SMolESY: an efficient and quantitative alternative to on-instrument macromolecular <sup>1</sup>H-NMR signal suppression

Takis, Panteleimon G.; Jiménez, Beatriz; Sands, Caroline; Chekmeneva, Elena; Lewis, Matthew R.

doi:10.1039/d0sc01421d

Cited by 18 publications

(19 citation statements)

References 32 publications

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“…( Figure S3a ). 5 Specifically, SMolESY-select utilizes a moving average filter spanning 11-points (see the Supporting Information ) across the high resolution data of the selected windows, which increases the s/n of signals belonging to tyrosine, phenylalanine, histidine, creatinine, and choline ( Figure S3b ), 16 while maintaining the attenuation of macromolecular signals/baseline background at the slight expense of resolution enhancement. The denoised SMolESY signals still maintain a slightly narrower Δ v 1/2 compared to the standard 1D 1 H NMR experiment, and their s/n is a maximum of ∼10% less than that of the CPMG spectra ( Figure S3b,c ).…”

Section: Resultsmentioning

confidence: 99%

“…This in turn increases the potential throughput of NMR metabolite panel measurement and maximizes the utility of existing 1D 1 H NMR data sets without preventing the traditional discovery analysis approach 2 of either the regularly processed or SMolESY enhanced profiles. 5 These advantages are particularly important in epidemiology, biobanking research applications that require the sequential automated analysis of thousands of human samples and the translation of NMR into clinical practice. The performance of SMolESY-select has been extensively validated across >8800 serum/plasma samples of varying phenotypes.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, we introduced Small Molecule Enhancement SpectroscopY (SMolESY), 5 a highly validated computational approach that exclusively utilizes the standard 1D 1 H NMR experiment to derive enhanced SM profiles from macromolecule-rich sample types such as serum and plasma. SMolESY maintains both the qualitative and quantitative features of the original experiment while suppressing the macromolecular signal and increasing spectral resolution (see more details in the Supporting Information ).…”

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confidence: 99%

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A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

et al. 2021

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Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1 H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional 1 H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

et al. 2021

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“…Other workflows, such as SigMa [ 23 ] require extensive compound libraries. Applied to serum and plasma samples, Takis et al [ 24 ] introduced a deconvolution-free integration method, SMolESY, which enables a suppression of the macromolecular background, which is particularly important in blood samples. Its application to urine samples remains unclear, as plasma, unlike urine, does not face extensive peak shifting.…”

Section: Discussionmentioning

confidence: 99%

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

2021

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Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.

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“…The NMR and MS data processing is described in detail in Supplementary Materials S2 (Sections S1.4.1 and S1.4.2, respectively) . Detailed description of the “SMolESY” (Small Molecule Enhancement SpectroscopY) algorithm is provided in [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Plasma Metabolomic Alterations Induced by COVID-19 Vaccination Reveal Putative Biomarkers Reflecting the Immune Response

Dagla

Iliou

Benaki

et al. 2022

Cells

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Vaccination is currently the most effective strategy for the mitigation of the COVID-19 pandemic. mRNA vaccines trigger the immune system to produce neutralizing antibodies (NAbs) against SARS-CoV-2 spike proteins. However, the underlying molecular processes affecting immune response after vaccination remain poorly understood, while there is significant heterogeneity in the immune response among individuals. Metabolomics have often been used to provide a deeper understanding of immune cell responses, but in the context of COVID-19 vaccination such data are scarce. Mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR)-based metabolomics were used to provide insights based on the baseline metabolic profile and metabolic alterations induced after mRNA vaccination in paired blood plasma samples collected and analysed before the first and second vaccination and at 3 months post first dose. Based on the level of NAbs just before the second dose, two groups, “low” and “high” responders, were defined. Distinct plasma metabolic profiles were observed in relation to the level of immune response, highlighting the role of amino acid metabolism and the lipid profile as predictive markers of response to vaccination. Furthermore, levels of plasma ceramides along with certain amino acids could emerge as predictive biomarkers of response and severity of inflammation.

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SMolESY: an efficient and quantitative alternative to on-instrument macromolecular ¹H-NMR signal suppression

Abstract: Small Molecule Enhancement SpectroscopY (SMolESY) by computational suppression of 1H-NMR macromolecular signals: a functional replacement for on-instrument spin-echo experiments.

Cited by 18 publications

References 32 publications

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Plasma Metabolomic Alterations Induced by COVID-19 Vaccination Reveal Putative Biomarkers Reflecting the Immune Response

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SMolESY: an efficient and quantitative alternative to on-instrument macromolecular 1H-NMR signal suppression

Abstract: Small Molecule Enhancement SpectroscopY (SMolESY) by computational suppression of 1H-NMR macromolecular signals: a functional replacement for on-instrument spin-echo experiments.

Cited by 18 publications

References 32 publications

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D 1H NMR

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

Plasma Metabolomic Alterations Induced by COVID-19 Vaccination Reveal Putative Biomarkers Reflecting the Immune Response

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Product

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SMolESY: an efficient and quantitative alternative to on-instrument macromolecular ¹H-NMR signal suppression

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR

A Computationally Lightweight Algorithm for Deriving Reliable Metabolite Panel Measurements from 1D ¹H NMR