Plasmonic silver nanoshells for drug and metabolite detection

Hollow Mesoporous Carbon Nanospheres/Ni Hybrids Aid in Metabolic Encoding for COVID-19 Recovery Assessment in Mothers and Fetuses

Metabolite analysis of body fluids is an advanced method for disease diagnosis and status assessment. Laser desorption/ionization-mass spectrometry (LDI-MS) has been widely employed for metabolic analysis due to the fast detection speed and simple sample pretreatment. Here, we designed and synthesized hollow mesoporous carbon nanospheres anchored with Ni (HMCSs/Ni) to simultaneously enhance the ionization and thermal desorption processes of the LDI process owing to their hollow and mesoporous structure, large surface area, and abundant Ni-N bonds. Based on HMCSs/Ni, we built an LDI-MS platform that can be used for metabolic information extraction and achieved the rapid detection (about seconds per sample) of metabolic fingerprints in trace serum samples (∼0.1 μL) without complicated preprocessing procedures. Then, we conducted serum metabolic screening in a cohort of COVID-19-recovered pregnant women. The optimized machine learning model could distinguish recovered pregnant women from uninfected pregnant women based on metabolic features with an AUC value of 0.901. In addition, the model indicates that maternal COVID-19 infection does not significantly affect the metabolic fingerprints of the fetuses. Overall, our work shows the prospect of HMCSs/Ni-assisted LDI-MS in disease recovery assessment and metabolite analysis.

Cross-Referencing Multifluid Metabolic Profiles on Hollow Dodecahedral Nanocages for Enhanced Disease Status Identification

The development of matrices has shown great potential for fluid metabolic analysis in disease detection. However, single-fluid metabolomic analysis has been recognized as insufficient to fully capture the complexities of diseases such as liver disease, which limits detection accuracy. To this end, the hollow dodecahedral nanocages-based analytical tool is developed, featuring four-high characteristics of speed, throughput, efficiency, and patient compliance, to enhance extraction of multifluid metabolic profiles. The cross-referencing of these profiles among different liver diseases, including hepatocellular carcinoma (HCC), chronic liver disease (CLD), and healthy controls, enhances the diagnosis of liver diseases, particularly achieving near-perfect discrimination for HCC with an AUC value of 0.990, significantly outperforming any single fluid analysis. Additionally, the dynamic changes in expression levels of the key biomarkers throughout disease progression are explored, providing insights into their temporal evolution, and highlighting their role in monitoring disease status. This work highlights that multifluid metabolic analysis can comprehensively and sensitively reflect the disease status, enabling precise identification of complex diseases and facilitating personalized treatment.

Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning

Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non-invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle-enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non-invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.

A tunable LDI-MS platform assisted by metal-phenolic network-coated AuNPs for sensitive and customized detection of amino acids

This study introduces a novel approach for the sensitive and accurate detection of small molecule metabolites, employing metal-phenolic network (MPN) functionalized AuNPs as both adsorbent and matrix to enhance laser desorption/ionization mass spectrometry (LDI-MS) performance. The MPN comprising tannic acid (TA) and transition metal ions (Fe3+, Co2+, Ni2+, Cu2+, or Zn2+) was coated on the surface of AuNPs, forming metal-TA network-coated AuNPs (M-TA@AuNPs). The M-TA@AuNPs provided a tunable surface for specific interactions with analytes, displaying distinct enrichment efficacies for different amino acids, especially for Cu-TA@AuNPs exhibiting the highest affinity for histidine (His). Under the optimized condition, the proposed method enabled ultrasensitive detection of His, with good linearity (R2 = 0.9627) in the low-concentration range (50 nM-1 μM) and a limit of detection (LOD) as low as 0.9 nM. Furthermore, the method was successfully applied to detect His from human urine samples, showcasing its practical applications in clinical diagnostics, particularly in the realm of amino acid-based targeted metabolomics.

Noble Metal Nanoparticle Assisted Mass Spectrometry for Metabolite-Based In Vitro Diagnostics

In vitro diagnostics (IVD) makes clinical diagnosis rapid, simple, and noninvasive to patients, playing a crucial role in the early diagnosis and monitoring of diseases. Metabolic biomarkers are closely correlated to the phenotype of diseases. However, most IVD platforms are constrained by the sensitivity and throughput of assay. In recent years, noble-metal-nanoparticle (NMNP)-assisted laser desorption/ionization mass spectrometry (LDI MS) has generated major advances in metabolite analysis, significantly improving the sensitivity, accuracy, and throughput of IVD due to the unique optical and electrical properties of NMNPs. This review systematically assesses the development of NMNPs as LDI MS matrices in the detection of metabolites for IVD application. The analysis of several NMNP structures, such as core-shell, porous, and 2D nanoparticles, elucidates their significant contribution to the enhancement of MS performance. Furthermore, the recent advancements in the application of NMNPs for diagnosing various systemic diseases are summarized. Finally, the prospects and challenges of NMNP-assisted MS for IVD are discussed. This review elucidates the roles of NMNPs' structure in enhancing MS-based metabolic detection and provides an overview of various IVD applications, consequently offering comprehensive insights for researchers and developers in this field.

Graphene-Perovskite Fibre Photodetectors

The integration of optoelectronic devices, such as transistors and photodetectors (PDs), into wearables and textiles is of great interest for applications such as healthcare and physiological monitoring. These require flexible/wearable systems adaptable to body motions, thus materials conformable to non-planar surfaces, and able to maintain performance under mechanical distortions. Here, fibre PDs are prepared by combining rolled graphene layers and photoactive perovskites. Conductive fibres (~500 Ωcm-1) are made by rolling single-layer graphene (SLG) around silica fibres, followed by deposition of a dielectric layer (Al2O3 and parylene C), another rolled SLG as a channel, and perovskite as photoactive component. The resulting gate-tunable PD has a response time~9ms, with an external responsivity~22kAW-1 at 488nm for a 1V bias. The external responsivity is two orders of magnitude higher, and the response time one order of magnitude faster, than state-of-the-art wearable fibre-based PDs. Under bending at 4mm radius, up to~80% photocurrent is maintained. Washability tests show~72% of initial photocurrent after 30 cycles, promising for wearable applications.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

One-step electrodeposited hybrid nanofilms in amperometric biosensor development

This review summarizes recent developments in amperometric biosensors, based on one-step electrodeposited organic-inorganic hybrid layers, used for analysis of low molecular weight compounds. The factors affecting self-assembly of one-step electrodeposited films, methods for verifying their composition, advantages, limitations and approaches affecting the electroanalytical performance of amperometric biosensors based on organic-inorganic hybrid layers were systemized. Moreover, issues related to the formation of one-step organic-inorganic hybrid functional layers with different structures in biosensors produced under the same electrodeposition parameters are discussed. The systemized dependencies can support the preliminary choice of functional sensing layers with architectures tuned for specific biotechnology and life science applications. Finally, the capabilities of one-step electrodeposition of organic-inorganic hybrid functional films beyond amperometric biosensors were highlighted.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Tuning Optical Properties of Plasmonic Aerosols through Ligand-Solvent Interactions

Plasmonic nanoparticles are highly tunable light-harvesting materials with a wide array of applications in photonics and catalysis. More recently, there has been interest in using aerosolized plasmonic nanoparticles for cloud formation, airborne photocatalysts, and molecular sensors, all of which take advantage of the large scattering cross sections and the ability of these particles to support intense local field enhancement ("hot spots"). While extensive research has investigated properties of plasmonic particles in the solution phase, surfaces, and films, aerosolized plasmonics are relatively unexplored. Here, we demonstrate how the capping ligand, suspension solvent, and atomization conditions used for aerosol generation control the steady-state optical properties of aerosolized Silica@Au plasmonic nanoshells. Our experimental results, supported with spectral simulations, illustrate that ligand coverage and atomization conditions control the degree of solvent retention and thus the spectral characteristics and potential access to surfaces for catalysis in the aerosol phase, opening a new regime for tunable applications of plasmonic metamaterials.

Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes

Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.

Functional Melanin Nanoparticles-Assisted Laser Desorption Ionization Mass Spectrometry for High-Sensitivity Detection of TBBPA and TBBPS Contaminations in Animal-Derived Foodstuffs

Tetrabromobisphenol A (TBBPA) and tetrabromobisphenol S (TBBPS) have been widely used as additives in various products; however, their residues damage human health mainly via dietary ingestion. The current detection techniques remain challenging in directly and sensitively identifying TBBPA and TBBPS from food samples. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has great potential as an alternative tool for the analysis of low-mass environmental pollution. Herein, we successfully screened and optimized COOH-MNP-COOH as a novel MALDI matrix to enhance deprotonation for the analysis of TBBPA and TBBPS from animal-derived food samples in negative-ion mode. Notably, COOH-MNP-COOH was synthesized by a facile self-assembly strategy and characterized by TEM, FT-IR, UV-vis, and zeta potential analysis. Compared with conventional and control matrices, the COOH-MNP-COOH matrix exhibited excellent performance of TBBPA and TBBPS with high chemical stability, favorable reproducibility, remarkable salt and protein tolerance, and high sensitivity owing to abundant active groups, stronger UV-vis absorption at 355 nm, and better hydrophilicity and biocompatibility. TBBPA and TBBPS were detected with the assistance of an internal standard with limits of detection (LODs) of 300 and 200 pg/mL, respectively. Moreover, this method was applied to directly identify the residues of TBBPA and TBBPS in milk products, followed by basa catfish and meat. This research may provide a promising approach for the analysis of environmental pollutants in foodstuffs.

Metabolic profiling of bacteria with the application of polypyrrole-MOF SPME fibers and plasmonic nanostructured LDI-MS substrates

Here we present application of innovative lab-made analytical devices such as plasmonic silver nanostructured substrates and polypyrrole-MOF solid-phase microextraction fibers for metabolic profiling of bacteria. For the first time, comprehensive metabolic profiling of both volatile and non-volatile low-molecular weight compounds in eight bacterial strains was carried out with utilization of lab-made devices. Profiles of low molecular weight metabolites were analyzed for similarities and differences using principal component analysis, hierarchical cluster analysis and random forest algorithm. The results showed clear differentiation between Gram positive (G+) and Gram negative (G-) species which were identified as distinct clusters according to their volatile metabolites. In case of non-volatile metabolites, differentiation between G+ and G- species and clustering for all eight species were observed for the chloroform fraction of the Bligh & Dyer extract, while methanolic fraction failed to recover specific ions in the profile. Furthermore, the results showed correlation between volatile and non-volatile metabolites, which suggests that lab-made devices presented in the current study might be complementary and therefore, useful for species differentiation and gaining insights into bacterial metabolic pathways.

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

Nanotechnology's frontier in combatting infectious and inflammatory diseases: prevention and treatment

Inflammation-associated diseases encompass a range of infectious diseases and non-infectious inflammatory diseases, which continuously pose one of the most serious threats to human health, attributed to factors such as the emergence of new pathogens, increasing drug resistance, changes in living environments and lifestyles, and the aging population. Despite rapid advancements in mechanistic research and drug development for these diseases, current treatments often have limited efficacy and notable side effects, necessitating the development of more effective and targeted anti-inflammatory therapies. In recent years, the rapid development of nanotechnology has provided crucial technological support for the prevention, treatment, and detection of inflammation-associated diseases. Various types of nanoparticles (NPs) play significant roles, serving as vaccine vehicles to enhance immunogenicity and as drug carriers to improve targeting and bioavailability. NPs can also directly combat pathogens and inflammation. In addition, nanotechnology has facilitated the development of biosensors for pathogen detection and imaging techniques for inflammatory diseases. This review categorizes and characterizes different types of NPs, summarizes their applications in the prevention, treatment, and detection of infectious and inflammatory diseases. It also discusses the challenges associated with clinical translation in this field and explores the latest developments and prospects. In conclusion, nanotechnology opens up new possibilities for the comprehensive management of infectious and inflammatory diseases.

Recent Studies on Metal-Embedded Silica Nanoparticles for Biological Applications

Recently, silica nanoparticles (NPs) have attracted considerable attention as biocompatible and stable templates for embedding noble metals. Noble-metal-embedded silica NPs utilize the exceptional optical properties of novel metals while overcoming the limitations of individual novel metal NPs. In addition, the structure of metal-embedded silica NPs decorated with small metal NPs around the silica core results in strong signal enhancement in localized surface plasmon resonance and surface-enhanced Raman scattering. This review summarizes recent studies on metal-embedded silica NPs, focusing on their unique designs and applications. The characteristics of the metal-embedded silica NPs depend on the type and structure of the embedded metals. Based on this progress, metal-embedded silica NPs are currently utilized in various spectroscopic applications, serving as nanozymes, detection and imaging probes, drug carriers, photothermal inducers, and bioactivation molecule screening identifiers. Owing to their versatile roles, metal-embedded silica NPs are expected to be applied in various fields, such as biology and medicine, in the future.

Dopamine-based selective spectrophotometry p-aminosalicylic acid assay by hydrolyzate-triggered formation of azamonardine-like products

BACKGROUND

The selective recognition of drugs and its metabolism or decomposition products is significant to drug development and drug resistance research. Fluorescence-based techniques provide satisfying sensitivity by target-triggered chemical reaction. However, the interference from the matrix or additives usually restricts the specific detection. It is highly desirable to explore specific chemical reactions for achieving selective perception of these species.

RESULTS

We report a specific m-aminophenol (MAP)-dopamine (DA) reaction, which generates highly fluorescent azamonardine-like products. Based on this reaction, fluorometric and indirect detection of p-aminosalicylic acid (typical antituberculosis drug, PAS) can be realized using the DA-based probe with high sensitivity. The acid induces the decarboxylation of PAS and produces MAP, which reacts with DA and generates fluorescent azamonardine-like products. The practical application of the proposed method is validated by the accurate PAS analysis in urine samples and Pasinazid tablets. Interestingly, none of additives in the Pasinazid tablets contribute comparable fluorescence variation.

SIGNIFICANCE

This work discovers a new MAP-DA reaction for the first time, it not only explores sensitive PAS drug detection probe, but also demonstrates the feasibility of the development of novel drug analysis platform by recognizing decomposition product with specific reaction. Thus, new avenues for the exploration of simple and rapid spectrophotometric probes toward various drug analytes with high specify and sensitivity based on this tactic might be possible in analytical and drug-related fields.

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.

A polymer dot-based NADH-sensitive electrochemiluminescence biosensor for analysis of metabolites in serum

Nicotinamide adenine dinucleotide (NADH) plays a pivotal role in metabolism. Convenient detection of NADH and its related metabolites has the pursuit of point-of-care and clinical analysis. Here, we propose a polymer dots (Pdots)-based NADH-sensitive electrochemiluminescence (ECL) biosensor for detection of NADH and three metabolites. Pdots acted as the efficient ECL emitters without additional modification to construct this biosensor. Specially, NADH both acted as the final detection target and at the same time as the bio-coreactants to sensitively influence the ECL intensities, in which NADH was generated or consumed in the presence of the target analyte and their specific enzyme. For glucose and lactic acid detection, NAD+ was reduced to NADH to generate an enhanced ECL signal. Conversely, for pyruvate detection, NADH was consumed to further decrease the ECL. The designed Pdots-based ECL biosensor showed wide detection ranges, high selectivity and low limits of detection of 4.6 μM, 0.7 μM and 0.5 μM for the analysis of three analytes, respectively. This strategy was successfully applied in quantifying the concentrations of glucose, lactic acid and pyruvate in human serum, which also has the potential to be implemented as a powerful and fast tool for ECL sensing of NADH and other related metabolites for point-of-care use and disease monitoring.

Deciphering the metabolic heterogeneity of hematopoietic stem cells with single-cell resolution

Metabolic status is crucial for stem cell functions; however, the metabolic heterogeneity of endogenous stem cells has never been directly assessed. Here, we develop a platform for high-throughput single-cell metabolomics (hi-scMet) of hematopoietic stem cells (HSCs). By combining flow cytometric isolation and nanoparticle-enhanced laser desorption/ionization mass spectrometry, we routinely detected >100 features from single cells. We mapped the single-cell metabolomes of all hematopoietic cell populations and HSC subpopulations with different division times, detecting 33 features whose levels exhibited trending changes during HSC proliferation. We found progressive activation of the oxidative pentose phosphate pathway (OxiPPP) from dormant to active HSCs. Genetic or pharmacological interference with OxiPPP increased reactive oxygen species level in HSCs, reducing HSC self-renewal upon oxidative stress. Together, our work uncovers the metabolic dynamics during HSC proliferation, reveals a role of OxiPPP for HSC activation, and illustrates the utility of hi-scMet in dissecting metabolic heterogeneity of immunophenotypically defined cell populations.

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.

Construction of Porous Perovskite Oxide Microrods with Au Nanoparticle Anchor for Precise Metabolic Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive illness, and early diagnosis and treatment can help delay its progression. However, clinics still lack high-throughput, low-invasive, precise, and objective diagnostic strategies. Herein, the Au nanoparticles anchored porous perovskite oxide microrods (CTO@Au) with designed superior properties is developed to construct a high-throughput detection platform. Specifically, a single metabolic fingerprinting is obtained from only 30 nL of serum within seconds, enabling the rapid acquisition of 239 × 8 high-quality fingerprints in ≈ 2 h. AD is distinguish from health controls and Parkinson's disease with an area under the curve (AUC) of 1.000. Moreover, eight specific metabolites are identified as a biomarker panel, based on which precise diagnosis of AD is achieved, with an AUC of 1.000 in blind test. The possible relevant pathways and potential mechanism involved in these biomarkers are investigated and discussed. This work provides a high-performance platform for metabolic diagnostic analysis.

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.

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.

In Situ Self-Assembled Formation of Nitrogen-Rich Ag@Ti3C2 Film for Sensitive Detection and Spatial Imaging of Pesticides with Laser Desorption/Ionization Mass Spectrometry (LDI-MS)

Pesticide residues are hazardous to human health; thus, developing a rapid and sensitive method for pesticide detection is an urgent need. Herein, novel nitrogen-rich Ag@Ti₃C₂ (Ag@N-Ti₃C₂) was synthesized via an ecofriendly, ultraviolet-assisted strategy, followed by in situ formation of a highly homogeneous film on target carriers via a facile water evaporation-induced self-assembly process. Ag@N-Ti₃C₂ shows greater surface area, electrical conductivity, and thermal conductivity than Ti₃C₂. This Ag@N-Ti₃C₂ film overcomes the limitations of conventional matrixes and allows laser desorption/ionization mass spectrometry (LDI-MS) to provide fast and high-throughput analysis of pesticides (e.g., carbendazim, thiamethoxam, propoxur, dimethoate, malathion, and cypermethrin) with ultrahigh sensitivity (detection limits of 0.5-200 ng/L), enhanced reproducibility, extremely low background, and good salt tolerance. Furthermore, the levels of pesticides were quantified with a linear range of 0-4 μg/L (R² > 0.99). This Ag@N-Ti₃C₂ film was used for high-throughput analysis of pesticides spiked in traditional Chinese herbs and soft drink samples. Meanwhile, high-resolution Ag@N-Ti₃C₂ film-assisted LDI-MS imaging (LDI MSI) was used to successfully explore spatial distributions of xenobiotic pesticides and other endogenous small molecules (e.g., amino acids, saccharides, hormones, and saponin) in the roots of plants. This study presents the new Ag@N-Ti₃C₂ self-assembled film equably deposits on the ITO slides and provides a dual platform for pesticide monitoring and has the advantages of high conductivity, accuracy, simplicity, rapid analysis, minimal sample volume requirement, and an imaging function.

Ratiometric electrochemical OR gate assay for NSCLC-derived exosomes

Non-small cell lung cancer (NSCLC) is the most common pathological type of LC and ranks as the leading cause of cancer deaths. Circulating exosomes have emerged as a valuable biomarker for the diagnosis of NSCLC, while the performance of current electrochemical assays for exosome detection is constrained by unsatisfactory sensitivity and specificity. Here we integrated a ratiometric biosensor with an OR logic gate to form an assay for surface protein profiling of exosomes from clinical serum samples. By using the specific aptamers for recognition of clinically validated biomarkers (EpCAM and CEA), the assay enabled ultrasensitive detection of trace levels of NSCLC-derived exosomes in complex serum samples (15.1 particles μL^-1 within a linear range of 10²-10⁸ particles μL^-1). The assay outperformed the analysis of six serum biomarkers for the accurate diagnosis, staging, and prognosis of NSCLC, displaying a diagnostic sensitivity of 93.3% even at an early stage (Stage I). The assay provides an advanced tool for exosome quantification and facilitates exosome-based liquid biopsies for cancer management in clinics.

Enhanced Photoluminescence of R6G Dyes from Metal Decorated Silicon Nanowires Fabricated through Metal Assisted Chemical Etching

In this study, we developed active substrates consisting of Ag-decorated silicon nanowires on a Si substrate using a single-step Metal Assisted Chemical Etching (MACE) process, and evaluated their performance in the identification of low concentrations of Rhodamine 6G using surface-enhanced photoluminescence spectroscopy. Different structures with Ag-aggregates as well as Ag-dendrites were fabricated and studied depending on the etching parameters. Moreover, the addition of Au nanoparticles by simple drop-casting on the MACE-treated surfaces can enhance the photoluminescence significantly, and the structures have shown a Limit of Detection of Rhodamine 6G down to 10-12 M for the case of the Ag-dendrites enriched with Au nanoparticles.

Silver Nanoshells with Optimized Infrared Optical Response: Synthesis for Thin-Shell Formation, and Optical/Thermal Properties after Embedding in Polymeric Films

We describe a new approach to making ultrathin Ag nanoshells with a higher level of extinction in the infrared than in the visible. The combination of near-infrared active ultrathin nanoshells with their isotropic optical properties is of interest for energy-saving applications. For such applications, the morphology must be precisely controlled, since the optical response is sensitive to nanometer-scale variations. To achieve this precision, we use a multi-step, reproducible, colloidal chemical synthesis. It includes the reduction of Tollens' reactant onto Sn²⁺-sensitized silica particles, followed by silver-nitrate reduction by formaldehyde and ammonia. The smooth shells are about 10 nm thick, on average, and have different morphologies: continuous, percolated, and patchy, depending on the quantity of the silver nitrate used. The shell-formation mechanism, studied by optical spectroscopy and high-resolution microscopy, seems to consist of two steps: the formation of very thin and flat patches, followed by their guided regrowth around the silica particle, which is favored by a high reaction rate. The optical and thermal properties of the core-shell particles, embedded in a transparent poly(vinylpyrrolidone) film on a glass substrate, were also investigated. We found that the Ag-nanoshell films can convert 30% of the power of incident near-infrared light into heat, making them very suitable in window glazing for radiative screening from solar light.

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.

Multi-channel AgNWs-doped interdigitated organic electrochemical transistors enable sputum-based device towards noninvasive and portable diagnosis of lung cancer

Biochemical monitoring of bodily fluidics such as sweat, urine, and tears have been extensively developed, but reliable biochemical analysis of sputum biospecimens remains limited and challenging due to the low abundance of biomarkers in intrinsically viscous sputum. We reported a portable multi-channel sputum-based interdigitated organic electrochemical transistors (SiOECTs) device for noninvasive sputum diagnosis. We tailored the AgNWs-doped organic electrochemical transistors, integrating with multiplexed aptamer-antigen assays, to realize the signal amplification and simultaneous detection of biomarkers in raw sputum biospecimens from lung cancer patients. Clinical validation studies demonstrated favorable correlation coefficients between the sputum and serum biospecimens. By utilizing our portable multi-channel iOECTs devices, lung cancer patients were differentiated from health control with an optimum area under the curve (AUC) of 0.931, sensitivity of 87.0%, and specificity of 86.5%. Our miniaturized and portable device could even realize the continuous in-home tracking of the biomarkers change for lung cancer patients after radiotherapy/chemotherapy. It is envisaged that the SiOECTs will shed light on noninvasive diagnostics platforms for sputum-related diseases.

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.

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.

Aptamer-conjugated graphene oxide-based surface assisted laser desorption ionization mass spectrometry for selective extraction and detection of Aβ1–42 in an Alzheimer’s disease SH-SY5 cell model

The generation and accumulation of amyloid-beta peptide (Aβ_1–42) in amyloid plaques are key characteristics of Alzheimer’s disease (AD); thus, specific detection of Aβ_1–42 is essential for the diagnosis and treatment of AD. Herein, an aptamer-conjugated graphene oxide (Apt-GO) sensor was synthesized by π-π and hydrophobic interactions using thiol poly (ethylene glycol) amine (SH-PEG-NH₂) as a spacer unit. Then, it was applied to selective capture of Aβ_1–42, and the resulting complex was directly analyzed by surface-assisted laser desorption ionization mass spectrometry (SALDI-MS). The results revealed that the Apt-GO could enhance the detection specificity and reduce non-specific adsorption. This method was validated to be sensitive in detecting Aβ_1–42 at a low level in human serum (ca. 0.1 μM) within a linear range from 0.1 to 10 μM. The immobilizing amount of aptamer on the GO was calculated to be 36.1 nmol/mg (RSD = 11.5%). In conclusion, this Apt-GO-based SALDI-MS method was sensitive and efficient in selective extraction and detection of Aβ_1–42, which proved to be a good option for early AD diagnosis.

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.

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

Liver cancer (LC) is the third frequent cause of death worldwide, so early diagnosis of liver cancer patients is crucial for disease management. Herein, we applied NH₂-coated polystyrene@Fe₃O₄ magnetic beads (PS@Fe₃O₄-NH₂ MBs) as a matrix material in laser desorption/ionization mass spectrometry (LDI-MS). Rapid, sensitive, and selective metabolic profiling of the native biofluids was achieved without any inconvenient enrichment or purification. Then, based on the selected m/z features, LC patients were discriminated from healthy controls (HCs) by machine learning, with the high area under the curve (AUC) values for urine and serum assessments (0.962 and 0.935). Moreover, initial-diagnosed and subsequent-visited LC patients were also differentiated, which indicates potential applications of this method in early diagnosis. Furthermore, among these identified compounds by FT-ICR MS, the expression level of some metabolites changed from HCs to LCs, including 29 and 12 characteristic metabolites in human urine and serum samples, respectively. These results suggest that PS@Fe₃O₄-NH₂ MBs-assisted LDI-MS coupled with machine learning is feasible for LC clinical diagnosis.

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.

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.

Top-Down Rational Engineering of Heteroatom-Doped Graphene Quantum Dots for Laser Desorption/Ionization Mass Spectrometry Detection and Imaging of Small Biomolecules

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is widely applied in mapping macrobiomolecules in tissues, but it is still limited in profiling low-molecular-weight (MW) compounds (typically metabolites) due to ion interference and suppression by organic matrices. Here, we present a versatile "top-down" strategy for rational engineering of carbon material-based matrices, by which heteroatom-doped graphene quantum dots (HGQDs) were manufactured for LDI MS detection and imaging of small biomolecules. The HGQDs derived from parent materials inherited the π-conjugated networks and doping sites for promoting energy transfer and negative ion generation, while their extremely small size guaranteed the matrix uniformity and signal reproducibility in LDI MSI. Compared to other HGQDs, nitrogen-doped graphene quantum dots (NGQDs) exhibited superior capability of assisting LDI of various small molecules, including amino acids, fatty acids, saccharides, small peptides, nucleobases, anticancer drugs, and bisphenol pollutants. Density functional theory simulations also corroborated that the LDI efficiency was markedly raised by the proton-capturing pyridinic nitrogen species and compromised by the electron-deficient boron dopants. NGQDs-assisted LDI MS further enabled label-free investigation on enzyme kinetics using an ordinary short peptide as the substrate. Moreover, due to the high salt tolerance and signal reproducibility, the proposed negative-ion NGQDs-assisted LDI MSI was able to reveal the abundance and distribution of low-MW species in rat brain tissue and achieved the imaging of low-MW lipids in coronally sectioned rat brains subjected to traumatic brain injury. Our work offers a new route for customizing nanomaterial matrices toward LDI MSI of small biomolecules in biomedical and pathological research.

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.

Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.