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

Advancements in DNA computing: exploring DNA logic systems and their biomedical applications

DNA computing is regarded as one of the most promising candidates for the next generation of molecular computers, utilizing DNA to execute Boolean logic operations. In recent decades, DNA computing has garnered widespread attention due to its powerful programmable and parallel computing capabilities, demonstrating significant potential in intelligent biological analysis. This review summarizes the latest advancements in DNA logic systems and their biomedical applications. Firstly, it introduces recent DNA logic systems based on various materials such as functional DNA sequences, nanomaterials, and three-dimensional DNA nanostructures. The material innovations driving DNA computing have been summarized, highlighting novel molecular reactions and analytical performance metrics like efficiency, sensitivity, and selectivity. Subsequently, it outlines the biomedical applications of DNA computing-based multi-biomarker analysis in cellular imaging, clinical diagnosis, and disease treatment. Additionally, it discusses the existing challenges and future research directions for the development of DNA computing.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn's disease

Mesenchymal stem cell (MSC) therapy offers a promising cure for Crohn's disease (CD), however, its therapeutic effects vary significantly due to individual differences. Therefore, identifying easily detectable biomarkers is essential to assess the efficacy of MSC therapy. In this study, SAMP1/Yit mice were used as a model of CD, which develop spontaneous chronic ileitis, closely resembling the characteristics present in CD patients. Serum metabolic alterations during treatment were analyzed, through the application of differential 12C-/13C-dansylation labeling liquid chromatography-mass spectrometry. Based on the significant differences and time-varying trends of serum amine/phenol-containing metabolites abundance between the control group, the model group, and the treatment group, four serum biomarkers were ultimately screened for evaluating the efficacy of MSC treatment for CD, namely 4-hydroxyphenylpyruvate, 4-hydroxyphenylacetaldehyde, caffeate, and N-acetyltryptamine, whose abundances both increased in the serum of CD model mice and decreased after MSC treatment. These metabolic alterations were associated with tyrosine metabolism, which was validated by the dysregulation of related enzymes. The discovery of biomarkers may help to improve the targeting and effectiveness of treatment and provide innovative prospects for the clinical application of MSC for CD.

High-Performance Metabolic Profiling of High-Risk Thyroid Nodules by ZrMOF Hybrids

Thyroid nodules (TNs) have emerged as the most prevalent endocrine disorder in China. Fine-needle aspiration (FNA) remains the standard diagnostic method for assessing TN malignancy, although a majority of FNA results indicate benign conditions. Balancing diagnostic accuracy while mitigating overdiagnosis in patients with benign nodules poses a significant clinical challenge. Precise, noninvasive, and high-throughput screening methods for high-risk TN diagnosis are highly desired but remain less explored. Developing such approaches can improve the accuracy of noninvasive methods like ultrasound imaging and reduce overdiagnosis of benign nodule patients caused by invasive procedures. Herein, we investigate the application of gold-doped zirconium-based metal-organic framework (ZrMOF/Au) nanostructures for metabolic profiling of thyroid diseases. This approach enables the efficient extraction of urine metabolite fingerprints with high throughput, low background noise, and reproducibility. Utilizing partial least-squares discriminant analysis and four machine learning models, including neural network (NN), random forest (RF), logistic regression (LR), and support vector machine (SVM), we achieved an enhanced diagnostic accuracy (98.6%) for discriminating thyroid cancer (TC) from low-risk TNs by using a diagnostic panel. Through the analysis of metabolic differences, potential pathway changes between benign nodule and malignancy are identified. This work explores the potential of rapid thyroid disease screening using the ZrMOF/Au-assisted LDI-MS platform, providing a potential method for noninvasive screening of thyroid malignant tumors. Integrating this approach with imaging technologies such as ultrasound can enhance the reliability of noninvasive diagnostic methods for malignant tumor screening, helping to prevent unnecessary invasive procedures and reducing the risk of overdiagnosis and overtreatment in patients with benign nodules.

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.

Simultaneous fingerprinting of multiplex collagen biomarkers in connective tissues by multicolor quantum dots-based peptide probes

The accurate detection of multiplex collagen biomarkers is vital for diagnosing and treating various critical diseases such as tumors and fibrosis. Despite the attractive optical properties of quantum dots (QDs), it remains technically challenging to create stable and specific QDs-based probes for multiplex biological imaging. We report for the first time the construction of multi-color QDs-based peptide probes for the simultaneous fingerprinting of multiplex collagen biomarkers in connective tissues. A bipeptide system composed of a glutathione (GSH) host peptide and a collagen-targeting guest peptide (CTP) has been developed, yielding CTP-QDs probes that exhibit exceptional luminescence stability when exposed to ultraviolet irradiation and mildly acidic conditions. The versatile bipeptide system allows for facile one-pot synthesis of high-quality multicolor CTP-QDs probes, exhibiting superior selectivity in targeting critical collagen biomarkers including denatured collagen, type I collagen, type II collagen, and type IV collagen. The multicolor CTP-QDs probes have demonstrated remarkable efficacy in simultaneously fingerprinting multiple collagen types in diverse connective tissues, irrespective of their status, whether affected by injury, diseases, or undergoing remodeling processes. The innovative multicolor CTP-QDs probes offer a robust toolkit for the multiplex fingerprinting of the collagen suprafamily, demonstrating significant potential in the diagnosis and treatment of collagen-related diseases.

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

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

Quantitative Detection of Multiple Cardiovascular Biomarkers by an Antibody Microarray-Based Metal-Enhanced Fluorescence Assay

Accurate detection of multiple cardiovascular biomarkers is crucial for the timely screening of acute coronary syndrome (ACS) and differential diagnosis from acute aortic syndrome (AAS). Herein, an antibody microarray-based metal-enhanced fluorescence assay (AMMEFA) has been developed to quantitatively detect 7 cardiovascular biomarkers through the formation of a sandwich immunoassay on the poly(glycidyl methacrylate-co-2-hydroxyethyl methacrylate)-decorated GNR-modified slide (GNR@P(GMA-HEMA) slide). The AMMEFA exhibits high specificity and sensitivity, the linear ranges span 5 orders of magnitude, and the limits of detection (LODs) of cardiac troponin I (cTnI), heart-type fatty acid binding protein (H-FABP), C-reactive protein (CRP), copeptin, myoglobin, D-Dimer, and N-terminal pro-brain natriuretic peptide (NT-proBNP) reach 0.07, 0.2, 65.7, 0.6, 0.2, 8.3, and 0.3 pg mL-1, respectively. To demonstrate its practicability, the AMMEFA has been applied to quantitatively analyze 7 cardiovascular biomarkers in 140 clinical plasma samples. In addition, the expression levels of cardiovascular biomarkers were analyzed by the least absolute shrinkage and selector operator (LASSO) regression, and the area under receiver operator characteristic curves (AUCs) of healthy donors (HDs), ACS patients, and AAS patients are 0.99, 0.98, and 0.97, respectively.

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

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

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.

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.

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

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

AuNPs/CNC Nanocomposite with A "Dual Dispersion" Effect for LDI-TOF MS Analysis of Intact Proteins in NSCLC Serum Exosomes

Detecting exosomal markers using laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) is a novel approach for examining liquid biopsies of non-small cell lung cancer (NSCLC) samples. However, LDI-TOF MS is limited by low sensitivity and poor reproducibility when analyzing intact proteins directly. In this report, gold nanoparticles/cellulose nanocrystals (AuNPs/CNC) is introduced as the matrix for direct analysis of intact proteins in NSCLC serum exosomes. AuNPs/CNC with "dual dispersion" effects dispersed and stabilized AuNPs and improved ion inhibition effects caused by protein aggregation. These features increased the signal-to-noise ratio of [M+H]+ peaks by two orders of magnitude and lowered the detection limit of intact proteins to 0.01 mg mL-1. The coefficient of variation with or without AuNPs/CNC is measured as 10.2% and 32.5%, respectively. The excellent reproducibility yielded a linear relationship (y = 15.41x - 7.983, R2 = 0.989) over the protein concentration range of 0.01 to 20 mg mL-1. Finally, AuNPs/CNC-assisted LDI-TOF MS provides clinically relevant fingerprint information of exosomal proteins in NSCLC serum, and characteristic proteins S100 calcium-binding protein A10, Urokinase plasminogen activator surface receptor, Plasma protease C1 inhibitor, Tyrosine-protein kinase Fgr and Mannose-binding lectin associated serine protease 2 represented excellent predictive biomarkers of NSCLC risk.

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.

Quantitative mass spectrometry imaging: therapeutics & biomolecules

Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.