Hierarchical superstructure aerogels for in situ biofluid metabolomics.

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

µ-PESI-based MS profiling combined with untargeted metabolomics analysis for rapid identification of red wine geographical origin.

BACKGROUND: The commercial value of red wine is strongly linked to its geographical origin. Given the large global market, there is great demand for high-throughput screening methods to authenticate the geographical source of red wine. However, only limited techniques have been established up to now. RESULTS: Herein, a sensitive and robust method, namely probe electrospray ionization mass spectrometry (µ-PESI-MS), was established to achieve rapid analysis at approximately 1.2 min per sample without any pretreatment. A scotch near the needle tip provides a fixed micro-volume for each analysis to achieve satisfactory ion signal reproducibility (RSD < 26.7%). In combination with a machine learning algorithm, 16 characteristic ions were discovered from thousands of detected ions and were utilized for differentiating red wine origin. Among them, the relative abundances of two characteristic metabolites (trigonelline and proline) correlated with geographical conditions (sun exposure and water stress) were identified, providing the rationale for differentiation of the geographical origin. CONCLUSION: The proposed µ-PESI-MS-based method demonstrates a promising high-throughput determination capability in red wine traceability.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples.

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

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

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