Nanoparticle‐Assisted Cation Adduction and Fragmentation of Small Metabolites

Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Tuberculosis (TB) stands as the second most fatal infectious disease after COVID-19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics-based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle-enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost-effectiveness (approximately $3). A panel of 14 m z-1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z-1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964-0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806-0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end-TB strategy.

Isoporous Membrane Mediated Imprinting Mass Spectrometry Imaging for Spatially-Resolved Metabolomics and Rapid Histopathological Diagnosis

Surface-assisted laser desorption/ionization (SALDI) mass spectrometry imaging (MSI) holds great value in spatial metabolomics and tumor diagnosis. Tissue imprinting on the SALDI target can avoid laser-induced tissue ablation and simplifies the sample preparation. However, the tissue imprinting process always causes lateral diffusion of biomolecules, thereby losing the fidelity of metabolite distribution on tissue. Herein, a membrane-mediated imprinting mass spectrometry imaging (MMI-MSI) strategy is proposed using isoporous nuclepore track-etched membrane as a mediating imprinting layer to selectively transport metabolites through uniform and vertical pores onto silicon nanowires (SiNWs) array. Compared with conventional direct imprinting technique, MMI-MSI can not only exclude the adsorption of large biomolecules but also avoid the lateral diffusion of metabolites. The whole time for MMI-based sample preparation can be reduced to 2 min, and the lipid peak number can increase from 46 to 113 in kidney tissue detection. Meanwhile, higher resolution of MSI can be achieved due to the confinement effect of the pore channel in the diffusion of metabolites. Based on MMI-MSI, the tumor margins of liver cancer can be clearly discriminated and their different subtypes can be precisely classified. This work demonstrates MMI-MSI is a rapid, highly sensitive, robust and high-resolution technique for spatially-resolved metabolomics and pathological diagnosis.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

A single atom cobalt anchored MXene bifunctional platform for rapid, label-free and high-throughput biomarker analysis and tissue imaging

Few of single-atom materials have been served as platform to analyze small molecules for surface assisted laser desorption/ionization mass spectrometry (SALDI-MS). Herein, a novel single Co atom-anchored MXene (Co-N-Ti3C2) is prepared to achieve enhanced SALDI-MS and mass spectrometry imaging (MSI) performance for the first time. The Co-N-Ti3C2 films were prepared by a simple in situ self-assembly strategy to generate an efficient SALDI-MS platform. Compared to typical inorganic/organic matrices, Co-N-Ti3C2 films exhibit superior performance in small molecules detection with ultra-high sensitivity (LOD at amol level), excellent repeatability (CV <4%), clean background and wide analyte coverage, enabling accurate quantitative analysis of various low-concentration metabolites from 1 μL biofluid in seconds. Its usage efficiently enhanced SALDI-MS detection of various small-molecule biomarkers such as amino acids, succinic acid, itaconic acid, arachidonic acid, citrulline, prostaglandin E2, creatinine, uric acid, glutamine, D-mannose, cholesterol and inositol in positive ion mode. The blood glucose level in humans was successfully determined from a linearity concentration range (0.25-10 mM). Notably, the Co-N-Ti3C2 assisted SALDI-MSI enables study the spatial distribution of small molecules covering the range central to metabolomics at a high resolution on a tissue section. Furthermore, Co-N-Ti3C2 platform revealed a specific peak profile that distinguishes osteoarthritis (OA) from rheumatoid arthritis (RA) tissue. Density functional theory theoretical investigation revealed that single Co atoms anchored on Ti3C2 could highly enhanced the ionization ability of metabolites, resulting in high-sensitivity and heterogeneous metabolome coverage.

Comprehensive Metabolic Fingerprints Characterize Neuromyelitis Optica Spectrum Disorder by Nanoparticle-Enhanced Laser Desorption/Ionization Mass Spectrometry

Timely screening of neuromyelitis optica spectrum disorder (NMOSD) and differential diagnosis from myelin oligodendrocyte glycoprotein associated disorder (MOGAD) are the keys to improving the quality of life of patients. Metabolic disturbance occurs with the development of NMOSD. Still, advanced tools are required to probe the metabolic phenotype of NMOSD. Here, we developed a fast nanoparticle-enhanced laser desorption/ionization mass spectrometry assay for multiplexing metabolic fingerprints (MFs) from trace plasma and cerebrospinal fluid (CSF) samples in 30 s. Machine learning of the plasma MFs achieved the timely screening of NMOSD from healthy donors with an area under receiver operator characteristic curve (AUROC) of 0.998, and it comprehensively revealed the dysregulated neurotransmitter and energy metabolisms. Combining comprehensive MFs from both plasma and CSF, we constructed an integrated panel for differential diagnosis of NMOSD versus MOGAD with an AUROC of 0.923. This approach demonstrated performance superior to that of human experts in classifying two diseases, especially in antibody assay-limited regions. Together, this approach provides an advanced nanomaterial-based tool for identifying vulnerable populations below the antibody threshold of aquaporin-4 positivity.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.

Construction of Exosome SORL1 Detection Platform Based on 3D Porous Microfluidic Chip and its Application in Early Diagnosis of Colorectal Cancer

Exosomes are promising new biomarkers for colorectal cancer (CRC) diagnosis, due to their rich biological fingerprints and high level of stability. However, the accurate detection of exosomes with specific surface receptors is limited to clinical application. Herein, an exosome enrichment platform on a 3D porous sponge microfluidic chip is constructed and the exosome capture efficiency of this chip is ≈90%. Also, deep mass spectrometry analysis followed by multi-level expression screenings revealed a CRC-specific exosome membrane protein (SORL1). A method of SORL1 detection by specific quantum dot labeling is further designed and the ensemble classification system is established by extracting features from 64-patched fluorescence images. Importantly, the area under the curve (AUC) using this system is 0.99, which is significantly higher (p < 0.001) than that using a conventional biomarker (carcinoembryonic antigen (CEA), AUC of 0.71). The above system showed similar diagnostic performance, dealing with early-stage CRC, young CRC, and CEA-negative CRC patients.

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.

SiO2@Au nanoshell-assisted laser desorption/ionization mass spectrometry for coronary heart disease diagnosis

Cardiovascular diseases have threatened human health, amongst which coronary heart disease (CHD) is the third most common cause of death. CHD is considered to be a metabolic disease; however, there is little research on the CHD metabolism. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has enabled the development of a suitable nanomaterial that can be used to obtain considerable high-quality metabolic information without complex pretreatment of biological fluid samples. This study combines SiO₂@Au nanoshells with minute plasma to obtain metabolic fingerprints of CHD. The thickness of the SiO₂@Au shell was also optimized to maximize the laser desorption/ionization effect. The results demonstrated 84% sensitivity at 85% specificity for distinguishing CHD patients from controls in the validation cohort.

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

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.

Autoxidation Kinetics of Tetrahydrobiopterin-Giving Quinonoid Dihydrobiopterin the Consideration It Deserves

In humans, tetrahydrobiopterin (H4Bip) is the cofactor of several essential hydroxylation reactions which dysfunction cause very serious diseases at any age. Hence, the determination of pterins in biological media is of outmost importance in the diagnosis and monitoring of H4Bip deficiency. More than half a century after the discovery of the physiological role of H4Bip and the recent advent of gene therapy for dopamine and serotonin disorders linked to H4Bip deficiency, the quantification of quinonoid dihydrobiopterin (qH2Bip), the transient intermediate of H4Bip, has not been considered yet. This is mainly due to its short half-life, which goes from 0.9 to 5 min according to previous studies. Based on our recent disclosure of the specific MS/MS transition of qH2Bip, here, we developed an efficient HPLC-MS/MS method to achieve the separation of qH2Bip from H4Bip and other oxidation products in less than 3.5 min. The application of this method to the investigation of H4Bip autoxidation kinetics clearly shows that qH2Bip's half-life is much longer than previously reported, and mostly longer than that of H4Bip, irrespective of the considered experimental conditions. These findings definitely confirm that an accurate method of H4Bip analysis should include the quantification of qH2Bip.

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

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.

Water promoted 9-fluorenylmethyloxycarbonyl detachment from amino acids in charged microdroplets

Aqueous microdroplets exhibit unique properties and can trigger reactions that do not occur in bulk solution. Herein, we have demonstrated that water, in microdroplets, can reduce the energy barrier for the lone H transfer of 9-fluorenylmethyloxycarbonyl and promote its detachment from the amino group. This strategy works on various amino acids and opens opportunities of aqueous microdroplets in triggering organic reactions.

Exosome Metabolic Patterns on Aptamer-Coupled Polymorphic Carbon for Precise Detection of Early Gastric Cancer

Gastric cancer (GC) presents high mortality worldwide because of delayed diagnosis. Currently, exosome-based liquid biopsy has been applied in diagnosis and monitoring of diseases including cancers, whereas disease detection based on exosomes at the metabolic level is rarely reported. Herein, the specific aptamer-coupled Au-decorated polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of urinary exosomes from early GC patients and healthy controls (HCs) and the subsequent exosome metabolic pattern profiling without extra elution process. Combining with machine learning algorithm on all exosome metabolic patterns, the early GC patients are excellently discriminated from HCs, with an accuracy of 100% for both the discovery set and blind test. Ulteriorly, three key metabolic features with clear identities are determined as a biomarker panel, obtaining a more than 90% diagnostic accuracy for early GC in the discovery set and validation set. Moreover, the change law of the key metabolic features along with GC development is revealed through making a comparison among HCs and GC at early stage and advanced stage, manifesting their monitoring ability toward GC. This work illustrates the high specificity of exosomes and the great prospective of exosome metabolic analysis in disease diagnosis and monitoring, which will promote exosome-driven precision medicine toward practical clinical application.

Rapid and selective detection of dopamine in human serum using an electrochemical sensor based on zinc oxide nanoparticles, nickel phthalocyanines, and carbon nanotubes

Composite materials have gained significant attention owing to the synergistic effects of their constituent materials, thereby facilitating their utilization in new applications or in improving the existing ones. In this study, a composite based on nickel phthalocyanine (NiTsPc), zinc oxide nanoparticles (ZnONPs), and carbon nanotubes (CNT) was developed and subsequently immobilized on a pyrolytic graphite electrode (PGE). The PGE/NiTsPc-ZnONPs-CNT was identified as a selective catalytic hybrid system for detection of neurotransmitter dopamine (DA). The electrochemical and morphological characterizations were conducted using atomic force microscopy (AFM). Chronoamperometry and differential pulse voltammetry (DPV) were used to detect DA and detection limits of 24 nM and 7.0 nM was found, respectively. In addition, the effects of some possible DA interferents, such as ascorbic acid, uric acid, and serotonin, on DA response were evaluated. Their presence did not show significant variations in the DA electrochemical response. The high specificity and sensitivity of PGE/NiTsPc-ZnONPs-CNT for DA enabled its direct detection in human serum without sample pretreatment as well as in DA-enriched serum samples, whose recovery levels were close to 100%, thereby confirming the effectiveness of the proposed method. In general, PGE/NiTsPc-ZnONPs-CNT is a promising candidate for future applications in clinical diagnosis.

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.

Metabolic Molecular Diagnosis of Inflammatory Bowel Disease by Synergistical Promotion of Layered Titania Nanosheets with Graphitized Carbon

Due to inefficient diagnostic methods, inflammatory bowel disease (IBD) normally progresses into severe complications including cancer. Highly efficient extraction and identification of metabolic fingerprints are of significance for disease surveillance. In this work, we synthesized a layered titania nanosheet doped with graphitized carbon (2D-GC-mTNS) through a simple one-step assembly process for assisting laser desorption ionization mass spectrometry (LDI-MS) for metabolite analysis. Based on the synergistic effect of graphitized carbon and mesoporous titania, 2D-GC-mTNS exhibits good extraction ability including high sensitivity (< 1 fmol µL^-1) and great repeatability toward metabolites. A total of 996 fingerprint spectra were collected from hundreds of native urine samples (including IBD patients and healthy controls), each of which contained 1220 m/z metabolite features. Diagnostic model was further established for precise discrimination of patients from healthy controls, with high area under the curve value of 0.972 and 0.981 toward discovery cohort and validation cohort, respectively. The 2D-GC-mTNS promotes LDI-MS to be close to clinical application, with rapid speed, minimum sample consumption and free of sample pretreatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00055-0.

NPvis: An Interactive Visualizer of Peptidic Natural Product–MS/MS Matches

Peptidic natural products (PNPs) represent a medically important class of secondary metabolites that includes antibiotics, anti-inflammatory and antitumor agents. Advances in tandem mass spectra (MS/MS) acquisition and in silico database search methods have enabled high-throughput PNP discovery. However, the resulting spectra annotations are often error-prone and their validation remains a bottleneck. Here, we present NPvis, a visualizer suitable for the evaluation of PNP–MS/MS matches. The tool interactively maps annotated spectrum peaks to the corresponding PNP fragments and allows researchers to assess the match correctness. NPvis accounts for the wide chemical diversity of PNPs that prevents the use of the existing proteomics visualizers. Moreover, NPvis works even if the exact chemical structure of the matching PNP is unknown. The tool is available online and as a standalone application. We hope that it will benefit the community by streamlining PNP data analysis and validation.

Defective Fe Metal-Organic Frameworks Enhance Metabolic Profiling for High-Accuracy Diagnosis of Human Cancers

Cancers heavily threaten human life; therefore, a high-accuracy diagnosis is vital to protect human beings from the suffering of cancers. While biopsies and imaging methods are widely used as current technologies for cancer diagnosis, a new detection platform by metabolic analysis is expected due to the significant advantages of fast, simple, and cost-effectiveness with high body tolerance. However, the signal of molecule biomarkers is too weak to acquire high-accuracy diagnosis. Herein, precisely engineered metal-organic frameworks for laser desorption/ionization mass spectrometry, allowing favorable charge transfer within the molecule-substrate interface and mitigated thermal dissipation by adjusting the phonon scattering with metal nodes, are developed. Consequently, a surprising signal enhancement of ≈10 000-fold is achieved, resulting in diagnosis of three major cancers (liver/lung/kidney cancer) with area-under-the-curve of 0.908-0.964 and accuracy of 83.2%-90.6%, which promises a universal detection tool for large-scale clinical diagnosis of human cancers.

The interaction mechanism of nickel ions with L929 cells based on integrative analysis of proteomics and metabolomics data

The aim of this paper was to study the toxicity mechanism of nickel ions (Ni2+) on L929 cells by combining proteomics and metabolomics. First, iTRAQ-based proteomics and LC/MS metabolomics analyses were used to determine the protein and metabolite expression profiles in L929 cells after treatment with 100 μM Ni2+ for 12, 24 and 48 h. A total of 177, 2191 and 2109 proteins and 40, 60 and 74 metabolites were found to be differentially expressed. Then, the metabolic pathways in which both differentially expressed proteins and metabolites were involved were identified, and three pathways with proteins and metabolites showing upstream and downstream relationships were affected at all three time points. Furthermore, the protein-metabolite-metabolic pathway network was constructed, and two important metabolic pathways involving 4 metabolites and 17 proteins were identified. Finally, the functions of the important screened metabolic pathways, metabolites and proteins were investigated and experimentally verified. Ni2+ mainly affected the expression of upstream proteins in the glutathione metabolic pathway and the arginine and proline metabolic pathway, which further regulated the synthesis of downstream metabolites, reduced the antioxidant capacity of cells, increased the level of superoxide anions and the ratio of GSSG to GSH, led to oxidative stress, affected energy metabolism and induced apoptosis.

A Versatile Electrochemical Biosensor for the Detection of Circulating MicroRNA toward Non-Small Cell Lung Cancer Diagnosis

Circulating microRNAs (miRNAs) can be used as noninvasive biomarkers and are also found circulating in body fluids such as blood. Dysregulated miRNA expression is associated with many diseases, including non-small cell lung cancer (NSCLC), and the miRNA assay is helpful in cancer diagnosis, prognosis, and monitoring. In this work, a versatile electrochemical biosensing system is developed for miRNA detection by DNAzyme-cleavage cycling amplification and hybridization chain reaction (HCR) amplification. With cleavage by Mn²⁺ targeted DNAzyme, DNA-walker can move along the predesigned DNA tracks and contribute to the transduction and enhancement of signals. For the electrochemical process, the formation of multiple G-quadruplex-incorporated long double-stranded DNA (dsDNA/G-quadruplex) structures is triggered through HCR amplification. The introduction of G-quadruplex allows sensitive measurement of miRNA down to 5.68 fM with good specificity. Furthermore, by profiling miRNA in the NSCLC cohort, this designed strategy shows high efficiency (area under the curve (AUC) of 0.879 using receiver operating characteristic (ROC) analysis) with the sensitivity of 80.0% for NSCLC early diagnosis (stage I). For the discrimination of NSCLC and benign disease, the assay displays an AUC of 0.907, superior to six clinically-acceptable protein tumor markers. Therefore, this platform holds promise in clinical application toward NSCLC diagnosis and prognosis.

Nanomaterials-assisted metabolic analysis toward in vitro diagnostics

In vitro diagnostics (IVD) has played an indispensable role in healthcare system by providing necessary information to indicate disease condition and guide therapeutic decision. Metabolic analysis can be the primary choice to facilitate the IVD since it characterizes the downstream metabolites and offers real-time feedback of the human body. Nanomaterials with well-designed composition and nanostructure have been developed for the construction of high-performance detection platforms toward metabolic analysis. Herein, we summarize the recent progress of nanomaterials-assisted metabolic analysis and the related applications in IVD. We first introduce the important role that nanomaterials play in metabolic analysis when coupled with different detection platforms, including electrochemical sensors, optical spectrometry, and mass spectrometry. We further highlight the nanomaterials-assisted metabolic analysis toward IVD applications, from the perspectives of both the targeted biomarker quantitation and untargeted fingerprint extraction. This review provides fundamental insights into the function of nanomaterials in metabolic analysis, thus facilitating the design of next-generation diagnostic devices in clinical practice.

Investigation of fragmentation pathways of norpimarane diterpenoids by mass spectrometry combined with computational chemistry

RATIONALE

Norpimarane diterpenes possess plentiful bioactivities and are widely distributed in herbs, such as Flickingeria fimbriata. Rapid characterization of these natural products in complicated plant extracts is of great importance, and electrospray ionization tandem mass spectrometry is a powerful tool for chemical constituent profiling. However, limited researches on their fragmentation mechanisms seriously hinder identification via mass spectrometry.

METHODS

Three norpimarane diterpenes isolated from F. fimbriata via multiple types of column chromatography served as reference compounds, and collision-induced dissociation experiments were performed on them with a series of variable collision energies. Plausible fragmentation pathways were proposed based on product ions. To further validate the fragmentation mechanisms, the proton affinity and product ion energy were simulated by density functional theory at the B3LYP/6-31+G(d, p) level.

RESULTS

Three main cleavage reactions induced skeleton breakage and resulted in characteristic ions, methyl (CH₃ -20) migration, hydrogen arrangement and Retro-Diels-Alder reaction, among which methyl migration was firstly proposed for pimarane diterpenes. A series of common diagnostic ions were identified, such as m/z 133.1012, 121.1012, 119.0805 and 107.0855. Additionally, the constructed fragmentation mechanisms were successfully applied for fragment ion rationalization of previously reported isopimarane diterpenes.

CONCLUSIONS

Fragmentation mechanisms of norpimarane diterpenes have been uncovered. Carbocation located at the C ring tends to result in methyl (CH₃ -20) migration which has been rarely reported before. This characteristic dissociation reaction allows multiple diagnostic ions to be rationalized and aids in rationalizing fragmentation patterns of other diterpenes. The uncovered mechanisms also shed light on rapid identification of norpimarane diterpenes.

Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring

Chemotherapy is a primary cancer treatment strategy, the monitoring of which is critical to enhancing the survival rate and quality of life of cancer patients. However, current chemotherapy monitoring mainly relies on imaging tools with inefficient sensitivity and radiation invasiveness. Herein, we develop the bowl-shaped submicroreactor chip of Au-loaded 3-aminophenol formaldehyde resin (denoted as APF-bowl&Au) with a specifically designed structure and Au loading content. The obtained APF-bowl&Au, used as the matrix of laser desorption/ionization mass spectrometry (LDI MS), possesses an enhanced localized electromagnetic field for strengthened small metabolite detection. The APF-bowl&Au enables the extraction of serum metabolic fingerprints (SMFs), and machine learning of the SMFs achieves chemotherapy monitoring of ovarian cancer with area-under-the-curve (AUC) of 0.81-0.98. Furthermore, a serum metabolic biomarker panel is preliminarily identified, exhibiting gradual changes as the chemotherapy cycles proceed. This work provides insights into the development of nanochips and contributes to a universal detection platform for chemotherapy monitoring.

Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting

The most common intraocular pediatric malignancy, retinoblastoma (RB), accounts for ≈10% of cancer in children. Efficient monitoring can enhance living quality of patients and 5-year survival ratio of RB up to 95%. However, RB monitoring is still insufficient in regions with limited resources and the mortality may even reach over 70% in such areas. Here, an RB monitoring platform by machine learning of aqueous humor metabolic fingerprinting (AH-MF) is developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (LDI MS). The direct AH-MF of RB free of sample pre-treatment is recorded, with both high reproducibility (coefficient of variation < 10%) and sensitivity (low to 0.3 pmol) at sample volume down to 40 nL only. Further, early and advanced RB patients with area-under-the-curve over 0.9 and accuracy over 80% are differentiated, through machine learning of AH-MF. Finally, a metabolic biomarker panel of 7 metabolites through accurate MS and tandem MS (MS/MS) with pathway analysis to monitor RB is identified. This work can contribute to advanced metabolic analysis of eye diseases including but not limited to RB and screening of new potential metabolic targets toward therapeutic intervention.

Ultra-Fast Label-Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer

Although mass spectrometry (MS) of metabolites has the potential to provide real-time monitoring of patient status for diagnostic purposes, the diagnostic application of MS is limited due to sample treatment and data quality/reproducibility. Here, the generation of a deep stabilizer for ultra-fast, label-free MS detection and the application of this method for serum metabolic diagnosis of coronary heart disease (CHD) are reported. Nanoparticle-assisted laser desorption/ionization-MS is used to achieve direct metabolic analysis of trace unprocessed serum in seconds. Furthermore, a deep stabilizer is constructed to map native MS results to high-quality results obtained by established methods. Finally, using the newly developed protocol and diagnosis variation characteristic surface to characterize sensitivity/specificity and variation, CHD is diagnosed with advanced accuracy in a high-throughput/speed manner. This work advances design of metabolic analysis tools for disease detection as it provides a direct label-free, ultra-fast, and stabilized platform for future protocol development in clinics.