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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.

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Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

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Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

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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.

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Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

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OBJECTIVES: To explore the metabolic fingerprints of diabetic retinopathy (DR) in individuals with type 2 diabetes using a newly-developed laser desorption/ionization mass spectrometry (LDI-MS) platform assisted by ferric particles. METHODS: Metabolic fingerprinting was performed using a ferric particle-assisted LDI-MS platform. A nested population-based case-control study was performed on 216 DR cases and 216 control individuals with type 2 diabetes. RESULTS: DR cases and control individuals with type 2 diabetes were comparable for a list of clinical factors. The newly-developed LDI-MS platform allowed us to draw the blueprint of plasma metabolic fingerprints from participants with and without DR. The neural network afforded diagnostic performance with an average area under curve value of 0.928 for discovery cohort and 0.905 for validation cohort (95 % confidence interval: 0.902-0.954 and 0.845-0.965, respectively). Tandem MS and Fourier transform ion cyclotron resonance MS with ultrahigh resolution identified seven specific metabolites that were significantly associated with DR in fully adjusted models. Of these metabolites, dihydrobiopterin, phosphoserine, N-arachidonoylglycine, and 3-methylhistamine levels in plasma were first reported to show the associations. CONCLUSIONS: This work advances the design of metabolic analysis for DR and holds the potential to promise as an efficient tool for clinical management of DR.

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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.

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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.

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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.

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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.

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Peak detection of untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) data is a key step to identify the metabolic status of the drugable chemicals and extracts from functional foods or herbs. Nevertheless, the existing approaches are difficult to obtain ideal results with low false positives and false negatives. In this paper, we proposed an automatic method based on convolutional neural network (CNN) for image classification and Faster R-CNN for peak location/classification in untargeted LC-HRMS data, and named it Peak_CF. It can achieve detection of target peaks with high accuracy and high recall (both >90%) as verified by an evaluation data-set. In terms of detecting the m/z peaks of known compounds, Peak_CF is better than Peakonly, and it can effectively have an overall peak shape judgment of split peaks. For the same evaluation data, the recall of MZmine2 (ADAP) is slightly higher than that of Peak_CF, however, the F1 score of Peak_CF is higher, indicating that it has higher accuracy. In addition, the Peak_ CF training model with strong generalization ability can be achieved and verified. At last, Peak_CF was applied in real metabolic fingerprints of total flavonoids from Glycyrrhiza uralensis Fisch, also a contrast was conducted based on 40 m/z peaks of 40 prototypes in serum data-set. The result showed that the recall rate of Peak_CF and Peakonly all reached 95%, higher than 70% of MZmine2 (ADAP), and Peak_CF is more accurate when detecting EIC that has serious drifts. In conclusion, Peak_CF provides a new route for data mining of LC-HRMS datasets of drug (or herbs, or functional foods) metabolites.

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Heterocyclic aromatic amines (HAAs) were not only present in cooked foods and cigarette smoke, but also measured in airborne particles and diesel-exhaust particles. Typical HAAs have been reported to induce carcinogenicity and metabolic disturbances, but how these hazardous compounds interfere with metabolic networks by regulating metabolic pathways and fingerprinting signature metabolites as biomarkers remains ambiguous. We developed an advanced strategy that adopted chemical isotope labeling ultrahigh-performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry for urinary nontargeted metabolomics analysis to gain new insight into in vivo physiological responses stimulated by exposure to typical HAAs. Rats were orally administered with a single dose of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) or 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx) (1 and 10 mg/kg bw) and their D3-isotopic compounds, respectively, and urine samples were then continuously collected within 36 h. Metabolomics data were acquired and processed by classical multivariate statistical analysis, while urinary metabolites were further identified and characterized according to mass spectrometric fragmentation rules, time- and dose-dependent profiles, and calibration of synthesized standards. We monitored 23 and 37 urinary metabolites as the biotransformation products of PhIP and MeIQx, respectively, and first identified demethylated metabolites of PhIP, tentatively named 2-amino-6-phenylimidazo[4,5-b]pyridine, and dihydroxylation products of classical HAAs as short-term biomarkers of exposure to further unravel the metabolic networks. In addition, our findings revealed that both HAAs significantly disturb histidine metabolism, arginine and proline metabolism, tryptophan metabolism, pyrimidine metabolism, tricarboxylic acid cycle, etc. Furthermore, we found that histamine, methionine, alanine, and 4-guanidinobutanoic acid could be considered potential characteristic biomarkers for the oncogenicity or carcinogenicity of both PhIP and MeIQx and screened their specific key pivotal metabolites. The current metabolomics approach is applicable in mapping updated urinary metabolic fingerprints and identifying potential specific biomarkers for HAAs-induced early tumorigenesis.

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Diabetes and high myopia as well-known high-risk factors can aggravate cataracts, yet clinical coping strategy remains a bottleneck. Metabolic analysis tends to be powerful for precisely detection and mechanism exploration since most of diseases including cataracts are accompanied by metabolic disorder. Herein, a layered binary co-ionizers assisted aqueous humor metabolic analysis tool is proposed for potentially etiological typing and detection of cataracts, including age-related cataracts (ARC), cataracts with diabetes mellitus (CDM), and cataracts with high myopia (CHM). Startlingly, taking advantage of the optimal machine learning algorithm and all metabolic fingerprints, 100% of accuracy, precision, and recall rates are achieved for arbitrary comparison between groups. Moreover, 11, 9, and 7 key metabolites with explicit identities are confirmed as markers of discriminating CDM from ARC, CHM from ARC, and CDM from CHM, and the corresponding area under the curve values of validation cohorts are 0.985, 1.000, and 1.000. Finally, the critical impact of diabetes/high myopia on cataracts is revealed by excavating the change levels and metabolic pathways of key metabolites. This work updates the insights of prevention and treatment about cataracts at metabolic level and throws out huge surprises and progresses metabolic diagnosis toward a reality.

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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.

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Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.

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In this study, we aimed to identify acute respiratory distress syndrome (ARDS) metabolic fingerprints in selected patient cohorts and compare the metabolic profiles of direct versus indirect ARDS and hypoinflammatory versus hyperinflammatory ARDS. We hypothesized that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints that set ARDS apart from other intensive care unit (ICU) conditions and could help examine ARDS subphenotypes and clinical subgroups. Patients with ARDS (n = 108) and ICU ventilated controls (n = 27) were included. Samples were randomly divided into 2/3 training and 1/3 test sets. Samples were analyzed using 1H nuclear magnetic resonance spectroscopy and gas chromatography-mass spectrometry. Twelve proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct versus indirect ARDS subgroups and hypoinflammatory versus hyperinflammatory ARDS subgroups were compared. Serum metabolomics and proteins/cytokines had similar area under receiver operator curves when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibited correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups were metabolically distinct. However, our identified metabolic fingerprints are not ARDS diagnostic biomarkers, and further research is required to ascertain generalizability. In conclusion, patients with ARDS are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.

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Stroke is a leading cause of mortality and disability worldwide, expected to result in 61 million disability-adjusted life-years in 2020. Rapid diagnostics is the core of stroke management for early prevention and medical treatment. Serum metabolic fingerprints (SMFs) reflect underlying disease progression, predictive of patient phenotypes. Deep learning (DL) encoding SMFs with clinical indexes outperforms single biomarkers, while posing challenges with poor prediction to interpret by feature selection. Herein, rapid computer-aided diagnosis of stroke is performed using SMF based multi-modal recognition by DL, to combine adaptive machine learning with a novel feature selection approach. SMFs are extracted by nano-assisted laser desorption/ionization mass spectrometry (LDI MS), consuming 100 nL of serum in seconds. A multi-modal recognition is constructed by integrating SMFs and clinical indexes with an enhanced area under curve (AUC) up to 0.845 for stroke screening, compared to single-modal diagnosis by only SMFs or clinical indexes. The prediction of DL is addressed by selecting 20 key metabolite features with differential regulation through a saliency map approach, shedding light on the molecular mechanisms in stroke. The approach highlights the emerging role of DL in precision medicine and suggests an expanding utility for computational analysis of SMFs in stroke screening.

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Leaves of semi-domesticated Diospyros digyna and wild D. rekoi trees, sampled seasonally in Mexico in 2014, were analyzed. Metabolic fingerprints revealed higher metabolite diversity in D. rekoi leaves. The TLC bands characteristic of glycosylated flavonoids, predominant in this species, matched the detection of quercetin and quercetin 3-O-glucuronides by liquid chromatography (UPLC-MS) of spring leaf extracts (LEs). Further gas chromatography (GC-MS) analysis revealed abundant fatty acids, organic acids, and secondary metabolites including trigonelline, p-coumaric, and ferulic and nicotinic acids. Phenolic-like compounds prevailed in D. digyna LEs, while unidentified triterpenoids and dihydroxylated coumarins were detected by UPLC-MS and GC-MS. A paucity of leaf metabolites in leaves of this species, compared to D. rekoi, was evident. Higher antioxidant capacity (AOC) was detected in D. digyna LEs. The AOC was season-independent in D. digyna but not in D. rekoi. The AOC in both species was concentrated in distinct TLC single bands, although seasonal variation in band intensity was observed among trees sampled. The AOC in D. digyna LEs could be ascribed to the coumarin esculetin. The LEs moderately inhibited phytopathogenic bacteria but not fungi. Leaf chemistry differences in these Mesoamerican Diospyros species substantiated previous variability reported in tree physiology and fruit physical chemistry, postulated to result from domestication and seasonality.

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Metabolomics is the area of research, which strives to obtain complete metabolic fingerprints, to detect differences between them, and to provide hypothesis to explain those differences [1]. But obtaining complete metabolic fingerprints is not an easy task. Metabolite extraction is a key step during this process, and much research has been devoted to finding the best solvent mixture to extract as much metabolites as possible.Here a procedure is described for analysis of both polar and apolar metabolites using a two-phase extraction system. D2O and CDCl3 are the solvents of choice, and their major advantage is that, for the identification of the compounds, standard databases can be used because D2O and CDCl3 are the solvents most commonly used for pure compound NMR spectra. The procedure enables the absolute quantification of components via the addition of suitable internal standards. The extracts are also suitable for further analysis with other systems like LC-MS or GC-MS.

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