Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics

Cho, Kevin; Schwaiger-Haber, Michaela; Naser, Fuad J.; Stancliffe, Ethan; Sindelar, Miriam; Patti, Gary J.

doi:10.1016/j.aca.2021.338210

Cited by 27 publications

(36 citation statements)

References 36 publications

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“…To filter contaminants and features redundant for the same metabolite, we applied the AcquireX software as previously described. 21 In brief, a blank sample and a pooled-reference sample were analyzed in MS1 mode. After peak detection, isotopes and adducts were grouped.…”

Section: Resultsmentioning

confidence: 99%

“… 21 MS1 data were acquired at a resolution of 120 K with an automatic gain control (AGC) target of 2e5 and a maximum injection time of 200 ms. MS1 scans for data-dependent acquisition (DDA) runs were acquired at a resolution of 60 K with an AGC target of 1e5 and a maximum injection time of 70 ms. MS2 spectra were collected on [M + H] + ions in positive polarity and [M – H] − ions in negative polarity over eight runs by using iterative DDA, which can be accomplished with AcquireX. 21 The MS2 isolation window was set to 1.5 m / z . Normalized collision energies (NCEs) of 10, 20, 25, 30, 35, 40, 50, 60, 70, and 80% were used as the collision energy ( Figure S1 ).…”

Section: Methodsmentioning

confidence: 99%

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A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

et al. 2021

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The thousands of features commonly observed when performing untargeted metabolomics with quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers often correspond to only a few hundred unique metabolites of biological origin, which is in the range of what can be assayed in a single targeted metabolomics experiment by using a triple quadrupole (QqQ) mass spectrometer. A major benefit of performing targeted metabolomics with QqQ mass spectrometry is the affordability of the instruments relative to high-resolution QTOF and Orbitrap platforms. Optimizing targeted methods to profile hundreds of metabolites on a QqQ mass spectrometer, however, has historically been limited by the availability of authentic standards, particularly for “unknowns” that have yet to be structurally identified. Here, we report a strategy to develop multiple reaction monitoring (MRM) methods for QqQ instruments on the basis of high-resolution spectra, thereby enabling us to use data from untargeted metabolomics to design targeted experiments without the need for authentic standards. We demonstrate that using high-resolution fragmentation data alone to design MRM methods results in the same quantitative performance as when methods are optimized by measuring authentic standards on QqQ instruments, as is conventionally done. The approach was validated by showing that Orbitrap ID-X data can be used to establish MRM methods on a Thermo TSQ Altis and two Agilent QqQs for hundreds of metabolites, including unknowns, without a dependence on standards. Finally, we highlight an application where metabolite profiling was performed on an ID-X and a QqQ by using the strategy introduced here, with both data sets yielding the same result. The described approach therefore allows us to use QqQ instruments, which are often associated with targeted metabolomics, to profile knowns and unknowns at a comprehensive scale that is typical of untargeted metabolomics.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

et al. 2021

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“…The use of different collision energies has been previously shown to facilitate structural elucidation, findings suggesting the exploration of different collision energy settings for broader coverage of the metabolome. [18][19][20] For this analysis we used a total set of 2240 compounds which had fragment ion spectra acquired with up to 29 different collision energy settings, resulting in ~3,000,000 spectral pairs for analysis when using an MS1-only acquisition at 25 Da (Fig. 5).…”

Section: Theoretical Saturation Of Compound Uniqueness Based On Number Of Transitions Matrixmentioning

confidence: 99%

Analyzing Assay Specificity in Metabolomics using Unique Ion Signature Simulations

Shanthamoorthy¹,

Röst²

2021

Preprint

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Targeted, untargeted and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have enjoyed popularity in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex co-isolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite identification in complex backgrounds using precursor and fragment ion mass-to-charge separation, comparing three benchmarked MS methods (MS1, MRM, DIA). We simulated MS1, MRM and DIA using the NIST LC-MS library as a complex background (8274 compounds at collision energy=35) and compared these methods with regards to unambiguous detection using unique ion signatures. Our simulations show that data generated with DIA with 25 Da mass windows outperformed MS1-only and MRM-based methods by a factor of 13.6-fold and 8.7-fold, respectively. Additionally, we use saturation analysis to show that for highly complex samples, a large portion of MS1-only detection (44.9%) is ambiguous while MRM (6.6%) and DIA (0.6%) present lower ambiguity. Our analysis demonstrates the power of using both high resolution precursor and high resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both DDA and MRM with regards to unique compound identification potential.

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“…For example, the sweat metabolome has been primarily defined by amino acids and amino acid like compounds with connections of metabolite abundance to health and disease Macedo et al, 2017;Delgado-Povedano et al, 2018;Harshman et al, 2018). However, inherent difficulties exist in metabolite compound identification, such as adducts and dimerization, access to neat standards, competing background ions, instrument noise, co-elution of isomeric species, source fragmentation, and the relatively simplistic nature of metabolites (Xu et al, 2015;Domingo-Almenara et al, 2017;Cho et al, 2021). Furthermore, MS detector speed can limit the type of quantitative and/or qualitative data to be collected, guiding the experiment to be targeted, untargeted, or semi-targeted in a blended data dependent acquisition (DDA) approach (Cho et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…However, inherent difficulties exist in metabolite compound identification, such as adducts and dimerization, access to neat standards, competing background ions, instrument noise, co-elution of isomeric species, source fragmentation, and the relatively simplistic nature of metabolites (Xu et al, 2015;Domingo-Almenara et al, 2017;Cho et al, 2021). Furthermore, MS detector speed can limit the type of quantitative and/or qualitative data to be collected, guiding the experiment to be targeted, untargeted, or semi-targeted in a blended data dependent acquisition (DDA) approach (Cho et al, 2021). Because of these intrinsic conditions, novel sweat metabolite identifications have remained sparce.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

et al. 2021

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Sweat is emerging as a prominent biosource for real-time human performance monitoring applications. Although promising, sources of variability must be identified to truly utilize sweat for biomarker applications. In this proof-of-concept study, a targeted metabolomics method was applied to sweat collected from the forearms of participants in a 12-week exercise program who ingested either low or high nutritional supplementation twice daily. The data establish the use of dried powder mass as a method for metabolomic data normalization from sweat samples. Additionally, the results support the hypothesis that ingestion of regular nutritional supplementation semi-quantitatively impact the sweat metabolome. For example, a receiver operating characteristic (ROC) curve of relative normalized metabolite quantities show an area under the curve of 0.82 suggesting the sweat metabolome can moderately predict if an individual is taking nutritional supplementation. Finally, a significant correlation between physical performance and the sweat metabolome are established. For instance, the data illustrate that by utilizing multiple linear regression modeling approaches, sweat metabolite quantities can predict VO2 max (p = 0.0346), peak lower body Windage (p = 0.0112), and abdominal circumference (p = 0.0425). The results illustrate the need to account for dietary nutrition in biomarker discovery applications involving sweat as a biosource.

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Targeting unique biological signals on the fly to improve MS/MS coverage and identification efficiency in metabolomics

Cited by 27 publications

References 36 publications

A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

Analyzing Assay Specificity in Metabolomics using Unique Ion Signature Simulations

The Impact of Nutritional Supplementation on Sweat Metabolomic Content: A Proof-of-Concept Study

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