Enabling one- and two-dimensional mass spectrometry in a linear quadrupole ion trap

Dziekonski, Eric T.; Eakins, Gregory; Cooks, R. Graham

doi:10.1016/j.ijms.2023.117099

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…As described previously, an external waveform generator applies a constant DC potential proportional to the RF trapping voltage to trap precursor ions. , Precursor ions were dissociated in turn using a frequency-swept excitation waveform applied to the Y electrodes, and their respective product ions were then ejected using a second swept waveform applied in the X dimension. Excitation and ejection occurred at frequencies corresponding to the ion secular parameters, which destabilize each precursor and product ion, respectively. , Figure shows the principle of the 2D MS/MS experiment and the data it provides.…”

Section: Methodsmentioning

confidence: 99%

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Machine-Learning Classification of Bacteria Using Two-Dimensional Tandem Mass Spectrometry

Gonzalez,

Snyder,

Casey

et al. 2023

Anal. Chem.

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Biothreat detection has continued to gain attention. Samples suspected to fall into any of the CDC's biothreat categories require identification by processes that require specialized expertise and facilities. Recent developments in analytical instrumentation and machine learning algorithms offer rapid and accurate classification of Gram-positive and Gramnegative bacterial species. This is achieved by analyzing the negative ions generated from bacterial cell extracts with a modified linear quadrupole ion-trap mass spectrometer fitted with twodimensional tandem mass spectrometry capabilities (2D MS/MS). The 2D MS/MS data domain of a bacterial cell extract is recorded within five s using a five-scan average after sample preparation by a simple extraction. Bacteria were classified at the species level by their lipid profiles using the random forest, k-nearest neighbor, and multilayer perceptron machine learning models. 2D MS/MS data can also be treated as image data for use with image recognition algorithms such as convolutional neural networks. The classification accuracy of all models tested was greater than 99%. Adding to previously published work on the 2D MS/MS analysis of bacterial growth and the profiling of sporulating bacteria, this study demonstrates the utility and information-rich nature of 2D MS/MS in the identification of bacterial pathogens at the species level when coupled with machine learning.

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Section: Methodsmentioning

confidence: 99%

“…5 μm) with −1.8 kV potential applied to an inserted stainless-steel wire electrode (Warner Instruments, Holliston, MA). The nESI-2D MS/MS spectra were recorded using a modified linear ion-trap instrument , (LTQ XL, Thermo Fisher Scientific, San Jose, CA).…”

Section: Methodsmentioning

confidence: 99%

Machine-Learning Classification of Bacteria Using Two-Dimensional Tandem Mass Spectrometry

Gonzalez,

Snyder,

Casey

et al. 2023

Anal. Chem.

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“…The 2D MS/MS data include all possible precursor, product, and neutral loss MS/MS relationships and was first implemented using FT‐ICR [4] . Later we developed 2D MS/MS using a modified linear ion trap mass spectrometer to perform untargeted MS/MS fragmentation of each precursor ion within a mixture [5,6] . This MS/MS technique provides full coverage of the 2D MS/MS data domain in times on the order of one second [3] .…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Phenolics in Mixtures by Two‐Dimensional Tandem Mass Spectrometry with Microdroplet Accelerated Derivatization Reactions

Hu,

Dziekonski,

Wang

et al. 2024

Analysis & Sensing

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Complex mixtures are typically analyzed by chromatographic separation followed by mass spectrometry. Two‐dimensional tandem mass spectrometry (2D MS/MS) may eliminate the need for separation and therefore significantly reduce analysis time while expanding the information space by recording the intensities of all fragment ions generated from all trapped precursor ions. Additional chemical specificity can be provided by using chemical derivatization. In this study phenolic compounds are identified after triazole click derivatization by the observation of two diagnostic features in the 2D MS/MS data domain, a mass shift of 175 Da on the precursor ion axis and a neutral loss (NL) of 119 Da. Additional NL transitions were highly correlated with particular substituents including carbonyl, ester, amino, and alkyl/phenyl groups. Substituent position information was also easily diagnosed. The derivatization reactions are accelerated in the charged electrosprayed microdroplets. Using this method, members of this single class of compounds can be identified within 1–2 minutes while structural information on the individual compounds is acquired simultaneously.

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Enabling one- and two-dimensional mass spectrometry in a linear quadrupole ion trap

Cited by 2 publications

References 30 publications

Machine-Learning Classification of Bacteria Using Two-Dimensional Tandem Mass Spectrometry

Machine-Learning Classification of Bacteria Using Two-Dimensional Tandem Mass Spectrometry

Rapid Identification of Phenolics in Mixtures by Two‐Dimensional Tandem Mass Spectrometry with Microdroplet Accelerated Derivatization Reactions

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