab can be realized from BevNF, exhibiting inhibitive cytotoxicity toward human umbilical vein endothelial cells whereas non-cytotoxicity toward BMSCs-induced chondrocytes. A model is also established by encapsulating BEC within BevNF, aiming to realize an antiangiogenic niche under conditions of sustained and localized release of bevacizumab to inhibit the process of vascular invasion, resulting in the eventual stabilization of the cartilaginous phenotype and promotion of the process of cartilage maturation in the submuscular environment. These results also confirm that the chondrogenesis stability of BEC increases with an increase in the bevacizumab concentration, and 10% BevNF is sufficient to protect BEC from vascularization. This demonstrates that the use of BevNF can potentially help develop an effective strategy for regulating the submuscular stability of BEC to repair the defects formed in tracheal cartilage.

Glaucoma Characterization by Machine Learning of Tear Metabolic Fingerprinting

Glaucoma is a common optic neuropathy disease affecting over 76 million people. Both timely diagnosis and progression monitoring are critical but challenging. Conventional characterization of glaucoma needs a combination of methods, calling for tedious procedures and experienced doctors. Herein, a platform through machine learning of tear metabolic fingerprinting (TMF) using nanoparticle enhanced laser desorption-ionization mass spectrometry is built. Direct TMF is obtained noninvasively, with fast speed and high reproducibility, using trace tear samples (down to 10 nL). Consequently, glaucoma patients are screened against healthy controls with the area under the curve (AUC) of 0.866, through machine learning of TMF. Further, primary open-angle glaucoma (POAG) is differentiated from primary angle-closure glaucoma (PACG) and an early-stage POAG is identified. Finally, a biomarker panel of six metabolites for glaucoma characterization (including screening, subtyping, and early diagnosis) with AUC of 0.827-0.891 is constructed, showing related metabolic pathways. The work will provide insights into eye diseases not limited to glaucoma.

A Copper-Based Biosensor for Dual-Mode Glucose Detection

Glucose is a source of energy for daily activities of the human body and is regarded as a clinical biomarker, due to the abnormal glucose level in the blood leading to many endocrine metabolic diseases. Thus, it is indispensable to develop simple, accurate, and sensitive methods for glucose detection. However, the current methods mainly depend on natural enzymes, which are unstable, hard to prepare, and expensive, limiting the extensive applications in clinics. Herein, we propose a dual-mode Cu₂O nanoparticles (NPs) based biosensor for glucose analysis based on colorimetric assay and laser desorption/ionization mass spectrometry (LDI MS). Cu₂O NPs exhibited excellent peroxidase-like activity and served as a matrix for LDI MS analysis, achieving visual and accurate quantitative analysis of glucose in serum. Our proposed method possesses promising application values in clinical disease diagnostics and monitoring.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Human metabolite detection by surface-enhanced Raman spectroscopy

Metabolites are important biomarkers in human body fluids, conveying direct information of cellular activities and physical conditions. Metabolite detection has long been a research hotspot in the field of biology and medicine. Surface-enhanced Raman spectroscopy (SERS), based on the molecular “fingerprint” of Raman spectrum and the enormous signal enhancement (down to a single-molecule level) by plasmonic nanomaterials, has proven to be a novel and powerful tool for metabolite detection. SERS provides favorable properties such as ultra-sensitive, label-free, rapid, specific, and non-destructive detection processes. In this review, we summarized the progress in recent 10 years on SERS-based sensing of endogenous metabolites at the cellular level, in tissues, and in biofluids, as well as drug metabolites in biofluids. We made detailed discussions on the challenges and optimization methods of SERS technique in metabolite detection. The combination of SERS with modern biomedical technology were also anticipated.

Porous Inorganic Materials for Bioanalysis and Diagnostic Applications

Porous inorganic materials play an important role in adsorbing targeted analytes and supporting efficient reactions in analytical science. The detection performance relies on the structural properties of porous materials, considering the tunable pore size, shape, connectivity, etc. Herein, we first clarify the enhancement mechanisms of porous materials for bioanalysis, concerning the detection sensitivity and selectivity. The diagnostic applications of porous material-assisted platforms by coupling with various analytical techniques, including electrochemical sensing, optical spectrometry, and mass spectrometry, etc., are then reviewed. We foresee that advanced porous materials will bring far-reaching implications in bioanalysis toward real-case applications, especially as diagnostic assays in clinical settings.