A surface-enhanced Raman spectroscopy database of 63 metabolites

Sherman, Lindy M.; Петров, А. П.; Karger, Leonhard F. P.; Tetrick, Maxwell; Dovic̀hi, Norman J.; Camden, Jon P.

doi:10.1016/j.talanta.2019.120645

Cited by 23 publications

(23 citation statements)

References 39 publications

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“…This method could also be extended to the vast library of metabolites, especially those with N -terminal aromatic groups, for increased sensitivity of SERS detection. 54 These results show that through simple sample preparation of CB[7]-functionalized silver nanoparticles, quantitative SERS detection can be significantly improved and scaled to larger, clinically relevant proteins.…”

Section: Resultsmentioning

confidence: 86%

Capture of Phenylalanine and Phenylalanine-Terminated Peptides Using a Supramolecular Macrocycle for Surface-Enhanced Raman Scattering Detection

et al. 2020

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Section: Resultsmentioning

confidence: 86%

Capture of Phenylalanine and Phenylalanine-Terminated Peptides Using a Supramolecular Macrocycle for Surface-Enhanced Raman Scattering Detection

et al. 2020

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“…The right tool(s) for the right biological question(s) must be the rule. Besides NMR and LC-MS (and to a lesser extent GC-MS), which are the two most widespread platforms, other approaches have also been or are being explored, such as vibrational spectroscopy (i.e., FT-IR and Raman) (Du et al, 2020;Sherman et al, 2020) and capillary electrophoresis coupled or not with a MS detector (Maier and Schmitt-Kopplin, 2016;Sasaki et al, 2019).…”

Section: Clinical Metabolomics and Personalized Medicinementioning

confidence: 99%

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Letertre

Giraudeau

Tullio

2021

Front. Mol. Biosci.

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Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

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“…When the molecules of interest are adsorbed in the hot spot, they would be excited to emit strong SERS signals to provide vibration fingerprints of the unique molecular structure. [13][14][15][16][17][18] While analyzing organic molecules, SERS technology exhibits the following characteristics: first, SERS technology can perform non-destructive rapid detection of samples. 19 It has a simple process of sample preparation, and organic molecules can exist either in solution or in the solid state.…”

Section: Introductionmentioning

confidence: 99%

Surface-enhanced Raman spectroscopy detection of organic molecules and in situ monitoring of organic reactions by ion-induced silver nanoparticle clusters

Wang

Liu

et al. 2022

Phys. Chem. Chem. Phys.

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A surface-enhanced Raman spectroscopy database of 63 metabolites

Cited by 23 publications

References 39 publications

Capture of Phenylalanine and Phenylalanine-Terminated Peptides Using a Supramolecular Macrocycle for Surface-Enhanced Raman Scattering Detection

Capture of Phenylalanine and Phenylalanine-Terminated Peptides Using a Supramolecular Macrocycle for Surface-Enhanced Raman Scattering Detection

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Surface-enhanced Raman spectroscopy detection of organic molecules and in situ monitoring of organic reactions by ion-induced silver nanoparticle clusters

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